Sample records for synaptic weight distribution
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The Influence of Synaptic Weight Distribution on Neuronal Population Dynamics
Buice, Michael; Koch, Christof; Mihalas, Stefan
2013-01-01
The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations. PMID:24204219
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Distributed synaptic weights in a LIF neural network and learning rules
NASA Astrophysics Data System (ADS)
Perthame, Benoît; Salort, Delphine; Wainrib, Gilles
2017-09-01
Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connectivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorize a learned signal.
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Bouchard, Kristofer E.; Ganguli, Surya; Brainard, Michael S.
2015-01-01
The majority of distinct sensory and motor events occur as temporally ordered sequences with rich probabilistic structure. Sequences can be characterized by the probability of transitioning from the current state to upcoming states (forward probability), as well as the probability of having transitioned to the current state from previous states (backward probability). Despite the prevalence of probabilistic sequencing of both sensory and motor events, the Hebbian mechanisms that mold synapses to reflect the statistics of experienced probabilistic sequences are not well understood. Here, we show through analytic calculations and numerical simulations that Hebbian plasticity (correlation, covariance, and STDP) with pre-synaptic competition can develop synaptic weights equal to the conditional forward transition probabilities present in the input sequence. In contrast, post-synaptic competition can develop synaptic weights proportional to the conditional backward probabilities of the same input sequence. We demonstrate that to stably reflect the conditional probability of a neuron's inputs and outputs, local Hebbian plasticity requires balance between competitive learning forces that promote synaptic differentiation and homogenizing learning forces that promote synaptic stabilization. The balance between these forces dictates a prior over the distribution of learned synaptic weights, strongly influencing both the rate at which structure emerges and the entropy of the final distribution of synaptic weights. Together, these results demonstrate a simple correspondence between the biophysical organization of neurons, the site of synaptic competition, and the temporal flow of information encoded in synaptic weights by Hebbian plasticity while highlighting the utility of balancing learning forces to accurately encode probability distributions, and prior expectations over such probability distributions. PMID:26257637
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Mean-field theory of a plastic network of integrate-and-fire neurons.
Chen, Chun-Chung; Jasnow, David
2010-01-01
We consider a noise-driven network of integrate-and-fire neurons. The network evolves as result of the activities of the neurons following spike-timing-dependent plasticity rules. We apply a self-consistent mean-field theory to the system to obtain the mean activity level for the system as a function of the mean synaptic weight, which predicts a first-order transition and hysteresis between a noise-dominated regime and a regime of persistent neural activity. Assuming Poisson firing statistics for the neurons, the plasticity dynamics of a synapse under the influence of the mean-field environment can be mapped to the dynamics of an asymmetric random walk in synaptic-weight space. Using a master equation for small steps, we predict a narrow distribution of synaptic weights that scales with the square root of the plasticity rate for the stationary state of the system given plausible physiological parameter values describing neural transmission and plasticity. The dependence of the distribution on the synaptic weight of the mean-field environment allows us to determine the mean synaptic weight self-consistently. The effect of fluctuations in the total synaptic conductance and plasticity step sizes are also considered. Such fluctuations result in a smoothing of the first-order transition for low number of afferent synapses per neuron and a broadening of the synaptic-weight distribution, respectively.
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Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity
Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon
2011-01-01
Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800
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Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons
Setareh, Hesam; Deger, Moritz; Petersen, Carl C. H.; Gerstner, Wulfram
2017-01-01
Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data. Simulations of such a network with generalized integrate-and-fire neurons display regular and irregular slow oscillations akin to experimentally observed up/down state transitions in the activity of cortical neurons with a broad distribution of pairwise spike correlations. Moreover, stimulation of a model network in the presence or absence of assembly structure exhibits responses similar to light-evoked responses of cortical layers in optogenetically modified animals. We conclude that a high connection probability into and within assemblies of excitatory weight-hub neurons, as it likely is present in some but not all cortical layers, changes the dynamics of a layer of cortical microcircuitry significantly. PMID:28690508
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Suen, Jonathan Y; Navlakha, Saket
2017-05-01
Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.
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Network algorithmics and the emergence of the cortical synaptic-weight distribution
NASA Astrophysics Data System (ADS)
Nathan, Andre; Barbosa, Valmir C.
2010-02-01
When a neuron fires and the resulting action potential travels down its axon toward other neurons’ dendrites, the effect on each of those neurons is mediated by the strength of the synapse that separates it from the firing neuron. This strength, in turn, is affected by the postsynaptic neuron’s response through a mechanism that is thought to underlie important processes such as learning and memory. Although of difficult quantification, cortical synaptic strengths have been found to obey a long-tailed unimodal distribution peaking near the lowest values (approximately lognormal), thus confirming some of the predictive models built previously. Most of these models are causally local, in the sense that they refer to the situation in which a number of neurons all fire directly at the same postsynaptic neuron. Consequently, they necessarily embody assumptions regarding the generation of action potentials by the presynaptic neurons that have little biological interpretability. We introduce a network model of large groups of interconnected neurons and demonstrate, making none of the assumptions that characterize the causally local models, that its long-term behavior gives rise to a distribution of synaptic weights (the mathematical surrogates of synaptic strengths) with the same properties that were experimentally observed. In our model, the action potentials that create a neuron’s input are, ultimately, the product of network-wide causal chains relating what happens at a neuron to the firings of others. Our model is then of a causally global nature and predicates the emergence of the synaptic-weight distribution on network structure and function. As such, it has the potential to become instrumental also in the study of other emergent cortical phenomena.
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Stability versus neuronal specialization for STDP: long-tail weight distributions solve the dilemma.
Gilson, Matthieu; Fukai, Tomoki
2011-01-01
Spike-timing-dependent plasticity (STDP) modifies the weight (or strength) of synaptic connections between neurons and is considered to be crucial for generating network structure. It has been observed in physiology that, in addition to spike timing, the weight update also depends on the current value of the weight. The functional implications of this feature are still largely unclear. Additive STDP gives rise to strong competition among synapses, but due to the absence of weight dependence, it requires hard boundaries to secure the stability of weight dynamics. Multiplicative STDP with linear weight dependence for depression ensures stability, but it lacks sufficiently strong competition required to obtain a clear synaptic specialization. A solution to this stability-versus-function dilemma can be found with an intermediate parametrization between additive and multiplicative STDP. Here we propose a novel solution to the dilemma, named log-STDP, whose key feature is a sublinear weight dependence for depression. Due to its specific weight dependence, this new model can produce significantly broad weight distributions with no hard upper bound, similar to those recently observed in experiments. Log-STDP induces graded competition between synapses, such that synapses receiving stronger input correlations are pushed further in the tail of (very) large weights. Strong weights are functionally important to enhance the neuronal response to synchronous spike volleys. Depending on the input configuration, multiple groups of correlated synaptic inputs exhibit either winner-share-all or winner-take-all behavior. When the configuration of input correlations changes, individual synapses quickly and robustly readapt to represent the new configuration. We also demonstrate the advantages of log-STDP for generating a stable structure of strong weights in a recurrently connected network. These properties of log-STDP are compared with those of previous models. Through long-tail weight distributions, log-STDP achieves both stable dynamics for and robust competition of synapses, which are crucial for spike-based information processing.
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Bamford, Simeon A; Murray, Alan F; Willshaw, David J
2010-02-01
A distributed and locally reprogrammable address-event receiver has been designed, in which incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change the address of their presynaptic neuron, allowing the distributed implementation of a biologically realistic learning rule, with both synapse formation and elimination (synaptic rewiring). Probabilistic synapse formation leads to topographic map development, made possible by a cross-chip current-mode calculation of Euclidean distance. As well as synaptic plasticity in rewiring, synapses change weights using a competitive Hebbian learning rule (spike-timing-dependent plasticity). The weight plasticity allows receptive fields to be modified based on spatio-temporal correlations in the inputs, and the rewiring plasticity allows these modifications to become embedded in the network topology.
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Neural network for processing both spatial and temporal data with time based back-propagation
NASA Technical Reports Server (NTRS)
Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)
1993-01-01
Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.
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Depression-Biased Reverse Plasticity Rule Is Required for Stable Learning at Top-Down Connections
Burbank, Kendra S.; Kreiman, Gabriel
2012-01-01
Top-down synapses are ubiquitous throughout neocortex and play a central role in cognition, yet little is known about their development and specificity. During sensory experience, lower neocortical areas are activated before higher ones, causing top-down synapses to experience a preponderance of post-synaptic activity preceding pre-synaptic activity. This timing pattern is the opposite of that experienced by bottom-up synapses, which suggests that different versions of spike-timing dependent synaptic plasticity (STDP) rules may be required at top-down synapses. We consider a two-layer neural network model and investigate which STDP rules can lead to a distribution of top-down synaptic weights that is stable, diverse and avoids strong loops. We introduce a temporally reversed rule (rSTDP) where top-down synapses are potentiated if post-synaptic activity precedes pre-synaptic activity. Combining analytical work and integrate-and-fire simulations, we show that only depression-biased rSTDP (and not classical STDP) produces stable and diverse top-down weights. The conclusions did not change upon addition of homeostatic mechanisms, multiplicative STDP rules or weak external input to the top neurons. Our prediction for rSTDP at top-down synapses, which are distally located, is supported by recent neurophysiological evidence showing the existence of temporally reversed STDP in synapses that are distal to the post-synaptic cell body. PMID:22396630
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NASA Technical Reports Server (NTRS)
Villarreal, James A.; Shelton, Robert O.
1992-01-01
Concept of space-time neural network affords distributed temporal memory enabling such network to model complicated dynamical systems mathematically and to recognize temporally varying spatial patterns. Digital filters replace synaptic-connection weights of conventional back-error-propagation neural network.
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Emergence of small-world structure in networks of spiking neurons through STDP plasticity.
Basalyga, Gleb; Gleiser, Pablo M; Wennekers, Thomas
2011-01-01
In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.
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A synaptic organizing principle for cortical neuronal groups
Perin, Rodrigo; Berger, Thomas K.; Markram, Henry
2011-01-01
Neuronal circuitry is often considered a clean slate that can be dynamically and arbitrarily molded by experience. However, when we investigated synaptic connectivity in groups of pyramidal neurons in the neocortex, we found that both connectivity and synaptic weights were surprisingly predictable. Synaptic weights follow very closely the number of connections in a group of neurons, saturating after only 20% of possible connections are formed between neurons in a group. When we examined the network topology of connectivity between neurons, we found that the neurons cluster into small world networks that are not scale-free, with less than 2 degrees of separation. We found a simple clustering rule where connectivity is directly proportional to the number of common neighbors, which accounts for these small world networks and accurately predicts the connection probability between any two neurons. This pyramidal neuron network clusters into multiple groups of a few dozen neurons each. The neurons composing each group are surprisingly distributed, typically more than 100 μm apart, allowing for multiple groups to be interlaced in the same space. In summary, we discovered a synaptic organizing principle that groups neurons in a manner that is common across animals and hence, independent of individual experiences. We speculate that these elementary neuronal groups are prescribed Lego-like building blocks of perception and that acquired memory relies more on combining these elementary assemblies into higher-order constructs. PMID:21383177
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Measuring Symmetry, Asymmetry and Randomness in Neural Network Connectivity
Esposito, Umberto; Giugliano, Michele; van Rossum, Mark; Vasilaki, Eleni
2014-01-01
Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity. PMID:25006663
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Measuring symmetry, asymmetry and randomness in neural network connectivity.
Esposito, Umberto; Giugliano, Michele; van Rossum, Mark; Vasilaki, Eleni
2014-01-01
Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.
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Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity
Effenberger, Felix; Jost, Jürgen; Levina, Anna
2015-01-01
Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network. PMID:26335425
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Synchrony detection and amplification by silicon neurons with STDP synapses.
Bofill-i-petit, Adria; Murray, Alan F
2004-09-01
Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.
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Hiratani, Naoki; Fukai, Tomoki
2016-01-01
In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance. PMID:27303271
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Whittington, James C. R.; Bogacz, Rafal
2017-01-01
To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583
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Whittington, James C R; Bogacz, Rafal
2017-05-01
To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.
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Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.
Kuroda, S; Yamamoto, K; Miyamoto, H; Doya, K; Kawat, M
2001-03-01
Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike-occurrence time after stimulus onset must capture at least the first, second and third moments of the temporal distribution of error signals.
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From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation.
Soltoggio, Andrea; Stanley, Kenneth O
2012-10-01
Synaptic plasticity is a major mechanism for adaptation, learning, and memory. Yet current models struggle to link local synaptic changes to the acquisition of behaviors. The aim of this paper is to demonstrate a computational relationship between local Hebbian plasticity and behavior learning by exploiting two traditionally unwanted features: neural noise and synaptic weight saturation. A modulation signal is employed to arbitrate the sign of plasticity: when the modulation is positive, the synaptic weights saturate to express exploitative behavior; when it is negative, the weights converge to average values, and neural noise reconfigures the network's functionality. This process is demonstrated through simulating neural dynamics in the autonomous emergence of fearful and aggressive navigating behaviors and in the solution to reward-based problems. The neural model learns, memorizes, and modifies different behaviors that lead to positive modulation in a variety of settings. The algorithm establishes a simple relationship between local plasticity and behavior learning by demonstrating the utility of noise and weight saturation. Moreover, it provides a new tool to simulate adaptive behavior, and contributes to bridging the gap between synaptic changes and behavior in neural computation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Attractor neural networks with resource-efficient synaptic connectivity
NASA Astrophysics Data System (ADS)
Pehlevan, Cengiz; Sengupta, Anirvan
Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.
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Integrated neuron circuit for implementing neuromorphic system with synaptic device
NASA Astrophysics Data System (ADS)
Lee, Jeong-Jun; Park, Jungjin; Kwon, Min-Woo; Hwang, Sungmin; Kim, Hyungjin; Park, Byung-Gook
2018-02-01
In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).
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Volknandt, W; Naito, S; Ueda, T; Zimmermann, H
1987-08-01
Using an affinity-purified monospecific polyclonal antibody against bovine brain synapsin I, the distribution of antigenically related proteins was investigated in the electric organs of the three strongly electric fish Torpedo marmorata, Electrophorus electricus, Malapterurus electricus and in the rat diaphragm. On application of indirect fluorescein isothiocyanate-immunofluorescence and using alpha-bungarotoxin for identification of synaptic sites, intense and very selective staining of nerve terminals was found in all of these tissues. Immunotransfer blots of tissue homogenates revealed specific bands whose molecular weights are similar to those of synapsin Ia and synapsin Ib. Moreover, synapsin I-like proteins are still attached to the synaptic vesicles that were isolated in isotonic glycine solution from Torpedo electric organ by density gradient centrifugation and chromatography on Sephacryl-1000. Our results suggest that synapsin I-like proteins are also associated with cholinergic synaptic vesicles of electric organs and that the electric organ may be an ideal source for studying further the functional and molecular properties of synapsin.
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Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang
2013-01-01
The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941
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Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules
Sacramento, João; Wichert, Andreas; van Rossum, Mark C. W.
2015-01-01
It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L 1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum. PMID:26046817
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Synaptic vesicle distribution by conveyor belt.
Moughamian, Armen J; Holzbaur, Erika L F
2012-03-02
The equal distribution of synaptic vesicles among synapses along the axon is critical for robust neurotransmission. Wong et al. show that the continuous circulation of synaptic vesicles throughout the axon driven by molecular motors ultimately yields this even distribution. Copyright © 2012 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Wang, I.-Ting; Chang, Chih-Cheng; Chiu, Li-Wen; Chou, Teyuh; Hou, Tuo-Hung
2016-09-01
The implementation of highly anticipated hardware neural networks (HNNs) hinges largely on the successful development of a low-power, high-density, and reliable analog electronic synaptic array. In this study, we demonstrate a two-layer Ta/TaO x /TiO2/Ti cross-point synaptic array that emulates the high-density three-dimensional network architecture of human brains. Excellent uniformity and reproducibility among intralayer and interlayer cells were realized. Moreover, at least 50 analog synaptic weight states could be precisely controlled with minimal drifting during a cycling endurance test of 5000 training pulses at an operating voltage of 3 V. We also propose a new state-independent bipolar-pulse-training scheme to improve the linearity of weight updates. The improved linearity considerably enhances the fault tolerance of HNNs, thus improving the training accuracy.
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Pfeil, Thomas; Potjans, Tobias C; Schrader, Sven; Potjans, Wiebke; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz
2012-01-01
Large-scale neuromorphic hardware systems typically bear the trade-off between detail level and required chip resources. Especially when implementing spike-timing dependent plasticity, reduction in resources leads to limitations as compared to floating point precision. By design, a natural modification that saves resources would be reducing synaptic weight resolution. In this study, we give an estimate for the impact of synaptic weight discretization on different levels, ranging from random walks of individual weights to computer simulations of spiking neural networks. The FACETS wafer-scale hardware system offers a 4-bit resolution of synaptic weights, which is shown to be sufficient within the scope of our network benchmark. Our findings indicate that increasing the resolution may not even be useful in light of further restrictions of customized mixed-signal synapses. In addition, variations due to production imperfections are investigated and shown to be uncritical in the context of the presented study. Our results represent a general framework for setting up and configuring hardware-constrained synapses. We suggest how weight discretization could be considered for other backends dedicated to large-scale simulations. Thus, our proposition of a good hardware verification practice may rise synergy effects between hardware developers and neuroscientists.
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Pfeil, Thomas; Potjans, Tobias C.; Schrader, Sven; Potjans, Wiebke; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz
2012-01-01
Large-scale neuromorphic hardware systems typically bear the trade-off between detail level and required chip resources. Especially when implementing spike-timing dependent plasticity, reduction in resources leads to limitations as compared to floating point precision. By design, a natural modification that saves resources would be reducing synaptic weight resolution. In this study, we give an estimate for the impact of synaptic weight discretization on different levels, ranging from random walks of individual weights to computer simulations of spiking neural networks. The FACETS wafer-scale hardware system offers a 4-bit resolution of synaptic weights, which is shown to be sufficient within the scope of our network benchmark. Our findings indicate that increasing the resolution may not even be useful in light of further restrictions of customized mixed-signal synapses. In addition, variations due to production imperfections are investigated and shown to be uncritical in the context of the presented study. Our results represent a general framework for setting up and configuring hardware-constrained synapses. We suggest how weight discretization could be considered for other backends dedicated to large-scale simulations. Thus, our proposition of a good hardware verification practice may rise synergy effects between hardware developers and neuroscientists. PMID:22822388
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Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study
Allam, Sushmita L.; Bouteiller, Jean-Marie C.; Hu, Eric Y.; Ambert, Nicolas; Greget, Renaud; Bischoff, Serge; Baudry, Michel; Berger, Theodore W.
2015-01-01
Glutamatergic synapses are the most prevalent functional elements of information processing in the brain. Changes in pre-synaptic activity and in the function of various post-synaptic elements contribute to generate a large variety of synaptic responses. Previous studies have explored postsynaptic factors responsible for regulating synaptic strength variations, but have given far less importance to synaptic geometry, and more specifically to the subcellular distribution of ionotropic receptors. We analyzed the functional effects resulting from changing the subsynaptic localization of ionotropic receptors by using a hippocampal synaptic computational framework. The present study was performed using the EONS (Elementary Objects of the Nervous System) synaptic modeling platform, which was specifically developed to explore the roles of subsynaptic elements as well as their interactions, and that of synaptic geometry. More specifically, we determined the effects of changing the localization of ionotropic receptors relative to the presynaptic glutamate release site, on synaptic efficacy and its variations following single pulse and paired-pulse stimulation protocols. The results indicate that changes in synaptic geometry do have consequences on synaptic efficacy and its dynamics. PMID:26480028
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Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study.
Allam, Sushmita L; Bouteiller, Jean-Marie C; Hu, Eric Y; Ambert, Nicolas; Greget, Renaud; Bischoff, Serge; Baudry, Michel; Berger, Theodore W
2015-01-01
Glutamatergic synapses are the most prevalent functional elements of information processing in the brain. Changes in pre-synaptic activity and in the function of various post-synaptic elements contribute to generate a large variety of synaptic responses. Previous studies have explored postsynaptic factors responsible for regulating synaptic strength variations, but have given far less importance to synaptic geometry, and more specifically to the subcellular distribution of ionotropic receptors. We analyzed the functional effects resulting from changing the subsynaptic localization of ionotropic receptors by using a hippocampal synaptic computational framework. The present study was performed using the EONS (Elementary Objects of the Nervous System) synaptic modeling platform, which was specifically developed to explore the roles of subsynaptic elements as well as their interactions, and that of synaptic geometry. More specifically, we determined the effects of changing the localization of ionotropic receptors relative to the presynaptic glutamate release site, on synaptic efficacy and its variations following single pulse and paired-pulse stimulation protocols. The results indicate that changes in synaptic geometry do have consequences on synaptic efficacy and its dynamics.
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Yang, Paul; Park, Daehoon; Beom, Keonwon; Kim, Hyung Jun; Kang, Chi Jung; Yoon, Tae-Sik
2018-07-20
We report a variety of synaptic behaviors in a thin-film transistor (TFT) with a metal-oxide-semiconductor gate stack that has a Pt/HfO x /n-type indium-gallium-zinc oxide (n-IGZO) structure. The three-terminal synaptic TFT exhibits a tunable synaptic weight with a drain current modulation upon repeated application of gate and drain voltages. The synaptic weight modulation is analog, voltage-polarity dependent reversible, and strong with a dynamic range of multiple orders of magnitude (>10 4 ). This modulation process emulates biological synaptic potentiation, depression, excitatory-postsynaptic current, paired-pulse facilitation, and short-term to long-term memory transition behaviors as a result of repeated pulsing with respect to the pulse amplitude, width, repetition number, and the interval between pulses. These synaptic behaviors are interpreted based on the changes in the capacitance of the Pt/HfO x /n-IGZO gate stack, the channel mobility, and the threshold voltage that result from the redistribution of oxygen ions by the applied gate voltage. These results demonstrate the potential of this structure for three-terminal synaptic transistor using the gate stack composed of the HfO x gate insulator and the IGZO channel layer.
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NASA Astrophysics Data System (ADS)
Yang, Paul; Park, Daehoon; Beom, Keonwon; Kim, Hyung Jun; Kang, Chi Jung; Yoon, Tae-Sik
2018-07-01
We report a variety of synaptic behaviors in a thin-film transistor (TFT) with a metal-oxide-semiconductor gate stack that has a Pt/HfO x /n-type indium–gallium–zinc oxide (n-IGZO) structure. The three-terminal synaptic TFT exhibits a tunable synaptic weight with a drain current modulation upon repeated application of gate and drain voltages. The synaptic weight modulation is analog, voltage-polarity dependent reversible, and strong with a dynamic range of multiple orders of magnitude (>104). This modulation process emulates biological synaptic potentiation, depression, excitatory-postsynaptic current, paired-pulse facilitation, and short-term to long-term memory transition behaviors as a result of repeated pulsing with respect to the pulse amplitude, width, repetition number, and the interval between pulses. These synaptic behaviors are interpreted based on the changes in the capacitance of the Pt/HfO x /n-IGZO gate stack, the channel mobility, and the threshold voltage that result from the redistribution of oxygen ions by the applied gate voltage. These results demonstrate the potential of this structure for three-terminal synaptic transistor using the gate stack composed of the HfO x gate insulator and the IGZO channel layer.
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Liu, Bingbing; Ouyang, Lisi; Mu, Shuhua; Zhu, Yaxi; Li, Keyi; Zhan, Mali; Liu, Zongwei; Jia, Yu; Lei, Wanlong
2011-11-01
The glutamatergic projection from the cerebral cortex and the thalamus extensively innervates the neostriatal neurons. However, some conflicts in the published literatures about cortical and thalamic intrastriatal synaptic terminals still need to be resolved. The present study intends to further elucidate the morphological characteristics of these two types of the terminals and their neurons. The corticostriatal and thalamostriatal terminals were immunolabeled for vesicular glutamate transporter type 1 (VGluT1) and 2 (VGluT2), respectively, and their neurons were retrograde labeled by biotinylated dextran amine 3,000 molecular weight (BDA3k) injection into the dorsolateral striatum of rats. The characteristics of the corticostriatal and thalamostriatal terminals were observed at the LM and EM levels, and the data were statistically analyzed with SPSS10.0 software. We observed that 63.53% of VGluT1+ terminals synapsed on dendritic spines, which was different from VGluT2+ terminals with the equal percentage of synapses on spines and dendrites (14.88 and 17.86%, respectively). Notably, VGluT1+ axospinous synaptic terminals were remarkably larger than VGluT2+ axospinous synaptic terminals. Terminal size-frequency distribution analysis showed that VGluT1+ terminals were within the size ranges of 0.4-0.5 and 0.8-0.9 μm, and VGluT2+ terminals were in the ranges of 0.4-0.5 and 0.6-0.7 μm. Perforated-postsynaptic densities (-PSDs) were more frequently found in VGluT1+ axospinous synaptic terminals than in VGluT2+ axospinous terminals. Furthermore, BDA3k-labeled corticostrital neurons were larger in perikaryal diameter than the thalamostriatal neurons, and they were also categorized as the two main populations based on their size-frequency distribution. The morphological characteristics of corticostriatal and thalamostriatal terminals and neurons have implications for understanding the roles of synaptic plasticity in adaptive motor control by the basal ganglia, and they have facilitations for understanding the complexities of basal ganglia function.
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Random synaptic feedback weights support error backpropagation for deep learning
NASA Astrophysics Data System (ADS)
Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.
2016-11-01
The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.
2016-01-01
The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning. PMID:27824044
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Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.
Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent
2015-08-01
The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.
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Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses
Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent
2015-01-01
The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure. PMID:26291697
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A compound memristive synapse model for statistical learning through STDP in spiking neural networks
Bill, Johannes; Legenstein, Robert
2014-01-01
Memristors have recently emerged as promising circuit elements to mimic the function of biological synapses in neuromorphic computing. The fabrication of reliable nanoscale memristive synapses, that feature continuous conductance changes based on the timing of pre- and postsynaptic spikes, has however turned out to be challenging. In this article, we propose an alternative approach, the compound memristive synapse, that circumvents this problem by the use of memristors with binary memristive states. A compound memristive synapse employs multiple bistable memristors in parallel to jointly form one synapse, thereby providing a spectrum of synaptic efficacies. We investigate the computational implications of synaptic plasticity in the compound synapse by integrating the recently observed phenomenon of stochastic filament formation into an abstract model of stochastic switching. Using this abstract model, we first show how standard pulsing schemes give rise to spike-timing dependent plasticity (STDP) with a stabilizing weight dependence in compound synapses. In a next step, we study unsupervised learning with compound synapses in networks of spiking neurons organized in a winner-take-all architecture. Our theoretical analysis reveals that compound-synapse STDP implements generalized Expectation-Maximization in the spiking network. Specifically, the emergent synapse configuration represents the most salient features of the input distribution in a Mixture-of-Gaussians generative model. Furthermore, the network's spike response to spiking input streams approximates a well-defined Bayesian posterior distribution. We show in computer simulations how such networks learn to represent high-dimensional distributions over images of handwritten digits with high fidelity even in presence of substantial device variations and under severe noise conditions. Therefore, the compound memristive synapse may provide a synaptic design principle for future neuromorphic architectures. PMID:25565943
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Gilson, Matthieu; Burkitt, Anthony N; Grayden, David B; Thomas, Doreen A; van Hemmen, J Leo
2009-12-01
In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.
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Limited distal organelles and synaptic function in extensive monoaminergic innervation.
Tao, Juan; Bulgari, Dinara; Deitcher, David L; Levitan, Edwin S
2017-08-01
Organelles such as neuropeptide-containing dense-core vesicles (DCVs) and mitochondria travel down axons to supply synaptic boutons. DCV distribution among en passant boutons in small axonal arbors is mediated by circulation with bidirectional capture. However, it is not known how organelles are distributed in extensive arbors associated with mammalian dopamine neuron vulnerability, and with volume transmission and neuromodulation by monoamines and neuropeptides. Therefore, we studied presynaptic organelle distribution in Drosophila octopamine neurons that innervate ∼20 muscles with ∼1500 boutons. Unlike in smaller arbors, distal boutons in these arbors contain fewer DCVs and mitochondria, although active zones are present. Absence of vesicle circulation is evident by proximal nascent DCV delivery, limited impact of retrograde transport and older distal DCVs. Traffic studies show that DCV axonal transport and synaptic capture are not scaled for extensive innervation, thus limiting distal delivery. Activity-induced synaptic endocytosis and synaptic neuropeptide release are also reduced distally. We propose that limits in organelle transport and synaptic capture compromise distal synapse maintenance and function in extensive axonal arbors, thereby affecting development, plasticity and vulnerability to neurodegenerative disease. © 2017. Published by The Company of Biologists Ltd.
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Supervised learning of probability distributions by neural networks
NASA Technical Reports Server (NTRS)
Baum, Eric B.; Wilczek, Frank
1988-01-01
Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.
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Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State.
Lagzi, Fereshteh; Rotter, Stefan
2015-01-01
We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the "within" versus "between" connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed "winnerless competition", which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might suggest a general approach to study the dynamics of interacting populations of spiking networks.
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Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State
Lagzi, Fereshteh; Rotter, Stefan
2015-01-01
We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the “within” versus “between” connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed “winnerless competition”, which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might suggest a general approach to study the dynamics of interacting populations of spiking networks. PMID:26407178
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Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies
Mirzakhalili, Ehsan; Gourgou, Eleni; Booth, Victoria; Epureanu, Bogdan
2017-01-01
Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons. PMID:28659765
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Pattern classification by memristive crossbar circuits using ex situ and in situ training.
Alibart, Fabien; Zamanidoost, Elham; Strukov, Dmitri B
2013-01-01
Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.
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Pattern classification by memristive crossbar circuits using ex situ and in situ training
NASA Astrophysics Data System (ADS)
Alibart, Fabien; Zamanidoost, Elham; Strukov, Dmitri B.
2013-06-01
Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.
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Synaptic and nonsynaptic plasticity approximating probabilistic inference
Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders
2014-01-01
Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758
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Saviane, Chiara; Silver, R Angus
2006-06-15
Synapses play a crucial role in information processing in the brain. Amplitude fluctuations of synaptic responses can be used to extract information about the mechanisms underlying synaptic transmission and its modulation. In particular, multiple-probability fluctuation analysis can be used to estimate the number of functional release sites, the mean probability of release and the amplitude of the mean quantal response from fits of the relationship between the variance and mean amplitude of postsynaptic responses, recorded at different probabilities. To determine these quantal parameters, calculate their uncertainties and the goodness-of-fit of the model, it is important to weight the contribution of each data point in the fitting procedure. We therefore investigated the errors associated with measuring the variance by determining the best estimators of the variance of the variance and have used simulations of synaptic transmission to test their accuracy and reliability under different experimental conditions. For central synapses, which generally have a low number of release sites, the amplitude distribution of synaptic responses is not normal, thus the use of a theoretical variance of the variance based on the normal assumption is not a good approximation. However, appropriate estimators can be derived for the population and for limited sample sizes using a more general expression that involves higher moments and introducing unbiased estimators based on the h-statistics. Our results are likely to be relevant for various applications of fluctuation analysis when few channels or release sites are present.
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Fully parallel write/read in resistive synaptic array for accelerating on-chip learning
NASA Astrophysics Data System (ADS)
Gao, Ligang; Wang, I.-Ting; Chen, Pai-Yu; Vrudhula, Sarma; Seo, Jae-sun; Cao, Yu; Hou, Tuo-Hung; Yu, Shimeng
2015-11-01
A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaO x /TiO2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30× speed-up and >30× improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve ∼95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm.
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Characterization of emergent synaptic topologies in noisy neural networks
NASA Astrophysics Data System (ADS)
Miller, Aaron James
Learned behaviors are one of the key contributors to an animal's ultimate survival. It is widely believed that the brain's microcircuitry undergoes structural changes when a new behavior is learned. In particular, motor learning, during which an animal learns a sequence of muscular movements, often requires precisely-timed coordination between muscles and becomes very natural once ingrained. Experiments show that neurons in the motor cortex exhibit precisely-timed spike activity when performing a learned motor behavior, and constituent stereotypical elements of the behavior can last several hundred milliseconds. The subject of this manuscript concerns how organized synaptic structures that produce stereotypical spike sequences emerge from random, dynamical networks. After a brief introduction in Chapter 1, we begin Chapter 2 by introducing a spike-timing-dependent plasticity (STDP) rule that defines how the activity of the network drives changes in network topology. The rule is then applied to idealized networks of leaky integrate-and-fire neurons (LIF). These neurons are not subjected to the variability that typically characterize neurons in vivo. In noiseless networks, synapses develop closed loops of strong connectivity that reproduce stereotypical, precisely-timed spike patterns from an initially random network. We demonstrate the characteristics of the asymptotic synaptic configuration are dependent on the statistics of the initial random network. The spike timings of the neurons simulated in Chapter 2 are generated exactly by a computationally economical, nonlinear mapping which is extended to LIF neurons injected with fluctuating current in Chapter 3. Development of an economical mapping that incorporates noise provides a practical solution to the long simulation times required to produce asymptotic synaptic topologies in networks with STDP in the presence of realistic neuronal variability. The mapping relies on generating numerical solutions to the dynamics of a LIF neuron subjected to Gaussian white noise (GWN). The system reduces to the Ornstein-Uhlenbeck first passage time problem, the solution of which we build into the mapping method of Chapter 2. We demonstrate that simulations using the stochastic mapping have reduced computation time compared to traditional Runge-Kutta methods by more than a factor of 150. In Chapter 4, we use the stochastic mapping to study the dynamics of emerging synaptic topologies in noisy networks. With the addition of membrane noise, networks with dynamical synapses can admit states in which the distribution of the synaptic weights is static under spontaneous activity, but the random connectivity between neurons is dynamical. The widely cited problem of instabilities in networks with STDP is avoided with the implementation of a synaptic decay and an activation threshold on each synapse. When such networks are presented with stimulus modeled by a focused excitatory current, chain-like networks can emerge with the addition of an axon-remodeling plasticity rule, a topological constraint on the connectivity modeling the finite resources available to each neuron. The emergent topologies are the result of an iterative stochastic process. The dynamics of the growth process suggest a strong interplay between the network topology and the spike sequences they produce during development. Namely, the existence of an embedded spike sequence alters the distribution of synaptic weights through the entire network. The roles of model parameters that affect the interplay between network structure and activity are elucidated. Finally, we propose two mathematical growth models, which are complementary, that capture the essence of the growth dynamics observed in simulations. In Chapter 5, we present an extension of the stochastic mapping that allows the possibility of neuronal cooperation. We demonstrate that synaptic topologies admitting stereotypical sequences can emerge in yet higher, biologically realistic levels of membrane potential variability when neurons cooperate to innervate shared targets. The structure that is most robust to the variability is that of a synfire chain. The principles of growth dynamics detailed in Chapter 4 are the same that sculpt the emergent synfire topologies. We conclude by discussing avenues for extensions of these results.
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Insel, Nathan; Takehara-Nishiuchi, Kaori
2013-11-01
Daily experiences are represented by networks of neurons distributed across the neocortex, bound together for rapid storage and later retrieval by the hippocampus. While the hippocampus is necessary for retrieving recent episode-based memory associations, over time, consolidation processes take place that enable many of these associations to be expressed independent of the hippocampus. It is generally thought that mechanisms of consolidation involve synaptic weight changes between cortical regions; or, in other words, the formation of "horizontal" cortico-cortical connections. Here, we review anatomical, behavioral, and physiological data which suggest that the connections in and between the entorhinal and cingulate cortices may be uniquely important for the long-term storage of memories that initially depend on the hippocampus. We propose that current theories of consolidation that divide memory into dual systems of hippocampus and neocortex might be improved by introducing a third, middle layer of entorhinal and cingulate allocortex, the synaptic weights within which are necessary and potentially sufficient for maintaining initially hippocampus-dependent associations over long time periods. This hypothesis makes a number of still untested predictions, and future experiments designed to address these will help to fill gaps in the current understanding of the cortical structure of consolidated memory. Copyright © 2013 Elsevier Inc. All rights reserved.
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Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André
2015-01-01
We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 226 (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 236 (64G) synaptic adaptors on a current high-end FPGA platform. PMID:26041985
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A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks
Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo
2015-01-01
Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns. PMID:26291608
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A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo
2015-08-01
Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns.
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Retrieval Property of Attractor Network with Synaptic Depression
NASA Astrophysics Data System (ADS)
Matsumoto, Narihisa; Ide, Daisuke; Watanabe, Masataka; Okada, Masato
2007-08-01
Synaptic connections are known to change dynamically. High-frequency presynaptic inputs induce decrease of synaptic weights. This process is known as short-term synaptic depression. The synaptic depression controls a gain for presynaptic inputs. However, it remains a controversial issue what are functional roles of this gain control. We propose a new hypothesis that one of the functional roles is to enlarge basins of attraction. To verify this hypothesis, we employ a binary discrete-time associative memory model which consists of excitatory and inhibitory neurons. It is known that the excitatory-inhibitory balance controls an overall activity of the network. The synaptic depression might incorporate an activity control mechanism. Using a mean-field theory and computer simulations, we find that the synaptic depression enlarges the basins at a small loading rate while the excitatory-inhibitory balance enlarges them at a large loading rate. Furthermore the synaptic depression does not affect the steady state of the network if a threshold is set at an appropriate value. These results suggest that the synaptic depression works in addition to the effect of the excitatory-inhibitory balance, and it might improve an error-correcting ability in cortical circuits.
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Generalized Adaptive Artificial Neural Networks
NASA Technical Reports Server (NTRS)
Tawel, Raoul
1993-01-01
Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.
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Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.
Carpenter, Gail A.
1997-11-01
A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.
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Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis
Anton-Sanchez, Laura; Bielza, Concha; Merchán-Pérez, Angel; Rodríguez, José-Rodrigo; DeFelipe, Javier; Larrañaga, Pedro
2014-01-01
The biggest problem when analyzing the brain is that its synaptic connections are extremely complex. Generally, the billions of neurons making up the brain exchange information through two types of highly specialized structures: chemical synapses (the vast majority) and so-called gap junctions (a substrate of one class of electrical synapse). Here we are interested in exploring the three-dimensional spatial distribution of chemical synapses in the cerebral cortex. Recent research has showed that the three-dimensional spatial distribution of synapses in layer III of the neocortex can be modeled by a random sequential adsorption (RSA) point process, i.e., synapses are distributed in space almost randomly, with the only constraint that they cannot overlap. In this study we hypothesize that RSA processes can also explain the distribution of synapses in all cortical layers. We also investigate whether there are differences in both the synaptic density and spatial distribution of synapses between layers. Using combined focused ion beam milling and scanning electron microscopy (FIB/SEM), we obtained three-dimensional samples from the six layers of the rat somatosensory cortex and identified and reconstructed the synaptic junctions. A total volume of tissue of approximately 4500μm3 and around 4000 synapses from three different animals were analyzed. Different samples, layers and/or animals were aggregated and compared using RSA replicated spatial point processes. The results showed no significant differences in the synaptic distribution across the different rats used in the study. We found that RSA processes described the spatial distribution of synapses in all samples of each layer. We also found that the synaptic distribution in layers II to VI conforms to a common underlying RSA process with different densities per layer. Interestingly, the results showed that synapses in layer I had a slightly different spatial distribution from the other layers. PMID:25206325
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Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis.
Anton-Sanchez, Laura; Bielza, Concha; Merchán-Pérez, Angel; Rodríguez, José-Rodrigo; DeFelipe, Javier; Larrañaga, Pedro
2014-01-01
The biggest problem when analyzing the brain is that its synaptic connections are extremely complex. Generally, the billions of neurons making up the brain exchange information through two types of highly specialized structures: chemical synapses (the vast majority) and so-called gap junctions (a substrate of one class of electrical synapse). Here we are interested in exploring the three-dimensional spatial distribution of chemical synapses in the cerebral cortex. Recent research has showed that the three-dimensional spatial distribution of synapses in layer III of the neocortex can be modeled by a random sequential adsorption (RSA) point process, i.e., synapses are distributed in space almost randomly, with the only constraint that they cannot overlap. In this study we hypothesize that RSA processes can also explain the distribution of synapses in all cortical layers. We also investigate whether there are differences in both the synaptic density and spatial distribution of synapses between layers. Using combined focused ion beam milling and scanning electron microscopy (FIB/SEM), we obtained three-dimensional samples from the six layers of the rat somatosensory cortex and identified and reconstructed the synaptic junctions. A total volume of tissue of approximately 4500μm(3) and around 4000 synapses from three different animals were analyzed. Different samples, layers and/or animals were aggregated and compared using RSA replicated spatial point processes. The results showed no significant differences in the synaptic distribution across the different rats used in the study. We found that RSA processes described the spatial distribution of synapses in all samples of each layer. We also found that the synaptic distribution in layers II to VI conforms to a common underlying RSA process with different densities per layer. Interestingly, the results showed that synapses in layer I had a slightly different spatial distribution from the other layers.
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Structure and plasticity potential of neural networks in the cerebral cortex
NASA Astrophysics Data System (ADS)
Fares, Tarec Edmond
In this thesis, we first described a theoretical framework for the analysis of spine remodeling plasticity. We provided a quantitative description of two models of spine remodeling in which the presence of a bouton is either required or not for the formation of a new synapse. We derived expressions for the density of potential synapses in the neuropil, the connectivity fraction, which is the ratio of actual to potential synapses, and the number of structurally different circuits attainable with spine remodeling. We calculated these parameters in mouse occipital cortex, rat CA1, monkey V1, and human temporal cortex. We found that on average a dendritic spine can choose among 4-7 potential targets in rodents and 10-20 potential targets in primates. The neuropil's potential for structural circuit remodeling is highest in rat CA1 (7.1-8.6 bits/mum3) and lowest in monkey V1 (1.3-1.5 bits/mum 3 We next studied the role neuron morphology plays in defining synaptic connectivity. As previously stated it is clear that only pairs of neurons with closely positioned axonal and dendritic branches can be synaptically coupled. For excitatory neurons in the cerebral cortex, ). We also evaluated the lower bound of neuron selectivity in the choice of synaptic partners. Post-synaptic excitatory neurons in rodents make synaptic contacts with more than 21-30% of pre-synaptic axons encountered with new spine growth. Primate neurons appear to be more selective, making synaptic connections with more than 7-15% of encountered axons. We next studied the role neuron morphology plays in defining synaptic connectivity. As previously stated it is clear that only pairs of neurons with closely positioned axonal and dendritic branches can be synaptically coupled. For excitatory neurons in the cerebral cortex, such axo-dendritic oppositions, or potential synapses, must be bridged by dendritic spines to form synaptic connections. To explore the rules by which synaptic connections are formed within the constraints imposed by neuron morphology, we compared the distributions of the numbers of actual and potential synapses between pre- and post-synaptic neurons forming different laminar projections in rat barrel cortex. Quantitative comparison explicitly ruled out the hypothesis that individual synapses between neurons are formed independently of each other. Instead, the data are consistent with a cooperative scheme of synapse formation, where multiple-synaptic connections between neurons are stabilized, while neurons that do not establish a critical number of synapses are not likely to remain synaptically coupled. In the above two projects, analysis of potential synapse numbers played an important role in shaping our understanding of connectivity and structural plasticity. In the third part of this thesis, we shift our attention to the study of the distribution of potential synapse numbers. This distribution is dependent on the details of neuron morphology and it defines synaptic connectivity patterns attainable with spine remodeling. To better understand how the distribution of potential synapse numbers is influenced by the overlap and the shapes of axonal and dendritic arbors, we first analyzed uniform disconnected arbors generated in silico. The resulting distributions are well described by binomial functions. We used a dataset of neurons reconstructed in 3D and generated the potential synapse distributions for neurons of different classes. Quantitative analysis showed that the binomial distribution is a good fit to this data as well. All distributions considered clustered into two categories, inhibitory to inhibitory and excitatory to excitatory projections. We showed that the distributions of potential synapse numbers are universally described by a family of single parameter (p) binomial functions, where p = 0.08, and for the inhibitory and p = 0.19 for the excitatory projections. In the last part of this thesis an attempt is made to incorporate some of the biological constraints we considered thus far, into an artificial neural network model. It became clear that several features of synaptic connectivity are ubiquitous among different cortical networks: (1) neural networks are predominately excitatory, containing roughly 80% of excitatory neurons and synapses, (2) neural networks are only sparsely interconnected, where the probabilities of finding connected neurons are always less than 50% even for neighboring cells, (3) the distribution of connection strengths has been shown to have a slow non-exponential decay. In the attempt to understand the advantage of such network architecture for learning and memory, we analyzed the associative memory capacity of a biologically constrained perceptron-like neural network model. The artificial neural network we consider consists of robust excitatory and inhibitory McCulloch and Pitts neurons with a constant firing threshold. Our theoretical results show that the capacity for associative memory storage in such networks increases with an addition of a small fraction of inhibitory neurons, while the connection probability remains below 50%. (Abstract shortened by UMI.)
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NASA Astrophysics Data System (ADS)
Keller, P. E.; Gmitro, A. F.
1993-07-01
A prototype neutral network system of multifaceted, planar interconnection holograms and opto-electronic neurons is analyzed. This analysis shows that a hologram fabricated with electron-beam lithography has the capacity to connect 6700 neuron outputs to 6700 neuron inputs, and that, the encoded synaptic weights have a precision of approximately 5 bits. Higher interconnection densities can be achieved by accepting a lower synaptic weight accuracy. For systems employing laser diodes at the outputs of the neurons, processing rates in the range of 45 to 720 trillion connections per second can potentially be achieved.
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Real-Time Adaptive Color Segmentation by Neural Networks
NASA Technical Reports Server (NTRS)
Duong, Tuan A.
2004-01-01
Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.
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Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin
Taube Navaraj, William; García Núñez, Carlos; Shakthivel, Dhayalan; Vinciguerra, Vincenzo; Labeau, Fabrice; Gregory, Duncan H.; Dahiya, Ravinder
2017-01-01
This paper presents novel Neural Nanowire Field Effect Transistors (υ-NWFETs) based hardware-implementable neural network (HNN) approach for tactile data processing in electronic skin (e-skin). The viability of Si nanowires (NWs) as the active material for υ-NWFETs in HNN is explored through modeling and demonstrated by fabricating the first device. Using υ-NWFETs to realize HNNs is an interesting approach as by printing NWs on large area flexible substrates it will be possible to develop a bendable tactile skin with distributed neural elements (for local data processing, as in biological skin) in the backplane. The modeling and simulation of υ-NWFET based devices show that the overlapping areas between individual gates and the floating gate determines the initial synaptic weights of the neural network - thus validating the working of υ-NWFETs as the building block for HNN. The simulation has been further extended to υ-NWFET based circuits and neuronal computation system and this has been validated by interfacing it with a transparent tactile skin prototype (comprising of 6 × 6 ITO based capacitive tactile sensors array) integrated on the palm of a 3D printed robotic hand. In this regard, a tactile data coding system is presented to detect touch gesture and the direction of touch. Following these simulation studies, a four-gated υ-NWFET is fabricated with Pt/Ti metal stack for gates, source and drain, Ni floating gate, and Al2O3 high-k dielectric layer. The current-voltage characteristics of fabricated υ-NWFET devices confirm the dependence of turn-off voltages on the (synaptic) weight of each gate. The presented υ-NWFET approach is promising for a neuro-robotic tactile sensory system with distributed computing as well as numerous futuristic applications such as prosthetics, and electroceuticals. PMID:28979183
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Massengill, L W; Mundie, D B
1992-01-01
A neural network IC based on a dynamic charge injection is described. The hardware design is space and power efficient, and achieves massive parallelism of analog inner products via charge-based multipliers and spatially distributed summing buses. Basic synaptic cells are constructed of exponential pulse-decay modulation (EPDM) dynamic injection multipliers operating sequentially on propagating signal vectors and locally stored analog weights. Individually adjustable gain controls on each neutron reduce the effects of limited weight dynamic range. A hardware simulator/trainer has been developed which incorporates the physical (nonideal) characteristics of actual circuit components into the training process, thus absorbing nonlinearities and parametric deviations into the macroscopic performance of the network. Results show that charge-based techniques may achieve a high degree of neural density and throughput using standard CMOS processes.
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Tetzlaff, Christian; Kolodziejski, Christoph; Timme, Marc; Wörgötter, Florentin
2011-01-01
Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks. PMID:22203799
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Spatially Distributed Dendritic Resonance Selectively Filters Synaptic Input
Segev, Idan; Shamma, Shihab
2014-01-01
An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree. Synaptic plasticity enables this by strenghtening a subset of synapses that are, presumably, functionally relevant to the neuron. A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates. A widely held view is that a neuron has one resonant frequency and thus can pass through one rate. Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites, and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs. PMID:25144440
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Gating of Long-Term Potentiation by Nicotinic Acetylcholine Receptors at the Cerebellum Input Stage
Prestori, Francesca; Bonardi, Claudia; Mapelli, Lisa; Lombardo, Paola; Goselink, Rianne; De Stefano, Maria Egle; Gandolfi, Daniela; Mapelli, Jonathan; Bertrand, Daniel; Schonewille, Martijn; De Zeeuw, Chris; D’Angelo, Egidio
2013-01-01
The brain needs mechanisms able to correlate plastic changes with local circuit activity and internal functional states. At the cerebellum input stage, uncontrolled induction of long-term potentiation or depression (LTP or LTD) between mossy fibres and granule cells can saturate synaptic capacity and impair cerebellar functioning, which suggests that neuromodulators are required to gate plasticity processes. Cholinergic systems innervating the cerebellum are thought to enhance procedural learning and memory. Here we show that a specific subtype of acetylcholine receptors, the α7-nAChRs, are distributed both in cerebellar mossy fibre terminals and granule cell dendrites and contribute substantially to synaptic regulation. Selective α7-nAChR activation enhances the postsynaptic calcium increase, allowing weak mossy fibre bursts, which would otherwise cause LTD, to generate robust LTP. The local microperfusion of α7-nAChR agonists could also lead to in vivo switching of LTD to LTP following sensory stimulation of the whisker pad. In the cerebellar flocculus, α7-nAChR pharmacological activation impaired vestibulo-ocular-reflex adaptation, probably because LTP was saturated, preventing the fine adjustment of synaptic weights. These results show that gating mechanisms mediated by specific subtypes of nicotinic receptors are required to control the LTD/LTP balance at the mossy fibre-granule cell relay in order to regulate cerebellar plasticity and behavioural adaptation. PMID:23741401
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Dynamic Observation of Brain-Like Learning in a Ferroelectric Synapse Device
NASA Astrophysics Data System (ADS)
Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito; Fujii, Eiji; Tsujimura, Ayumu
2013-04-01
A brain-like learning function was implemented in an electronic synapse device using a ferroelectric-gate field effect transistor (FeFET). The FeFET was a bottom-gate type FET with a ZnO channel and a ferroelectric Pb(Zr,Ti)O3 (PZT) gate insulator. The synaptic weight, which is represented by the channel conductance of the FeFET, is updated by applying a gate voltage through a change in the ferroelectric polarization in the PZT. A learning function based on the symmetric spike-timing dependent synaptic plasticity was implemented in the synapse device using the multilevel weight update by applying a pulse gate voltage. The dynamic weighting and learning behavior in the synapse device was observed as a change in the membrane potential in a spiking neuron circuit.
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Asymmetric Operation of the Locomotor Central Pattern Generator in the Neonatal Mouse Spinal Cord
Endo, Toshiaki; Kiehn, Ole
2008-01-01
The rhythmic voltage oscillations in motor neurons (MNs) during locomotor movements reflect the operation of the pre-MN central pattern generator (CPG) network. Recordings from MNs can thus be used as a method to deduct the organization of CPGs. Here, we use continuous conductance measurements and decomposition methods to quantitatively assess the weighting and phase tuning of synaptic inputs to different flexor and extensor MNs during locomotor-like activity in the isolated neonatal mice lumbar spinal cord preparation. Whole cell recordings were obtained from 22 flexor and 18 extensor MNs in rostral and caudal lumbar segments. In all flexor and the large majority of extensor MNs the extracted excitatory and inhibitory synaptic conductances alternate but with a predominance of inhibitory conductances, most pronounced in extensors. These conductance changes are consistent with a “push–pull” operation of locomotor CPG. The extracted excitatory and inhibitory synaptic conductances varied between 2 and 56% of the mean total conductance. Analysis of the phase tuning of the extracted synaptic conductances in flexor and extensor MNs in the rostral lumbar cord showed that the flexor-phase–related synaptic conductance changes have sharper locomotor-phase tuning than the extensor-phase–related conductances, suggesting a modular organization of premotor CPG networks consisting of reciprocally coupled, but differently composed, flexor and extensor CPG networks. There was a clear difference between phase tuning in rostral and caudal MNs, suggesting a distinct operation of CPG networks in different lumbar segments. The highly asymmetric features were preserved throughout all ranges of locomotor frequencies investigated and with different combinations of locomotor-inducing drugs. The asymmetric nature of CPG operation and phase tuning of the conductance profiles provide important clues to the organization of the rodent locomotor CPG and are compatible with a multilayered and distributed structure of the network. PMID:18829847
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Dense modifiable interconnections utilizing photorefractive volume holograms
NASA Astrophysics Data System (ADS)
Psaltis, Demetri; Qiao, Yong
1990-11-01
This report describes an experimental two-layer optical neural network built at Caltech. The system uses photorefractive volume holograms to implement dense, modifiable synaptic interconnections and liquid crystal light valves (LCVS) to perform nonlinear thresholding operations. Kanerva's Sparse, Distributed Memory was implemented using this network and its ability to recognize handwritten character-alphabet (A-Z) has been demonstrated experimentally. According to Kanerva's model, the first layer has fixed, random weights of interconnections and the second layer is trained by sum-of-outer-products rule. After training, the recognition rates of the network on the training set (104 patterns) and test set (520 patterns) are 100 and 50 percent, respectively.
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Garza-Manero, Sylvia; Pichardo-Casas, Israel; Arias, Clorinda; Vaca, Luis; Zepeda, Angélica
2014-10-10
MicroRNAs (miRNAs) are small non-coding RNAs that control a wide range of functions in the cell. They act as post-transcriptional gene regulators throughout in development and in adulthood, although recent evidence suggests their potential role in the onset and development of various diseases and neuropathologies. In neurons miRNAs seem to play a key role as regulators of synaptic function. Synapses are vulnerable structures in neurodegenerative diseases. In particular, synaptic loss has been described as an early event in the pathogenesis of Alzheimer's Disease (AD). MicroRNA-mediated gene silencing represents a candidate event for the repression of specific mRNAs and protein synthesis that could account for synaptic dysfunction. In this work, we review the participation of miRNAs in synaptic function and consider their possible role in synaptic alterations in AD. First we review the biogenesis of miRNAs and their role as post-transcriptional regulators. Then we discuss recently published data on the distribution of miRNAs in the brain as well as their role in dynamic regulation at the synapse. In the second part, we briefly introduce the reader to AD, focusing on synaptic alterations in the progression of the pathology. Then we discuss possible implications of miRNAs in the associated synaptic dysfunction. Copyright © 2013 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Yang, Paul; Kim, Hyung Jun; Zheng, Hong; Beom, Geon Won; Park, Jong-Sung; Kang, Chi Jung; Yoon, Tae-Sik
2017-06-01
A synaptic transistor emulating the biological synaptic motion is demonstrated using the memcapacitance characteristics in a Pt/HfOx/n-indium-gallium-zinc-oxide (IGZO) memcapacitor. First, the metal-oxide-semiconductor (MOS) capacitor with Pt/HfOx/n-IGZO structure exhibits analog, polarity-dependent, and reversible memcapacitance in capacitance-voltage (C-V), capacitance-time (C-t), and voltage-pulse measurements. When a positive voltage is applied repeatedly to the Pt electrode, the accumulation capacitance increases gradually and sequentially. The depletion capacitance also increases consequently. The capacitances are restored by repeatedly applying a negative voltage, confirming the reversible memcapacitance. The analog and reversible memcapacitance emulates the potentiation and depression synaptic motions. The synaptic thin-film transistor (TFT) with this memcapacitor also shows the synaptic motion with gradually increasing drain current by repeatedly applying the positive gate and drain voltages and reversibly decreasing one by applying the negative voltages, representing synaptic weight modulation. The reversible and analog conductance change in the transistor at both the voltage sweep and pulse operations is obtained through the memcapacitance and threshold voltage shift at the same time. These results demonstrate the synaptic transistor operations with a MOS memcapacitor gate stack consisting of Pt/HfOx/n-IGZO.
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Yang, Paul; Jun Kim, Hyung; Zheng, Hong; Won Beom, Geon; Park, Jong-Sung; Jung Kang, Chi; Yoon, Tae-Sik
2017-06-02
A synaptic transistor emulating the biological synaptic motion is demonstrated using the memcapacitance characteristics in a Pt/HfOx/n-indium-gallium-zinc-oxide (IGZO) memcapacitor. First, the metal-oxide-semiconductor (MOS) capacitor with Pt/HfOx/n-IGZO structure exhibits analog, polarity-dependent, and reversible memcapacitance in capacitance-voltage (C-V), capacitance-time (C-t), and voltage-pulse measurements. When a positive voltage is applied repeatedly to the Pt electrode, the accumulation capacitance increases gradually and sequentially. The depletion capacitance also increases consequently. The capacitances are restored by repeatedly applying a negative voltage, confirming the reversible memcapacitance. The analog and reversible memcapacitance emulates the potentiation and depression synaptic motions. The synaptic thin-film transistor (TFT) with this memcapacitor also shows the synaptic motion with gradually increasing drain current by repeatedly applying the positive gate and drain voltages and reversibly decreasing one by applying the negative voltages, representing synaptic weight modulation. The reversible and analog conductance change in the transistor at both the voltage sweep and pulse operations is obtained through the memcapacitance and threshold voltage shift at the same time. These results demonstrate the synaptic transistor operations with a MOS memcapacitor gate stack consisting of Pt/HfOx/n-IGZO.
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Nonvolatile Array Of Synapses For Neural Network
NASA Technical Reports Server (NTRS)
Tawel, Raoul
1993-01-01
Elements of array programmed with help of ultraviolet light. A 32 x 32 very-large-scale integrated-circuit array of electronic synapses serves as building-block chip for analog neural-network computer. Synaptic weights stored in nonvolatile manner. Makes information content of array invulnerable to loss of power, and, by eliminating need for circuitry to refresh volatile synaptic memory, makes architecture simpler and more compact.
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Matias, Fernanda S.; Carelli, Pedro V.; Mirasso, Claudio R.; Copelli, Mauro
2015-01-01
Several cognitive tasks related to learning and memory exhibit synchronization of macroscopic cortical areas together with synaptic plasticity at neuronal level. Therefore, there is a growing effort among computational neuroscientists to understand the underlying mechanisms relating synchrony and plasticity in the brain. Here we numerically study the interplay between spike-timing dependent plasticity (STDP) and anticipated synchronization (AS). AS emerges when a dominant flux of information from one area to another is accompanied by a negative time lag (or phase). This means that the receiver region pulses before the sender does. In this paper we study the interplay between different synchronization regimes and STDP at the level of three-neuron microcircuits as well as cortical populations. We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations. We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity. Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries. PMID:26474165
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Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.
Burbank, Kendra S
2015-12-01
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.
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Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons
Burbank, Kendra S.
2015-01-01
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. PMID:26633645
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Hippocampal ripples down-regulate synapses.
Norimoto, Hiroaki; Makino, Kenichi; Gao, Mengxuan; Shikano, Yu; Okamoto, Kazuki; Ishikawa, Tomoe; Sasaki, Takuya; Hioki, Hiroyuki; Fujisawa, Shigeyoshi; Ikegaya, Yuji
2018-03-30
The specific effects of sleep on synaptic plasticity remain unclear. We report that mouse hippocampal sharp-wave ripple oscillations serve as intrinsic events that trigger long-lasting synaptic depression. Silencing of sharp-wave ripples during slow-wave states prevented the spontaneous down-regulation of net synaptic weights and impaired the learning of new memories. The synaptic down-regulation was dependent on the N -methyl-d-aspartate receptor and selective for a specific input pathway. Thus, our findings are consistent with the role of slow-wave states in refining memory engrams by reducing recent memory-irrelevant neuronal activity and suggest a previously unrecognized function for sharp-wave ripples. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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Bayesian analysis of the kinetics of quantal transmitter secretion at the neuromuscular junction.
Saveliev, Anatoly; Khuzakhmetova, Venera; Samigullin, Dmitry; Skorinkin, Andrey; Kovyazina, Irina; Nikolsky, Eugeny; Bukharaeva, Ellya
2015-10-01
The timing of transmitter release from nerve endings is considered nowadays as one of the factors determining the plasticity and efficacy of synaptic transmission. In the neuromuscular junction, the moments of release of individual acetylcholine quanta are related to the synaptic delays of uniquantal endplate currents recorded under conditions of lowered extracellular calcium. Using Bayesian modelling, we performed a statistical analysis of synaptic delays in mouse neuromuscular junction with different patterns of rhythmic nerve stimulation and when the entry of calcium ions into the nerve terminal was modified. We have obtained a statistical model of the release timing which is represented as the summation of two independent statistical distributions. The first of these is the exponentially modified Gaussian distribution. The mixture of normal and exponential components in this distribution can be interpreted as a two-stage mechanism of early and late periods of phasic synchronous secretion. The parameters of this distribution depend on both the stimulation frequency of the motor nerve and the calcium ions' entry conditions. The second distribution was modelled as quasi-uniform, with parameters independent of nerve stimulation frequency and calcium entry. Two different probability density functions for the distribution of synaptic delays suggest at least two independent processes controlling the time course of secretion, one of them potentially involving two stages. The relative contribution of these processes to the total number of mediator quanta released depends differently on the motor nerve stimulation pattern and on calcium ion entry into nerve endings.
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Moroz, Leonid L; Kohn, Andrea B
2015-12-01
Hypotheses of origins and evolution of neurons and synapses are controversial, mostly due to limited comparative data. Here, we investigated the genome-wide distribution of the bilaterian "synaptic" and "neuronal" protein-coding genes in non-bilaterian basal metazoans (Ctenophora, Porifera, Placozoa, and Cnidaria). First, there are no recognized genes uniquely expressed in neurons across all metazoan lineages. None of the so-called pan-neuronal genes such as embryonic lethal abnormal vision (ELAV), Musashi, or Neuroglobin are expressed exclusively in neurons of the ctenophore Pleurobrachia. Second, our comparative analysis of about 200 genes encoding canonical presynaptic and postsynaptic proteins in bilaterians suggests that there are no true "pan-synaptic" genes or genes uniquely and specifically attributed to all classes of synapses. The majority of these genes encode receptive and secretory complexes in a broad spectrum of eukaryotes. Trichoplax (Placozoa) an organism without neurons and synapses has more orthologs of bilaterian synapse-related/neuron-related genes than do ctenophores-the group with well-developed neuronal and synaptic organization. Third, the majority of genes encoding ion channels and ionotropic receptors are broadly expressed in unicellular eukaryotes and non-neuronal tissues in metazoans. Therefore, they cannot be viewed as neuronal markers. Nevertheless, the co-expression of multiple types of ion channels and receptors does correlate with the presence of neural and synaptic organization. As an illustrative example, the ctenophore genomes encode a greater diversity of ion channels and ionotropic receptors compared with the genomes of the placozoan Trichoplax and the demosponge Amphimedon. Surprisingly, both placozoans and sponges have a similar number of orthologs of "synaptic" proteins as we identified in the genomes of two ctenophores. Ctenophores have a distinct synaptic organization compared with other animals. Our analysis of transcriptomes from 10 different ctenophores did not detect recognized orthologs of synthetic enzymes encoding several classical, low-molecular-weight (neuro)transmitters; glutamate signaling machinery is one of the few exceptions. Novel peptidergic signaling molecules were predicted for ctenophores, together with the diversity of putative receptors including SCNN1/amiloride-sensitive sodium channel-like channels, many of which could be examples of a lineage-specific expansion within this group. In summary, our analysis supports the hypothesis of independent evolution of neurons and, as corollary, a parallel evolution of synapses. We suggest that the formation of synaptic machinery might occur more than once over 600 million years of animal evolution. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.
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Neural coding using telegraphic switching of magnetic tunnel junction
DOE Office of Scientific and Technical Information (OSTI.GOV)
Suh, Dong Ik; Bae, Gi Yoon; Oh, Heong Sik
2015-05-07
In this work, we present a synaptic transmission representing neural coding with spike trains by using a magnetic tunnel junction (MTJ). Telegraphic switching generates an artificial neural signal with both the applied magnetic field and the spin-transfer torque that act as conflicting inputs for modulating the number of spikes in spike trains. The spiking probability is observed to be weighted with modulation between 27.6% and 99.8% by varying the amplitude of the voltage input or the external magnetic field. With a combination of the reverse coding scheme and the synaptic characteristic of MTJ, an artificial function for the synaptic transmissionmore » is achieved.« less
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Li, Yi; Zhong, Yingpeng; Zhang, Jinjian; Xu, Lei; Wang, Qing; Sun, Huajun; Tong, Hao; Cheng, Xiaoming; Miao, Xiangshui
2014-05-09
Nanoscale inorganic electronic synapses or synaptic devices, which are capable of emulating the functions of biological synapses of brain neuronal systems, are regarded as the basic building blocks for beyond-Von Neumann computing architecture, combining information storage and processing. Here, we demonstrate a Ag/AgInSbTe/Ag structure for chalcogenide memristor-based electronic synapses. The memristive characteristics with reproducible gradual resistance tuning are utilised to mimic the activity-dependent synaptic plasticity that serves as the basis of memory and learning. Bidirectional long-term Hebbian plasticity modulation is implemented by the coactivity of pre- and postsynaptic spikes, and the sign and degree are affected by assorted factors including the temporal difference, spike rate and voltage. Moreover, synaptic saturation is observed to be an adjustment of Hebbian rules to stabilise the growth of synaptic weights. Our results may contribute to the development of highly functional plastic electronic synapses and the further construction of next-generation parallel neuromorphic computing architecture.
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Villa, Roberto Federico; Ferrari, Federica; Gorini, Antonella
2012-12-01
The effect of aging and CDP-choline treatment (20 mg kg⁻¹ body weight i.p. for 28 days) on the maximal rates (V(max)) of representative mitochondrial enzyme activities related to Krebs' cycle (citrate synthase, α-ketoglutarate dehydrogenase, malate dehydrogenase), glutamate and related amino acid metabolism (glutamate dehydrogenase, glutamate-oxaloacetate- and glutamate-pyruvate transaminases) were evaluated in non-synaptic and intra-synaptic "light" and "heavy" mitochondria from frontal cerebral cortex of male Wistar rats aged 4, 12, 18 and 24 months. During aging, enzyme activities vary in a complex way respect to the type of mitochondria, i.e. non-synaptic and intra-synaptic. This micro-heterogeneity is an important factor, because energy-related mitochondrial enzyme catalytic properties cause metabolic modifications of physiopathological significance in cerebral tissue in vivo, also discriminating pre- and post-synaptic sites of action for drugs and affecting tissue responsiveness to noxious stimuli. Results show that CDP-choline in vivo treatment enhances cerebral energy metabolism selectively at 18 months, specifically modifying enzyme catalytic activities in non-synaptic and intra-synaptic "light" mitochondrial sub-populations. This confirms that the observed changes in enzyme catalytic activities during aging reflect the bioenergetic state at each single age and the corresponding energy requirements, further proving that in vivo drug treatment is able to interfere with the neuronal energy metabolism. Copyright © 2012. Published by Elsevier Ltd.
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Excitement and synchronization of small-world neuronal networks with short-term synaptic plasticity.
Han, Fang; Wiercigroch, Marian; Fang, Jian-An; Wang, Zhijie
2011-10-01
Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically coupled network but impairs synchronization for the chemically coupled one. Both the introduction of shortcuts and the increase of the coupling strength improve synchronization and they are helpful in increasing the excitement for the chemically coupled network, but have little effect on the excitement of the electrically coupled one.
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E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.
Trapp, Philip; Echeveste, Rodrigo; Gros, Claudius
2018-06-12
Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.
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Cohen, Yaniv; Wilson, Donald A.; Barkai, Edi
2015-01-01
Learning of a complex olfactory discrimination (OD) task results in acquisition of rule learning after prolonged training. Previously, we demonstrated enhanced synaptic connectivity between the piriform cortex (PC) and its ascending and descending inputs from the olfactory bulb (OB) and orbitofrontal cortex (OFC) following OD rule learning. Here, using recordings of evoked field postsynaptic potentials in behaving animals, we examined the dynamics by which these synaptic pathways are modified during rule acquisition. We show profound differences in synaptic connectivity modulation between the 2 input sources. During rule acquisition, the ascending synaptic connectivity from the OB to the anterior and posterior PC is simultaneously enhanced. Furthermore, post-training stimulation of the OB enhanced learning rate dramatically. In sharp contrast, the synaptic input in the descending pathway from the OFC was significantly reduced until training completion. Once rule learning was established, the strength of synaptic connectivity in the 2 pathways resumed its pretraining values. We suggest that acquisition of olfactory rule learning requires a transient enhancement of ascending inputs to the PC, synchronized with a parallel decrease in the descending inputs. This combined short-lived modulation enables the PC network to reorganize in a manner that enables it to first acquire and then maintain the rule. PMID:23960200
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Self-organised criticality via retro-synaptic signals
NASA Astrophysics Data System (ADS)
Hernandez-Urbina, Victor; Herrmann, J. Michael
2016-12-01
The brain is a complex system par excellence. In the last decade the observation of neuronal avalanches in neocortical circuits suggested the presence of self-organised criticality in brain networks. The occurrence of this type of dynamics implies several benefits to neural computation. However, the mechanisms that give rise to critical behaviour in these systems, and how they interact with other neuronal processes such as synaptic plasticity are not fully understood. In this paper, we present a long-term plasticity rule based on retro-synaptic signals that allows the system to reach a critical state in which clusters of activity are distributed as a power-law, among other observables. Our synaptic plasticity rule coexists with other synaptic mechanisms such as spike-timing-dependent plasticity, which implies that the resulting synaptic modulation captures not only the temporal correlations between spiking times of pre- and post-synaptic units, which has been suggested as requirement for learning and memory in neural systems, but also drives the system to a state of optimal neural information processing.
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Analog hardware for learning neural networks
NASA Technical Reports Server (NTRS)
Eberhardt, Silvio P. (Inventor)
1991-01-01
This is a recurrent or feedforward analog neural network processor having a multi-level neuron array and a synaptic matrix for storing weighted analog values of synaptic connection strengths which is characterized by temporarily changing one connection strength at a time to determine its effect on system output relative to the desired target. That connection strength is then adjusted based on the effect, whereby the processor is taught the correct response to training examples connection by connection.
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2014-10-01
AD_________________ Award Number: W81XWH-13-1-0415 TITLE: Testing Brain Overgrowth and Synaptic Models of Autism Using NPCs and Neurons from...REPORT TYPE Annual 3. DATES COVERED 15 Sept 2013 – 14 Sept 2014 4. TITLE AND SUBTITLE Testing Brain Overgrowth and Synaptic Models of Autism Using...STATEMENT Approved for Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Autism and autism spectrum disorders (ASD
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Distance-dependent gradient in NMDAR-driven spine calcium signals along tapering dendrites
Walker, Alison S.; Grillo, Federico; Jackson, Rachel E.; Rigby, Mark; Lowe, Andrew S.; Vizcay-Barrena, Gema; Fleck, Roland A.; Burrone, Juan
2017-01-01
Neurons receive a multitude of synaptic inputs along their dendritic arbor, but how this highly heterogeneous population of synaptic compartments is spatially organized remains unclear. By measuring N-methyl-d-aspartic acid receptor (NMDAR)-driven calcium responses in single spines, we provide a spatial map of synaptic calcium signals along dendritic arbors of hippocampal neurons and relate this to measures of synapse structure. We find that quantal NMDAR calcium signals increase in amplitude as they approach a thinning dendritic tip end. Based on a compartmental model of spine calcium dynamics, we propose that this biased distribution in calcium signals is governed by a gradual, distance-dependent decline in spine size, which we visualized using serial block-face scanning electron microscopy. Our data describe a cell-autonomous feature of principal neurons, where tapering dendrites show an inverse distribution of spine size and NMDAR-driven calcium signals along dendritic trees, with important implications for synaptic plasticity rules and spine function. PMID:28209776
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Pedretti, G; Milo, V; Ambrogio, S; Carboni, R; Bianchi, S; Calderoni, A; Ramaswamy, N; Spinelli, A S; Ielmini, D
2017-07-13
Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10 4 ) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.
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NASA Astrophysics Data System (ADS)
Yan, Xiaodong; Tian, He; Xie, Yujun; Kostelec, Andrew; Zhao, Huan; Cha, Judy J.; Tice, Jesse; Wang, Han
Modulatory input-dependent plasticity is a well-known type of hetero-synaptic response where the release of neuromodulators can alter the efficacy of neurotransmission in a nearby chemical synapse. Solid-state devices that can mimic such phenomenon are desirable for enhancing the functionality and reconfigurability of neuromorphic electronics. In this work, we demonstrated a tunable artificial synaptic device concept based on the properties of graphene and tin oxide that can mimic the modulatory input-dependent plasticity. By using graphene as the contact electrode, a third electrode terminal can be used to modulate the conductive filament formation in the vertical tin oxide based resistive memory device. The resulting synaptic characteristics of this device, in terms of the profile of synaptic weight change and the spike-timing-dependent-plasticity, is tunable with the bias at the modulating terminal. Furthermore, the synaptic response can be reconfigured between excitatory and inhibitory modes by this modulating bias. The operation mechanism of the device is studied with combined experimental and theoretical analysis. The device is attractive for application in neuromorphic electronics. This work is supported by ARO and NG-ION2 at USC.
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Statistical theory of synaptic connectivity in the neocortex
NASA Astrophysics Data System (ADS)
Escobar, Gina
Learning and long-term memory rely on plasticity of neural circuits. In adult cerebral cortex plasticity can be mediated by modulation of existing synapses and structural reorganization of circuits through growth and retraction of dendritic spines. In the first part of this thesis, we describe a theoretical framework for the analysis of spine remodeling plasticity. New synaptic contacts appear in the neuropil where gaps between axonal and dendritic branches can be bridged by dendritic spines. Such sites are termed potential synapses. We derive expressions for the densities of potential synapses in the neuropil. We calculate the ratio of actual to potential synapses, called the connectivity fraction, and use it to find the number of structurally different circuits attainable with spine remodeling. These parameters are calculated in four systems: mouse occipital cortex, rat hippocampal area CA1, monkey primary visual (V1), and human temporal cortex. The neurogeometric results indicate that a dendritic spine can choose among an average of 4-7 potential targets in rodents, while in primates it can choose from 10-20 potential targets. The potential of the neuropil to undergo circuit remodeling is found to be highest in rat CA1 (4.9-6.0 nats/mum 3) and lowest in monkey V1 (0.9-1.0 nats/mum3). We evaluate the lower bound of neuron selectivity in the choice of synaptic partners and find that post-synaptic excitatory neurons in rodents make synaptic contacts with more than 21-30% of pre-synaptic axons encountered with new spine growth. Primate neurons appear to be more selective, making synaptic connections with more than 7-15% of encountered axons. Another plasticity mechanism is included in the second part of this work: long-term potentiation and depression of excitatory synaptic connections. Because synaptic strength is correlated with the size of the synapse, the former can be inferred from the distribution of spine head volumes. To this end we analyze and compare 166 distributions of spine head volumes and spine lengths from mouse, rat, monkey, and human brains. We develope a statistical theory in which the equilibrium distribution of dendritic spine shapes is governed by the principle of synaptic entropy maximization under a "generalized cost" constraint. We find the generalized cost of dendritic spines and show that it universally depends on the spine shape, i.e. the dependence is the same in all the considered systems. We show that the modulatory and structural plasticity mechanisms in adults are in a statistical equilibrium with each other, the numbers of dendritic spines in different cortical areas are nearly optimally chosen for memory storage, and the distribution of spine shapes is governed by a single parameter -- the effective temperature. Our results suggest that the effective temperature of a cortical area may be viewed as a measure of longevity of stored memories. Finally, we test the hypothesis that the number of spines in the neuropil is chosen to optimize its storage information capacity.
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Zhao, LiYing; Sakagami, Hiroyuki; Suzuki, Tatsuo
2014-10-01
We systematically investigated the purification process of post-synaptic density (PSD) and post-synaptic membrane rafts (PSRs) from the rat forebrain synaptic plasma membranes by examining the components and the structures of the materials obtained after the treatment of synaptic plasma membranes with TX-100, n-octyl β-d-glucoside (OG) or 3-([3-cholamidopropyl]dimethylammonio)-2-hydroxy-1-propanesulfonate (CHAPSO). These three detergents exhibited distinct separation profiles for the synaptic subdomains. Type I and type II PSD proteins displayed mutually exclusive distribution. After TX-100 treatment, type I PSD was recovered in two fractions: a pellet and an insoluble fraction 8, which contained partially broken PSD-PSR complexes. Conventional PSD was suggested to be a mixture of these two PSD pools and did not contain type II PSD. An association of type I PSD with PSRs was identified in the TX-100 treatment, and those with type II PSD in the OG and CHAPSO treatments. An association of GABA receptors with gephyrin was easily dissociated. OG at a high concentration solubilized the type I PSD proteins. CHAPSO treatment resulted in a variety of distinct fractions, which contained certain novel structures. Two different pools of GluA, either PSD or possibly raft-associated, were identified in the OG and CHAPSO treatments. These results are useful in advancing our understanding of the structural organization of synapses at the molecular level. We systematically investigated the purification process of post-synaptic density (PSD) and synaptic membrane rafts by examining the structures obtained after treatment of the SPMs with TX-100, n-octyl β-d-glucoside or CHAPSO. Differential distribution of type I and type II PSD, synaptic membrane rafts, and other novel subdomains in the SPM give clues to understand the structural organization of synapses at the molecular level. © 2014 International Society for Neurochemistry.
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Attention Enhances Synaptic Efficacy and Signal-to-Noise in Neural Circuits
Briggs, Farran; Mangun, George R.; Usrey, W. Martin
2013-01-01
Summary Attention is a critical component of perception. However, the mechanisms by which attention modulates neuronal communication to guide behavior are poorly understood. To elucidate the synaptic mechanisms of attention, we developed a sensitive assay of attentional modulation of neuronal communication. In alert monkeys performing a visual spatial attention task, we probed thalamocortical communication by electrically stimulating neurons in the lateral geniculate nucleus of the thalamus while simultaneously recording shock-evoked responses from monosynaptically connected neurons in primary visual cortex. We found that attention enhances neuronal communication by (1) increasing the efficacy of presynaptic input in driving postsynaptic responses, (2) increasing synchronous responses among ensembles of postsynaptic neurons receiving independent input, and (3) decreasing redundant signals between postsynaptic neurons receiving common input. These results demonstrate that attention finely tunes neuronal communication at the synaptic level by selectively altering synaptic weights, enabling enhanced detection of salient events in the noisy sensory milieu. PMID:23803766
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Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations
Wei, Yina; Krishnan, Giri P.
2016-01-01
Sleep is critical for regulation of synaptic efficacy, memories, and learning. However, the underlying mechanisms of how sleep rhythms contribute to consolidating memories acquired during wakefulness remain unclear. Here we studied the role of slow oscillations, 0.2–1 Hz rhythmic transitions between Up and Down states during stage 3/4 sleep, on dynamics of synaptic connectivity in the thalamocortical network model implementing spike-timing-dependent synaptic plasticity. We found that the spatiotemporal pattern of Up-state propagation determines the changes of synaptic strengths between neurons. Furthermore, an external input, mimicking hippocampal ripples, delivered to the cortical network results in input-specific changes of synaptic weights, which persisted after stimulation was removed. These synaptic changes promoted replay of specific firing sequences of the cortical neurons. Our study proposes a neuronal mechanism on how an interaction between hippocampal input, such as mediated by sharp wave-ripple events, cortical slow oscillations, and synaptic plasticity, may lead to consolidation of memories through preferential replay of cortical cell spike sequences during slow-wave sleep. SIGNIFICANCE STATEMENT Sleep is critical for memory and learning. Replay during sleep of temporally ordered spike sequences related to a recent experience was proposed to be a neuronal substrate of memory consolidation. However, specific mechanisms of replay or how spike sequence replay leads to synaptic changes that underlie memory consolidation are still poorly understood. Here we used a detailed computational model of the thalamocortical system to report that interaction between slow cortical oscillations and synaptic plasticity during deep sleep can underlie mapping hippocampal memory traces to persistent cortical representation. This study provided, for the first time, a mechanistic explanation of how slow-wave sleep may promote consolidation of recent memory events. PMID:27076422
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Synaptic behaviors of a single metal-oxide-metal resistive device
NASA Astrophysics Data System (ADS)
Choi, Sang-Jun; Kim, Guk-Bae; Lee, Kyoobin; Kim, Ki-Hong; Yang, Woo-Young; Cho, Soohaeng; Bae, Hyung-Jin; Seo, Dong-Seok; Kim, Sang-Il; Lee, Kyung-Jin
2011-03-01
The mammalian brain is far superior to today's electronic circuits in intelligence and efficiency. Its functions are realized by the network of neurons connected via synapses. Much effort has been extended in finding satisfactory electronic neural networks that act like brains, i.e., especially the electronic version of synapse that is capable of the weight control and is independent of the external data storage. We demonstrate experimentally that a single metal-oxide-metal structure successfully stores the biological synaptic weight variations (synaptic plasticity) without any external storage node or circuit. Our device also demonstrates the reliability of plasticity experimentally with the model considering the time dependence of spikes. All these properties are embodied by the change of resistance level corresponding to the history of injected voltage-pulse signals. Moreover, we prove the capability of second-order learning of the multi-resistive device by applying it to the circuit composed of transistors. We anticipate our demonstration will invigorate the study of electronic neural networks using non-volatile multi-resistive device, which is simpler and superior compared to other storage devices.
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2015-12-01
AWARD NUMBER: W81XWH-13-1-0414 TITLE: Testing Brain Overgrowth and Synaptic Models of Autism Using NPCs and Neurons From Patient-Derived iPS...3. DATES COVERED 15 Sep 2013 - 14 Sep 2015 4. TITLE AND SUBTITLE Testing Brain Overgrowth and Synaptic Models of Autism Using NPCs and Neurons From...AVAILABILITY STATEMENT Approved for Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Autism and autism spectrum disorders (ASD
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Luque, Niceto R.; Garrido, Jesús A.; Carrillo, Richard R.; D'Angelo, Egidio; Ros, Eduardo
2014-01-01
The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network. PMID:25177290
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Su, Bo; Ji, Yun-Song; Sun, Xu-lu; Liu, Xiang-Hua; Chen, Zhe-Yu
2014-01-17
Appropriate mitochondrial transport and distribution are essential for neurons because of the high energy and Ca(2+) buffering requirements at synapses. Brain-derived neurotrophic factor (BDNF) plays an essential role in regulating synaptic transmission and plasticity. However, whether and how BDNF can regulate mitochondrial transport and distribution are still unclear. Here, we find that in cultured hippocampal neurons, application of BDNF for 15 min decreased the percentage of moving mitochondria in axons, a process dependent on the activation of the TrkB receptor and its downstream PI3K and phospholipase-Cγ signaling pathways. Moreover, the BDNF-induced mitochondrial stopping requires the activation of transient receptor potential canonical 3 and 6 (TRPC3 and TRPC6) channels and elevated intracellular Ca(2+) levels. The Ca(2+) sensor Miro1 plays an important role in this process. Finally, the BDNF-induced mitochondrial stopping leads to the accumulation of more mitochondria at presynaptic sites. Mutant Miro1 lacking the ability to bind Ca(2+) prevents BDNF-induced mitochondrial presynaptic accumulation and synaptic transmission, suggesting that Miro1-mediated mitochondrial motility is involved in BDNF-induced mitochondrial presynaptic docking and neurotransmission. Together, these data suggest that mitochondrial transport and distribution play essential roles in BDNF-mediated synaptic transmission.
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The super-Turing computational power of plastic recurrent neural networks.
Cabessa, Jérémie; Siegelmann, Hava T
2014-12-01
We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.
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Farinella, Matteo; Ruedt, Daniel T.; Gleeson, Padraig; Lanore, Frederic; Silver, R. Angus
2014-01-01
In vivo, cortical pyramidal cells are bombarded by asynchronous synaptic input arising from ongoing network activity. However, little is known about how such ‘background’ synaptic input interacts with nonlinear dendritic mechanisms. We have modified an existing model of a layer 5 (L5) pyramidal cell to explore how dendritic integration in the apical dendritic tuft could be altered by the levels of network activity observed in vivo. Here we show that asynchronous background excitatory input increases neuronal gain and extends both temporal and spatial integration of stimulus-evoked synaptic input onto the dendritic tuft. Addition of fast and slow inhibitory synaptic conductances, with properties similar to those from dendritic targeting interneurons, that provided a ‘balanced’ background configuration, partially counteracted these effects, suggesting that inhibition can tune spatio-temporal integration in the tuft. Excitatory background input lowered the threshold for NMDA receptor-mediated dendritic spikes, extended their duration and increased the probability of additional regenerative events occurring in neighbouring branches. These effects were also observed in a passive model where all the non-synaptic voltage-gated conductances were removed. Our results show that glutamate-bound NMDA receptors arising from ongoing network activity can provide a powerful spatially distributed nonlinear dendritic conductance. This may enable L5 pyramidal cells to change their integrative properties as a function of local network activity, potentially allowing both clustered and spatially distributed synaptic inputs to be integrated over extended timescales. PMID:24763087
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Miner, Daniel; Triesch, Jochen
2016-01-01
Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function. Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random, exhibiting, for example, above-chance bidirectionality and an overrepresentation of certain triangular motifs. Additional evidence suggests a significant distance dependency to connectivity over a local scale of a few hundred microns, and particular patterns of synaptic turnover dynamics, including a heavy-tailed distribution of synaptic efficacies, a power law distribution of synaptic lifetimes, and a tendency for stronger synapses to be more stable over time. Understanding how many of these non-random features simultaneously arise would provide valuable insights into the development and function of the cortex. While previous work has modeled some of the individual features of local cortical wiring, there is no model that begins to comprehensively account for all of them. We present a spiking network model of a rodent Layer 5 cortical slice which, via the interactions of a few simple biologically motivated intrinsic, synaptic, and structural plasticity mechanisms, qualitatively reproduces these non-random effects when combined with simple topological constraints. Our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed non-random features of recurrent cortical wiring. Interestingly, similar mechanisms have been shown to endow recurrent networks with powerful learning abilities, suggesting that these mechanism are central to understanding both structure and function of cortical synaptic wiring. PMID:26866369
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Panda, Priyadarshini; Roy, Kaushik
2017-01-01
Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations. PMID:29311774
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Regulation of Synaptic Structure by the Ubiquitin C-terminal Hydrolase UCH-L1
Cartier, Anna E.; Djakovic, Stevan N.; Salehi, Afshin; Wilson, Scott M.; Masliah, Eliezer; Patrick, Gentry N.
2009-01-01
UCH-L1 is a de-ubiquitinating enzyme that is selectively and abundantly expressed in the brain, and its activity is required for normal synaptic function. Here, we show that UCH-L1 functions in maintaining normal synaptic structure in hippocampal neurons. We have found that UCH-L1 activity is rapidly up-regulated by NMDA receptor activation which leads to an increase in the levels of free monomeric ubiquitin. Conversely, pharmacological inhibition of UCH-L1 significantly reduces monomeric ubiquitin levels and causes dramatic alterations in synaptic protein distribution and spine morphology. Inhibition of UCH-L1 activity increases spine size while decreasing spine density. Furthermore, there is a concomitant increase in the size of pre and postsynaptic protein clusters. Interestingly, however, ectopic expression of ubiquitin restores normal synaptic structure in UCH-L1 inhibited neurons. These findings point to a significant role of UCH-L1 in synaptic remodeling most likely by modulating free monomeric ubiquitin levels in an activity-dependent manner. PMID:19535597
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Regulation of synaptic structure by ubiquitin C-terminal hydrolase L1.
Cartier, Anna E; Djakovic, Stevan N; Salehi, Afshin; Wilson, Scott M; Masliah, Eliezer; Patrick, Gentry N
2009-06-17
Ubiquitin C-terminal hydrolase L1 (UCH-L1) is a deubiquitinating enzyme that is selectively and abundantly expressed in the brain, and its activity is required for normal synaptic function. Here, we show that UCH-L1 functions in maintaining normal synaptic structure in hippocampal neurons. We found that UCH-L1 activity is rapidly upregulated by NMDA receptor activation, which leads to an increase in the levels of free monomeric ubiquitin. Conversely, pharmacological inhibition of UCH-L1 significantly reduces monomeric ubiquitin levels and causes dramatic alterations in synaptic protein distribution and spine morphology. Inhibition of UCH-L1 activity increases spine size while decreasing spine density. Furthermore, there is a concomitant increase in the size of presynaptic and postsynaptic protein clusters. Interestingly, however, ectopic expression of ubiquitin restores normal synaptic structure in UCH-L1-inhibited neurons. These findings point to a significant role of UCH-L1 in synaptic remodeling, most likely by modulating free monomeric ubiquitin levels in an activity-dependent manner.
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Synaptic organization of the Drosophila antennal lobe and its regulation by the Teneurins
Mosca, Timothy J; Luo, Liqun
2014-01-01
Understanding information flow through neuronal circuits requires knowledge of their synaptic organization. In this study, we utilized fluorescent pre- and postsynaptic markers to map synaptic organization in the Drosophila antennal lobe, the first olfactory processing center. Olfactory receptor neurons (ORNs) produce a constant synaptic density across different glomeruli. Each ORN within a class contributes nearly identical active zone number. Active zones from ORNs, projection neurons (PNs), and local interneurons have distinct subglomerular and subcellular distributions. The correct number of ORN active zones and PN acetylcholine receptor clusters requires the Teneurins, conserved transmembrane proteins involved in neuromuscular synapse organization and synaptic partner matching. Ten-a acts in ORNs to organize presynaptic active zones via the spectrin cytoskeleton. Ten-m acts in PNs autonomously to regulate acetylcholine receptor cluster number and transsynaptically to regulate ORN active zone number. These studies advanced our ability to assess synaptic architecture in complex CNS circuits and their underlying molecular mechanisms. DOI: http://dx.doi.org/10.7554/eLife.03726.001 PMID:25310239
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Li, Chunyan; Tripathi, Pradeep K; Armstrong, William E
2007-01-01
The firing pattern of magnocellular neurosecretory neurons is intimately related to hormone release, but the relative contribution of synaptic versus intrinsic factors to the temporal dispersion of spikes is unknown. In the present study, we examined the firing patterns of vasopressin (VP) and oxytocin (OT) supraoptic neurons in coronal slices from virgin female rats, with and without blockade of inhibitory and excitatory synaptic currents. Inhibitory postsynaptic currents (IPSCs) were twice as prevalent as their excitatory counterparts (EPSCs), and both were more prevalent in OT compared with VP neurons. Oxytocin neurons fired more slowly and irregularly than VP neurons near threshold. Blockade of Cl− currents (including tonic and synaptic currents) with picrotoxin reduced interspike interval (ISI) variability of continuously firing OT and VP neurons without altering input resistance or firing rate. Blockade of EPSCs did not affect firing pattern. Phasic bursting neurons (putative VP neurons) were inconsistently affected by broad synaptic blockade, suggesting that intrinsic factors may dominate the ISI distribution during this mode in the slice. Specific blockade of synaptic IPSCs with gabazine also reduced ISI variability, but only in OT neurons. In all cases, the effect of inhibitory blockade on firing pattern was independent of any consistent change in input resistance or firing rate. Since the great majority of IPSCs are randomly distributed, miniature events (mIPSCs) in the coronal slice, these findings imply that even mIPSCs can impart irregularity to the firing pattern of OT neurons in particular, and could be important in regulating spike patterning in vivo. For example, the increased firing variability that precedes bursting in OT neurons during lactation could be related to significant changes in synaptic activity. PMID:17332000
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Spontaneously emerging direction selectivity maps in visual cortex through STDP.
Wenisch, Oliver G; Noll, Joachim; Hemmen, J Leo van
2005-10-01
It is still an open question as to whether, and how, direction-selective neuronal responses in primary visual cortex are generated by feedforward thalamocortical or recurrent intracortical connections, or a combination of both. Here we present an investigation that concentrates on and, only for the sake of simplicity, restricts itself to intracortical circuits, in particular, with respect to the developmental aspects of direction selectivity through spike-timing-dependent synaptic plasticity. We show that directional responses can emerge in a recurrent network model of visual cortex with spiking neurons that integrate inputs mainly from a particular direction, thus giving rise to an asymmetrically shaped receptive field. A moving stimulus that enters the receptive field from this (preferred) direction will activate a neuron most strongly because of the increased number and/or strength of inputs from this direction and since delayed isotropic inhibition will neither overlap with, nor cancel excitation, as would be the case for other stimulus directions. It is demonstrated how direction-selective responses result from spatial asymmetries in the distribution of synaptic contacts or weights of inputs delivered to a neuron by slowly conducting intracortical axonal delay lines. By means of spike-timing-dependent synaptic plasticity with an asymmetric learning window this kind of coupling asymmetry develops naturally in a recurrent network of stochastically spiking neurons in a scenario where the neurons are activated by unidirectionally moving bar stimuli and even when only intrinsic spontaneous activity drives the learning process. We also present simulation results to show the ability of this model to produce direction preference maps similar to experimental findings.
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[Influence of mediator diffusion on trigger mode of a synapse].
Vasilev, A N; Kulish, M V
2014-01-01
The model of postsynaptic membrane activation, is proposed in the paper. This model takes into account inhomogeneity of mediator's space distribution in the region of the synaptic cleft as well as nonlinear nature of interaction between the mediator and receptors on the postsynaptic membrane. Based on equations of this model stationary solutions are calculated for mediator distribution in the synaptic cleft and the number of activated receptors. Kinetics of reactions for activation and deactivation of receptors is analyzed within the concept of a trigger mode of the synapse. It is shown that activation-deactivation processes and redistribution of the mediator in the cleft can be interpreted as successive transitions between two stationary states of the system. Time of transitions between these states is found and its dependence on system parameters (in particular on the width of the synaptic cleft) is analyzed.
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NASA Astrophysics Data System (ADS)
Yuan, Manman; Wang, Weiping; Luo, Xiong; Li, Lixiang; Kurths, Jürgen; Wang, Xiao
2018-03-01
This paper is concerned with the exponential lag function projective synchronization of memristive multidirectional associative memory neural networks (MMAMNNs). First, we propose a new model of MMAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying discrete delays and distributed time delays. Second, we design two kinds of hybrid controllers. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the controllers are carefully designed to confirm the process of different types of synchronization in the MMAMNNs. Third, sufficient criteria guaranteeing the synchronization of system are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.
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Cohen, Laurie D.; Zuchman, Rina; Sorokina, Oksana; Müller, Anke; Dieterich, Daniela C.; Armstrong, J. Douglas; Ziv, Tamar; Ziv, Noam E.
2013-01-01
Chemical synapses contain multitudes of proteins, which in common with all proteins, have finite lifetimes and therefore need to be continuously replaced. Given the huge numbers of synaptic connections typical neurons form, the demand to maintain the protein contents of these connections might be expected to place considerable metabolic demands on each neuron. Moreover, synaptic proteostasis might differ according to distance from global protein synthesis sites, the availability of distributed protein synthesis facilities, trafficking rates and synaptic protein dynamics. To date, the turnover kinetics of synaptic proteins have not been studied or analyzed systematically, and thus metabolic demands or the aforementioned relationships remain largely unknown. In the current study we used dynamic Stable Isotope Labeling with Amino acids in Cell culture (SILAC), mass spectrometry (MS), Fluorescent Non–Canonical Amino acid Tagging (FUNCAT), quantitative immunohistochemistry and bioinformatics to systematically measure the metabolic half-lives of hundreds of synaptic proteins, examine how these depend on their pre/postsynaptic affiliation or their association with particular molecular complexes, and assess the metabolic load of synaptic proteostasis. We found that nearly all synaptic proteins identified here exhibited half-lifetimes in the range of 2–5 days. Unexpectedly, metabolic turnover rates were not significantly different for presynaptic and postsynaptic proteins, or for proteins for which mRNAs are consistently found in dendrites. Some functionally or structurally related proteins exhibited very similar turnover rates, indicating that their biogenesis and degradation might be coupled, a possibility further supported by bioinformatics-based analyses. The relatively low turnover rates measured here (∼0.7% of synaptic protein content per hour) are in good agreement with imaging-based studies of synaptic protein trafficking, yet indicate that the metabolic load synaptic protein turnover places on individual neurons is very substantial. PMID:23658807
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García-Cáceres, Cristina; Fuente-Martín, Esther; Burgos-Ramos, Emma; Granado, Miriam; Frago, Laura M.; Barrios, Vicente; Horvath, Tamas
2011-01-01
Astrocytes participate in neuroendocrine functions partially through modulation of synaptic input density in the hypothalamus. Indeed, glial ensheathing of neurons is modified by specific hormones, thus determining the availability of neuronal membrane space for synaptic inputs, with the loss of this plasticity possibly being involved in pathological processes. Leptin modulates synaptic inputs in the hypothalamus, but whether astrocytes participate in this action is unknown. Here we report that astrocyte structural proteins, such as glial fibrillary acidic protein (GFAP) and vimentin, are induced and astrocyte morphology modified by chronic leptin administration (intracerebroventricular, 2 wk), with these changes being inversely related to modifications in synaptic protein densities. Similar changes in glial structural proteins were observed in adult male rats that had increased body weight and circulating leptin levels due to neonatal overnutrition (overnutrition: four pups/litter vs. control: 12 pups/litter). However, acute leptin treatment reduced hypothalamic GFAP levels and induced synaptic protein levels 1 h after administration, with no effect on vimentin. In primary hypothalamic astrocyte cultures leptin also reduced GFAP levels at 1 h, with an induction at 24 h, indicating a possible direct effect of leptin. Hence, one mechanism by which leptin may affect metabolism is by modifying hypothalamic astrocyte morphology, which in turn could alter synaptic inputs to hypothalamic neurons. Furthermore, the responses to acute and chronic leptin exposure are inverse, raising the possibility that increased glial activation in response to chronic leptin exposure could be involved in central leptin resistance. PMID:21343257
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Robust short-term memory without synaptic learning.
Johnson, Samuel; Marro, J; Torres, Joaquín J
2013-01-01
Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.
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USDA-ARS?s Scientific Manuscript database
Peptidergic neurons are not easily integrated into current connectomics concepts, since their peptide messages can be distributed via non-synaptic paracrine signaling or even via volume transmission. Moreover, and especially in insects, the polarity of peptidergic interneurons in terms of in- and o...
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Moore, Eugene L; Haspel, Gal; Libersat, Frederic; Adams, Michael E
2006-07-01
The wasp Ampulex compressa injects venom directly into the prothoracic ganglion of its cockroach host to induce a transient paralysis of the front legs. To identify the biochemical basis for this paralysis, we separated venom components according to molecular size and tested fractions for inhibition of synaptic transmission at the cockroach cercal-giant synapse. Only fractions in the low molecular weight range (<2 kDa) caused synaptic block. Dabsylation of venom components and analysis by HPLC and MALDI-TOF-MS revealed high levels of GABA (25 mM), and its receptor agonists beta-alanine (18 mM), and taurine (9 mM) in the active fractions. Each component produces transient block of synaptic transmission at the cercal-giant synapse and block of efferent motor output from the prothoracic ganglion, which mimics effects produced by injection of whole venom. Whole venom evokes picrotoxin-sensitive chloride currents in cockroach central neurons, consistent with a GABAergic action. Together these data demonstrate that Ampulex utilizes GABAergic chloride channel activation as a strategy for central synaptic block to induce transient and focal leg paralysis in its host. Copyright 2006 Wiley Periodicals, Inc.
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The formation and distribution of hippocampal synapses on patterned neuronal networks
NASA Astrophysics Data System (ADS)
Dowell-Mesfin, Natalie M.
Communication within the central nervous system is highly orchestrated with neurons forming trillions of specialized junctions called synapses. In vivo, biochemical and topographical cues can regulate neuronal growth. Biochemical cues also influence synaptogenesis and synaptic plasticity. The effects of topography on the development of synapses have been less studied. In vitro, neuronal growth is unorganized and complex making it difficult to study the development of networks. Patterned topographical cues guide and control the growth of neuronal processes (axons and dendrites) into organized networks. The aim of this dissertation was to determine if patterned topographical cues can influence synapse formation and distribution. Standard fabrication and compression molding procedures were used to produce silicon masters and polystyrene replicas with topographical cues presented as 1 mum high pillars with diameters of 0.5 and 2.0 mum and gaps of 1.0 to 5.0 mum. Embryonic rat hippocampal neurons grown unto patterned surfaces. A developmental analysis with immunocytochemistry was used to assess the distribution of pre- and post-synaptic proteins. Activity-dependent pre-synaptic vesicle uptake using functional imaging dyes was also performed. Adaptive filtering computer algorithms identified synapses by segmenting juxtaposed pairs of pre- and post-synaptic labels. Synapse number and area were automatically extracted from each deconvolved data set. In addition, neuronal processes were traced automatically to assess changes in synapse distribution. The results of these experiments demonstrated that patterned topographic cues can induce organized and functional neuronal networks that can serve as models for the study of synapse formation and plasticity as well as for the development of neuroprosthetic devices.
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The orientating reflex: the "targeting reaction" and "searchlight of attention".
Sokolov, E N; Nezlina, N I; Polyanskii, V B; Evtikhin, D V
2002-01-01
A concept of the orientating reflex is presented, based on the principle of vector coding of cognitive and executive processes. The orientating reflex is a complex of orientating reactions of motor, autonomic, and subjective types, accentuating new and significant stimuli. Two main systems form the orientating reflex: the "targeting reaction" and the "searchlight of attention:" In the visual system, the targeting reaction ensures that the image of the object falls onto the fovea; this is mediated by involvement of premotor neurons which are excited by saccade command neurons in the superior colliculi. The "searchlight of attention" is activated as a result of resonance within the gamma frequency range, selectively enhancing cortical detectors and involving the reticular nucleus of the thalamus. Novelty signals arise in novelty neurons of the hippocampus. The synaptic weightings of neocortical detectors for hippocampal novelty neurons is initially characterized by high efficiency, which assigns a significant level of excitation of these neurons to the new stimulus. During repeated stimulation, the synaptic weightings of all the detectors representing a given stimulus decrease, with the result that the novelty signal becomes weaker. When the stimulus changes, it acts on other detectors, whose weightings for novelty neurons remain high, which strengthens the novelty signal. Decreases in the synaptic weightings on repetition of a standard stimulus form a trace of this stimulus in the novelty neurons - this is the "neural model of the stimulus." The novelty signal is determined by the non-concordance of the new stimulus with this "neural model," which is formed under the influence of the standard stimulus. The greater the difference between the new stimulus and the previously formed neural model, the stronger the novelty signal.
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Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level.
Bono, Jacopo; Clopath, Claudia
2017-09-26
Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the local non-linear processing of synaptic inputs allowed for by dendrites. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. We also explore how the connectivity between two cells is affected by these plasticity rules and by different synaptic distributions. Finally, we show that how memory retention during associative learning can be prolonged in networks of neurons by including dendrites.Synaptic plasticity is the neuronal mechanism underlying learning. Here the authors construct biophysical models of pyramidal neurons that reproduce observed plasticity gradients along the dendrite and show that dendritic spike dependent LTP which is predominant in distal sections can prolong memory retention.
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Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity
NASA Astrophysics Data System (ADS)
Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro
2013-05-01
The collective dynamics of excitatory pulse coupled neurons with spike-timing dependent plasticity is studied. The introduction of spike-timing dependent plasticity induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.
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Hippocampal Spike-Timing Correlations Lead to Hexagonal Grid Fields
NASA Astrophysics Data System (ADS)
Monsalve-Mercado, Mauro M.; Leibold, Christian
2017-07-01
Space is represented in the mammalian brain by the activity of hippocampal place cells, as well as in their spike-timing correlations. Here, we propose a theory for how this temporal code is transformed to spatial firing rate patterns via spike-timing-dependent synaptic plasticity. The resulting dynamics of synaptic weights resembles well-known pattern formation models in which a lateral inhibition mechanism gives rise to a Turing instability. We identify parameter regimes in which hexagonal firing patterns develop as they have been found in medial entorhinal cortex.
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Ablation of SNX6 leads to defects in synaptic function of CA1 pyramidal neurons and spatial memory
Niu, Yang; Dai, Zhonghua; Liu, Wenxue; Zhang, Cheng; Yang, Yanrui; Guo, Zhenzhen; Li, Xiaoyu; Xu, Chenchang; Huang, Xiahe; Wang, Yingchun; Shi, Yun S; Liu, Jia-Jia
2017-01-01
SNX6 is a ubiquitously expressed PX-BAR protein that plays important roles in retromer-mediated retrograde vesicular transport from endosomes. Here we report that CNS-specific Snx6 knockout mice exhibit deficits in spatial learning and memory, accompanied with loss of spines from distal dendrites of hippocampal CA1 pyramidal cells. SNX6 interacts with Homer1b/c, a postsynaptic scaffold protein crucial for the synaptic distribution of other postsynaptic density (PSD) proteins and structural integrity of dendritic spines. We show that SNX6 functions independently of retromer to regulate distribution of Homer1b/c in the dendritic shaft. We also find that Homer1b/c translocates from shaft to spines by protein diffusion, which does not require SNX6. Ablation of SNX6 causes reduced distribution of Homer1b/c in distal dendrites, decrease in surface levels of AMPAR and impaired AMPAR-mediated synaptic transmission. These findings reveal a physiological role of SNX6 in CNS excitatory neurons. DOI: http://dx.doi.org/10.7554/eLife.20991.001 PMID:28134614
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Yao, Pamela J; Bushlin, Ittai; Petralia, Ronald S
2006-01-10
Synapses of neurons use clathrin-mediated endocytic pathways for recycling of synaptic vesicles and trafficking of neurotransmitter receptors. Epsin 1 and huntingtin-interacting protein 1 (HIP1) are endocytic accessory proteins. Both proteins interact with clathrin and the AP2 adaptor complex and also bind to the phosphoinositide-containing plasma membrane via an epsin/AP180 N-terminal homology (ENTH/ANTH) domain. Epsin1 and HIP1 are found in neurons; however, their precise roles in synapses remain largely unknown. Using immunogold electron microscopy, we examine and compare the synaptic distribution of epsin1 and HIP1 in rat CA1 hippocampal synapse. We find that epsin1 is located across both sides of the synapse, whereas HIP1 displays a preference for the postsynaptic compartment. Within the synaptic compartments, espin1 is distributed similarly throughout, whereas postsynaptic HIP1 is concentrated near the plasma membrane. Our results suggest a dual role for epsin1 and HIP1 in the synapse: as broadly required factors for promoting clathrin assembly and as adaptors for specific endocytic pathways.
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Liu, Zhigang; Patil, Ishan; Sancheti, Harsh; Yin, Fei; Cadenas, Enrique
2017-07-14
High-fat diet (HFD)-induced obesity is accompanied by insulin resistance and compromised brain synaptic plasticity through the impairment of insulin-sensitive pathways regulating neuronal survival, learning, and memory. Lipoic acid is known to modulate the redox status of the cell and has insulin mimetic effects. This study was aimed at determining the effects of dietary administration of lipoic acid on a HFD-induced obesity model in terms of (a) insulin signaling, (b) brain glucose uptake and neuronal- and astrocytic metabolism, and (c) synaptic plasticity. 3-Month old C57BL/6J mice were divided into 4 groups exposed to their respective treatments for 9 weeks: (1) normal diet, (2) normal diet plus lipoic acid, (3) HFD, and (4) HFD plus lipoic acid. HFD resulted in higher body weight, development of insulin resistance, lower brain glucose uptake and glucose transporters, alterations in glycolytic and acetate metabolism in neurons and astrocytes, and ultimately synaptic plasticity loss evident by a decreased long-term potentiation (LTP). Lipoic acid treatment in mice on HFD prevented several HFD-induced metabolic changes and preserved synaptic plasticity. The metabolic and physiological changes in HFD-fed mice, including insulin resistance, brain glucose uptake and metabolism, and synaptic function, could be preserved by the insulin-like effect of lipoic acid.
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Hu, Jun; Jiang, Lin; Low, Malcolm J; Rui, Liangyou
2014-01-01
Hypothalamic POMC neurons are required for glucose and energy homeostasis. POMC neurons have a wide synaptic connection with neurons both within and outside the hypothalamus, and their activity is controlled by a balance between excitatory and inhibitory synaptic inputs. Brain glucose-sensing plays an essential role in the maintenance of normal body weight and metabolism; however, the effect of glucose on synaptic transmission in POMC neurons is largely unknown. Here we identified three types of POMC neurons (EPSC(+), EPSC(-), and EPSC(+/-)) based on their glucose-regulated spontaneous excitatory postsynaptic currents (sEPSCs), using whole-cell patch-clamp recordings. Lowering extracellular glucose decreased the frequency of sEPSCs in EPSC(+) neurons, but increased it in EPSC(-) neurons. Unlike EPSC(+) and EPSC(-) neurons, EPSC(+/-) neurons displayed a bi-phasic sEPSC response to glucoprivation. In the first phase of glucoprivation, both the frequency and the amplitude of sEPSCs decreased, whereas in the second phase, they increased progressively to the levels above the baseline values. Accordingly, lowering glucose exerted a bi-phasic effect on spontaneous action potentials in EPSC(+/-) neurons. Glucoprivation decreased firing rates in the first phase, but increased them in the second phase. These data indicate that glucose induces distinct excitatory synaptic plasticity in different subpopulations of POMC neurons. This synaptic remodeling is likely to regulate the sensitivity of the melanocortin system to neuronal and hormonal signals.
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Moroz, Leonid L.; Kohn, Andrea B.
2015-01-01
Hypotheses of origins and evolution of neurons and synapses are controversial, mostly due to limited comparative data. Here, we investigated the genome-wide distribution of the bilaterian “synaptic” and “neuronal” protein-coding genes in non-bilaterian basal metazoans (Ctenophora, Porifera, Placozoa, and Cnidaria). First, there are no recognized genes uniquely expressed in neurons across all metazoan lineages. None of the so-called pan-neuronal genes such as embryonic lethal abnormal vision (ELAV), Musashi, or Neuroglobin are expressed exclusively in neurons of the ctenophore Pleurobrachia. Second, our comparative analysis of about 200 genes encoding canonical presynaptic and postsynaptic proteins in bilaterians suggests that there are no true “pan-synaptic” genes or genes uniquely and specifically attributed to all classes of synapses. The majority of these genes encode receptive and secretory complexes in a broad spectrum of eukaryotes. Trichoplax (Placozoa) an organism without neurons and synapses has more orthologs of bilaterian synapse-related/neuron-related genes than do ctenophores—the group with well-developed neuronal and synaptic organization. Third, the majority of genes encoding ion channels and ionotropic receptors are broadly expressed in unicellular eukaryotes and non-neuronal tissues in metazoans. Therefore, they cannot be viewed as neuronal markers. Nevertheless, the co-expression of multiple types of ion channels and receptors does correlate with the presence of neural and synaptic organization. As an illustrative example, the ctenophore genomes encode a greater diversity of ion channels and ionotropic receptors compared with the genomes of the placozoan Trichoplax and the demosponge Amphimedon. Surprisingly, both placozoans and sponges have a similar number of orthologs of “synaptic” proteins as we identified in the genomes of two ctenophores. Ctenophores have a distinct synaptic organization compared with other animals. Our analysis of transcriptomes from 10 different ctenophores did not detect recognized orthologs of synthetic enzymes encoding several classical, low-molecular-weight (neuro)transmitters; glutamate signaling machinery is one of the few exceptions. Novel peptidergic signaling molecules were predicted for ctenophores, together with the diversity of putative receptors including SCNN1/amiloride-sensitive sodium channel-like channels, many of which could be examples of a lineage-specific expansion within this group. In summary, our analysis supports the hypothesis of independent evolution of neurons and, as corollary, a parallel evolution of synapses. We suggest that the formation of synaptic machinery might occur more than once over 600 million years of animal evolution. PMID:26454853
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Synaptic clustering within dendrites: an emerging theory of memory formation
Kastellakis, George; Cai, Denise J.; Mednick, Sara C.; Silva, Alcino J.; Poirazi, Panayiota
2015-01-01
It is generally accepted that complex memories are stored in distributed representations throughout the brain, however the mechanisms underlying these representations are not understood. Here, we review recent findings regarding the subcellular mechanisms implicated in memory formation, which provide evidence for a dendrite-centered theory of memory. Plasticity-related phenomena which affect synaptic properties, such as synaptic tagging and capture, synaptic clustering, branch strength potentiation and spinogenesis provide the foundation for a model of memory storage that relies heavily on processes operating at the dendrite level. The emerging picture suggests that clusters of functionally related synapses may serve as key computational and memory storage units in the brain. We discuss both experimental evidence and theoretical models that support this hypothesis and explore its advantages for neuronal function. PMID:25576663
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Ogawa, Hiroto; Mitani, Ruriko
2015-11-13
The spatial dynamics of action potentials, including their propagation and the location of spike initiation zone (SIZ), are crucial for the computation of a single neuron. Compared with mammalian central neurons, the spike dynamics of invertebrate neurons remain relatively unknown. Thus, we examined the spike dynamics based on single spike-induced Ca(2+) signals in the dendrites of cricket mechanosensory projection neurons, known as giant interneurons (GIs). The Ca(2+) transients induced by a synaptically evoked single spike were larger than those induced by an antidromic spike, whereas subthreshold synaptic potentials caused no elevation of Ca(2+). These results indicate that synaptic activity enhances the dendritic Ca(2+) influx through voltage-gated Ca(2+) channels. Stimulation of the presynaptic sensory afferents ipsilateral to the recording site evoked a dendritic spike with higher amplitude than contralateral stimulation, thereby suggesting that alteration of the spike waveform resulted in synaptic enhancement of the dendritic Ca(2+) transients. The SIZ estimated from the spatial distribution of the difference in the Ca(2+) amplitude was distributed throughout the right and left dendritic branches across the primary neurite connecting them in GIs. Copyright © 2015 Elsevier Inc. All rights reserved.
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Dynamical model of long-term synaptic plasticity
Abarbanel, Henry D. I.; Huerta, R.; Rabinovich, M. I.
2002-01-01
Long-term synaptic plasticity leading to enhancement in synaptic efficacy (long-term potentiation, LTP) or decrease in synaptic efficacy (long-term depression, LTD) is widely regarded as underlying learning and memory in nervous systems. LTP and LTD at excitatory neuronal synapses are observed to be induced by precise timing of pre- and postsynaptic events. Modification of synaptic transmission in long-term plasticity is a complex process involving many pathways; for example, it is also known that both forms of synaptic plasticity can be induced by various time courses of Ca2+ introduction into the postsynaptic cell. We present a phenomenological description of a two-component process for synaptic plasticity. Our dynamical model reproduces the spike time-dependent plasticity of excitatory synapses as a function of relative timing between pre- and postsynaptic events, as observed in recent experiments. The model accounts for LTP and LTD when the postsynaptic cell is voltage clamped and depolarized (LTP) or hyperpolarized (LTD) and no postsynaptic action potentials are evoked. We are also able to connect our model with the Bienenstock, Cooper, and Munro rule. We give model predictions for changes in synaptic strength when periodic spike trains of varying frequency and Poisson distributed spike trains with varying average frequency are presented pre- and postsynaptically. When the frequency of spike presentation exceeds ≈30–40 Hz, only LTP is induced. PMID:12114531
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Microscopy in Space Research: Learning More About Gravitational Effects on Living Systems
NASA Technical Reports Server (NTRS)
Ross, Muriel D.
1994-01-01
Investigators are using light, scanning and transmission electron microscopic (TEM) methods to investigate the effects of microgravity on the development, maintenance and aging of biological systems. The capabilities of the spacelab for life sciences research in space will be described. Among the many results to be discussed are the effects of microgravity on amphibian fertilization and early development, and on the rodent musculoskeletal and neural systems. Xenopus laevis eggs fertilized in space developed normally during and after an eight day spaceflight. Ultrastructural studies of rodent tissue demonstrated that spaceflight-induced atrophy of antigravity skeletal muscles renders muscle fibers susceptible to structural failure upon return to weight bearing postflight. Principle TEM changes in neuromuscular junctions are the decrease or absence of synaptic vesicles and degeneration of axon terminals. In bone, architectural rather than compositional changes may be the primary perturbation. Thus, many techniques used on Earth (such as density determinations) would not detect significant changes in bone strength. An increment in synaptic number and changes in synapse distribution occur in peripheral gravity sensors. There is a decrease in muscarinic cholinergic receptor density in the striatum. Striatal receptor changes suggest spaceflight-related alterations in motor activity. Opportunities for future life sciences research in space will be discussed.
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Spike-Based Bayesian-Hebbian Learning of Temporal Sequences
Lindén, Henrik; Lansner, Anders
2016-01-01
Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison. PMID:27213810
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Mutant Huntingtin Causes a Selective Decrease in the Expression of Synaptic Vesicle Protein 2C.
Peng, Chaohua; Zhu, Gaochun; Liu, Xiangqian; Li, He
2018-04-30
Huntington's disease (HD) is a neurodegenerative disease caused by a polyglutamine expansion in the huntingtin (Htt) protein. Mutant Htt causes synaptic transmission dysfunctions by interfering in the expression of synaptic proteins, leading to early HD symptoms. Synaptic vesicle proteins 2 (SV2s), a family of synaptic vesicle proteins including 3 members, SV2A, SV2B, and SV2C, plays important roles in synaptic physiology. Here, we investigated whether the expression of SV2s is affected by mutant Htt in the brains of HD transgenic (TG) mice and Neuro2a mouse neuroblastoma cells (N2a cells) expressing mutant Htt. Western blot analysis showed that the protein levels of SV2A and SV2B were not significantly changed in the brains of HD TG mice expressing mutant Htt with 82 glutamine repeats. However, in the TG mouse brain there was a dramatic decrease in the protein level of SV2C, which has a restricted distribution pattern in regions particularly vulnerable in HD. Immunostaining revealed that the immunoreactivity of SV2C was progressively weakened in the basal ganglia and hippocampus of TG mice. RT-PCR demonstrated that the mRNA level of SV2C progressively declined in the TG mouse brain without detectable changes in the mRNA levels of SV2A and SV2B, indicating that mutant Htt selectively inhibits the transcriptional expression of SV2C. Furthermore, we found that only SV2C expression was progressively inhibited in N2a cells expressing a mutant Htt containing 120 glutamine repeats. These findings suggest that the synaptic dysfunction in HD results from the mutant Htt-mediated inhibition of SV2C transcriptional expression. These data also imply that the restricted distribution and decreased expression of SV2C contribute to the brain region-selective pathology of HD.
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Clarinet (CLA-1), a novel active zone protein required for synaptic vesicle clustering and release
Nelson, Jessica; Richmond, Janet E; Colón-Ramos, Daniel A; Shen, Kang
2017-01-01
Active zone proteins cluster synaptic vesicles at presynaptic terminals and coordinate their release. In forward genetic screens, we isolated a novel Caenorhabditis elegans active zone gene, clarinet (cla-1). cla-1 mutants exhibit defects in synaptic vesicle clustering, active zone structure and synapse number. As a result, they have reduced spontaneous vesicle release and increased synaptic depression. cla-1 mutants show defects in vesicle distribution near the presynaptic dense projection, with fewer undocked vesicles contacting the dense projection and more docked vesicles at the plasma membrane. cla-1 encodes three isoforms containing common C-terminal PDZ and C2 domains with homology to vertebrate active zone proteins Piccolo and RIM. The C-termini of all isoforms localize to the active zone. Specific loss of the ~9000 amino acid long isoform results in vesicle clustering defects and increased synaptic depression. Our data indicate that specific isoforms of clarinet serve distinct functions, regulating synapse development, vesicle clustering and release. PMID:29160205
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An Attractive Reelin Gradient Establishes Synaptic Lamination in the Vertebrate Visual System.
Di Donato, Vincenzo; De Santis, Flavia; Albadri, Shahad; Auer, Thomas Oliver; Duroure, Karine; Charpentier, Marine; Concordet, Jean-Paul; Gebhardt, Christoph; Del Bene, Filippo
2018-03-07
A conserved organizational and functional principle of neural networks is the segregation of axon-dendritic synaptic connections into laminae. Here we report that targeting of synaptic laminae by retinal ganglion cell (RGC) arbors in the vertebrate visual system is regulated by a signaling system relying on target-derived Reelin and VLDLR/Dab1a on the projecting neurons. Furthermore, we find that Reelin is distributed as a gradient on the target tissue and stabilized by heparan sulfate proteoglycans (HSPGs) in the extracellular matrix (ECM). Through genetic manipulations, we show that this Reelin gradient is important for laminar targeting and that it is attractive for RGC axons. Finally, we suggest a comprehensive model of synaptic lamina formation in which attractive Reelin counter-balances repulsive Slit1, thereby guiding RGC axons toward single synaptic laminae. We establish a mechanism that may represent a general principle for neural network assembly in vertebrate species and across different brain areas. Copyright © 2018 Elsevier Inc. All rights reserved.
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Leptin regulation of hippocampal synaptic function in health and disease
Irving, Andrew J.; Harvey, Jenni
2014-01-01
The endocrine hormone leptin plays a key role in regulating food intake and body weight via its actions in the hypothalamus. However, leptin receptors are highly expressed in many extra-hypothalamic brain regions and evidence is growing that leptin influences many central processes including cognition. Indeed, recent studies indicate that leptin is a potential cognitive enhancer as it markedly facilitates the cellular events underlying hippocampal-dependent learning and memory, including effects on glutamate receptor trafficking, neuronal morphology and activity-dependent synaptic plasticity. However, the ability of leptin to regulate hippocampal synaptic function markedly declines with age and aberrant leptin function has been linked to neurodegenerative disorders such as Alzheimer's disease (AD). Here, we review the evidence supporting a cognitive enhancing role for the hormone leptin and discuss the therapeutic potential of using leptin-based agents to treat AD. PMID:24298156
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Dao Duc, Khanh; Parutto, Pierre; Chen, Xiaowei; Epsztein, Jérôme; Konnerth, Arthur; Holcman, David
2015-01-01
The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.
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Synaptic and extrasynaptic traces of long-term memory: the ID molecule theory.
Legéndy, Charles R
2016-08-01
It is generally assumed at the time of this writing that memories are stored in the form of synaptic weights. However, it is now also clear that the synapses are not permanent; in fact, synaptic patterns undergo significant change in a matter of hours. This means that to implement the long survival of distant memories (for several decades in humans), the brain must possess a molecular backup mechanism in some form, complete with provisions for the storage and retrieval of information. It is found below that the memory-supporting molecules need not contain a detailed description of mental entities, as had been envisioned in the 'memory molecule papers' from 50 years ago, they only need to contain unique identifiers of various entities, and that this can be achieved using relatively small molecules, using a random code ('ID molecules'). In this paper, the logistics of information flow are followed through the steps of storage and retrieval, and the conclusion reached is that the ID molecules, by carrying a sufficient amount of information (entropy), can effectively control the recreation of complex multineuronal patterns. In illustrations, it is described how ID molecules can be made to revive a selected cell assembly by waking up its synapses and how they cause a selected cell assembly to ignite by sending slow inward currents into its cells. The arrangement involves producing multiple copies of the ID molecules and distributing them at strategic locations at selected sets of synapses, then reaching them through small noncoding RNA molecules. This requires the quick creation of entropy-rich messengers and matching receptors, and it suggests that these are created from each other by small-scale transcription and reverse transcription.
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Wang, Dongmei; Mitchell, Ellen S
2016-01-01
Brain glucose hypometabolism is a common feature of Alzheimer's disease (AD). Previous studies have shown that cognition is improved by providing AD patients with an alternate energy source: ketones derived from either ketogenic diet or supplementation with medium chain triglycerides (MCT). Recently, data on the neuroprotective capacity of MCT-derived medium chain fatty acids (MCFA) suggest 8-carbon and 10-carbon MCFA may have cognition-enhancing properties which are not related to ketone production. We investigated the effect of 8 week treatment with MCT8, MCT10 or sunflower oil supplementation (5% by weight of chow diet) in 21 month old Wistar rats. Both MCT diets increased ketones plasma similarly compared to control diet, but MCT diets did not increase ketones in the brain. Treatment with MCT10, but not MCT8, significantly improved novel object recognition memory compared to control diet, while social recognition increased in both MCT groups. MCT8 and MCT10 diets decreased weight compared to control diet, where MCFA plasma levels were higher in MCT10 groups than in MCT8 groups. Both MCT diets increased IRS-1 (612) phosphorylation and decreased S6K phosphorylation (240/244) but only MCT10 increased Akt phosphorylation (473). MCT8 supplementation increased synaptophysin, but not PSD-95, in contrast MCT10 had no effect on either synaptic marker. Expression of Ube3a, which controls synaptic stability, was increased by both MCT diets. Cortex transcription via qPCR showed that immediate early genes related to synaptic plasticity (arc, plk3, junb, egr2, nr4a1) were downregulated by both MCT diets while MCT8 additionally down-regulated fosb and egr1 but upregulated grin1 and gba2. These results demonstrate that treatment of 8- and 10-carbon length MCTs in aged rats have slight differential effects on synaptic stability, protein synthesis and behavior that may be independent of brain ketone levels.
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Wang, Dongmei; Mitchell, Ellen S.
2016-01-01
Brain glucose hypometabolism is a common feature of Alzheimer’s disease (AD). Previous studies have shown that cognition is improved by providing AD patients with an alternate energy source: ketones derived from either ketogenic diet or supplementation with medium chain triglycerides (MCT). Recently, data on the neuroprotective capacity of MCT-derived medium chain fatty acids (MCFA) suggest 8-carbon and 10-carbon MCFA may have cognition-enhancing properties which are not related to ketone production. We investigated the effect of 8 week treatment with MCT8, MCT10 or sunflower oil supplementation (5% by weight of chow diet) in 21 month old Wistar rats. Both MCT diets increased ketones plasma similarly compared to control diet, but MCT diets did not increase ketones in the brain. Treatment with MCT10, but not MCT8, significantly improved novel object recognition memory compared to control diet, while social recognition increased in both MCT groups. MCT8 and MCT10 diets decreased weight compared to control diet, where MCFA plasma levels were higher in MCT10 groups than in MCT8 groups. Both MCT diets increased IRS-1 (612) phosphorylation and decreased S6K phosphorylation (240/244) but only MCT10 increased Akt phosphorylation (473). MCT8 supplementation increased synaptophysin, but not PSD-95, in contrast MCT10 had no effect on either synaptic marker. Expression of Ube3a, which controls synaptic stability, was increased by both MCT diets. Cortex transcription via qPCR showed that immediate early genes related to synaptic plasticity (arc, plk3, junb, egr2, nr4a1) were downregulated by both MCT diets while MCT8 additionally down-regulated fosb and egr1 but upregulated grin1 and gba2. These results demonstrate that treatment of 8- and 10-carbon length MCTs in aged rats have slight differential effects on synaptic stability, protein synthesis and behavior that may be independent of brain ketone levels. PMID:27517611
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NASA Astrophysics Data System (ADS)
Lim, Hyungkwang; Kim, Inho; Kim, Jin-Sang; Hwang, Cheol Seong; Jeong, Doo Seok
2013-09-01
Chemical synapses are important components of the large-scaled neural network in the hippocampus of the mammalian brain, and a change in their weight is thought to be in charge of learning and memory. Thus, the realization of artificial chemical synapses is of crucial importance in achieving artificial neural networks emulating the brain’s functionalities to some extent. This kind of research is often referred to as neuromorphic engineering. In this study, we report short-term memory behaviours of electrochemical capacitors (ECs) utilizing TiO2 mixed ionic-electronic conductor and various reactive electrode materials e.g. Ti, Ni, and Cr. By experiments, it turned out that the potentiation behaviours did not represent unlimited growth of synaptic weight. Instead, the behaviours exhibited limited synaptic weight growth that can be understood by means of an empirical equation similar to the Bienenstock-Cooper-Munro rule, employing a sliding threshold. The observed potentiation behaviours were analysed using the empirical equation and the differences between the different ECs were parameterized.
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Takeda, Shuko; Wegmann, Susanne; Cho, Hansang; DeVos, Sarah L.; Commins, Caitlin; Roe, Allyson D.; Nicholls, Samantha B.; Carlson, George A.; Pitstick, Rose; Nobuhara, Chloe K.; Costantino, Isabel; Frosch, Matthew P.; Müller, Daniel J.; Irimia, Daniel; Hyman, Bradley T.
2015-01-01
Tau pathology is known to spread in a hierarchical pattern in Alzheimer's disease (AD) brain during disease progression, likely by trans-synaptic tau transfer between neurons. However, the tau species involved in inter-neuron propagation remains unclear. To identify tau species responsible for propagation, we examined uptake and propagation properties of different tau species derived from postmortem cortical extracts and brain interstitial fluid of tau-transgenic mice, as well as human AD cortices. Here we show that PBS-soluble phosphorylated high-molecular-weight (HMW) tau, though very low in abundance, is taken up, axonally transported, and passed on to synaptically connected neurons. Our findings suggest that a rare species of soluble phosphorylated HMW tau is the endogenous form of tau involved in propagation and could be a target for therapeutic intervention and biomarker development. PMID:26458742
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Mean Field Analysis of Stochastic Neural Network Models with Synaptic Depression
NASA Astrophysics Data System (ADS)
Yasuhiko Igarashi,; Masafumi Oizumi,; Masato Okada,
2010-08-01
We investigated the effects of synaptic depression on the macroscopic behavior of stochastic neural networks. Dynamical mean field equations were derived for such networks by taking the average of two stochastic variables: a firing-state variable and a synaptic variable. In these equations, the average product of thesevariables is decoupled as the product of their averages because the two stochastic variables are independent. We proved the independence of these two stochastic variables assuming that the synaptic weight Jij is of the order of 1/N with respect to the number of neurons N. Using these equations, we derived macroscopic steady-state equations for a network with uniform connections and for a ring attractor network with Mexican hat type connectivity and investigated the stability of the steady-state solutions. An oscillatory uniform state was observed in the network with uniform connections owing to a Hopf instability. For the ring network, high-frequency perturbations were shown not to affect system stability. Two mechanisms destabilize the inhomogeneous steady state, leading to two oscillatory states. A Turing instability leads to a rotating bump state, while a Hopf instability leads to an oscillatory bump state, which was previously unreported. Various oscillatory states take place in a network with synaptic depression depending on the strength of the interneuron connections.
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Memristor-based cellular nonlinear/neural network: design, analysis, and applications.
Duan, Shukai; Hu, Xiaofang; Dong, Zhekang; Wang, Lidan; Mazumder, Pinaki
2015-06-01
Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.
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Ultralow power artificial synapses using nanotextured magnetic Josephson junctions.
Schneider, Michael L; Donnelly, Christine A; Russek, Stephen E; Baek, Burm; Pufall, Matthew R; Hopkins, Peter F; Dresselhaus, Paul D; Benz, Samuel P; Rippard, William H
2018-01-01
Neuromorphic computing promises to markedly improve the efficiency of certain computational tasks, such as perception and decision-making. Although software and specialized hardware implementations of neural networks have made tremendous accomplishments, both implementations are still many orders of magnitude less energy efficient than the human brain. We demonstrate a new form of artificial synapse based on dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier. The spiking energy per pulse varies with the magnetic configuration, but in our demonstration devices, the spiking energy is always less than 1 aJ. This compares very favorably with the roughly 10 fJ per synaptic event in the human brain. Each artificial synapse is composed of a Si barrier containing Mn nanoclusters with superconducting Nb electrodes. The critical current of each synapse junction, which is analogous to the synaptic weight, can be tuned using input voltage spikes that change the spin alignment of Mn nanoclusters. We demonstrate synaptic weight training with electrical pulses as small as 3 aJ. Further, the Josephson plasma frequencies of the devices, which determine the dynamical time scales, all exceed 100 GHz. These new artificial synapses provide a significant step toward a neuromorphic platform that is faster, more energy-efficient, and thus can attain far greater complexity than has been demonstrated with other technologies.
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Ultralow power artificial synapses using nanotextured magnetic Josephson junctions
Schneider, Michael L.; Donnelly, Christine A.; Russek, Stephen E.; Baek, Burm; Pufall, Matthew R.; Hopkins, Peter F.; Dresselhaus, Paul D.; Benz, Samuel P.; Rippard, William H.
2018-01-01
Neuromorphic computing promises to markedly improve the efficiency of certain computational tasks, such as perception and decision-making. Although software and specialized hardware implementations of neural networks have made tremendous accomplishments, both implementations are still many orders of magnitude less energy efficient than the human brain. We demonstrate a new form of artificial synapse based on dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier. The spiking energy per pulse varies with the magnetic configuration, but in our demonstration devices, the spiking energy is always less than 1 aJ. This compares very favorably with the roughly 10 fJ per synaptic event in the human brain. Each artificial synapse is composed of a Si barrier containing Mn nanoclusters with superconducting Nb electrodes. The critical current of each synapse junction, which is analogous to the synaptic weight, can be tuned using input voltage spikes that change the spin alignment of Mn nanoclusters. We demonstrate synaptic weight training with electrical pulses as small as 3 aJ. Further, the Josephson plasma frequencies of the devices, which determine the dynamical time scales, all exceed 100 GHz. These new artificial synapses provide a significant step toward a neuromorphic platform that is faster, more energy-efficient, and thus can attain far greater complexity than has been demonstrated with other technologies. PMID:29387787
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[Neuronal and synaptic properties: fundamentals of network plasticity].
Le Masson, G
2000-02-01
Neurons, within the nervous system, are organized in different neural networks through synaptic connections. Two fundamental components are dynamically interacting in these functional units. The first one are the neurons themselves, and far from being simple action potential generators, they are capable of complex electrical integrative properties due to various types, number, distribution and modulation of voltage-gated ionic channels. The second elements are the synapses where a similar complexity and plasticity is found. Identifying both cellular and synaptic intrinsic properties is necessary to understand the links between neural networks behavior and physiological function, and is a useful step towards a better control of neurological diseases.
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Richardson, Magnus J E; Gerstner, Wulfram
2005-04-01
The subthreshold membrane voltage of a neuron in active cortical tissue is a fluctuating quantity with a distribution that reflects the firing statistics of the presynaptic population. It was recently found that conductance-based synaptic drive can lead to distributions with a significant skew. Here it is demonstrated that the underlying shot noise caused by Poissonian spike arrival also skews the membrane distribution, but in the opposite sense. Using a perturbative method, we analyze the effects of shot noise on the distribution of synaptic conductances and calculate the consequent voltage distribution. To first order in the perturbation theory, the voltage distribution is a gaussian modulated by a prefactor that captures the skew. The gaussian component is identical to distributions derived using current-based models with an effective membrane time constant. The well-known effective-time-constant approximation can therefore be identified as the leading-order solution to the full conductance-based model. The higher-order modulatory prefactor containing the skew comprises terms due to both shot noise and conductance fluctuations. The diffusion approximation misses these shot-noise effects implying that analytical approaches such as the Fokker-Planck equation or simulation with filtered white noise cannot be used to improve on the gaussian approximation. It is further demonstrated that quantities used for fitting theory to experiment, such as the voltage mean and variance, are robust against these non-Gaussian effects. The effective-time-constant approximation is therefore relevant to experiment and provides a simple analytic base on which other pertinent biological details may be added.
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Hu, Jun; Jiang, Lin; Low, Malcolm J.; Rui, Liangyou
2014-01-01
Hypothalamic POMC neurons are required for glucose and energy homeostasis. POMC neurons have a wide synaptic connection with neurons both within and outside the hypothalamus, and their activity is controlled by a balance between excitatory and inhibitory synaptic inputs. Brain glucose-sensing plays an essential role in the maintenance of normal body weight and metabolism; however, the effect of glucose on synaptic transmission in POMC neurons is largely unknown. Here we identified three types of POMC neurons (EPSC(+), EPSC(−), and EPSC(+/−)) based on their glucose-regulated spontaneous excitatory postsynaptic currents (sEPSCs), using whole-cell patch-clamp recordings. Lowering extracellular glucose decreased the frequency of sEPSCs in EPSC(+) neurons, but increased it in EPSC(−) neurons. Unlike EPSC(+) and EPSC(−) neurons, EPSC(+/−) neurons displayed a bi-phasic sEPSC response to glucoprivation. In the first phase of glucoprivation, both the frequency and the amplitude of sEPSCs decreased, whereas in the second phase, they increased progressively to the levels above the baseline values. Accordingly, lowering glucose exerted a bi-phasic effect on spontaneous action potentials in EPSC(+/−) neurons. Glucoprivation decreased firing rates in the first phase, but increased them in the second phase. These data indicate that glucose induces distinct excitatory synaptic plasticity in different subpopulations of POMC neurons. This synaptic remodeling is likely to regulate the sensitivity of the melanocortin system to neuronal and hormonal signals. PMID:25127258
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Martinez, Tara L; Kong, Lingling; Wang, Xueyong; Osborne, Melissa A; Crowder, Melissa E; Van Meerbeke, James P; Xu, Xixi; Davis, Crystal; Wooley, Joe; Goldhamer, David J; Lutz, Cathleen M; Rich, Mark M; Sumner, Charlotte J
2012-06-20
The inherited motor neuron disease spinal muscular atrophy (SMA) is caused by deficient expression of survival motor neuron (SMN) protein and results in severe muscle weakness. In SMA mice, synaptic dysfunction of both neuromuscular junctions (NMJs) and central sensorimotor synapses precedes motor neuron cell death. To address whether this synaptic dysfunction is due to SMN deficiency in motor neurons, muscle, or both, we generated three lines of conditional SMA mice with tissue-specific increases in SMN expression. All three lines of mice showed increased survival, weights, and improved motor behavior. While increased SMN expression in motor neurons prevented synaptic dysfunction at the NMJ and restored motor neuron somal synapses, increased SMN expression in muscle did not affect synaptic function although it did improve myofiber size. Together these data indicate that both peripheral and central synaptic integrity are dependent on motor neurons in SMA, but SMN may have variable roles in the maintenance of these different synapses. At the NMJ, it functions at the presynaptic terminal in a cell-autonomous fashion, but may be necessary for retrograde trophic signaling to presynaptic inputs onto motor neurons. Importantly, SMN also appears to function in muscle growth and/or maintenance independent of motor neurons. Our data suggest that SMN plays distinct roles in muscle, NMJs, and motor neuron somal synapses and that restored function of SMN at all three sites will be necessary for full recovery of muscle power.
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Neuronal Activity and the Expression of Clathrin Assembly Protein AP180
Wu, Fangbai; Mattson, Mark P.; Yao, Pamela J.
2010-01-01
The clathrin assembly protein AP180 is known to promote the assembly of clathrin-coated vesicles in the neuron. However, it is unknown whether the expression of AP180 is influenced by neuronal activity. In this study, we report that chronic depolarization results in a reduction of AP180 from hippocampal neurons, while acute depolarization causes a dispersed synaptic distribution of AP180. Activity-induced effects are observed only for AP180, but not for the structurally-related clathrin assembly proteins CALM, epsin1, or HIP1. These findings suggest that AP180 levels and synaptic distribution are highly sensitive to neuronal activity. PMID:20937255
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Simple modification of Oja rule limits L1-norm of weight vector and leads to sparse connectivity.
Aparin, Vladimir
2012-03-01
This letter describes a simple modification of the Oja learning rule, which asymptotically constrains the L1-norm of an input weight vector instead of the L2-norm as in the original rule. This constraining is local as opposed to commonly used instant normalizations, which require the knowledge of all input weights of a neuron to update each one of them individually. The proposed rule converges to a weight vector that is sparser (has more zero weights) than the vector learned by the original Oja rule with or without the zero bound, which could explain the developmental synaptic pruning.
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Activity-dependent trafficking of lysosomes in dendrites and dendritic spines.
Goo, Marisa S; Sancho, Laura; Slepak, Natalia; Boassa, Daniela; Deerinck, Thomas J; Ellisman, Mark H; Bloodgood, Brenda L; Patrick, Gentry N
2017-08-07
In neurons, lysosomes, which degrade membrane and cytoplasmic components, are thought to primarily reside in somatic and axonal compartments, but there is little understanding of their distribution and function in dendrites. Here, we used conventional and two-photon imaging and electron microscopy to show that lysosomes traffic bidirectionally in dendrites and are present in dendritic spines. We find that lysosome inhibition alters their mobility and also decreases dendritic spine number. Furthermore, perturbing microtubule and actin cytoskeletal dynamics has an inverse relationship on the distribution and motility of lysosomes in dendrites. We also find trafficking of lysosomes is correlated with synaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptors. Strikingly, lysosomes traffic to dendritic spines in an activity-dependent manner and can be recruited to individual spines in response to local activation. These data indicate the position of lysosomes is regulated by synaptic activity and thus plays an instructive role in the turnover of synaptic membrane proteins. © 2017 Goo et al.
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Activity-dependent trafficking of lysosomes in dendrites and dendritic spines
Sancho, Laura; Slepak, Natalia; Boassa, Daniela; Deerinck, Thomas J.; Ellisman, Mark H.
2017-01-01
In neurons, lysosomes, which degrade membrane and cytoplasmic components, are thought to primarily reside in somatic and axonal compartments, but there is little understanding of their distribution and function in dendrites. Here, we used conventional and two-photon imaging and electron microscopy to show that lysosomes traffic bidirectionally in dendrites and are present in dendritic spines. We find that lysosome inhibition alters their mobility and also decreases dendritic spine number. Furthermore, perturbing microtubule and actin cytoskeletal dynamics has an inverse relationship on the distribution and motility of lysosomes in dendrites. We also find trafficking of lysosomes is correlated with synaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid–type glutamate receptors. Strikingly, lysosomes traffic to dendritic spines in an activity-dependent manner and can be recruited to individual spines in response to local activation. These data indicate the position of lysosomes is regulated by synaptic activity and thus plays an instructive role in the turnover of synaptic membrane proteins. PMID:28630145
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Domínguez-Álvaro, M; Montero-Crespo, M; Blazquez-Llorca, L; Insausti, R; DeFelipe, J; Alonso-Nanclares, L
2018-03-02
Synaptic dysfunction or loss in early stages of Alzheimer's disease (AD) is thought to be a major structural correlate of cognitive dysfunction. Early loss of episodic memory, which occurs at the early stage of AD, is closely associated with the progressive degeneration of medial temporal lobe (MTL) structures of which the transentorhinal cortex (TEC) is the first affected area. However, no ultrastructural studies have been performed in this region in human brain samples from AD patients. In the present study, we have performed a detailed three-dimensional (3D) ultrastructural analysis using focused ion beam/scanning electron microscopy (FIB/SEM) to investigate possible synaptic alterations in the TEC of patients with AD. Surprisingly, the analysis of the density, morphological features and spatial distribution of synapses in the neuropil showed no significant differences between AD and control samples. However, light microscopy studies showed that cortical thickness of the TEC was severely reduced in AD samples, but there were no changes in the volume occupied by neuronal and glial cell bodies, blood vessels, and neuropil. Thus, the present results indicate that there is a dramatic loss of absolute number of synapses, while the morphology of synaptic junctions and synaptic spatial distribution are maintained. How these changes affect cognitive impairment in AD remains to be elucidated.
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Ultrafast Synaptic Events in a Chalcogenide Memristor
NASA Astrophysics Data System (ADS)
Li, Yi; Zhong, Yingpeng; Xu, Lei; Zhang, Jinjian; Xu, Xiaohua; Sun, Huajun; Miao, Xiangshui
2013-04-01
Compact and power-efficient plastic electronic synapses are of fundamental importance to overcoming the bottlenecks of developing a neuromorphic chip. Memristor is a strong contender among the various electronic synapses in existence today. However, the speeds of synaptic events are relatively slow in most attempts at emulating synapses due to the material-related mechanism. Here we revealed the intrinsic memristance of stoichiometric crystalline Ge2Sb2Te5 that originates from the charge trapping and releasing by the defects. The device resistance states, representing synaptic weights, were precisely modulated by 30 ns potentiating/depressing electrical pulses. We demonstrated four spike-timing-dependent plasticity (STDP) forms by applying programmed pre- and postsynaptic spiking pulse pairs in different time windows ranging from 50 ms down to 500 ns, the latter of which is 105 times faster than the speed of STDP in human brain. This study provides new opportunities for building ultrafast neuromorphic computing systems and surpassing Von Neumann architecture.
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Ultrafast synaptic events in a chalcogenide memristor.
Li, Yi; Zhong, Yingpeng; Xu, Lei; Zhang, Jinjian; Xu, Xiaohua; Sun, Huajun; Miao, Xiangshui
2013-01-01
Compact and power-efficient plastic electronic synapses are of fundamental importance to overcoming the bottlenecks of developing a neuromorphic chip. Memristor is a strong contender among the various electronic synapses in existence today. However, the speeds of synaptic events are relatively slow in most attempts at emulating synapses due to the material-related mechanism. Here we revealed the intrinsic memristance of stoichiometric crystalline Ge2Sb2Te5 that originates from the charge trapping and releasing by the defects. The device resistance states, representing synaptic weights, were precisely modulated by 30 ns potentiating/depressing electrical pulses. We demonstrated four spike-timing-dependent plasticity (STDP) forms by applying programmed pre- and postsynaptic spiking pulse pairs in different time windows ranging from 50 ms down to 500 ns, the latter of which is 10(5) times faster than the speed of STDP in human brain. This study provides new opportunities for building ultrafast neuromorphic computing systems and surpassing Von Neumann architecture.
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NASA Astrophysics Data System (ADS)
Mizusaki, Beatriz E. P.; Agnes, Everton J.; Erichsen, Rubem; Brunnet, Leonardo G.
2017-08-01
The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.
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From a meso- to micro-scale connectome: array tomography and mGRASP
Rah, Jong-Cheol; Feng, Linqing; Druckmann, Shaul; Lee, Hojin; Kim, Jinhyun
2015-01-01
Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors. PMID:26089781
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Interregional synaptic maps among engram cells underlie memory formation.
Choi, Jun-Hyeok; Sim, Su-Eon; Kim, Ji-Il; Choi, Dong Il; Oh, Jihae; Ye, Sanghyun; Lee, Jaehyun; Kim, TaeHyun; Ko, Hyoung-Gon; Lim, Chae-Seok; Kaang, Bong-Kiun
2018-04-27
Memory resides in engram cells distributed across the brain. However, the site-specific substrate within these engram cells remains theoretical, even though it is generally accepted that synaptic plasticity encodes memories. We developed the dual-eGRASP (green fluorescent protein reconstitution across synaptic partners) technique to examine synapses between engram cells to identify the specific neuronal site for memory storage. We found an increased number and size of spines on CA1 engram cells receiving input from CA3 engram cells. In contextual fear conditioning, this enhanced connectivity between engram cells encoded memory strength. CA3 engram to CA1 engram projections strongly occluded long-term potentiation. These results indicate that enhanced structural and functional connectivity between engram cells across two directly connected brain regions forms the synaptic correlate for memory formation. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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Bi, Zedong; Zhou, Changsong
2016-01-01
In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP) when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis). Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons). Neurons (including the post-synaptic neuron) in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV) induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV) induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1) synchronous firing and burstiness tend to increase DiffV, (2) heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3) heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our work important for understanding functional processes of neuronal networks (such as memory) and neural development. PMID:26941634
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Dendrodendritic Synapses in the Mouse Olfactory Bulb External Plexiform Layer
Bartel, Dianna L.; Rela, Lorena; Hsieh, Lawrence; Greer, Charles A.
2014-01-01
Odor information relayed by olfactory bulb projection neurons, mitral and tufted cells (M/T), is modulated by pairs of reciprocal dendrodendritic synaptic circuits in the external plexiform layer (EPL). Interneurons, which are accounted for largely by granule cells, receive depolarizing input from M/T dendrites and in turn inhibit current spread in M/T dendrites via hyperpolarizing reciprocal dendrodendritic synapses. Because the location of dendrodendritic synapses may significantly affect the cascade of odor information, we assessed synaptic properties and density within sublaminae of the EPL and along the length of M/T secondary dendrites. In electron micrographs the M/T to granule cell synapse appeared to predominate and were equivalent in both the outer and inner EPL. However, the dendrodendritic synapses from granule cell spines onto M/T dendrites, were more prevalent in the outer EPL. In contrast, individual gephyrin-IR puncta, a postsynaptic scaffolding protein at inhibitory synapses used here as a proxy for the granule to M/T dendritic synapse was equally distributed throughout the EPL. Of significance to the organization of intrabulbar circuits, gephyrin-IR synapses are not uniformly distributed along M/T secondary dendrites. Synaptic density, expressed as a function of surface area, increases distal to the cell body. Furthermore, the distributions of gephyrin-IR puncta are heterogeneous and appear as clusters along the length of the M/T dendrites. Consistent with computational models, our data suggest that temporal coding in M/T cells is achieved by precisely located inhibitory input and that distance from the soma is compensated with an increase in synaptic density. PMID:25420934
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Goutman, Juan D; Auclair, Sarah Marie; Boutet de Monvel, Jacques; Tertrais, Margot; Emptoz, Alice; Parrin, Alexandre; Nouaille, Sylvie; Guillon, Marc; Sachse, Martin; Ciric, Danica; Bahloul, Amel; Hardelin, Jean-Pierre; Sutton, Roger Bryan; Avan, Paul; Krishnakumar, Shyam S; Rothman, James E
2017-01-01
Hearing relies on rapid, temporally precise, and sustained neurotransmitter release at the ribbon synapses of sensory cells, the inner hair cells (IHCs). This process requires otoferlin, a six C2-domain, Ca2+-binding transmembrane protein of synaptic vesicles. To decipher the role of otoferlin in the synaptic vesicle cycle, we produced knock-in mice (Otof Ala515,Ala517/Ala515,Ala517) with lower Ca2+-binding affinity of the C2C domain. The IHC ribbon synapse structure, synaptic Ca2+ currents, and otoferlin distribution were unaffected in these mutant mice, but auditory brainstem response wave-I amplitude was reduced. Lower Ca2+ sensitivity and delay of the fast and sustained components of synaptic exocytosis were revealed by membrane capacitance measurement upon modulations of intracellular Ca2+ concentration, by varying Ca2+ influx through voltage-gated Ca2+-channels or Ca2+ uncaging. Otoferlin thus functions as a Ca2+ sensor, setting the rates of primed vesicle fusion with the presynaptic plasma membrane and synaptic vesicle pool replenishment in the IHC active zone. PMID:29111973
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Oscillations in Spurious States of the Associative Memory Model with Synaptic Depression
NASA Astrophysics Data System (ADS)
Murata, Shin; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato
2014-12-01
The associative memory model is a typical neural network model that can store discretely distributed fixed-point attractors as memory patterns. When the network stores the memory patterns extensively, however, the model has other attractors besides the memory patterns. These attractors are called spurious memories. Both spurious states and memory states are in equilibrium, so there is little difference between their dynamics. Recent physiological experiments have shown that the short-term dynamic synapse called synaptic depression decreases its efficacy of transmission to postsynaptic neurons according to the activities of presynaptic neurons. Previous studies revealed that synaptic depression destabilizes the memory states when the number of memory patterns is finite. However, it is very difficult to study the dynamical properties of the spurious states if the number of memory patterns is proportional to the number of neurons. We investigate the effect of synaptic depression on spurious states by Monte Carlo simulation. The results demonstrate that synaptic depression does not affect the memory states but mainly destabilizes the spurious states and induces periodic oscillations.
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Michalski, Nicolas; Goutman, Juan D; Auclair, Sarah Marie; Boutet de Monvel, Jacques; Tertrais, Margot; Emptoz, Alice; Parrin, Alexandre; Nouaille, Sylvie; Guillon, Marc; Sachse, Martin; Ciric, Danica; Bahloul, Amel; Hardelin, Jean-Pierre; Sutton, Roger Bryan; Avan, Paul; Krishnakumar, Shyam S; Rothman, James E; Dulon, Didier; Safieddine, Saaid; Petit, Christine
2017-11-07
Hearing relies on rapid, temporally precise, and sustained neurotransmitter release at the ribbon synapses of sensory cells, the inner hair cells (IHCs). This process requires otoferlin, a six C 2 -domain, Ca 2+ -binding transmembrane protein of synaptic vesicles. To decipher the role of otoferlin in the synaptic vesicle cycle, we produced knock-in mice ( Otof Ala515,Ala517/Ala515,Ala517 ) with lower Ca 2+ -binding affinity of the C 2 C domain. The IHC ribbon synapse structure, synaptic Ca 2+ currents, and otoferlin distribution were unaffected in these mutant mice, but auditory brainstem response wave-I amplitude was reduced. Lower Ca 2+ sensitivity and delay of the fast and sustained components of synaptic exocytosis were revealed by membrane capacitance measurement upon modulations of intracellular Ca 2+ concentration, by varying Ca 2+ influx through voltage-gated Ca 2+ -channels or Ca 2+ uncaging. Otoferlin thus functions as a Ca 2+ sensor, setting the rates of primed vesicle fusion with the presynaptic plasma membrane and synaptic vesicle pool replenishment in the IHC active zone.
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2015-12-01
Award Number: W81XWH-13-1-0415 TITLE: Testing Brain Overgrowth and Synaptic Models of Autism Using NPC’s and Neurons from Patient-Derived IPS...Approved for Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Autism and autism spectrum disorders (ASD) are complex...impaired social interaction, and limited and repetitive interests and behavior. Recent studies have led to two major hypotheses for autism
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NASA Astrophysics Data System (ADS)
Kim, E. J.; Kim, K. A.; Yoon, S. M.
2016-02-01
Synaptic plasticity can be mimicked by electronic synaptic devices. By using ferroelectric thin films as gate insulator for thin-film transistors (TFT), channel conductance can be defined as the synaptic plasticity, and gradually modulated by the variations in amounts of aligned ferroelectric dipoles. Poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)]-poly(methyl methacrylate) (PMMA) blended films are chosen and their switching kinetics are investigated by using the Kolmogorov-Avrami-Ishibashi model. The switching time for ferroelectric polarization is sensitively influenced by the amplitude of applied electric field and volumetric ratio of ferroelectric beta-phases in the P(VDF-TrFE)-PMMA films. The switching time of the P(VDF-TrFE) increases with decreasing the pulse amplitude and/or the ratio of ferroelectric beta-phases by incorporation of PMMA. The activation electric field is also found to increase as the increase in blended amount of PMMA. Synapse TFTs are fabricated using the P(VDF-TrFE)-PMMA as gate insulator and In-Ga-Zn-O active channels. The drain currents of the synapse TFTs gradually increased when the voltage pulse signals with given duration are repeatedly applied. This suggests that the synaptic weights can be modulated by the number of external pulse signals, and that the proposed synapse TFT can be applied for mimicking the operations of bio-synapses.
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Intracellular GPCRs Play Key Roles in Synaptic Plasticity.
Jong, Yuh-Jiin I; Harmon, Steven K; O'Malley, Karen L
2018-02-16
The trillions of synaptic connections within the human brain are shaped by experience and neuronal activity, both of which underlie synaptic plasticity and ultimately learning and memory. G protein-coupled receptors (GPCRs) play key roles in synaptic plasticity by strengthening or weakening synapses and/or shaping dendritic spines. While most studies of synaptic plasticity have focused on cell surface receptors and their downstream signaling partners, emerging data point to a critical new role for the very same receptors to signal from inside the cell. Intracellular receptors have been localized to the nucleus, endoplasmic reticulum, lysosome, and mitochondria. From these intracellular positions, such receptors may couple to different signaling systems, display unique desensitization patterns, and/or show distinct patterns of subcellular distribution. Intracellular GPCRs can be activated at the cell surface, endocytosed, and transported to an intracellular site or simply activated in situ by de novo ligand synthesis, diffusion of permeable ligands, or active transport of non-permeable ligands. Current findings reinforce the notion that intracellular GPCRs play a dynamic role in synaptic plasticity and learning and memory. As new intracellular GPCR roles are defined, the need to selectively tailor agonists and/or antagonists to both intracellular and cell surface receptors may lead to the development of more effective therapeutic tools.
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Democratization in a passive dendritic tree: an analytical investigation.
Timofeeva, Y; Cox, S J; Coombes, S; Josić, K
2008-10-01
One way to achieve amplification of distal synaptic inputs on a dendritic tree is to scale the amplitude and/or duration of the synaptic conductance with its distance from the soma. This is an example of what is often referred to as "dendritic democracy". Although well studied experimentally, to date this phenomenon has not been thoroughly explored from a mathematical perspective. In this paper we adopt a passive model of a dendritic tree with distributed excitatory synaptic conductances and analyze a number of key measures of democracy. In particular, via moment methods we derive laws for the transport, from synapse to soma, of strength, characteristic time, and dispersion. These laws lead immediately to synaptic scalings that overcome attenuation with distance. We follow this with a Neumann approximation of Green's representation that readily produces the synaptic scaling that democratizes the peak somatic voltage response. Results are obtained for both idealized geometries and for the more realistic geometry of a rat CA1 pyramidal cell. For each measure of democratization we produce and contrast the synaptic scaling associated with treating the synapse as either a conductance change or a current injection. We find that our respective scalings agree up to a critical distance from the soma and we reveal how this critical distance decreases with decreasing branch radius.
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NASA Astrophysics Data System (ADS)
Kim, Hyung Jun; Park, Daehoon; Yang, Paul; Beom, Keonwon; Kim, Min Ju; Shin, Chansun; Kang, Chi Jung; Yoon, Tae-Sik
2018-06-01
A crossbar array of Pt/CeO2/Pt memristors exhibited the synaptic characteristics such as analog, reversible, and strong resistance change with a ratio of ∼103, corresponding to wide dynamic range of synaptic weight modulation as potentiation and depression with respect to the voltage polarity. In addition, it presented timing-dependent responses such as paired-pulse facilitation and the short-term to long-term memory transition by increasing amplitude, width, and repetition number of voltage pulse and reducing the interval time between pulses. The memory loss with a time was fitted with a stretched exponential relaxation model, revealing the relation of memory stability with the input stimuli strength. The resistance change was further enhanced but its stability got worse as increasing measurement temperature, indicating that the resistance was changed as a result of voltage- and temperature-dependent electrical charging and discharging to alter the energy barrier for charge transport. These detailed synaptic characteristics demonstrated the potential of crossbar array of Pt/CeO2/Pt memristors as artificial synapses in highly connected neuron-synapse network.
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Kim, Hyung Jun; Park, Daehoon; Yang, Paul; Beom, Keonwon; Kim, Min Ju; Shin, Chansun; Kang, Chi Jung; Yoon, Tae-Sik
2018-06-29
A crossbar array of Pt/CeO 2 /Pt memristors exhibited the synaptic characteristics such as analog, reversible, and strong resistance change with a ratio of ∼10 3 , corresponding to wide dynamic range of synaptic weight modulation as potentiation and depression with respect to the voltage polarity. In addition, it presented timing-dependent responses such as paired-pulse facilitation and the short-term to long-term memory transition by increasing amplitude, width, and repetition number of voltage pulse and reducing the interval time between pulses. The memory loss with a time was fitted with a stretched exponential relaxation model, revealing the relation of memory stability with the input stimuli strength. The resistance change was further enhanced but its stability got worse as increasing measurement temperature, indicating that the resistance was changed as a result of voltage- and temperature-dependent electrical charging and discharging to alter the energy barrier for charge transport. These detailed synaptic characteristics demonstrated the potential of crossbar array of Pt/CeO 2 /Pt memristors as artificial synapses in highly connected neuron-synapse network.
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Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change.
Leibold, Christian; Monsalve-Mercado, Mauro M
2016-08-01
Synaptic change is a costly resource, particularly for brain structures that have a high demand of synaptic plasticity. For example, building memories of object positions requires efficient use of plasticity resources since objects can easily change their location in space and yet we can memorize object locations. But how should a neural circuit ideally be set up to integrate two input streams (object location and identity) in case the overall synaptic changes should be minimized during ongoing learning? This letter provides a theoretical framework on how the two input pathways should ideally be specified. Generally the model predicts that the information-rich pathway should be plastic and encoded sparsely, whereas the pathway conveying less information should be encoded densely and undergo learning only if a neuronal representation of a novel object has to be established. As an example, we consider hippocampal area CA1, which combines place and object information. The model thereby provides a normative account of hippocampal rate remapping, that is, modulations of place field activity by changes of local cues. It may as well be applicable to other brain areas (such as neocortical layer V) that learn combinatorial codes from multiple input streams.
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Enhanced polychronization in a spiking network with metaplasticity.
Guise, Mira; Knott, Alistair; Benuskova, Lubica
2015-01-01
Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002). In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004). Polychronous groups (PNGs) develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP), but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.
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Li, Wen-Chang; Cooke, Tom; Sautois, Bart; Soffe, Stephen R; Borisyuk, Roman; Roberts, Alan
2007-09-10
How specific are the synaptic connections formed as neuronal networks develop and can simple rules account for the formation of functioning circuits? These questions are assessed in the spinal circuits controlling swimming in hatchling frog tadpoles. This is possible because detailed information is now available on the identity and synaptic connections of the main types of neuron. The probabilities of synapses between 7 types of identified spinal neuron were measured directly by making electrical recordings from 500 pairs of neurons. For the same neuron types, the dorso-ventral distributions of axons and dendrites were measured and then used to calculate the probabilities that axons would encounter particular dendrites and so potentially form synaptic connections. Surprisingly, synapses were found between all types of neuron but contact probabilities could be predicted simply by the anatomical overlap of their axons and dendrites. These results suggested that synapse formation may not require axons to recognise specific, correct dendrites. To test the plausibility of simpler hypotheses, we first made computational models that were able to generate longitudinal axon growth paths and reproduce the axon distribution patterns and synaptic contact probabilities found in the spinal cord. To test if probabilistic rules could produce functioning spinal networks, we then made realistic computational models of spinal cord neurons, giving them established cell-specific properties and connecting them into networks using the contact probabilities we had determined. A majority of these networks produced robust swimming activity. Simple factors such as morphogen gradients controlling dorso-ventral soma, dendrite and axon positions may sufficiently constrain the synaptic connections made between different types of neuron as the spinal cord first develops and allow functional networks to form. Our analysis implies that detailed cellular recognition between spinal neuron types may not be necessary for the reliable formation of functional networks to generate early behaviour like swimming.
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Mizerna, O P; Fedulova, S A; Veselovs'kyĭ, M S
2010-01-01
In the present study, we investigated the sensitivity of GABAergic short-term plasticity to the selective P- and P/Q-type calcium channels blocker omega-agatoxin-IVA. To block the P-type channels we used 30 nM of this toxin and 200 nM of the toxin was used to block the P/Q channel types. The evoked inhibitory postsynaptic currents (eIPSC) were studied using patch-clamp technique in whole-cell configuration in postsynaptic neuron and local extracellular stimulation of single presynaptic axon by rectangular pulse. The present data show that the contribution of P- and P/Q-types channels to GABAergic synaptic transmission in cultured hippocampal neurons are 30% and 45%, respectively. It was shown that the mediate contribution of the P- and P/Q-types channels to the amplitudes of eIPSC is different to every discovered neuron. It means that distribution of these channels is non-uniform. To study the short-term plasticity of inhibitory synaptic transmission, axons of presynaptic neurons were paired-pulse stimulated with the interpulse interval of 150 ms. Neurons demonstrated both the depression and facilitation. The application of 30 nM and 200 nM of the blocker decreased the depression and increased facilitation to 8% and 11%, respectively. In addition, we found that the mediate contribution of the P- and P/Q-types channels to realization of synaptic transmission after the second stimuli is 4% less compared to that after the first one. Therefore, blocking of both P- and P/Q-types calcium channels can change the efficiency of synaptic transmission. In this instance it facilitates realization of the transmission via decreased depression or increased facilitation. These results confirm that the P- and P/Q-types calcium channels are involved in regulation of the short-term inhibitory synaptic plasticity in cultured hippocampal neurons.
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Stabilization of memory States by stochastic facilitating synapses.
Miller, Paul
2013-12-06
Bistability within a small neural circuit can arise through an appropriate strength of excitatory recurrent feedback. The stability of a state of neural activity, measured by the mean dwelling time before a noise-induced transition to another state, depends on the neural firing-rate curves, the net strength of excitatory feedback, the statistics of spike times, and increases exponentially with the number of equivalent neurons in the circuit. Here, we show that such stability is greatly enhanced by synaptic facilitation and reduced by synaptic depression. We take into account the alteration in times of synaptic vesicle release, by calculating distributions of inter-release intervals of a synapse, which differ from the distribution of its incoming interspike intervals when the synapse is dynamic. In particular, release intervals produced by a Poisson spike train have a coefficient of variation greater than one when synapses are probabilistic and facilitating, whereas the coefficient of variation is less than one when synapses are depressing. However, in spite of the increased variability in postsynaptic input produced by facilitating synapses, their dominant effect is reduced synaptic efficacy at low input rates compared to high rates, which increases the curvature of neural input-output functions, leading to wider regions of bistability in parameter space and enhanced lifetimes of memory states. Our results are based on analytic methods with approximate formulae and bolstered by simulations of both Poisson processes and of circuits of noisy spiking model neurons.
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Kojic, L; Gu, Q; Douglas, R M; Cynader, M S
2001-02-28
Both cholinergic and serotonergic modulatory projections to mammalian striate cortex have been demonstrated to be involved in the regulation of postnatal plasticity, and a striking alteration in the number and intracortical distribution of cholinergic and serotonergic receptors takes place during the critical period for cortical plasticity. As well, agonists of cholinergic and serotonergic receptors have been demonstrated to facilitate induction of long-term synaptic plasticity in visual cortical slices supporting their involvement in the control of activity-dependent plasticity. We recorded field potentials from layers 4 and 2/3 in visual cortex slices of 60--80 day old kittens after white matter stimulation, before and after a period of high frequency stimulation (HFS), in the absence or presence of either cholinergic or serotonergic agonists. At these ages, the HFS protocol alone almost never induced long-term changes of synaptic plasticity in either layers 2/3 or 4. In layer 2/3, agonist stimulation of m1 receptors facilitated induction of long-term potentiation (LTP) with HFS stimulation, while the activation of serotonergic receptors had only a modest effect. By contrast, a strong serotonin-dependent LTP facilitation and insignificant muscarinic effects were observed after HFS within layer 4. The results show that receptor-dependent laminar stratification of synaptic modifiability occurs in the cortex at these ages. This plasticity may underly a control system gating the experience-dependent changes of synaptic organization within developing visual cortex.
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Garrido, Jesús A.; Luque, Niceto R.; D'Angelo, Egidio; Ros, Eduardo
2013-01-01
Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr, 1969). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar adaptation. We therefore tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. The incorporation of further plasticity mechanisms and of spiking signal processing will allow this concept to be extended in a more realistic computational scenario. PMID:24130518
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Fernández-Alfonso, Tomás; Nadella, K.M. Naga Srinivas; Iacaruso, M. Florencia; Pichler, Bruno; Roš, Hana; Kirkby, Paul A.; Silver, R. Angus
2014-01-01
Background Two-photon microscopy is widely used to study brain function, but conventional microscopes are too slow to capture the timing of neuronal signalling and imaging is restricted to one plane. Recent development of acousto-optic-deflector-based random access functional imaging has improved the temporal resolution, but the utility of these technologies for mapping 3D synaptic activity patterns and their performance at the excitation wavelengths required to image genetically encoded indicators have not been investigated. New method Here, we have used a compact acousto-optic lens (AOL) two-photon microscope to make high speed [Ca2+] measurements from spines and dendrites distributed in 3D with different excitation wavelengths (800–920 nm). Results We show simultaneous monitoring of activity from many synaptic inputs distributed over the 3D arborisation of a neuronal dendrite using both synthetic as well as genetically encoded indicators. We confirm the utility of AOL-based imaging for fast in vivo recordings by measuring, simultaneously, visually evoked responses in 100 neurons distributed over a 150 μm focal depth range. Moreover, we explore ways to improve the measurement of timing of neuronal activation by choosing specific regions within the cell soma. Comparison with existing methods These results establish that AOL-based 3D random access two-photon microscopy has a wider range of neuroscience applications than previously shown. Conclusions Our findings show that the compact AOL microscope design has the speed, spatial resolution, sensitivity and wavelength flexibility to measure 3D patterns of synaptic and neuronal activity on individual trials. PMID:24200507
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Potjans, Wiebke; Morrison, Abigail; Diesmann, Markus
2010-01-01
A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e., on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator, or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity. PMID:21151370
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Expression of synapsin I correlates with maturation of the neuromuscular synapse.
Lu, B; Czernik, A J; Popov, S; Wang, T; Poo, M M; Greengard, P
1996-10-01
Synapsins are a family of neuron-specific phosphoproteins that are localized within the presynaptic terminals in adult brain. Previous work has demonstrated that introduction of exogenous synapsins I(a + b) or IIa into Xenopus spinal neurons promoted maturation of the neuromuscular synapse in a nerve-muscle co-culture system. We have now studied the expression of endogenous Xenopus synapsin I during synaptic maturation in vivo and in culture, using a polyclonal antibody raised against Xenopus synapsin I. Immunoprecipitation experiments indicated that synapsin I was not detectable during the early phase of synaptogenesis in vivo, and exhibited a marked increase during the period of synaptic maturation. In contrast, the expression of synaptophysin, another synaptic vesicle protein, was detected at the start of nervous system formation, and remained at a high level thereafter. Similar expression profiles for the two proteins were also observed in immunocytochemical studies of Xenopus spinal neurons in culture: intense staining of synaptophysin was found on the first day, while synapsin I was not detected until after three days in culture. The expression of synapsin I correlated very well with the appearance of a bell-shaped amplitude distribution of spontaneous synaptic currents, a physiological parameter which reflects functional maturation of the neuromuscular synapse. In one-day-old cultures grown in the absence of laminin, an extracellular matrix protein known to be present at the neuromuscular junction, the amplitude distribution of virtually all synapses was skewed towards smaller values. In contrast, when laminin was used as a culture substrate, many synapses exhibited a bell-shaped amplitude distribution. Laminin treatment also induced synapsin I expression in one-day-old cultures. These results suggest that the expression of endogenous synapsin I may regulate maturation at neuromuscular synapses.
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Active subthreshold dendritic conductances shape the local field potential
Ness, Torbjørn V.; Remme, Michiel W. H.
2016-01-01
Key points The local field potential (LFP), the low‐frequency part of extracellular potentials recorded in neural tissue, is often used for probing neural circuit activity. Interpreting the LFP signal is difficult, however.While the cortical LFP is thought mainly to reflect synaptic inputs onto pyramidal neurons, little is known about the role of the various subthreshold active conductances in shaping the LFP.By means of biophysical modelling we obtain a comprehensive qualitative understanding of how the LFP generated by a single pyramidal neuron depends on the type and spatial distribution of active subthreshold currents.For pyramidal neurons, the h‐type channels probably play a key role and can cause a distinct resonance in the LFP power spectrum.Our results show that the LFP signal can give information about the active properties of neurons and imply that preferred frequencies in the LFP can result from those cellular properties instead of, for example, network dynamics. Abstract The main contribution to the local field potential (LFP) is thought to stem from synaptic input to neurons and the ensuing subthreshold dendritic processing. The role of active dendritic conductances in shaping the LFP has received little attention, even though such ion channels are known to affect the subthreshold neuron dynamics. Here we used a modelling approach to investigate the effects of subthreshold dendritic conductances on the LFP. Using a biophysically detailed, experimentally constrained model of a cortical pyramidal neuron, we identified conditions under which subthreshold active conductances are a major factor in shaping the LFP. We found that, in particular, the hyperpolarization‐activated inward current, I h, can have a sizable effect and cause a resonance in the LFP power spectral density. To get a general, qualitative understanding of how any subthreshold active dendritic conductance and its cellular distribution can affect the LFP, we next performed a systematic study with a simplified model. We found that the effect on the LFP is most pronounced when (1) the synaptic drive to the cell is asymmetrically distributed (i.e. either basal or apical), (2) the active conductances are distributed non‐uniformly with the highest channel densities near the synaptic input and (3) when the LFP is measured at the opposite pole of the cell relative to the synaptic input. In summary, we show that subthreshold active conductances can be strongly reflected in LFP signals, opening up the possibility that the LFP can be used to characterize the properties and cellular distributions of active conductances. PMID:27079755
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The Physiology of Moral Maturity.
ERIC Educational Resources Information Center
Hemming, James
1991-01-01
Discusses an evolutionary approach to human morality. Emphasizes the rapid development of brain weight, neural circuits, and synaptic systems during early childhood. Concludes that the human brain has resources for generating responsible, caring behavior but must be nurtured and educated. Urges that moral training in a proper social climate be…
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Santini, Emanuela; Huynh, Thu N.; Klann, Eric
2018-01-01
The complexity of memory formation and its persistence is a phenomenon that has been studied intensely for centuries. Memory exists in many forms and is stored in various brain regions. Generally speaking, memories are reorganized into broadly distributed cortical networks over time through systems level consolidation. At the cellular level, storage of information is believed to initially occur via altered synaptic strength by processes such as long-term potentiation (LTP). New protein synthesis is required for long-lasting synaptic plasticity as well as for the formation of long-term memory. The mammalian target of rapamycin complex 1 (mTORC1) is a critical regulator of cap-dependent protein synthesis and is required for numerous forms of long-lasting synaptic plasticity and long-term memory. As such, the study of mTORC1 and protein factors that control translation initiation and elongation have enhanced our understanding of how the process of protein synthesis is regulated during memory formation. Herein we will discuss the molecular mechanisms that regulate protein synthesis as well as pharmacological and genetic manipulations that demonstrate the requirement for proper translational control in long-lasting synaptic plasticity and long-term memory formation. PMID:24484700
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Merchán-Pérez, Angel; Rodríguez, José-Rodrigo; González, Santiago; Robles, Víctor; DeFelipe, Javier; Larrañaga, Pedro; Bielza, Concha
2014-01-01
In the cerebral cortex, most synapses are found in the neuropil, but relatively little is known about their 3-dimensional organization. Using an automated dual-beam electron microscope that combines focused ion beam milling and scanning electron microscopy, we have been able to obtain 10 three-dimensional samples with an average volume of 180 µm3 from the neuropil of layer III of the young rat somatosensory cortex (hindlimb representation). We have used specific software tools to fully reconstruct 1695 synaptic junctions present in these samples and to accurately quantify the number of synapses per unit volume. These tools also allowed us to determine synapse position and to analyze their spatial distribution using spatial statistical methods. Our results indicate that the distribution of synaptic junctions in the neuropil is nearly random, only constrained by the fact that synapses cannot overlap in space. A theoretical model based on random sequential absorption, which closely reproduces the actual distribution of synapses, is also presented. PMID:23365213
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Merchán-Pérez, Angel; Rodríguez, José-Rodrigo; González, Santiago; Robles, Víctor; Defelipe, Javier; Larrañaga, Pedro; Bielza, Concha
2014-06-01
In the cerebral cortex, most synapses are found in the neuropil, but relatively little is known about their 3-dimensional organization. Using an automated dual-beam electron microscope that combines focused ion beam milling and scanning electron microscopy, we have been able to obtain 10 three-dimensional samples with an average volume of 180 µm(3) from the neuropil of layer III of the young rat somatosensory cortex (hindlimb representation). We have used specific software tools to fully reconstruct 1695 synaptic junctions present in these samples and to accurately quantify the number of synapses per unit volume. These tools also allowed us to determine synapse position and to analyze their spatial distribution using spatial statistical methods. Our results indicate that the distribution of synaptic junctions in the neuropil is nearly random, only constrained by the fact that synapses cannot overlap in space. A theoretical model based on random sequential absorption, which closely reproduces the actual distribution of synapses, is also presented.
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Rodríguez, José-Rodrigo; DeFelipe, Javier
2018-01-01
Abstract Changes in the size of the synaptic junction are thought to have significant functional consequences. We used focused ion beam milling and scanning electron microscopy (FIB/SEM) to obtain stacks of serial sections from the six layers of the rat somatosensory cortex. We have segmented in 3D a large number of synapses (n = 6891) to analyze the size and shape of excitatory (asymmetric) and inhibitory (symmetric) synapses, using dedicated software. This study provided three main findings. Firstly, the mean synaptic sizes were smaller for asymmetric than for symmetric synapses in all cortical layers. In all cases, synaptic junction sizes followed a log-normal distribution. Secondly, most cortical synapses had disc-shaped postsynaptic densities (PSDs; 93%). A few were perforated (4.5%), while a smaller proportion (2.5%) showed a tortuous horseshoe-shaped perimeter. Thirdly, the curvature was larger for symmetric than for asymmetric synapses in all layers. However, there was no correlation between synaptic area and curvature. PMID:29387782
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Santuy, Andrea; Rodríguez, José-Rodrigo; DeFelipe, Javier; Merchán-Pérez, Angel
2018-01-01
Changes in the size of the synaptic junction are thought to have significant functional consequences. We used focused ion beam milling and scanning electron microscopy (FIB/SEM) to obtain stacks of serial sections from the six layers of the rat somatosensory cortex. We have segmented in 3D a large number of synapses ( n = 6891) to analyze the size and shape of excitatory (asymmetric) and inhibitory (symmetric) synapses, using dedicated software. This study provided three main findings. Firstly, the mean synaptic sizes were smaller for asymmetric than for symmetric synapses in all cortical layers. In all cases, synaptic junction sizes followed a log-normal distribution. Secondly, most cortical synapses had disc-shaped postsynaptic densities (PSDs; 93%). A few were perforated (4.5%), while a smaller proportion (2.5%) showed a tortuous horseshoe-shaped perimeter. Thirdly, the curvature was larger for symmetric than for asymmetric synapses in all layers. However, there was no correlation between synaptic area and curvature.
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1993-01-01
Stiff-Man syndrome (SMS) is a rare disease of the central nervous system (CNS) characterized by progressive rigidity of the body musculature with superimposed painful spasms. An autoimmune origin of the disease has been proposed. In a caseload of more than 100 SMS patients, 60% were found positive for autoantibodies directed against the GABA-synthesizing enzyme glutamic acid decarboxylase (GAD). Few patients, all women affected by breast cancer, were negative for GAD autoantibodies but positive for autoantibodies directed against a 128- kD synaptic protein. We report here that this antigen is amphiphysin. GAD and amphiphysin are nonintrinsic membrane proteins that are concentrated in nerve terminals, where a pool of both proteins is associated with the cytoplasmic surface of synaptic vesicles. GAD and amphiphysin are the only two known targets of CNS autoimmunity with this distribution. This finding suggests a possible link between autoimmunity directed against cytoplasmic proteins associated with synaptic vesicles and SMS. PMID:8245793
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Ding, Juan; Xi, Yuan-Di; Zhang, Dan-Di; Zhao, Xia; Liu, Jin-Meng; Li, Chao-Qun; Han, Jing; Xiao, Rong
2013-12-01
This research aims to investigate whether soybean isoflavone (SIF) could alleviate the learning and memory deficit induced by β-amyloid peptides 1-42 (Aβ 1-42) by protecting the synapses of rats. Adult male Wistar rats were randomly allocated to the following groups: (1) control group; (2) Aβ 1-42 group; (3) SIF group; (4) SIF + Aβ 1-42 group (SIF pretreatment group) according to body weight. The 80 mg/kg/day of SIF was administered orally by gavage to the rats in SIF and SIF+Aβ 1-42 groups. Aβ 1-42 was injected into the lateral cerebral ventricle of rats in Aβ 1-42 and SIF+Aβ 1-42 groups. The ability of learning and memory, ultramicrostructure of hippocampal synapses, and expression of synaptic related proteins were investigated. The Morris water maze results showed the escape latency and total distance were decreased in the rats of SIF pretreatment group compared to the rats in Aβ1-42 group. Furthermore, SIF pretreatment could alleviate the synaptic structural damage and antagonize the down-regulation expressions of below proteins induced by Aβ1-42: (1) mRNA and protein of the synaptophysin and postsynaptic density protein 95 (PSD-95); (2) protein of calmodulin (CaM), Ca(2+) /calmodulin-dependent protein kinase II (CaMK II), and cAMP response element binding protein (CREB); (3) phosphorylation levels of CaMK II and CREB (pCAMK II, pCREB). These results suggested that SIF pretreatment could ameliorate the impairment of learning and memory ability in rats induced by Aβ 1-42, and its mechanism might be associated with the protection of synaptic plasticity by improving the synaptic structure and regulating the synaptic related proteins. Copyright © 2013 Wiley Periodicals, Inc.
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Bi, Zedong; Zhou, Changsong
2016-01-01
Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816
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A Cognitive Model Based on Neuromodulated Plasticity
Ruan, Xiaogang
2016-01-01
Associative learning, including classical conditioning and operant conditioning, is regarded as the most fundamental type of learning for animals and human beings. Many models have been proposed surrounding classical conditioning or operant conditioning. However, a unified and integrated model to explain the two types of conditioning is much less studied. Here, a model based on neuromodulated synaptic plasticity is presented. The model is bioinspired including multistored memory module and simulated VTA dopaminergic neurons to produce reward signal. The synaptic weights are modified according to the reward signal, which simulates the change of associative strengths in associative learning. The experiment results in real robots prove the suitability and validity of the proposed model. PMID:27872638
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Origin of the computational hardness for learning with binary synapses.
Huang, Haiping; Kabashima, Yoshiyuki
2014-11-01
Through supervised learning in a binary perceptron one is able to classify an extensive number of random patterns by a proper assignment of binary synaptic weights. However, to find such assignments in practice is quite a nontrivial task. The relation between the weight space structure and the algorithmic hardness has not yet been fully understood. To this end, we analytically derive the Franz-Parisi potential for the binary perceptron problem by starting from an equilibrium solution of weights and exploring the weight space structure around it. Our result reveals the geometrical organization of the weight space; the weight space is composed of isolated solutions, rather than clusters of exponentially many close-by solutions. The pointlike clusters far apart from each other in the weight space explain the previously observed glassy behavior of stochastic local search heuristics.
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Emergence of low noise frustrated states in E/I balanced neural networks.
Recio, I; Torres, J J
2016-12-01
We study emerging phenomena in binary neural networks where, with a probability c synaptic intensities are chosen according with a Hebbian prescription, and with probability (1-c) there is an extra random contribution to synaptic weights. This new term, randomly taken from a Gaussian bimodal distribution, balances the synaptic population in the network so that one has 80%-20% relation in E/I population ratio, mimicking the balance observed in mammals cortex. For some regions of the relevant parameters, our system depicts standard memory (at low temperature) and non-memory attractors (at high temperature). However, as c decreases and the level of the underlying noise also decreases below a certain temperature T t , a kind of memory-frustrated state, which resembles spin-glass behavior, sharply emerges. Contrary to what occurs in Hopfield-like neural networks, the frustrated state appears here even in the limit of the loading parameter α→0. Moreover, we observed that the frustrated state in fact corresponds to two states of non-vanishing activity uncorrelated with stored memories, associated, respectively, to a high activity or Up state and to a low activity or Down state. Using a linear stability analysis, we found regions in the space of relevant parameters for locally stable steady states and demonstrated that frustrated states coexist with memory attractors below T t . Then, multistability between memory and frustrated states is present for relatively small c, and metastability of memory attractors can emerge as c decreases even more. We studied our system using standard mean-field techniques and with Monte Carlo simulations, obtaining a perfect agreement between theory and simulations. Our study can be useful to explain the role of synapse heterogeneity on the emergence of stable Up and Down states not associated to memory attractors, and to explore the conditions to induce transitions among them, as in sleep-wake transitions. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Sun, Rong; Zhang, Bin; Qi, Lei; Shivakoti, Sakar; Tian, Chong-Li; Lau, Pak-Ming
2018-01-01
As key functional units in neural circuits, different types of neuronal synapses play distinct roles in brain information processing, learning, and memory. Synaptic abnormalities are believed to underlie various neurological and psychiatric disorders. Here, by combining cryo-electron tomography and cryo-correlative light and electron microscopy, we distinguished intact excitatory and inhibitory synapses of cultured hippocampal neurons, and visualized the in situ 3D organization of synaptic organelles and macromolecules in their native state. Quantitative analyses of >100 synaptic tomograms reveal that excitatory synapses contain a mesh-like postsynaptic density (PSD) with thickness ranging from 20 to 50 nm. In contrast, the PSD in inhibitory synapses assumes a thin sheet-like structure ∼12 nm from the postsynaptic membrane. On the presynaptic side, spherical synaptic vesicles (SVs) of 25–60 nm diameter and discus-shaped ellipsoidal SVs of various sizes coexist in both synaptic types, with more ellipsoidal ones in inhibitory synapses. High-resolution tomograms obtained using a Volta phase plate and electron filtering and counting reveal glutamate receptor-like and GABAA receptor-like structures that interact with putative scaffolding and adhesion molecules, reflecting details of receptor anchoring and PSD organization. These results provide an updated view of the ultrastructure of excitatory and inhibitory synapses, and demonstrate the potential of our approach to gain insight into the organizational principles of cellular architecture underlying distinct synaptic functions. SIGNIFICANCE STATEMENT To understand functional properties of neuronal synapses, it is desirable to analyze their structure at molecular resolution. We have developed an integrative approach combining cryo-electron tomography and correlative fluorescence microscopy to visualize 3D ultrastructural features of intact excitatory and inhibitory synapses in their native state. Our approach shows that inhibitory synapses contain uniform thin sheet-like postsynaptic densities (PSDs), while excitatory synapses contain previously known mesh-like PSDs. We discovered “discus-shaped” ellipsoidal synaptic vesicles, and their distributions along with regular spherical vesicles in synaptic types are characterized. High-resolution tomograms further allowed identification of putative neurotransmitter receptors and their heterogeneous interaction with synaptic scaffolding proteins. The specificity and resolution of our approach enables precise in situ analysis of ultrastructural organization underlying distinct synaptic functions. PMID:29311144
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Oscillations, Timing, Plasticity, and Learning in the Cerebellum.
Cheron, G; Márquez-Ruiz, J; Dan, B
2016-04-01
The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.
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HIRANO, ARLENE A.; BRANDSTÄTTER, JOHANN H.; BRECHA, NICHOLAS C.
2010-01-01
The mechanism underlying transmitter release from retinal horizontal cells is poorly understood. We investigated the possibility of vesicular transmitter release from mammalian horizontal cells by examining the expression of synaptic proteins that participate in vesicular transmitter release at chemical synapses. Using immunocytochemistry, we evaluated the cellular and subcellular distribution of complexin I/II, syntaxin-1, and synapsin I in rabbit retina. Strong labeling for complexin I/II, proteins that regulate a late step in vesicular transmitter release, was found in both synaptic layers of the retina, and in somata of A- and B-type horizontal cells, of γ-aminobutyric acid (GABA)- and glycinergic amacrine cells, and of ganglion cells. Immunoelectron microscopy demonstrated the presence of complexin I/II in horizontal cell processes postsynaptic to rod and cone ribbon synapses. Syntaxin-1, a core protein of the soluble N-ethylmaleimide-sensitive-factor attachment protein receptor (SNARE) complex known to bind to complexin, and synapsin I, a synaptic vesicle-associated protein involved in the Ca2+-dependent recruitment of synaptic vesicles for transmitter release, were also present in the horizontal cells and their processes at photoreceptor synapses. Photoreceptors and bipolar cells did not express any of these proteins at their axon terminals. The presence of complexin I/II, syntaxin-1, and synapsin I in rabbit horizontal cell processes and tips suggests that a vesicular mechanism may underlie transmitter release from mammalian horizontal cells. PMID:15912504
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Model of reversible vesicular transport with exclusion
NASA Astrophysics Data System (ADS)
Bressloff, Paul C.; Karamched, Bhargav R.
2016-08-01
A major question in neurobiology concerns the mechanics behind the motor-driven transport and delivery of vesicles to synaptic targets along the axon of a neuron. Experimental evidence suggests that the distribution of vesicles along the axon is relatively uniform and that vesicular delivery to synapses is reversible. A recent modeling study has made explicit the crucial role that reversibility in vesicular delivery to synapses plays in achieving uniformity in vesicle distribution, so called synaptic democracy (Bressloff et al 2015 Phys. Rev. Lett. 114 168101). In this paper we generalize the previous model by accounting for exclusion effects (hard-core repulsion) that may occur between molecular motor-cargo complexes (particles) moving along the same microtubule track. The resulting model takes the form of an exclusion process with four internal states, which distinguish between motile and stationary particles, and whether or not a particle is carrying vesicles. By applying a mean field approximation and an adiabatic approximation we reduce the system of ODEs describing the evolution of occupation numbers of the sites on a 1D lattice to a system of hydrodynamic equations in the continuum limit. We find that reversibility in vesicular delivery allows for synaptic democracy even in the presence of exclusion effects, although exclusion does exacerbate nonuniform distributions of vesicles in an axon when compared with a model without exclusion. We also uncover the relationship between our model and other models of exclusion processes with internal states.
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Palisade pattern of mormyrid Purkinje cells: a correlated light and electron microscopic study.
Meek, J; Nieuwenhuys, R
1991-04-01
The present study is devoted to a detailed analysis of the structural and synaptic organization of mormyrid Purkinje cells in order to evaluate the possible functional significance of their dendritic palisade pattern. For this purpose, the properties of Golgi-impregnated as well as unimpregnated Purkinje cells in lobe C1 and C3 of the cerebellum of Gnathonemus petersii were light and electron microscopically analyzed, quantified, reconstructed, and mutually compared. Special attention was paid to the degree of regularity of their dendritic trees, their relations with Bergmann glia, and the distribution and numerical properties of their synaptic connections with parallel fibers, stellate cells, "climbing" fibers, and Purkinje axonal boutons. The highest degree of palisade specialization was encountered in lobe C1, where Purkinje cells have on average 50 palisade dendrites with a very regular distribution in a sagittal plane. Their spine density decreases from superficial to deep (from 14 to 6 per micron dendritic length), a gradient correlated with a decreasing parallel fiber density but an increasing parallel fiber diameter. Each Purkinje cell makes on average 75,000 synaptic contacts with parallel fibers, some of which are rather coarse (0.45 microns), and provided with numerous short collaterals. Climbing fibers do not climb, since their synaptic contacts are restricted to the ganglionic layer (i.e., the layer of Purkinje and eurydendroid projection cells), where they make about 130 synaptic contacts per cell with 2 or 3 clusters of thorns on the proximal dendrites. These clusters contain also a type of "shunting" elements that make desmosome-like junctions with both the climbing fiber boutons and the necks of the thorns. The axons of Purkinje cells in lobe C1 make small terminal arborizations, with about 20 boutons, that may be substantially (up to 500 microns) displaced rostrally or caudally with respect to the soma. Purkinje axonal boutons were observed to make synaptic contacts with eurydendroid projection cells and with the proximal dendritic and somatic receptive surface of Purkinje cells, where about 15 randomly distributed boutons per neuron occur. The organization of Purkinje cells in lobe C3 differs markedly from that in C1 and seems to be less regular and specialized, although the overall palisade pattern is even more regular than in lobe C1 because of the absence of large eurydendroid neurons. However, individual neurons have a less regular dendritic tree, there is no apical-basal gradient in spine density or parallel fiber density and diameter, and there are no "shunting" elements in the climbing fiber glomeruli.(ABSTRACT TRUNCATED AT 400 WORDS)
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Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity.
Cazé, Romain D; Jarvis, Sarah; Foust, Amanda J; Schultz, Simon R
2017-09-01
Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.
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Bazzani, Armando; Castellani, Gastone C; Cooper, Leon N
2010-05-01
We analyze the effects of noise correlations in the input to, or among, Bienenstock-Cooper-Munro neurons using the Wigner semicircular law to construct random, positive-definite symmetric correlation matrices and compute their eigenvalue distributions. In the finite dimensional case, we compare our analytic results with numerical simulations and show the effects of correlations on the lifetimes of synaptic strengths in various visual environments. These correlations can be due either to correlations in the noise from the input lateral geniculate nucleus neurons, or correlations in the variability of lateral connections in a network of neurons. In particular, we find that for fixed dimensionality, a large noise variance can give rise to long lifetimes of synaptic strengths. This may be of physiological significance.
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Distribution of serine/threonine kinase SAD-B in mouse peripheral nerve synapse.
Hagiwara, Akari; Harada, Kenu; Hida, Yamato; Kitajima, Isao; Ohtsuka, Toshihisa
2011-05-11
The serine/threonine kinase SAD regulates neural functions such as axon/dendrite polarization and neurotransmitter release. In the vertebrate central nervous system, SAD-B, a homolog of Caenorhabditis elegans SAD-1, is associated with synaptic vesicles and the active zone cytomatrix in nerve terminals. However, the distribution of SAD-B in the peripheral nervous system remains elusive. Here, we show that SAD-B is specifically localized to neuromuscular junctions. Although the active zone protein bassoon showed a punctated signal indicating its localization to motor end plates, SAD-B shows relatively diffuse localization indicating its association with both the active zone and synaptic vesicles. Therefore, SAD kinase may regulate neurotransmitter release from motor end plates in a similar manner to its regulation of neurotransmitter release in the central nervous system.
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One parameter family of master equations for logistic growth and BCM theory
NASA Astrophysics Data System (ADS)
De Oliveira, L. R.; Castellani, C.; Turchetti, G.
2015-02-01
We propose a one parameter family of master equations, for the evolution of a population, having the logistic equation as mean field limit. The parameter α determines the relative weight of linear versus nonlinear terms in the population number n ⩽ N entering the loss term. By varying α from 0 to 1 the equilibrium distribution changes from maximum growth to almost extinction. The former is a Gaussian centered at n = N, the latter is a power law peaked at n = 1. A bimodal distribution is observed in the transition region. When N grows and tends to ∞, keeping the value of α fixed, the distribution tends to a Gaussian centered at n = N whose limit is a delta function corresponding to the stable equilibrium of the mean field equation. The choice of the master equation in this family depends on the equilibrium distribution for finite values of N. The presence of an absorbing state for n = 0 does not change this picture since the extinction mean time grows exponentially fast with N. As a consequence for α close to zero extinction is not observed, whereas when α approaches 1 the relaxation to a power law is observed before extinction occurs. We extend this approach to a well known model of synaptic plasticity, the so called BCM theory in the case of a single neuron with one or two synapses.
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Synaptic tagging, evaluation of memories, and the distal reward problem.
Päpper, Marc; Kempter, Richard; Leibold, Christian
2011-01-01
Long-term synaptic plasticity exhibits distinct phases. The synaptic tagging hypothesis suggests an early phase in which synapses are prepared, or "tagged," for protein capture, and a late phase in which those proteins are integrated into the synapses to achieve memory consolidation. The synapse specificity of the tags is consistent with conventional neural network models of associative memory. Memory consolidation through protein synthesis, however, is neuron specific, and its functional role in those models has not been assessed. Here, using a theoretical network model, we test the tagging hypothesis on its potential to prolong memory lifetimes in an online-learning paradigm. We find that protein synthesis, though not synapse specific, prolongs memory lifetimes if it is used to evaluate memory items on a cellular level. In our model we assume that only "important" memory items evoke protein synthesis such that these become more stable than "unimportant" items, which do not evoke protein synthesis. The network model comprises an equilibrium distribution of synaptic states that is very susceptible to the storage of new items: Most synapses are in a state in which they are plastic and can be changed easily, whereas only those synapses that are essential for the retrieval of the important memory items are in the stable late phase. The model can solve the distal reward problem, where the initial exposure of a memory item and its evaluation are temporally separated. Synaptic tagging hence provides a viable mechanism to consolidate and evaluate memories on a synaptic basis.
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Zinc at glutamatergic synapses.
Paoletti, P; Vergnano, A M; Barbour, B; Casado, M
2009-01-12
It has long been known that the mammalian forebrain contains a subset of glutamatergic neurons that sequester zinc in their synaptic vesicles. This zinc may be released into the synaptic cleft upon neuronal activity. Extracellular zinc has the potential to interact with and modulate many different synaptic targets, including glutamate receptors and transporters. Among these targets, NMDA receptors appear particularly interesting because certain NMDA receptor subtypes (those containing the NR2A subunit) contain allosteric sites exquisitely sensitive to extracellular zinc. The existence of these high-affinity zinc binding sites raises the possibility that zinc may act both in a phasic and tonic mode. Changes in zinc concentration and subcellular zinc distribution have also been described in several pathological conditions linked to glutamatergic transmission dysfunctions. However, despite intense investigation, the functional significance of vesicular zinc remains largely a mystery. In this review, we present the anatomy and the physiology of the glutamatergic zinc-containing synapse. Particular emphasis is put on the molecular and cellular mechanisms underlying the putative roles of zinc as a messenger involved in excitatory synaptic transmission and plasticity. We also highlight the many controversial issues and unanswered questions. Finally, we present and compare two widely used zinc chelators, CaEDTA and tricine, and show why tricine should be preferred to CaEDTA when studying fast transient zinc elevations as may occur during synaptic activity.
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Harnett, Mark T.; Magee, Jeffrey C.
2015-01-01
The apical tuft is the most remote area of the dendritic tree of neocortical pyramidal neurons. Despite its distal location, the apical dendritic tuft of layer 5 pyramidal neurons receives substantial excitatory synaptic drive and actively processes corticocortical input during behavior. The properties of the voltage-activated ion channels that regulate synaptic integration in tuft dendrites have, however, not been thoroughly investigated. Here, we use electrophysiological and optical approaches to examine the subcellular distribution and function of hyperpolarization-activated cyclic nucleotide-gated nonselective cation (HCN) channels in rat layer 5B pyramidal neurons. Outside-out patch recordings demonstrated that the amplitude and properties of ensemble HCN channel activity were uniform in patches excised from distal apical dendritic trunk and tuft sites. Simultaneous apical dendritic tuft and trunk whole-cell current-clamp recordings revealed that the pharmacological blockade of HCN channels decreased voltage compartmentalization and enhanced the generation and spread of apical dendritic tuft and trunk regenerative activity. Furthermore, multisite two-photon glutamate uncaging demonstrated that HCN channels control the amplitude and duration of synaptically evoked regenerative activity in the distal apical dendritic tuft. In contrast, at proximal apical dendritic trunk and somatic recording sites, the blockade of HCN channels decreased excitability. Dynamic-clamp experiments revealed that these compartment-specific actions of HCN channels were heavily influenced by the local and distributed impact of the high density of HCN channels in the distal apical dendritic arbor. The properties and subcellular distribution pattern of HCN channels are therefore tuned to regulate the interaction between integration compartments in layer 5B pyramidal neurons. PMID:25609619
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Synapse Specificity of Long-Term Potentiation Breaks Down with Aging
ERIC Educational Resources Information Center
Ris, Laurence; Godaux, Emile
2007-01-01
Memory shows age-related decline. According to the current prevailing theoretical model, encoding of memories relies on modifications in the strength of the synapses connecting the different cells within a neuronal network. The selective increases in synaptic weight are thought to be biologically implemented by long-term potentiation (LTP). Here,…
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Theta Coordinated Error Driven Learning in the Hippocampus (Open Access, Publisher’s Version)
2013-06-06
assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of...together as part of a memory or engram representation, e.g., in the central area CA3 of the hippocampus. With these connections strengthened, the ability
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Obis, Teresa; Besalduch, Núria; Hurtado, Erica; Nadal, Laura; Santafe, Manel M; Garcia, Neus; Tomàs, Marta; Priego, Mercedes; Lanuza, Maria A; Tomàs, Josep
2015-02-10
Protein kinase C (PKC) regulates a variety of neural functions, including neurotransmitter release. Although various PKC isoforms can be expressed at the synaptic sites and specific cell distribution may contribute to their functional diversity, little is known about the isoform-specific functions of PKCs in neuromuscular synapse. The present study is designed to examine the location of the novel isoform nPKCε at the neuromuscular junction (NMJ), their synaptic activity-related expression changes, its regulation by muscle contraction, and their possible involvement in acetylcholine release. We use immunohistochemistry and confocal microscopy to demonstrate that the novel isoform nPKCε is exclusively located in the motor nerve terminals of the adult rat NMJ. We also report that electrical stimulation of synaptic inputs to the skeletal muscle significantly increased the amount of nPKCε isoform as well as its phosphorylated form in the synaptic membrane, and muscle contraction is necessary for these nPKCε expression changes. The results also demonstrate that synaptic activity-induced muscle contraction promotes changes in presynaptic nPKCε through the brain-derived neurotrophic factor (BDNF)-mediated tyrosine kinase receptor B (TrkB) signaling. Moreover, nPKCε activity results in phosphorylation of the substrate MARCKS involved in actin cytoskeleton remodeling and related with neurotransmission. Finally, blocking nPKCε with a nPKCε-specific translocation inhibitor peptide (εV1-2) strongly reduces phorbol ester-induced ACh release potentiation, which further indicates that nPKCε is involved in neurotransmission. Together, these results provide a mechanistic insight into how synaptic activity-induced muscle contraction could regulate the presynaptic action of the nPKCε isoform and suggest that muscle contraction is an important regulatory step in TrkB signaling at the NMJ.
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High abundance of BDNF within glutamatergic presynapses of cultured hippocampal neurons
Andreska, Thomas; Aufmkolk, Sarah; Sauer, Markus; Blum, Robert
2014-01-01
In the mammalian brain, the neurotrophin brain-derived neurotrophic factor (BDNF) has emerged as a key factor for synaptic refinement, plasticity and learning. Although BDNF-induced signaling cascades are well known, the spatial aspects of the synaptic BDNF localization remained unclear. Recent data provide strong evidence for an exclusive presynaptic location and anterograde secretion of endogenous BDNF at synapses of the hippocampal circuit. In contrast, various studies using BDNF overexpression in cultured hippocampal neurons support the idea that postsynaptic elements and other dendritic structures are the preferential sites of BDNF localization and release. In this study we used rigorously tested anti-BDNF antibodies and achieved a dense labeling of endogenous BDNF close to synapses. Confocal microscopy showed natural BDNF close to many, but not all glutamatergic synapses, while neither GABAergic synapses nor postsynaptic structures carried a typical synaptic BDNF label. To visualize the BDNF distribution within the fine structure of synapses, we implemented super resolution fluorescence imaging by direct stochastic optical reconstruction microscopy (dSTORM). Two-color dSTORM images of neurites were acquired with a spatial resolution of ~20 nm. At this resolution, the synaptic scaffold proteins Bassoon and Homer exhibit hallmarks of mature synapses and form juxtaposed bars, separated by a synaptic cleft. BDNF imaging signals form granule-like clusters with a mean size of ~60 nm and are preferentially found within the fine structure of the glutamatergic presynapse. Individual glutamatergic presynapses carried up to 90% of the synaptic BDNF immunoreactivity, and only a minor fraction of BDNF molecules was found close to the postsynaptic bars. Our data proof that hippocampal neurons are able to enrich and store high amounts of BDNF in small granules within the mature glutamatergic presynapse, at a principle site of synaptic plasticity. PMID:24782711
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Ye, Xuan; Chang, Qing; Jeong, Yu Young; Cai, Huaibin; Kusnecov, Alexander
2017-01-01
Amyloid-β (Aβ) peptides play a key role in synaptic damage and memory deficits in the early pathogenesis of Alzheimer's disease (AD). Abnormal accumulation of Aβ at nerve terminals leads to synaptic pathology and ultimately to neurodegeneration. β-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1) is the major neuronal β-secretase for Aβ generation. However, the mechanisms regulating BACE1 distribution in axons and β cleavage of APP at synapses remain largely unknown. Here, we reveal that dynein–Snapin-mediated retrograde transport regulates BACE1 trafficking in axons and APP processing at presynaptic terminals. BACE1 is predominantly accumulated within late endosomes at the synapses of AD-related mutant human APP (hAPP) transgenic (Tg) mice and patient brains. Defective retrograde transport by genetic ablation of snapin in mice recapitulates late endocytic retention of BACE1 and increased APP processing at presynaptic sites. Conversely, overexpressing Snapin facilitates BACE1 trafficking and reduces synaptic BACE1 accumulation by enhancing the removal of BACE1 from distal AD axons and presynaptic terminals. Moreover, elevated Snapin expression via stereotactic hippocampal injections of adeno-associated virus particles in mutant hAPP Tg mouse brains decreases synaptic Aβ levels and ameliorates synapse loss, thus rescuing cognitive impairments associated with hAPP mice. Altogether, our study provides new mechanistic insights into the complex regulation of BACE1 trafficking and presynaptic localization through Snapin-mediated dynein-driven retrograde axonal transport, thereby suggesting a potential approach of modulating Aβ levels and attenuating synaptic deficits in AD. SIGNIFICANCE STATEMENT β-Site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1) trafficking and synaptic localization significantly influence its β secretase activity and amyloid-β (Aβ) production. In AD brains, BACE1 is accumulated within dystrophic neurites, which is thought to augment Aβ-induced synaptotoxicity by Aβ overproduction. However, it remains largely unknown whether axonal transport regulates synaptic APP processing. Here, we demonstrate that Snapin-mediated retrograde transport plays a critical role in removing BACE1 from presynaptic terminals toward the soma, thus reducing synaptic Aβ production. Adeno-associated virus–mediated Snapin overexpression in the hippocampus of mutant hAPP mice significantly decreases synaptic Aβ levels, attenuates synapse loss, and thus rescues cognitive deficits. Our study uncovers a new pathway that controls synaptic APP processing by enhancing axonal BACE1 trafficking, thereby advancing our fundamental knowledge critical for ameliorating Aβ-linked synaptic pathology. PMID:28159908
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Ajemian, Robert; D’Ausilio, Alessandro; Moorman, Helene; Bizzi, Emilio
2013-01-01
During the process of skill learning, synaptic connections in our brains are modified to form motor memories of learned sensorimotor acts. The more plastic the adult brain is, the easier it is to learn new skills or adapt to neurological injury. However, if the brain is too plastic and the pattern of synaptic connectivity is constantly changing, new memories will overwrite old memories, and learning becomes unstable. This trade-off is known as the stability–plasticity dilemma. Here a theory of sensorimotor learning and memory is developed whereby synaptic strengths are perpetually fluctuating without causing instability in motor memory recall, as long as the underlying neural networks are sufficiently noisy and massively redundant. The theory implies two distinct stages of learning—preasymptotic and postasymptotic—because once the error drops to a level comparable to that of the noise-induced error, further error reduction requires altered network dynamics. A key behavioral prediction derived from this analysis is tested in a visuomotor adaptation experiment, and the resultant learning curves are modeled with a nonstationary neural network. Next, the theory is used to model two-photon microscopy data that show, in animals, high rates of dendritic spine turnover, even in the absence of overt behavioral learning. Finally, the theory predicts enhanced task selectivity in the responses of individual motor cortical neurons as the level of task expertise increases. From these considerations, a unique interpretation of sensorimotor memory is proposed—memories are defined not by fixed patterns of synaptic weights but, rather, by nonstationary synaptic patterns that fluctuate coherently. PMID:24324147
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Autism-like Deficits in Shank3-Deficient Mice Are Rescued by Targeting Actin Regulators.
Duffney, Lara J; Zhong, Ping; Wei, Jing; Matas, Emmanuel; Cheng, Jia; Qin, Luye; Ma, Kaijie; Dietz, David M; Kajiwara, Yuji; Buxbaum, Joseph D; Yan, Zhen
2015-06-09
Haploinsufficiency of the Shank3 gene, which encodes a scaffolding protein at glutamatergic synapses, is a highly prevalent and penetrant risk factor for autism. Using combined behavioral, electrophysiological, biochemical, imaging, and molecular approaches, we find that Shank3-deficient mice exhibit autism-like social deficits and repetitive behaviors, as well as the significantly diminished NMDA receptor (NMDAR) synaptic function and synaptic distribution in prefrontal cortex. Concomitantly, Shank3-deficient mice have a marked loss of cortical actin filaments, which is associated with the reduced Rac1/PAK activity and increased activity of cofilin, the major actin depolymerizing factor. The social deficits and NMDAR hypofunction are rescued by inhibiting cofilin or activating Rac1 in Shank3-deficient mice and are induced by inhibiting PAK or Rac1 in wild-type mice. These results indicate that the aberrant regulation of synaptic actin filaments and loss of synaptic NMDARs contribute to the manifestation of autism-like phenotypes. Thus, targeting actin regulators provides a strategy for autism treatment. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
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The schizophrenia risk gene product miR-137 alters presynaptic plasticity
Siegert, Sandra; Seo, Jinsoo; Kwon, Ester J.; Rudenko, Andrii; Cho, Sukhee; Wang, Wenyuan; Flood, Zachary; Martorell, Anthony J.; Ericsson, Maria; Mungenast, Alison E.; Tsai, Li-Huei
2015-01-01
Non-coding variants in the human MIR137 gene locus increase schizophrenia risk at a genome-wide significance level. However, the functional consequence of these risk alleles is unknown. Here, we examined induced human neurons harboring the minor alleles of four disease-associated single nucleotide polymorphisms (SNPs) in MIR137, and observed increased MIR137 levels compared to major allele-carrying cells. We found that miR-137 gain-of-function causes downregulation of the presynaptic target genes, Complexin-1 (Cplx1), Nsf, and Synaptotagmin-1 (Syt1), leading to impaired vesicle release. In vivo, miR-137 gain-of-function results in changes in synaptic vesicle pool distribution, impaired mossy fiber-LTP induction and deficits in hippocampus-dependent learning and memory. By sequestering endogenous miR-137, we were able to ameliorate the synaptic phenotypes. Moreover, reinstatement of Syt1 expression partially restored synaptic plasticity, demonstrating the importance of Syt1 as a miR-137 target. Our data provide new insight into the mechanism by which miR-137 dysregulation can impair synaptic plasticity in the hippocampus. PMID:26005852
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Mishina, Masahiro; Senda, Michio; Kiyosawa, Motohiro; Ishiwata, Kiichi; De Volder, Anne G; Nakano, Hideki; Toyama, Hinako; Oda, Kei-ichi; Kimura, Yuichi; Ishii, Kenji; Sasaki, Touru; Ohyama, Masashi; Komaba, Yuichi; Kobayashi, Shirou; Kitamura, Shin; Katayama, Yasuo
2003-05-01
Before the completion of visual development, visual deprivation impairs synaptic elimination in the visual cortex. The purpose of this study was to determine whether the distribution of central benzodiazepine receptor (BZR) is also altered in the visual cortex in subjects with early-onset blindness. Positron emission tomography was carried out with [(15)O]water and [(11)C]flumazenil on six blind subjects and seven sighted controls at rest. We found that the CBF was significantly higher in the visual cortex for the early-onset blind subjects than for the sighted control subjects. However, there was no significant difference in the BZR distribution in the visual cortex for the subject with early-onset blindness than for the sighted control subjects. These results demonstrated that early visual deprivation does not affect the distribution of GABA(A) receptors in the visual cortex with the sensitivity of our measurements. Synaptic elimination may be independent of visual experience in the GABAergic system of the human visual cortex during visual development.
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Moreno-Bote, Rubén; Parga, Néstor
2010-06-01
Delivery of neurotransmitter produces on a synapse a current that flows through the membrane and gets transmitted into the soma of the neuron, where it is integrated. The decay time of the current depends on the synaptic receptor's type and ranges from a few (e.g., AMPA receptors) to a few hundred milliseconds (e.g., NMDA receptors). The role of the variety of synaptic timescales, several of them coexisting in the same neuron, is at present not understood. A prime question to answer is which is the effect of temporal filtering at different timescales of the incoming spike trains on the neuron's response. Here, based on our previous work on linear synaptic filtering, we build a general theory for the stationary firing response of integrate-and-fire (IF) neurons receiving stochastic inputs filtered by one, two, or multiple synaptic channels, each characterized by an arbitrary timescale. The formalism applies to arbitrary IF model neurons and arbitrary forms of input noise (i.e., not required to be gaussian or to have small amplitude), as well as to any form of synaptic filtering (linear or nonlinear). The theory determines with exact analytical expressions the firing rate of an IF neuron for long synaptic time constants using the adiabatic approach. The correlated spiking (cross-correlations function) of two neurons receiving common as well as independent sources of noise is also described. The theory is illustrated using leaky, quadratic, and noise-thresholded IF neurons. Although the adiabatic approach is exact when at least one of the synaptic timescales is long, it provides a good prediction of the firing rate even when the timescales of the synapses are comparable to that of the leak of the neuron; it is not required that the synaptic time constants are longer than the mean interspike intervals or that the noise has small variance. The distribution of the potential for general IF neurons is also characterized. Our results provide powerful analytical tools that can allow a quantitative description of the dynamics of neuronal networks with realistic synaptic dynamics.
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Activity-dependent control of NMDA receptor subunit composition at hippocampal mossy fibre synapses.
Carta, Mario; Srikumar, Bettadapura N; Gorlewicz, Adam; Rebola, Nelson; Mulle, Christophe
2018-02-15
CA3 pyramidal cells display input-specific differences in the subunit composition of synaptic NMDA receptors (NMDARs). Although at low density, GluN2B contributes significantly to NMDAR-mediated EPSCs at mossy fibre synapses. Long-term potentiation (LTP) of NMDARs triggers a modification in the subunit composition of synaptic NMDARs by insertion of GluN2B. GluN2B subunits are essential for the expression of LTP of NMDARs at mossy fibre synapses. Single neurons express NMDA receptors (NMDARs) with distinct subunit composition and biophysical properties that can be segregated in an input-specific manner. The dynamic control of the heterogeneous distribution of synaptic NMDARs is crucial to control input-dependent synaptic integration and plasticity. In hippocampal CA3 pyramidal cells from mice of both sexes, we found that mossy fibre (MF) synapses display a markedly lower proportion of GluN2B-containing NMDARs than associative/commissural synapses. The mechanism involved in such heterogeneous distribution of GluN2B subunits is not known. Here we show that long-term potentiation (LTP) of NMDARs, which is selectively expressed at MF-CA3 pyramidal cell synapses, triggers a modification in the subunit composition of synaptic NMDARs by insertion of GluN2B. This activity-dependent recruitment of GluN2B at mature MF-CA3 pyramidal cell synapses contrasts with the removal of GluN2B subunits at other glutamatergic synapses during development and in response to activity. Furthermore, although expressed at low levels, GluN2B is necessary for the expression of LTP of NMDARs at MF-CA3 pyramidal cell synapses. Altogether, we reveal a previously unknown activity-dependent regulation and function of GluN2B subunits that may contribute to the heterogeneous plasticity induction rules in CA3 pyramidal cells. © 2017 Centre Nationnal de la Recherche Scientifique. The Journal of Physiology © 2017 The Physiological Society.
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Uzunova, Genoveva; Hollander, Eric; Shepherd, Jason
2014-01-01
Autism spectrum disorder (ASD) and Fragile X syndrome (FXS) are relatively common childhood neurodevelopmental disorders with increasing incidence in recent years. They are currently accepted as disorders of the synapse with alterations in different forms of synaptic communication and neuronal network connectivity. The major excitatory neurotransmitter system in brain, the glutamatergic system, is implicated in learning and memory, synaptic plasticity, neuronal development. While much attention is attributed to the role of metabotropic glutamate receptors in ASD and FXS, studies indicate that the ionotropic glutamate receptors (iGluRs) and their regulatory proteins are also altered in several brain regions. Role of iGluRs in the neurobiology of ASD and FXS is supported by a weight of evidence that ranges from human genetics to in vitro cultured neurons. In this review we will discuss clinical, molecular, cellular and functional changes in NMDA, AMPA and kainate receptors and the synaptic proteins that regulate them in the context of ASD and FXS. We will also discuss the significance for the development of translational biomarkers and treatments for the core symptoms of ASD and FXS.
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Experience-Driven Formation of Parts-Based Representations in a Model of Layered Visual Memory
Jitsev, Jenia; von der Malsburg, Christoph
2009-01-01
Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces. PMID:19862345
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Nishimune, Hiroshi; Numata, Tomohiro; Chen, Jie; Aoki, Yudai; Wang, Yonghong; Starr, Miranda P; Mori, Yasuo; Stanford, John A
2012-01-01
The P/Q-type voltage-dependent calcium channels (VDCCs) are essential for synaptic transmission at adult mammalian neuromuscular junctions (NMJs); however, the subsynaptic location of VDCCs relative to active zones in rodent NMJs, and the functional modification of VDCCs by the interaction with active zone protein Bassoon remain unknown. Here, we show that P/Q-type VDCCs distribute in a punctate pattern within the NMJ presynaptic terminals and align in three dimensions with Bassoon. This distribution pattern of P/Q-type VDCCs and Bassoon in NMJs is consistent with our previous study demonstrating the binding of VDCCs and Bassoon. In addition, we now show that the interaction between P/Q-type VDCCs and Bassoon significantly suppressed the inactivation property of P/Q-type VDCCs, suggesting that the Ca(2+) influx may be augmented by Bassoon for efficient synaptic transmission at NMJs. However, presynaptic Bassoon level was significantly attenuated in aged rat NMJs, which suggests an attenuation of VDCC function due to a lack of this interaction between VDCC and Bassoon. Importantly, the decreased Bassoon level in aged NMJs was ameliorated by isometric strength training of muscles for two months. The training increased Bassoon immunoreactivity in NMJs without affecting synapse size. These results demonstrated that the P/Q-type VDCCs preferentially accumulate at NMJ active zones and play essential role in synaptic transmission in conjunction with the active zone protein Bassoon. This molecular mechanism becomes impaired by aging, which suggests altered synaptic function in aged NMJs. However, Bassoon level in aged NMJs can be improved by muscle exercise.
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Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations
Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus
2017-01-01
Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509
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Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck
2015-11-01
This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.
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Biologically plausible learning in neural networks: a lesson from bacterial chemotaxis.
Shimansky, Yury P
2009-12-01
Learning processes in the brain are usually associated with plastic changes made to optimize the strength of connections between neurons. Although many details related to biophysical mechanisms of synaptic plasticity have been discovered, it is unclear how the concurrent performance of adaptive modifications in a huge number of spatial locations is organized to minimize a given objective function. Since direct experimental observation of even a relatively small subset of such changes is not feasible, computational modeling is an indispensable investigation tool for solving this problem. However, the conventional method of error back-propagation (EBP) employed for optimizing synaptic weights in artificial neural networks is not biologically plausible. This study based on computational experiments demonstrated that such optimization can be performed rather efficiently using the same general method that bacteria employ for moving closer to an attractant or away from a repellent. With regard to neural network optimization, this method consists of regulating the probability of an abrupt change in the direction of synaptic weight modification according to the temporal gradient of the objective function. Neural networks utilizing this method (regulation of modification probability, RMP) can be viewed as analogous to swimming in the multidimensional space of their parameters in the flow of biochemical agents carrying information about the optimality criterion. The efficiency of RMP is comparable to that of EBP, while RMP has several important advantages. Since the biological plausibility of RMP is beyond a reasonable doubt, the RMP concept provides a constructive framework for the experimental analysis of learning in natural neural networks.
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Tao, Chang-Lu; Liu, Yun-Tao; Sun, Rong; Zhang, Bin; Qi, Lei; Shivakoti, Sakar; Tian, Chong-Li; Zhang, Peijun; Lau, Pak-Ming; Zhou, Z Hong; Bi, Guo-Qiang
2018-02-07
As key functional units in neural circuits, different types of neuronal synapses play distinct roles in brain information processing, learning, and memory. Synaptic abnormalities are believed to underlie various neurological and psychiatric disorders. Here, by combining cryo-electron tomography and cryo-correlative light and electron microscopy, we distinguished intact excitatory and inhibitory synapses of cultured hippocampal neurons, and visualized the in situ 3D organization of synaptic organelles and macromolecules in their native state. Quantitative analyses of >100 synaptic tomograms reveal that excitatory synapses contain a mesh-like postsynaptic density (PSD) with thickness ranging from 20 to 50 nm. In contrast, the PSD in inhibitory synapses assumes a thin sheet-like structure ∼12 nm from the postsynaptic membrane. On the presynaptic side, spherical synaptic vesicles (SVs) of 25-60 nm diameter and discus-shaped ellipsoidal SVs of various sizes coexist in both synaptic types, with more ellipsoidal ones in inhibitory synapses. High-resolution tomograms obtained using a Volta phase plate and electron filtering and counting reveal glutamate receptor-like and GABA A receptor-like structures that interact with putative scaffolding and adhesion molecules, reflecting details of receptor anchoring and PSD organization. These results provide an updated view of the ultrastructure of excitatory and inhibitory synapses, and demonstrate the potential of our approach to gain insight into the organizational principles of cellular architecture underlying distinct synaptic functions. SIGNIFICANCE STATEMENT To understand functional properties of neuronal synapses, it is desirable to analyze their structure at molecular resolution. We have developed an integrative approach combining cryo-electron tomography and correlative fluorescence microscopy to visualize 3D ultrastructural features of intact excitatory and inhibitory synapses in their native state. Our approach shows that inhibitory synapses contain uniform thin sheet-like postsynaptic densities (PSDs), while excitatory synapses contain previously known mesh-like PSDs. We discovered "discus-shaped" ellipsoidal synaptic vesicles, and their distributions along with regular spherical vesicles in synaptic types are characterized. High-resolution tomograms further allowed identification of putative neurotransmitter receptors and their heterogeneous interaction with synaptic scaffolding proteins. The specificity and resolution of our approach enables precise in situ analysis of ultrastructural organization underlying distinct synaptic functions. Copyright © 2018 Tao, Liu et al.
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Kriener, Birgit; Enger, Håkon; Tetzlaff, Tom; Plesser, Hans E.; Gewaltig, Marc-Oliver; Einevoll, Gaute T.
2014-01-01
Random networks of integrate-and-fire neurons with strong current-based synapses can, unlike previously believed, assume stable states of sustained asynchronous and irregular firing, even without external random background or pacemaker neurons. We analyze the mechanisms underlying the emergence, lifetime and irregularity of such self-sustained activity states. We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network. Thus, by increasing the synaptic coupling strength, the system can become bistable: In addition to the quiescent state, a second stable fixed-point at moderate firing rates can emerge by a saddle-node bifurcation. Inherently generated fluctuations of the population firing rate around this non-trivial fixed-point can trigger transitions into the quiescent state. Hence, the trade-off between the magnitude of the population-rate fluctuations and the size of the basin of attraction of the non-trivial rate fixed-point determines the onset and the lifetime of self-sustained activity states. During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states. We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state. PMID:25400575
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Kriener, Birgit; Enger, Håkon; Tetzlaff, Tom; Plesser, Hans E; Gewaltig, Marc-Oliver; Einevoll, Gaute T
2014-01-01
Random networks of integrate-and-fire neurons with strong current-based synapses can, unlike previously believed, assume stable states of sustained asynchronous and irregular firing, even without external random background or pacemaker neurons. We analyze the mechanisms underlying the emergence, lifetime and irregularity of such self-sustained activity states. We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network. Thus, by increasing the synaptic coupling strength, the system can become bistable: In addition to the quiescent state, a second stable fixed-point at moderate firing rates can emerge by a saddle-node bifurcation. Inherently generated fluctuations of the population firing rate around this non-trivial fixed-point can trigger transitions into the quiescent state. Hence, the trade-off between the magnitude of the population-rate fluctuations and the size of the basin of attraction of the non-trivial rate fixed-point determines the onset and the lifetime of self-sustained activity states. During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states. We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state.
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Efficient Transmission of Subthreshold Signals in Complex Networks of Spiking Neurons
Torres, Joaquin J.; Elices, Irene; Marro, J.
2015-01-01
We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances—that naturally balances the network with excitatory and inhibitory synapses—and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest. PMID:25799449
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Hanley, M.R.
1978-11-01
The crude venom of the Formosan banded krait, Bungarus multicinctus, was separated into eleven lethal protein fractions. Nine fractions were purified to final homogeneous toxins, designated ..cap alpha..-bungarotoxin, ..beta..-bungarotoxin, and toxins 7, 8, 9A, 11, 12, 13, and 14. Three of the toxins, ..cap alpha..-bungarotoxin, 7, and 8, were identified as post-synaptic curarimimetic neurotoxins. The remaining toxins were identified as pre-synaptic neurotoxins. ..cap alpha..-Bungarotoxin, toxin 7, and toxin 8 are all highly stable basic polypeptides of approx. 8000 daltons molecular weight. The pre-synaptic toxins fell into two structural groups: toxin 9A and 14 which were single basic chains of approx.more » 14,000 daltons, and ..beta..-bungarotoxin, and toxins 11 thru 13 which were composed of two chains of approx. 8000 and approx. 13,000 daltons covalently linked by disulfides. All the pre-synaptic neurotoxins were shown to have intrinsic calcium-dependent phospholipase A activities. Under certain conditions, intact synaptic membranes were hydrolyzed more rapidly than protein-free extracted synaptic-lipid liposomes which, in turn, were hydrolyzed more rapidly than any other tested liposomes. It was speculated that cell-surface arrays of phosphatidyl serine/glycolipids created high affinity target sites for ..beta..-bungarotoxin. Single-chain toxins were found to be qualitatively different from the two-chain toxins in their ability to block the functioning of acetylcholine receptors, and were quantitatively different in their enzymatic and membrane disruptive activities. ..beta..-Bungarotoxin was shown to be an extremely potent neuronal lesioning agent. There was no apparent selectivity for cholinergic over non-cholinergic neurons, nor for nerve terminals over cell bodies. It was suggested that ..beta..-bungarotoxin can be considered a useful new histological tool, which may exhibit some regional selectivity.« less
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ElBasiouny, Sherif M.; Rymer, W. Zev; Heckman, C. J.
2012-01-01
Motoneuron discharge patterns reflect the interaction of synaptic inputs with intrinsic conductances. Recent work has focused on the contribution of conductances mediating persistent inward currents (PICs), which amplify and prolong the effects of synaptic inputs on motoneuron discharge. Certain features of human motor unit discharge are thought to reflect a relatively stereotyped activation of PICs by excitatory synaptic inputs; these features include rate saturation and de-recruitment at a lower level of net excitation than that required for recruitment. However, PIC activation is also influenced by the pattern and spatial distribution of inhibitory inputs that are activated concurrently with excitatory inputs. To estimate the potential contributions of PIC activation and synaptic input patterns to motor unit discharge patterns, we examined the responses of a set of cable motoneuron models to different patterns of excitatory and inhibitory inputs. The models were first tuned to approximate the current- and voltage-clamp responses of low- and medium-threshold spinal motoneurons studied in decerebrate cats and then driven with different patterns of excitatory and inhibitory inputs. The responses of the models to excitatory inputs reproduced a number of features of human motor unit discharge. However, the pattern of rate modulation was strongly influenced by the temporal and spatial pattern of concurrent inhibitory inputs. Thus, even though PIC activation is likely to exert a strong influence on firing rate modulation, PIC activation in combination with different patterns of excitatory and inhibitory synaptic inputs can produce a wide variety of motor unit discharge patterns. PMID:22031773
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Harnett, Mark T; Magee, Jeffrey C; Williams, Stephen R
2015-01-21
The apical tuft is the most remote area of the dendritic tree of neocortical pyramidal neurons. Despite its distal location, the apical dendritic tuft of layer 5 pyramidal neurons receives substantial excitatory synaptic drive and actively processes corticocortical input during behavior. The properties of the voltage-activated ion channels that regulate synaptic integration in tuft dendrites have, however, not been thoroughly investigated. Here, we use electrophysiological and optical approaches to examine the subcellular distribution and function of hyperpolarization-activated cyclic nucleotide-gated nonselective cation (HCN) channels in rat layer 5B pyramidal neurons. Outside-out patch recordings demonstrated that the amplitude and properties of ensemble HCN channel activity were uniform in patches excised from distal apical dendritic trunk and tuft sites. Simultaneous apical dendritic tuft and trunk whole-cell current-clamp recordings revealed that the pharmacological blockade of HCN channels decreased voltage compartmentalization and enhanced the generation and spread of apical dendritic tuft and trunk regenerative activity. Furthermore, multisite two-photon glutamate uncaging demonstrated that HCN channels control the amplitude and duration of synaptically evoked regenerative activity in the distal apical dendritic tuft. In contrast, at proximal apical dendritic trunk and somatic recording sites, the blockade of HCN channels decreased excitability. Dynamic-clamp experiments revealed that these compartment-specific actions of HCN channels were heavily influenced by the local and distributed impact of the high density of HCN channels in the distal apical dendritic arbor. The properties and subcellular distribution pattern of HCN channels are therefore tuned to regulate the interaction between integration compartments in layer 5B pyramidal neurons. Copyright © 2015 the authors 0270-6474/15/351024-14$15.00/0.
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Weber, A J; Stanford, L R
1994-05-15
It has long been known that a number of functionally different types of ganglion cells exist in the cat retina, and that each responds differently to visual stimulation. To determine whether the characteristic response properties of different retinal ganglion cell types might reflect differences in the number and distribution of their bipolar and amacrine cell inputs, we compared the percentages and distributions of the synaptic inputs from bipolar and amacrine cells to the entire dendritic arbors of physiologically characterized retinal X- and Y-cells. Sixty-two percent of the synaptic input to the Y-cell was from amacrine cell terminals, while the X-cells received approximately equal amounts of input from amacrine and bipolar cells. We found no significant difference in the distributions of bipolar or amacrine cell inputs to X- and Y-cells, or ON-center and OFF-center cells, either as a function of dendritic branch order or distance from the origin of the dendritic arbor. While, on the basis of these data, we cannot exclude the possibility that the difference in the proportion of bipolar and amacrine cell input contributes to the functional differences between X- and Y-cells, the magnitude of this difference, and the similarity in the distributions of the input from the two afferent cell types, suggest that mechanisms other than a simple predominance of input from amacrine or bipolar cells underlie the differences in their response properties. More likely, perhaps, is that the specific response features of X- and Y-cells originate in differences in the visual responses of the bipolar and amacrine cells that provide their input, or in the complex synaptic arrangements found among amacrine and bipolar cell terminals and the dendrites of specific types of retinal ganglion cells.
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Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex.
Steyn-Ross, Moira L; Steyn-Ross, D A; Sleigh, J W; Wilson, M T; Wilcocks, Lara C
2005-12-01
Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer's dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.
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Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex
NASA Astrophysics Data System (ADS)
Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Sleigh, J. W.; Wilson, M. T.; Wilcocks, Lara C.
2005-12-01
Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer’s dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.
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What do dendrites and their synapses tell the neuron?
Segev, Idan
2006-03-01
This essay looks at the historical significance of four APS classic papers that are freely available online: Rall W. Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. J Neurophysiol 30: 1138-1168, 1967 (http://jn.physiology.org/cgi/reprint/30/5/1138). Rall W, Burke RE, Smith TG, Nelson PG, and Frank K. Dendritic location of synapses and possible mechanisms for the monosynaptic EPSP in motoneurons. J Neurophysiol 30: 1169-1193, 1967 (http://jn.physiology.org/cgi/reprint/30/5/1169). Rall W and Shepherd GM. Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J Neurophysiol 31: 884-915, 1968 (http://jn.physiology.org/cgi/reprint/31/6/884). Segev I and Rall W. Computational study of an excitable dendritic spine. J Neurophysiol 60: 499-523, 1988 (http://jn.physiology.org/cgi/reprint/60/2/499).
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Shang, Xueliang; Shang, Yingchun; Fu, Jingxuan; Zhang, Tao
2017-08-01
The aim of this study was to examine if nicotine was able to improve cognition deficits in a mouse model of chronic mild stress. Twenty-four male C57BL/6 mice were divided into three groups: control, stress, and stress with nicotine treatment. The animal model was established by combining chronic unpredictable mild stress (CUMS) and isolated feeding. Mice were exposed to CUMS continued for 28 days, while nicotine (0.2 mg/kg) was also administrated for 28 days. Weight and sucrose consumption were measured during model establishing period. The anxiety and behavioral despair were analyzed using the forced swim test (FST) and open-field test (OFT). Spatial cognition was evaluated using Morris water maze (MWM) test. Following behavioral assessment, both long-term potentiation (LTP) and depotentiation (DEP) were recorded in the hippocampal dentate gyrus (DG) region. Both synaptic and Notch1 proteins were measured by Western. Nicotine increased stressed mouse's sucrose consumption. The MWM test showed that spatial learning and reversal learning in stressed animals were remarkably affected relative to controls, whereas nicotine partially rescued cognitive functions. Additionally, nicotine considerably alleviated the level of anxiety and the degree of behavioral despair in stressed mice. It effectively mitigated the depression-induced impairment of hippocampal synaptic plasticity, in which both the LTP and DEP were significantly inhibited in stressed mice. Moreover, nicotine enhanced the expression of synaptic and Notch1 proteins in stressed animals. The results suggest that nicotine ameliorates the depression-like symptoms and improves the hippocampal synaptic plasticity closely associated with activating transmembrane ion channel receptors and Notch signaling components. Graphical Abstract ᅟ.
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Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model
Luque, Niceto R.; Garrido, Jesús A.; Naveros, Francisco; Carrillo, Richard R.; D'Angelo, Egidio; Ros, Eduardo
2016-01-01
Deep cerebellar nuclei neurons receive both inhibitory (GABAergic) synaptic currents from Purkinje cells (within the cerebellar cortex) and excitatory (glutamatergic) synaptic currents from mossy fibers. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP) located at different cerebellar sites (parallel fibers to Purkinje cells, mossy fibers to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells) in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibers to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP) and inhibitory (i-STDP) mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibers to Purkinje cells synapses and then transferred to mossy fibers to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation toward optimizing its working range). PMID:26973504
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Burkitt, A N
2006-08-01
The integrate-and-fire neuron model describes the state of a neuron in terms of its membrane potential, which is determined by the synaptic inputs and the injected current that the neuron receives. When the membrane potential reaches a threshold, an action potential (spike) is generated. This review considers the model in which the synaptic input varies periodically and is described by an inhomogeneous Poisson process, with both current and conductance synapses. The focus is on the mathematical methods that allow the output spike distribution to be analyzed, including first passage time methods and the Fokker-Planck equation. Recent interest in the response of neurons to periodic input has in part arisen from the study of stochastic resonance, which is the noise-induced enhancement of the signal-to-noise ratio. Networks of integrate-and-fire neurons behave in a wide variety of ways and have been used to model a variety of neural, physiological, and psychological phenomena. The properties of the integrate-and-fire neuron model with synaptic input described as a temporally homogeneous Poisson process are reviewed in an accompanying paper (Burkitt in Biol Cybern, 2006).
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Chang, Karen T.; Min, Kyung-Tai
2009-01-01
At the neuronal level of Down syndrome (DS) brains, there are evidences of altered shape, number, and density of synapses, as well as aberrant endocytosis associated with accumulation of enlarged endosomes, suggesting that proteins involved in synaptic vesicle recycling may play key roles in DS neurons. However, the exact mechanism underlying those anomalies is not well understood. We hypothesize that overexpression of three genes, dap160/itsn1, synj/synj1, and nla/dscr1, located on human chromosome 21 play important roles in DS neurons. Here, we systematically investigate the effects of multiple gene overexpression on synaptic morphology and endocytosis to identify possible dominant gene or genes. We found that overexpression of individual genes lead to abnormal synaptic morphology, but all three genes are necessary to cause impaired vesicle recycling and affect locomotor vigor. Furthermore, we report that dap160 overexpression alters the subcellular distribution of synaptojanin, and overexpression of nla regulates the phosphoinositol 5′ phosphatase activity of synaptojanin. These findings imply that restoring the level of any one of these genes may reduce endocytic defects seen in DS. PMID:19805187
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Child, Nicholas D; Benarroch, Eduardo E
2014-03-18
Neurons contain different functional somatodendritic and axonal domains, each with a characteristic distribution of voltage-gated ion channels, synaptic inputs, and function. The dendritic tree of a cortical pyramidal neuron has 2 distinct domains, the basal and the apical dendrites, both containing dendritic spines; the different domains of the axon are the axonal initial segment (AIS), axon proper (which in myelinated axons includes the node of Ranvier, paranodes, juxtaparanodes, and internodes), and the axon terminals. In the cerebral cortex, the dendritic spines of the pyramidal neurons receive most of the excitatory synapses; distinct populations of γ-aminobutyric acid (GABA)ergic interneurons target specific cellular domains and thus exert different influences on pyramidal neurons. The multiple synaptic inputs reaching the somatodendritic region and generating excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) sum and elicit changes in membrane potential at the AIS, the site of initiation of the action potential.
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Super-resolution Imaging of Chemical Synapses in the Brain
Dani, Adish; Huang, Bo; Bergan, Joseph; Dulac, Catherine; Zhuang, Xiaowei
2010-01-01
Determination of the molecular architecture of synapses requires nanoscopic image resolution and specific molecular recognition, a task that has so far defied many conventional imaging approaches. Here we present a super-resolution fluorescence imaging method to visualize the molecular architecture of synapses in the brain. Using multicolor, three-dimensional stochastic optical reconstruction microscopy, the distributions of synaptic proteins can be measured with nanometer precision. Furthermore, the wide-field, volumetric imaging method enables high-throughput, quantitative analysis of a large number of synapses from different brain regions. To demonstrate the capabilities of this approach, we have determined the organization of ten protein components of the presynaptic active zone and the postsynaptic density. Variations in synapse morphology, neurotransmitter receptor composition, and receptor distribution were observed both among synapses and across different brain regions. Combination with optogenetics further allowed molecular events associated with synaptic plasticity to be resolved at the single-synapse level. PMID:21144999
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NASA Astrophysics Data System (ADS)
Wan, Li; Zhou, Qinghua
2007-10-01
The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.
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The Beneficial Effects of Leptin on REM Sleep Deprivation-Induced Cognitive Deficits in Mice
ERIC Educational Resources Information Center
Chang, Hsiao-Fu; Su, Chun-Lin; Chang, Chih-Hua; Chen, Yu-Wen; Gean, Po-Wu
2013-01-01
Leptin, a 167 amino acid peptide, is synthesized predominantly in the adipose tissues and plays a key role in the regulation of food intake and body weight. Recent studies indicate that leptin receptor is expressed with high levels in many brain regions that may regulate synaptic plasticity. Here we show that deprivation of rapid eye movement…
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Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms
Vázquez, Roberto A.
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
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Rubinov, Mikail; Sporns, Olaf; Thivierge, Jean-Philippe; Breakspear, Michael
2011-06-01
Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.
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A spaceflight study of synaptic plasticity in adult rat vestibular maculas
NASA Technical Reports Server (NTRS)
Ross, M. D.
1994-01-01
Behavioral signs of vestibular perturbation in altered gravity have not been well correlated with structural modifications in neurovestibular centers. This ultrastructural research investigated synaptic plasticity in hair cells of adult rat utricular maculas exposed to microgravity for nine days on a space shuttle. The hypothesis was that synaptic plasticity would be more evident in type II hair cells because they are part of a distributed modifying macular circuitry. All rats were shared with other investigators and were subjected to treatments unrelated to this experiment. Maculas were obtained from flight and control rats after shuttle return (R + 0) and nine days post-flight (R + 9). R + 9 rats had chromodacryorrhea, a sign of acute stress. Tissues were prepared for ultrastructural study by conventional methods. Ribbon synapses were counted in fifty serial sections from medial utricular macular regions of three rats of each flight and control group. Counts in fifty additional consecutive sections from one sample in each group established method reliability. All synapses were photographed and located to specific cells on mosaics of entire sections. Pooled data were analyzed statistically. Flown rats showed abnormal posture and movement at R + 0. They had statistically significant increases in total ribbon synapses and in sphere-like ribbons in both kinds of hair cells; in type II cells, pairs of synapses nearly doubled and clusters of 3 to 6 synapses increased twelve-fold. At R + 9, behavioral signs were normal. However, synapse counts remained high in both kinds of hair cells of flight maculas and were elevated in control type II cells. Only counts in type I cells showed statistically significant differences at R + 9. High synaptic counts at R + 9 may have resulted from stress due to experimental treatments. The results nevertheless demonstrate that adult maculas retain the potential for synaptic plasticity. Type II cells exhibited more synaptic plasticity, but space flight induced synaptic plasticity in type I cells.
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AHaH computing-from metastable switches to attractors to machine learning.
Nugent, Michael Alexander; Molter, Timothy Wesley
2014-01-01
Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH) plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures-all key capabilities of biological nervous systems and modern machine learning algorithms with real world application.
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AHaH Computing–From Metastable Switches to Attractors to Machine Learning
Nugent, Michael Alexander; Molter, Timothy Wesley
2014-01-01
Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH) plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures–all key capabilities of biological nervous systems and modern machine learning algorithms with real world application. PMID:24520315
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Identification of PSD-95 Depalmitoylating Enzymes.
Yokoi, Norihiko; Fukata, Yuko; Sekiya, Atsushi; Murakami, Tatsuro; Kobayashi, Kenta; Fukata, Masaki
2016-06-15
Postsynaptic density (PSD)-95, the most abundant postsynaptic scaffolding protein, plays a pivotal role in synapse development and function. Continuous palmitoylation cycles on PSD-95 are essential for its synaptic clustering and regulation of AMPA receptor function. However, molecular mechanisms for palmitate cycling on PSD-95 remain incompletely understood, as PSD-95 depalmitoylating enzymes remain unknown. Here, we isolated 38 mouse or rat serine hydrolases and found that a subset specifically depalmitoylated PSD-95 in heterologous cells. These enzymes showed distinct substrate specificity. α/β-Hydrolase domain-containing protein 17 members (ABHD17A, 17B, and 17C), showing the strongest depalmitoylating activity to PSD-95, showed different localization from other candidates in rat hippocampal neurons, and were distributed to recycling endosomes, the dendritic plasma membrane, and the synaptic fraction. Expression of ABHD17 in neurons selectively reduced PSD-95 palmitoylation and synaptic clustering of PSD-95 and AMPA receptors. Furthermore, taking advantage of the acyl-PEGyl exchange gel shift (APEGS) method, we quantitatively monitored the palmitoylation stoichiometry and the depalmitoylation kinetics of representative synaptic proteins, PSD-95, GluA1, GluN2A, mGluR5, Gαq, and HRas. Unexpectedly, palmitate on all of them did not turn over in neurons. Uniquely, most of the PSD-95 population underwent rapid palmitoylation cycles, and palmitate cycling on PSD-95 decelerated accompanied by its increased stoichiometry as synapses developed, probably contributing to postsynaptic receptor consolidation. Finally, inhibition of ABHD17 expression dramatically delayed the kinetics of PSD-95 depalmitoylation. This study suggests that local palmitoylation machinery composed of synaptic DHHC palmitoylating enzymes and ABHD17 finely controls the amount of synaptic PSD-95 and synaptic function. Protein palmitoylation, the most common lipid modification, dynamically regulates neuronal protein localization and function. Its unique reversibility is conferred by DHHC-type palmitoyl acyl transferases (palmitoylating enzymes) and still controversial palmitoyl-protein thioesterases (depalmitoylating enzymes). Here, we identified the membrane-anchored serine hydrolases, ABHD17A, 17B, and 17C, as the physiological PSD-95 depalmitoylating enzymes that regulate PSD-95 palmitoylation cycles in neurons. This study describes the first direct evidence for the neuronal depalmitoylating enzyme and provides a new aspect of the dynamic regulatory mechanisms of synaptic development and synaptic plasticity. In addition, our established APEGS assay, which provides unbiased and quantitative information about the palmitoylation state and dynamics, revealed the distinct regulatory mechanisms for synaptic palmitoylation. Copyright © 2016 Yokoi, Fukata et al.
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Identification of PSD-95 Depalmitoylating Enzymes
Yokoi, Norihiko; Sekiya, Atsushi; Murakami, Tatsuro; Kobayashi, Kenta
2016-01-01
Postsynaptic density (PSD)-95, the most abundant postsynaptic scaffolding protein, plays a pivotal role in synapse development and function. Continuous palmitoylation cycles on PSD-95 are essential for its synaptic clustering and regulation of AMPA receptor function. However, molecular mechanisms for palmitate cycling on PSD-95 remain incompletely understood, as PSD-95 depalmitoylating enzymes remain unknown. Here, we isolated 38 mouse or rat serine hydrolases and found that a subset specifically depalmitoylated PSD-95 in heterologous cells. These enzymes showed distinct substrate specificity. α/β-Hydrolase domain-containing protein 17 members (ABHD17A, 17B, and 17C), showing the strongest depalmitoylating activity to PSD-95, showed different localization from other candidates in rat hippocampal neurons, and were distributed to recycling endosomes, the dendritic plasma membrane, and the synaptic fraction. Expression of ABHD17 in neurons selectively reduced PSD-95 palmitoylation and synaptic clustering of PSD-95 and AMPA receptors. Furthermore, taking advantage of the acyl-PEGyl exchange gel shift (APEGS) method, we quantitatively monitored the palmitoylation stoichiometry and the depalmitoylation kinetics of representative synaptic proteins, PSD-95, GluA1, GluN2A, mGluR5, Gαq, and HRas. Unexpectedly, palmitate on all of them did not turn over in neurons. Uniquely, most of the PSD-95 population underwent rapid palmitoylation cycles, and palmitate cycling on PSD-95 decelerated accompanied by its increased stoichiometry as synapses developed, probably contributing to postsynaptic receptor consolidation. Finally, inhibition of ABHD17 expression dramatically delayed the kinetics of PSD-95 depalmitoylation. This study suggests that local palmitoylation machinery composed of synaptic DHHC palmitoylating enzymes and ABHD17 finely controls the amount of synaptic PSD-95 and synaptic function. SIGNIFICANCE STATEMENT Protein palmitoylation, the most common lipid modification, dynamically regulates neuronal protein localization and function. Its unique reversibility is conferred by DHHC-type palmitoyl acyl transferases (palmitoylating enzymes) and still controversial palmitoyl-protein thioesterases (depalmitoylating enzymes). Here, we identified the membrane-anchored serine hydrolases, ABHD17A, 17B, and 17C, as the physiological PSD-95 depalmitoylating enzymes that regulate PSD-95 palmitoylation cycles in neurons. This study describes the first direct evidence for the neuronal depalmitoylating enzyme and provides a new aspect of the dynamic regulatory mechanisms of synaptic development and synaptic plasticity. In addition, our established APEGS assay, which provides unbiased and quantitative information about the palmitoylation state and dynamics, revealed the distinct regulatory mechanisms for synaptic palmitoylation. PMID:27307232
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Guo, M; Lu, Y; Garza, J C; Li, Y; Chua, S C; Zhang, W; Lu, B; Lu, X-Y
2012-01-01
The glutamatergic system has been implicated in the pathophysiology of depression and the mechanism of action of antidepressants. Leptin, an adipocyte-derived hormone, has antidepressant-like properties. However, the functional role of leptin receptor (Lepr) signaling in glutamatergic neurons remains to be elucidated. In this study, we generated conditional knockout mice in which the long form of Lepr was ablated selectively in glutamatergic neurons located in the forebrain structures, including the hippocampus and prefrontal cortex (Lepr cKO). Lepr cKO mice exhibit normal growth and body weight. Behavioral characterization of Lepr cKO mice reveals depression-like behavioral deficits, including anhedonia, behavioral despair, enhanced learned helplessness and social withdrawal, with no evident signs of anxiety. In addition, loss of Lepr in forebrain glutamatergic neurons facilitates N-methyl--aspartate (NMDA)-induced hippocampal long-term synaptic depression (LTD), whereas conventional LTD or long-term potentiation (LTP) was not affected. The facilitated LTD induction requires activation of the NMDA receptor GluN2B (NR2B) subunit as it was completely blocked by a selective GluN2B antagonist. Moreover, Lepr cKO mice are highly sensitive to the antidepressant-like behavioral effects of the GluN2B antagonist but resistant to leptin. These results support important roles for Lepr signaling in glutamatergic neurons in regulating depression-related behaviors and modulating excitatory synaptic strength, suggesting a possible association between synaptic depression and behavioral manifestation of behavioral depression. PMID:22408745
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Analysis of nonlocal neural fields for both general and gamma-distributed connectivities
NASA Astrophysics Data System (ADS)
Hutt, Axel; Atay, Fatihcan M.
2005-04-01
This work studies the stability of equilibria in spatially extended neuronal ensembles. We first derive the model equation from statistical properties of the neuron population. The obtained integro-differential equation includes synaptic and space-dependent transmission delay for both general and gamma-distributed synaptic connectivities. The latter connectivity type reveals infinite, finite, and vanishing self-connectivities. The work derives conditions for stationary and nonstationary instabilities for both kernel types. In addition, a nonlinear analysis for general kernels yields the order parameter equation of the Turing instability. To compare the results to findings for partial differential equations (PDEs), two typical PDE-types are derived from the examined model equation, namely the general reaction-diffusion equation and the Swift-Hohenberg equation. Hence, the discussed integro-differential equation generalizes these PDEs. In the case of the gamma-distributed kernels, the stability conditions are formulated in terms of the mean excitatory and inhibitory interaction ranges. As a novel finding, we obtain Turing instabilities in fields with local inhibition-lateral excitation, while wave instabilities occur in fields with local excitation and lateral inhibition. Numerical simulations support the analytical results.
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Chang, C T; Zeng, F; Li, X J; Dong, W S; Lu, S H; Gao, S; Pan, F
2016-01-07
The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.
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Uzunova, Genoveva; Hollander, Eric; Shepherd, Jason
2014-01-01
Autism spectrum disorder (ASD) and Fragile X syndrome (FXS) are relatively common childhood neurodevelopmental disorders with increasing incidence in recent years. They are currently accepted as disorders of the synapse with alterations in different forms of synaptic communication and neuronal network connectivity. The major excitatory neurotransmitter system in brain, the glutamatergic system, is implicated in learning and memory, synaptic plasticity, neuronal development. While much attention is attributed to the role of metabotropic glutamate receptors in ASD and FXS, studies indicate that the ionotropic glutamate receptors (iGluRs) and their regulatory proteins are also altered in several brain regions. Role of iGluRs in the neurobiology of ASD and FXS is supported by a weight of evidence that ranges from human genetics to in vitro cultured neurons. In this review we will discuss clinical, molecular, cellular and functional changes in NMDA, AMPA and kainate receptors and the synaptic proteins that regulate them in the context of ASD and FXS. We will also discuss the significance for the development of translational biomarkers and treatments for the core symptoms of ASD and FXS. PMID:24533017
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Chang, C. T.; Zeng, F.; Li, X. J.; Dong, W. S.; Lu, S. H.; Gao, S.; Pan, F.
2016-01-01
The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP. PMID:26739613
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NASA Astrophysics Data System (ADS)
Chang, C. T.; Zeng, F.; Li, X. J.; Dong, W. S.; Lu, S. H.; Gao, S.; Pan, F.
2016-01-01
The simulation of synaptic plasticity using new materials is critical in the study of brain-inspired computing. Devices composed of Ba(CF3SO3)2-doped polyethylene oxide (PEO) electrolyte film were fabricated and with pulse responses found to resemble the synaptic short-term plasticity (STP) of both short-term depression (STD) and short-term facilitation (STF) synapses. The values of the charge and discharge peaks of the pulse responses did not vary with input number when the pulse frequency was sufficiently low(~1 Hz). However, when the frequency was increased, the charge and discharge peaks decreased and increased, respectively, in gradual trends and approached stable values with respect to the input number. These stable values varied with the input frequency, which resulted in the depressed and potentiated weight modifications of the charge and discharge peaks, respectively. These electrical properties simulated the high and low band-pass filtering effects of STD and STF, respectively. The simulations were consistent with biological results and the corresponding biological parameters were successfully extracted. The study verified the feasibility of using organic electrolytes to mimic STP.
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Ensemble stacking mitigates biases in inference of synaptic connectivity.
Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N
2018-01-01
A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.
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Guo, Ming-Lei; Xue, Bing; Jin, Dao-Zhong; Mao, Li-Min; Wang, John Q
2012-07-17
Postsynaptic density 93 (PSD-93) is a protein enriched at postsynaptic sites. As a key scaffolding protein, PSD-93 forms complexes with the clustering of various synaptic proteins to construct postsynaptic signaling networks and control synaptic transmission. Extracellular signal-regulated kinase (ERK) is a prototypic member of a serine/threonine protein kinase family known as mitogen-activated protein kinase (MAPK). This kinase, especially ERK2 isoform, noticeably resides in peripheral structures of neurons, such as dendritic spines and postsynaptic density areas, in addition to its distribution in the cytoplasm and nucleus, although little is known about specific substrates of ERK at synaptic sites. In this study, we found that synaptic PSD-93 is a direct target of ERK. This was demonstrated by direct protein-protein interactions between purified ERK2 and PSD-93 in vitro. The accurate ERK2-binding region seems to locate at an N-terminal region of PSD-93. In adult rat striatal neurons in vivo, native ERK from synaptosomal fractions also associated with PSD-93. In phosphorylation assays, active ERK2 phosphorylated PSD-93. An accurate phosphorylation site was identified at a serine site (S323). In striatal neurons, immunoprecipitated PSD-93 showed basal phosphorylation at an ERK-sensitive site. Our data provide evidence supporting PSD-93 as a new substrate of the synaptic species of ERK. ERK2 possesses the ability to interact with PSD-93 and phosphorylate PSD-93 at a specific site. Published by Elsevier B.V.
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Competitive STDP Learning of Overlapping Spatial Patterns.
Krunglevicius, Dalius
2015-08-01
Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.
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Learning Reward Uncertainty in the Basal Ganglia
Bogacz, Rafal
2016-01-01
Learning the reliability of different sources of rewards is critical for making optimal choices. However, despite the existence of detailed theory describing how the expected reward is learned in the basal ganglia, it is not known how reward uncertainty is estimated in these circuits. This paper presents a class of models that encode both the mean reward and the spread of the rewards, the former in the difference between the synaptic weights of D1 and D2 neurons, and the latter in their sum. In the models, the tendency to seek (or avoid) options with variable reward can be controlled by increasing (or decreasing) the tonic level of dopamine. The models are consistent with the physiology of and synaptic plasticity in the basal ganglia, they explain the effects of dopaminergic manipulations on choices involving risks, and they make multiple experimental predictions. PMID:27589489
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Genzel, Lisa; Kroes, Marijn C W; Dresler, Martin; Battaglia, Francesco P
2014-01-01
Sleep is strongly involved in memory consolidation, but its role remains unclear. 'Sleep replay', the active potentiation of relevant synaptic connections via reactivation of patterns of network activity that occurred during previous experience, has received considerable attention. Alternatively, sleep has been suggested to regulate synaptic weights homeostatically and nonspecifically, thereby improving the signal:noise ratio of memory traces. Here, we reconcile these theories by highlighting the distinction between light and deep nonrapid eye movement (NREM) sleep. Specifically, we draw on recent studies to suggest a link between light NREM and active potentiation, and between deep NREM and homeostatic regulation. This framework could serve as a key for interpreting the physiology of sleep stages and reconciling inconsistencies in terminology in this field. Copyright © 2013 Elsevier Ltd. All rights reserved.
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The synaptic terminations of certain midbrain-olivary fibers in the opossum.
King, J S; Hamos, J E; Maley, B E
1978-11-15
The nuclear origin and distribution of midbrain-olivary fibers has been described in a previous study utilizing axonal transport techniques (Linauts and Martin, '78a). The present report extends their results to the electron microscopic level and details the postsynaptic distribution of such fibers. Lesions within the ventral periaqueductal grey and adjacent tegmentum, the red nucleus or the nucleus subparafascicularis result in electron dense axon terminals within the olive at survival times of 48, 72 and 96 hours. At 72 hours, many degenerating presynaptic profiles shrink, become irregular in shape and are totally or partially surrounded by glial processes. The principal olivary nucleus contains the majority of these profiles. However, the subparafascicular terminals are more abundant in the rostral and intermediate parts of the medial accessory nucleus and the rubral terminals are concentrated within the dorsal lamella of the principal nucleus. The nuclear location of the degenerating terminals was determined by examination of 1 micrometer plastic sections cut in the transverse plane from each block face prior to thin sectioning. Degenerating terminals were counted in three cases, one from each of the three lesion sites described above. When taken together these cases show that just over 50% of the degenerating terminals are presynaptic to spiny appendages and are located within the synaptic clusters (glomeruli) described previously (King, '76). The percentage of degenerating terminals in the glomeruli increases to 70% when the lesion is in the ventral periaqueductal grey and adjacent tegmentum. The remaining degenerating terminals contact dendritic shafts outside the astrocytic boundaries of the synaptic clusters. The synpatic vesicle populations within the degenerating terminals vary with the location of the lesion. Lesions in the ventral periaqueductal grey and the adjacent tegmentum result in the degeneration of terminals with either clear spherical vesicles or endings with both clear spherical vesicles and a variable number of large dense core vesicles. In contrast, the primary degenerative changes that occur after destruction of the red nucleus or the nucleus subparafascicularis are in terminals with clear spherical vesicles. When the synaptic complex was present in the plane of section, regardless of the site of the lesion, the degenerating terminals could be classified as Gray's type I. Thus, we have demonstrated that afferents from the mesencephalon terminate within synpatic clusters located in the principal and medial accessory (part A) subnuclei of the inferior olive. Although the mesencephalic afferents have multiple origins (Linauts and Martin, '78a), many of their synaptic terminals contact spiny appendages within the synaptic clusters. This postsynaptic site also receives cerebellar terminals (King et al., '76). The origin of presynaptic profiles within the synaptic clusters that contain clear pleomorphlic vesicles is yet to be determined.
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Rudolph, Stephanie; Hull, Court; Regehr, Wade G
2015-11-25
Interneurons are essential to controlling excitability, timing, and synaptic integration in neuronal networks. Golgi cells (GoCs) serve these roles at the input layer of the cerebellar cortex by releasing GABA to inhibit granule cells (grcs). GoCs are excited by mossy fibers (MFs) and grcs and provide feedforward and feedback inhibition to grcs. Here we investigate two important aspects of GoC physiology: the properties of GoC dendrites and the role of calcium signaling in regulating GoC spontaneous activity. Although GoC dendrites are extensive, previous studies concluded they are devoid of voltage-gated ion channels. Hence, the current view holds that somatic voltage signals decay passively within GoC dendrites, and grc synapses onto distal dendrites are not amplified and are therefore ineffective at firing GoCs because of strong passive attenuation. Using whole-cell recording and calcium imaging in rat slices, we find that dendritic voltage-gated sodium channels allow somatic action potentials to activate voltage-gated calcium channels (VGCCs) along the entire dendritic length, with R-type and T-type VGCCs preferentially located distally. We show that R- and T-type VGCCs located in the dendrites can boost distal synaptic inputs and promote burst firing. Active dendrites are thus critical to the regulation of GoC activity, and consequently, to the processing of input to the cerebellar cortex. In contrast, we find that N-type channels are preferentially located near the soma, and control the frequency and pattern of spontaneous firing through their close association with calcium-activated potassium (KCa) channels. Thus, VGCC types are differentially distributed and serve specialized functions within GoCs. Interneurons are essential to neural processing because they modulate excitability, timing, and synaptic integration within circuits. At the input layer of the cerebellar cortex, a single type of interneuron, the Golgi cell (GoC), carries these functions. The extent of inhibition depends on both spontaneous activity of GoCs and the excitatory synaptic input they receive. In this study, we find that different types of calcium channels are differentially distributed, with dendritic calcium channels being activated by somatic activity, boosting synaptic inputs and enabling bursting, and somatic calcium cannels promoting regular firing. We therefore challenge the current view that GoC dendrites are passive and identify the mechanisms that contribute to GoCs regulating the flow of sensory information in the cerebellar cortex. Copyright © 2015 the authors 0270-6474/15/3515492-13$15.00/0.
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Interplay between population firing stability and single neuron dynamics in hippocampal networks
Slomowitz, Edden; Styr, Boaz; Vertkin, Irena; Milshtein-Parush, Hila; Nelken, Israel; Slutsky, Michael; Slutsky, Inna
2015-01-01
Neuronal circuits' ability to maintain the delicate balance between stability and flexibility in changing environments is critical for normal neuronal functioning. However, to what extent individual neurons and neuronal populations maintain internal firing properties remains largely unknown. In this study, we show that distributions of spontaneous population firing rates and synchrony are subject to accurate homeostatic control following increase of synaptic inhibition in cultured hippocampal networks. Reduction in firing rate triggered synaptic and intrinsic adaptive responses operating as global homeostatic mechanisms to maintain firing macro-stability, without achieving local homeostasis at the single-neuron level. Adaptive mechanisms, while stabilizing population firing properties, reduced short-term facilitation essential for synaptic discrimination of input patterns. Thus, invariant ongoing population dynamics emerge from intrinsically unstable activity patterns of individual neurons and synapses. The observed differences in the precision of homeostatic control at different spatial scales challenge cell-autonomous theory of network homeostasis and suggest the existence of network-wide regulation rules. DOI: http://dx.doi.org/10.7554/eLife.04378.001 PMID:25556699
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Role of NMDA Receptor-Mediated Glutamatergic Signaling in Chronic and Acute Neuropathologies
2016-01-01
N-Methyl-D-aspartate receptors (NMDARs) have two opposing roles in the brain. On the one hand, NMDARs control critical events in the formation and development of synaptic organization and synaptic plasticity. On the other hand, the overactivation of NMDARs can promote neuronal death in neuropathological conditions. Ca2+ influx acts as a primary modulator after NMDAR channel activation. An imbalance in Ca2+ homeostasis is associated with several neurological diseases including schizophrenia, Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. These chronic conditions have a lengthy progression depending on internal and external factors. External factors such as acute episodes of brain damage are associated with an earlier onset of several of these chronic mental conditions. Here, we will review some of the current evidence of how traumatic brain injury can hasten the onset of several neurological conditions, focusing on the role of NMDAR distribution and the functional consequences in calcium homeostasis associated with synaptic dysfunction and neuronal death present in this group of chronic diseases. PMID:27630777
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Besalduch, Núria; Tomàs, Marta; Santafé, Manel M; Garcia, Neus; Tomàs, Josep; Lanuza, Maria Angel
2010-01-10
Protein kinase C (PKC) is essential for signal transduction in a variety of cells, including neurons and myocytes, and is involved in both acetylcholine release and muscle fiber contraction. Here, we demonstrate that the increases in synaptic activity by nerve stimulation couple PKC to transmitter release in the rat neuromuscular junction and increase the level of alpha, betaI, and betaII isoforms in the membrane when muscle contraction follows the stimulation. The phosphorylation activity of these classical PKCs also increases. It seems that the muscle has to contract in order to maintain or increase classical PKCs in the membrane. We use immunohistochemistry to show that PKCalpha and PKCbetaI were located in the nerve terminals, whereas PKCalpha and PKCbetaII were located in the postsynaptic and the Schwann cells. Stimulation and contraction do not change these cellular distributions, but our results show that the localization of classical PKC isoforms in the membrane is affected by synaptic activity.
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Factors regulating the abundance and localization of synaptobrevin in the plasma membrane
Dittman, Jeremy S.; Kaplan, Joshua M.
2006-01-01
After synaptic vesicle fusion, vesicle proteins must be segregated from plasma membrane proteins and recycled to maintain a functional vesicle pool. We monitored the distribution of synaptobrevin, a vesicle protein required for exocytosis, in Caenorhabditis elegans motor neurons by using a pH-sensitive synaptobrevin GFP fusion protein, synaptopHluorin. We estimated that 30% of synaptobrevin was present in the plasma membrane. By using a panel of endocytosis and exocytosis mutants, we found that the majority of surface synaptobrevin derives from fusion of synaptic vesicles and that, in steady state, synaptobrevin equilibrates throughout the axon. The surface synaptobrevin was enriched near active zones, and its spatial extent was regulated by the clathrin adaptin AP180. These results suggest that there is a plasma membrane reservoir of synaptobrevin that is supplied by the synaptic vesicle cycle and available for retrieval throughout the axon. The size of the reservoir is set by the relative rates of exo- and endocytosis. PMID:16844789
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Feduccia, Allison A.; Chatterjee, Susmita; Bartlett, Selena E.
2012-01-01
Addictive drugs can activate systems involved in normal reward-related learning, creating long-lasting memories of the drug's reinforcing effects and the environmental cues surrounding the experience. These memories significantly contribute to the maintenance of compulsive drug use as well as cue-induced relapse which can occur even after long periods of abstinence. Synaptic plasticity is thought to be a prominent molecular mechanism underlying drug-induced learning and memories. Ethanol and nicotine are both widely abused drugs that share a common molecular target in the brain, the neuronal nicotinic acetylcholine receptors (nAChRs). The nAChRs are ligand-gated ion channels that are vastly distributed throughout the brain and play a key role in synaptic neurotransmission. In this review, we will delineate the role of nAChRs in the development of ethanol and nicotine addiction. We will characterize both ethanol and nicotine's effects on nAChR-mediated synaptic transmission and plasticity in several key brain areas that are important for addiction. Finally, we will discuss some of the behavioral outcomes of drug-induced synaptic plasticity in animal models. An understanding of the molecular and cellular changes that occur following administration of ethanol and nicotine will lead to better therapeutic strategies. PMID:22876217
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Location-dependent excitatory synaptic interactions in pyramidal neuron dendrites.
Behabadi, Bardia F; Polsky, Alon; Jadi, Monika; Schiller, Jackie; Mel, Bartlett W
2012-01-01
Neocortical pyramidal neurons (PNs) receive thousands of excitatory synaptic contacts on their basal dendrites. Some act as classical driver inputs while others are thought to modulate PN responses based on sensory or behavioral context, but the biophysical mechanisms that mediate classical-contextual interactions in these dendrites remain poorly understood. We hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric. Distal excitation lowers the local spike threshold for more proximal inputs, while having little effect on peak responses at the soma. In contrast, proximal excitation lowers the threshold, but also substantially increases the gain of distally-driven responses. Our findings support the view that PN basal dendrites possess significant analog computing capabilities, and suggest that the diverse forms of nonlinear response modulation seen in the neocortex, including uni-modal, cross-modal, and attentional effects, could depend in part on pathway-specific biases in the spatial distribution of excitatory synaptic contacts onto PN basal dendritic arbors.
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Ito, Shoko; Takeichi, Masatoshi
2009-08-04
Neural circuits are generated by precisely ordered synaptic connections among neurons, and this process is thought to rely on the ability of neurons to recognize specific partners. However, it is also known that neurons promiscuously form synapses with nonspecific partners, in particular when cultured in vitro, causing controversies about neural recognition mechanisms. Here we reexamined whether neurons can or cannot select particular partners in vitro. In the cerebellum, granule cell (GC) dendrites form synaptic connections specifically with mossy fibers, but not with climbing fibers. We cocultured GC neurons with pontine or inferior olivary axons, the major sources for mossy and climbing fibers, respectively, as well as with hippocampal axons as a control. The GC neurons formed synapses with pontine axons predominantly at the distal ends of their dendrites, reproducing the characteristic morphology of their synapses observed in vivo, whereas they failed to do so when combined with other axons. In the latter case, synaptic proteins could accumulate between axons and dendrites, but these synapses were randomly distributed throughout the contact sites, and also their synaptic vesicle recycling was anomalous. These observations suggest that GC dendrites can select their authentic partners for synaptogenesis even in vitro, forming the synapses with a GC-specific nature only with them.
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Dynamics of action potential initiation in the GABAergic thalamic reticular nucleus in vivo.
Muñoz, Fabián; Fuentealba, Pablo
2012-01-01
Understanding the neural mechanisms of action potential generation is critical to establish the way neural circuits generate and coordinate activity. Accordingly, we investigated the dynamics of action potential initiation in the GABAergic thalamic reticular nucleus (TRN) using in vivo intracellular recordings in cats in order to preserve anatomically-intact axo-dendritic distributions and naturally-occurring spatiotemporal patterns of synaptic activity in this structure that regulates the thalamic relay to neocortex. We found a wide operational range of voltage thresholds for action potentials, mostly due to intrinsic voltage-gated conductances and not synaptic activity driven by network oscillations. Varying levels of synchronous synaptic inputs produced fast rates of membrane potential depolarization preceding the action potential onset that were associated with lower thresholds and increased excitability, consistent with TRN neurons performing as coincidence detectors. On the other hand the presence of action potentials preceding any given spike was associated with more depolarized thresholds. The phase-plane trajectory of the action potential showed somato-dendritic propagation, but no obvious axon initial segment component, prominent in other neuronal classes and allegedly responsible for the high onset speed. Overall, our results suggest that TRN neurons could flexibly integrate synaptic inputs to discharge action potentials over wide voltage ranges, and perform as coincidence detectors and temporal integrators, supported by a dynamic action potential threshold.
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Cove, Joshua; Blinder, Pablo; Abi-Jaoude, Elia; Lafrenière-Roula, Myriam; Devroye, Luc; Baranes, Danny
2006-01-01
The integrative properties of dendrites are determined by several factors, including their morphology and the spatio-temporal patterning of their synaptic inputs. One of the great challenges is to discover the interdependency of these two factors and the mechanisms which sculpt dendrites' fine morphological details. We found a novel form of neurite growth behavior in neuronal cultures of the hippocampus and cortex, when axons and dendrites grew directly toward neurite-neurite contact sites and crossed them, forming multi-neurite intersections (MNIs). MNIs were found at a frequency higher than obtained by computer simulations of randomly distributed dendrites, involved many of the dendrites and were stable for days. They were formed specifically by neurites originating from different neurons and were extremely rare among neurites of individual neurons or among astrocytic processes. Axonal terminals were clustered at MNIs and exhibited higher synaptophysin content and release capability than in those located elsewhere. MNI formation, as well as enhancement of axonal terminal clustering and secretion at MNIs, was disrupted by inhibitors of synaptic activity. Thus, convergence of axons and dendrites to form MNIs is a non-random activity-regulated wiring behavior which shapes dendritic trees and affects the location, clustering level and strength of their presynaptic inputs.
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Random noise effects in pulse-mode digital multilayer neural networks.
Kim, Y C; Shanblatt, M A
1995-01-01
A pulse-mode digital multilayer neural network (DMNN) based on stochastic computing techniques is implemented with simple logic gates as basic computing elements. The pulse-mode signal representation and the use of simple logic gates for neural operations lead to a massively parallel yet compact and flexible network architecture, well suited for VLSI implementation. Algebraic neural operations are replaced by stochastic processes using pseudorandom pulse sequences. The distributions of the results from the stochastic processes are approximated using the hypergeometric distribution. Synaptic weights and neuron states are represented as probabilities and estimated as average pulse occurrence rates in corresponding pulse sequences. A statistical model of the noise (error) is developed to estimate the relative accuracy associated with stochastic computing in terms of mean and variance. Computational differences are then explained by comparison to deterministic neural computations. DMNN feedforward architectures are modeled in VHDL using character recognition problems as testbeds. Computational accuracy is analyzed, and the results of the statistical model are compared with the actual simulation results. Experiments show that the calculations performed in the DMNN are more accurate than those anticipated when Bernoulli sequences are assumed, as is common in the literature. Furthermore, the statistical model successfully predicts the accuracy of the operations performed in the DMNN.
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The neuronal encoding of information in the brain.
Rolls, Edmund T; Treves, Alessandro
2011-11-01
We describe the results of quantitative information theoretic analyses of neural encoding, particularly in the primate visual, olfactory, taste, hippocampal, and orbitofrontal cortex. Most of the information turns out to be encoded by the firing rates of the neurons, that is by the number of spikes in a short time window. This has been shown to be a robust code, for the firing rate representations of different neurons are close to independent for small populations of neurons. Moreover, the information can be read fast from such encoding, in as little as 20 ms. In quantitative information theoretic studies, only a little additional information is available in temporal encoding involving stimulus-dependent synchronization of different neurons, or the timing of spikes within the spike train of a single neuron. Feature binding appears to be solved by feature combination neurons rather than by temporal synchrony. The code is sparse distributed, with the spike firing rate distributions close to exponential or gamma. A feature of the code is that it can be read by neurons that take a synaptically weighted sum of their inputs. This dot product decoding is biologically plausible. Understanding the neural code is fundamental to understanding not only how the cortex represents, but also processes, information. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Devi, Suma Priya Sudarsana; Howe, James R.
2016-01-01
Key points Purkinje cells of the cerebellum receive ∼180,000 parallel fibre synapses, which have often been viewed as a homogeneous synaptic population and studied using single action potentials.Many parallel fibre synapses might be silent, however, and granule cells in vivo fire in bursts. Here, we used trains of stimuli to study parallel fibre inputs to Purkinje cells in rat cerebellar slices.Analysis of train EPSCs revealed two synaptic components, phase 1 and 2. Phase 1 is initially large and saturates rapidly, whereas phase 2 is initially small and facilitates throughout the train. The two components have a heterogeneous distribution at dendritic sites and different pharmacological profiles.The differential sensitivity of phase 1 and phase 2 to inhibition by pentobarbital and NBQX mirrors the differential sensitivity of AMPA receptors associated with the transmembrane AMPA receptor regulatory protein, γ‐2, gating in the low‐ and high‐open probability modes, respectively. Abstract Cerebellar granule cells fire in bursts, and their parallel fibre axons (PFs) form ∼180,000 excitatory synapses onto the dendritic tree of a Purkinje cell. As many as 85% of these synapses have been proposed to be silent, but most are labelled for AMPA receptors. Here, we studied PF to Purkinje cell synapses using trains of 100 Hz stimulation in rat cerebellar slices. The PF train EPSC consisted of two components that were present in variable proportions at different dendritic sites: one, with large initial EPSC amplitude, saturated after three stimuli and dominated the early phase of the train EPSC; and the other, with small initial amplitude, increased steadily throughout the train of 10 stimuli and dominated the late phase of the train EPSC. The two phases also displayed different pharmacological profiles. Phase 2 was less sensitive to inhibition by NBQX but more sensitive to block by pentobarbital than phase 1. Comparison of synaptic results with fast glutamate applications to recombinant receptors suggests that the high‐open‐probability gating mode of AMPA receptors containing the auxiliary subunit transmembrane AMPA receptor regulatory protein γ‐2 makes a substantial contribution to phase 2. We argue that the two synaptic components arise from AMPA receptors with different functional signatures and synaptic distributions. Comparisons of voltage‐ and current‐clamp responses obtained from the same Purkinje cells indicate that phase 1 of the EPSC arises from synapses ideally suited to transmit short bursts of action potentials, whereas phase 2 is likely to arise from low‐release‐probability or ‘silent’ synapses that are recruited during longer bursts. PMID:27094216
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The process of learning in neural net models with Poisson and Gauss connectivities.
Sivridis, L; Kotini, A; Anninos, P
2008-01-01
In this study we examined the dynamic behavior of isolated and non-isolated neural networks with chemical markers that follow a Poisson or Gauss distribution of connectivity. The Poisson distribution shows higher activity in comparison to the Gauss distribution although the latter has more connections that obliterated due to randomness. We examined 57 hematoxylin and eosin stained sections from an equal number of autopsy specimens with a diagnosis of "cerebral matter within normal limits". Neural counting was carried out in 5 continuous optic fields, with the use of a simple optical microscope connected to a computer (software programmer Nikon Act-1 vers-2). The number of neurons that corresponded to a surface was equal to 0.15 mm(2). There was a gradual reduction in the number of neurons as age increased. A mean value of 45.8 neurons /0.15 mm(2) was observed within the age range 21-25, 33 neurons /0.15 mm(2) within the age range 41-45, 19.3 neurons /0.15 mm(2) within the age range 56-60 years. After the age of 60 it was observed that the number of neurons per unit area stopped decreasing. A correlation was observed between these experimental findings and the theoretical neural model developed by professor Anninos and his colleagues. Equivalence between the mean numbers of neurons of the above mentioned age groups and the highest possible number of synaptic connections per neuron (highest number of synaptic connections corresponded to the age group 21-25) was created. We then used both inhibitory and excitatory post-synaptic potentials and applied these values to the Poisson and Gauss distributions, whereas the neuron threshold was varied between 3 and 5. According to the obtained phase diagrams, the hysteresis loops decrease as age increases. These findings were significant as the hysteresis loops can be regarded as the basis for short-term memory.
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Remodeling of the postsynaptic plasma membrane during neural development.
Tulodziecka, Karolina; Diaz-Rohrer, Barbara B; Farley, Madeline M; Chan, Robin B; Di Paolo, Gilbert; Levental, Kandice R; Waxham, M Neal; Levental, Ilya
2016-11-07
Neuronal synapses are the fundamental units of neural signal transduction and must maintain exquisite signal fidelity while also accommodating the plasticity that underlies learning and development. To achieve these goals, the molecular composition and spatial organization of synaptic terminals must be tightly regulated; however, little is known about the regulation of lipid composition and organization in synaptic membranes. Here we quantify the comprehensive lipidome of rat synaptic membranes during postnatal development and observe dramatic developmental lipidomic remodeling during the first 60 postnatal days, including progressive accumulation of cholesterol, plasmalogens, and sphingolipids. Further analysis of membranes associated with isolated postsynaptic densities (PSDs) suggests the PSD-associated postsynaptic plasma membrane (PSD-PM) as one specific location of synaptic remodeling. We analyze the biophysical consequences of developmental remodeling in reconstituted synaptic membranes and observe remarkably stable microdomains, with the stability of domains increasing with developmental age. We rationalize the developmental accumulation of microdomain-forming lipids in synapses by proposing a mechanism by which palmitoylation of the immobilized scaffold protein PSD-95 nucleates domains at the postsynaptic plasma membrane. These results reveal developmental changes in lipid composition and palmitoylation that facilitate the formation of postsynaptic membrane microdomains, which may serve key roles in the function of the neuronal synapse. © 2016 Tulodziecka et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
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Grande, Giovanbattista; Bui, Tuan V; Rose, P Ken
2007-06-01
In the presence of monoamines, L-type Ca(2+) channels on the dendrites of motoneurons contribute to persistent inward currents (PICs) that can amplify synaptic inputs two- to sixfold. However, the exact location of the L-type Ca(2+) channels is controversial, and the importance of the location as a means of regulating the input-output properties of motoneurons is unknown. In this study, we used a computational strategy developed previously to estimate the dendritic location of the L-type Ca(2+) channels and test the hypothesis that the location of L-type Ca(2+) channels varies as a function of motoneuron size. Compartmental models were constructed based on dendritic trees of five motoneurons that ranged in size from small to large. These models were constrained by known differences in PIC activation reported for low- and high-conductance motoneurons and the relationship between somatic PIC threshold and the presence or absence of tonic excitatory or inhibitory synaptic activity. Our simulations suggest that L-type Ca(2+) channels are concentrated in hotspots whose distance from the soma increases with the size of the dendritic tree. Moving the hotspots away from these sites (e.g., using the hotspot locations from large motoneurons on intermediate-sized motoneurons) fails to replicate the shifts in PIC threshold that occur experimentally during tonic excitatory or inhibitory synaptic activity. In models equipped with a size-dependent distribution of L-type Ca(2+) channels, the amplification of synaptic current by PICs depends on motoneuron size and the location of the synaptic input on the dendritic tree.
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Bidirectional control of postsynaptic density-95 (PSD-95) clustering by Huntingtin.
Parsons, Matthew P; Kang, Rujun; Buren, Caodu; Dau, Alejandro; Southwell, Amber L; Doty, Crystal N; Sanders, Shaun S; Hayden, Michael R; Raymond, Lynn A
2014-02-07
Huntington disease is associated with early alterations in corticostriatal synaptic function that precede cell death, and it is postulated that ameliorating such changes may delay clinical onset and/or prevent neurodegeneration. Although many of these synaptic alterations are thought to be attributable to a toxic gain of function of the mutant huntingtin protein, the role that nonpathogenic huntingtin (HTT) plays in synaptic function is relatively unexplored. Here, we compare the immunocytochemical localization of a major postsynaptic scaffolding protein, PSD-95, in striatal neurons from WT mice and mice overexpressing HTT with 18 glutamine repeats (YAC18, nonpathogenic). We found that HTT overexpression resulted in a palmitoylation- and BDNF-dependent increase in PSD-95 clustering at synaptic sites in striatal spiny projection neurons (SPNs) co-cultured with cortical neurons. Surprisingly, the latter effect was mediated presynaptically, as HTT overexpression in cortical neurons alone was sufficient to increase PSD-95 clustering in the postsynaptic SPNs. In contrast, antisense oligonucleotide knockdown of HTT in WT co-cultures resulted in a significant reduction of PSD-95 clustering in SPNs. Notably, despite these bidirectional changes in PSD-95 clustering, we did not observe an alteration in basal electrophysiological measures of AMPA and NMDA receptors. Thus, unlike in previous studies in the hippocampus, enhanced or decreased PSD-95 clustering alone was insufficient to drive AMPA or NMDA receptors into or out of SPN synapses. In all, our results demonstrate that nonpathogenic HTT can indeed influence synaptic protein localization and uncover a novel role of HTT in PSD-95 distribution.
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Bidirectional Control of Postsynaptic Density-95 (PSD-95) Clustering by Huntingtin*
Parsons, Matthew P.; Kang, Rujun; Buren, Caodu; Dau, Alejandro; Southwell, Amber L.; Doty, Crystal N.; Sanders, Shaun S.; Hayden, Michael R.; Raymond, Lynn A.
2014-01-01
Huntington disease is associated with early alterations in corticostriatal synaptic function that precede cell death, and it is postulated that ameliorating such changes may delay clinical onset and/or prevent neurodegeneration. Although many of these synaptic alterations are thought to be attributable to a toxic gain of function of the mutant huntingtin protein, the role that nonpathogenic huntingtin (HTT) plays in synaptic function is relatively unexplored. Here, we compare the immunocytochemical localization of a major postsynaptic scaffolding protein, PSD-95, in striatal neurons from WT mice and mice overexpressing HTT with 18 glutamine repeats (YAC18, nonpathogenic). We found that HTT overexpression resulted in a palmitoylation- and BDNF-dependent increase in PSD-95 clustering at synaptic sites in striatal spiny projection neurons (SPNs) co-cultured with cortical neurons. Surprisingly, the latter effect was mediated presynaptically, as HTT overexpression in cortical neurons alone was sufficient to increase PSD-95 clustering in the postsynaptic SPNs. In contrast, antisense oligonucleotide knockdown of HTT in WT co-cultures resulted in a significant reduction of PSD-95 clustering in SPNs. Notably, despite these bidirectional changes in PSD-95 clustering, we did not observe an alteration in basal electrophysiological measures of AMPA and NMDA receptors. Thus, unlike in previous studies in the hippocampus, enhanced or decreased PSD-95 clustering alone was insufficient to drive AMPA or NMDA receptors into or out of SPN synapses. In all, our results demonstrate that nonpathogenic HTT can indeed influence synaptic protein localization and uncover a novel role of HTT in PSD-95 distribution. PMID:24347167
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Addiction-like synaptic impairments in diet-induced obesity
Spencer, Sade; Garcia-Keller, Constanza; Spanswick, David C; Lawrence, Andrew John; Simonds, Stephanie Elise; Schwartz, Danielle Joy; Jordan, Kelsey Ann; Jhou, Thomas Clayton; Kalivas, Peter William
2016-01-01
Background There is increasing evidence that the pathological overeating underlying some forms of obesity is compulsive in nature, and therefore contains elements of an addictive disorder. However, direct physiological evidence linking obesity to synaptic plasticity akin to that occurring in addiction is lacking. We sought to establish whether the propensity to diet-induced obesity (DIO) is associated with addictive-like behavior, as well as synaptic impairments in the nucleus accumbens core (NAcore) considered hallmarks of addiction. Methods Sprague-Dawley rats were allowed free access to a palatable diet for 8 weeks then separated by weight gain into DIO prone (OP) and resistant (OR) subgroups. Access to palatable food was then restricted to daily operant self-administration sessions using fixed (FR1, 3 and 5) and progressive ratio (PR) schedules. Subsequently, NAcore brain slices were prepared and we tested for changes in the ratio between AMPA and NMDA currents (AMPA/NMDA) and the ability to exhibit long-term depression (LTD). Results We found that propensity to develop DIO is linked to deficits in the ability to induce LTD in the NAcore, as well as increased potentiation at these synapses as measured by AMPA/NMDA currents. Consistent with these impairments, we observed addictive-like behavior in OP rats, including i) heightened motivation for palatable food (ii) excessive intake and (iii) increased food-seeking when food was unavailable. Conclusions Our results show overlap between the propensity for DIO and the synaptic changes associated with facets of addictive behavior, supporting partial coincident neurological underpinnings for compulsive overeating and drug addiction. PMID:26826876
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Learning and memory disabilities in IUGR babies: Functional and molecular analysis in a rat model.
Camprubí Camprubí, Marta; Balada Caballé, Rafel; Ortega Cano, Juan A; Ortega de la Torre, Maria de Los Angeles; Duran Fernández-Feijoo, Cristina; Girabent-Farrés, Montserrat; Figueras-Aloy, Josep; Krauel, Xavier; Alcántara, Soledad
2017-03-01
1Intrauterine growth restriction (IUGR) is the failure of the fetus to achieve its inherent growth potential, and it has frequently been associated with neurodevelopmental problems in childhood. Neurological disorders are mostly associated with IUGR babies with an abnormally high cephalization index (CI) and a brain sparing effect. However, a similar correlation has never been demonstrated in an animal model. The aim of this study was to determine the correlations between CI, functional deficits in learning and memory and alterations in synaptic proteins in a rat model of IUGR. 2Utero-placental insufficiency was induced by meso-ovarian vessel cauterization (CMO) in pregnant rats at embryonic day 17 (E17). Learning performance in an aquatic learning test was evaluated 25 days after birth and during 10 days. Some synaptic proteins were analyzed (PSD95, Synaptophysin) by Western blot and immunohistochemistry. 3Placental insufficiency in CMO pups was associated with spatial memory deficits, which are correlated with a CI above the normal range. CMO pups presented altered levels of synaptic proteins PSD95 and synaptophysin in the hippocampus. 4The results of this study suggest that learning disabilities may be associated with altered development of excitatory neurotransmission and synaptic plasticity. Although interspecific differences in fetal response to placental insufficiency should be taken into account, the translation of these data to humans suggest that both IUGR babies and babies with a normal birth weight but with intrauterine Doppler alterations and abnormal CI should be closely followed to detect neurodevelopmental alterations during the postnatal period.
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FPGA Implementation of Generalized Hebbian Algorithm for Texture Classification
Lin, Shiow-Jyu; Hwang, Wen-Jyi; Lee, Wei-Hao
2012-01-01
This paper presents a novel hardware architecture for principal component analysis. The architecture is based on the Generalized Hebbian Algorithm (GHA) because of its simplicity and effectiveness. The architecture is separated into three portions: the weight vector updating unit, the principal computation unit and the memory unit. In the weight vector updating unit, the computation of different synaptic weight vectors shares the same circuit for reducing the area costs. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is physically implemented by Field Programmable Gate Array (FPGA). It is embedded in a System-On-Programmable-Chip (SOPC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs. PMID:22778640
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Cheng, Zengguang; Ríos, Carlos; Pernice, Wolfram H P; Wright, C David; Bhaskaran, Harish
2017-09-01
The search for new "neuromorphic computing" architectures that mimic the brain's approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses.
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Myostatin-like proteins regulate synaptic function and neuronal morphology.
Augustin, Hrvoje; McGourty, Kieran; Steinert, Joern R; Cochemé, Helena M; Adcott, Jennifer; Cabecinha, Melissa; Vincent, Alec; Halff, Els F; Kittler, Josef T; Boucrot, Emmanuel; Partridge, Linda
2017-07-01
Growth factors of the TGFβ superfamily play key roles in regulating neuronal and muscle function. Myostatin (or GDF8) and GDF11 are potent negative regulators of skeletal muscle mass. However, expression of myostatin and its cognate receptors in other tissues, including brain and peripheral nerves, suggests a potential wider biological role. Here, we show that Myoglianin (MYO), the Drosophila homolog of myostatin and GDF11, regulates not only body weight and muscle size, but also inhibits neuromuscular synapse strength and composition in a Smad2-dependent manner. Both myostatin and GDF11 affected synapse formation in isolated rat cortical neuron cultures, suggesting an effect on synaptogenesis beyond neuromuscular junctions. We also show that MYO acts in vivo to inhibit synaptic transmission between neurons in the escape response neural circuit of adult flies. Thus, these anti-myogenic proteins act as important inhibitors of synapse function and neuronal growth. © 2017. Published by The Company of Biologists Ltd.
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Palaniyandi, P; Rangarajan, Govindan
2017-08-21
We propose a mathematical model for storage and recall of images using coupled maps. We start by theoretically investigating targeted synchronization in coupled map systems wherein only a desired (partial) subset of the maps is made to synchronize. A simple method is introduced to specify coupling coefficients such that targeted synchronization is ensured. The principle of this method is extended to storage/recall of images using coupled Rulkov maps. The process of adjusting coupling coefficients between Rulkov maps (often used to model neurons) for the purpose of storing a desired image mimics the process of adjusting synaptic strengths between neurons to store memories. Our method uses both synchronisation and synaptic weight modification, as the human brain is thought to do. The stored image can be recalled by providing an initial random pattern to the dynamical system. The storage and recall of the standard image of Lena is explicitly demonstrated.
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The modeling and simulation of visuospatial working memory
Liang, Lina; Zhang, Zhikang
2010-01-01
Camperi and Wang (Comput Neurosci 5:383–405, 1998) presented a network model for working memory that combines intrinsic cellular bistability with the recurrent network architecture of the neocortex. While Fall and Rinzel (Comput Neurosci 20:97–107, 2006) replaced this intrinsic bistability with a biological mechanism-Ca2+ release subsystem. In this study, we aim to further expand the above work. We integrate the traditional firing-rate network with Ca2+ subsystem-induced bistability, amend the synaptic weights and suggest that Ca2+ concentration only increase the efficacy of synaptic input but has nothing to do with the external input for the transient cue. We found that our network model maintained the persistent activity in response to a brief transient stimulus like that of the previous two models and the working memory performance was resistant to noise and distraction stimulus if Ca2+ subsystem was tuned to be bistable. PMID:22132045
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Cheng, Zengguang; Ríos, Carlos; Pernice, Wolfram H. P.; Wright, C. David; Bhaskaran, Harish
2017-01-01
The search for new “neuromorphic computing” architectures that mimic the brain’s approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses. PMID:28959725
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Kulkarni, Shruti R; Rajendran, Bipin
2018-07-01
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Stepien, Anna E; Tripodi, Marco; Arber, Silvia
2010-11-04
Movement is the behavioral output of neuronal activity in the spinal cord. Motor neurons are grouped into motor neuron pools, the functional units innervating individual muscles. Here we establish an anatomical rabies virus-based connectivity assay in early postnatal mice. We employ it to study the connectivity scheme of premotor neurons, the neuronal cohorts monosynaptically connected to motor neurons, unveiling three aspects of organization. First, motor neuron pools are connected to segmentally widely distributed yet stereotypic interneuron populations, differing for pools innervating functionally distinct muscles. Second, depending on subpopulation identity, interneurons take on local or segmentally distributed positions. Third, cholinergic partition cells involved in the regulation of motor neuron excitability segregate into ipsilaterally and bilaterally projecting populations, the latter exhibiting preferential connections to functionally equivalent motor neuron pools bilaterally. Our study visualizes the widespread yet precise nature of the connectivity matrix for premotor interneurons and reveals exquisite synaptic specificity for bilaterally projecting cholinergic partition cells. Copyright © 2010 Elsevier Inc. All rights reserved.
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Eguchi, Akihiro; Neymotin, Samuel A.; Stringer, Simon M.
2014-01-01
Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red–green and blue–yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell). PMID:24659956
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Cholesterol asymmetry in synaptic plasma membranes.
Wood, W Gibson; Igbavboa, Urule; Müller, Walter E; Eckert, Gunter P
2011-03-01
Lipids are essential for the structural and functional integrity of membranes. Membrane lipids are not randomly distributed but are localized in different domains. A common characteristic of these membrane domains is their association with cholesterol. Lipid rafts and caveolae are examples of cholesterol enriched domains, which have attracted keen interest. However, two other important cholesterol domains are the exofacial and cytofacial leaflets of the plasma membrane. The two leaflets that make up the bilayer differ in their fluidity, electrical charge, lipid distribution, and active sites of certain proteins. The synaptic plasma membrane (SPM) cytofacial leaflet contains over 85% of the total SPM cholesterol as compared with the exofacial leaflet. This asymmetric distribution of cholesterol is not fixed or immobile but can be modified by different conditions in vivo: (i) chronic ethanol consumption; (ii) statins; (iii) aging; and (iv) apoE isoform. Several potential candidates have been proposed as mechanisms involved in regulation of SPM cholesterol asymmetry: apoE, low-density lipoprotein receptor, sterol carrier protein-2, fatty acid binding proteins, polyunsaturated fatty acids, P-glycoprotein and caveolin-1. This review examines cholesterol asymmetry in SPM, potential mechanisms of regulation and impact on membrane structure and function. © 2011 The Authors. Journal of Neurochemistry © 2011 International Society for Neurochemistry.
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Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios
2017-01-01
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387
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Hoshino, Osamu
2015-06-01
Perception of supraliminal stimuli might in general be reflected in bursts of action potentials (spikes), and their memory traces could be formed through spike-timing-dependent plasticity (STDP). Memory traces for subliminal stimuli might be formed in a different manner, because subliminal stimulation evokes a fraction (but not a burst) of spikes. Simulations of a cortical neural network model showed that a subliminal stimulus that was too brief (10 msec) to perceive transiently (more than about 500 msec) depolarized stimulus-relevant principal cells and hyperpolarized stimulus-irrelevant principal cells in a subthreshold manner. This led to a small increase or decrease in ongoing-spontaneous spiking activity frequency (less than 1 Hz). Synaptic modification based on STDP during this period effectively enhanced relevant synaptic weights, by which subliminal learning was improved. GABA transporters on GABAergic interneurons modulated local levels of ambient GABA. Ambient GABA molecules acted on extrasynaptic receptors, provided principal cells with tonic inhibitory currents, and contributed to achieving the subthreshold neuronal state. We suggest that ongoing-spontaneous synaptic alteration through STDP following subliminal stimulation may be a possible neuronal mechanism for leaving its memory trace in cortical circuitry. Regulation of local ambient GABA levels by transporter-mediated GABA import and export may be crucial for subliminal learning.
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Speed, Haley E.; Kouser, Mehreen; Xuan, Zhong; Reimers, Jeremy M.; Ochoa, Christine F.; Gupta, Natasha; Liu, Shunan
2015-01-01
SHANK3 (also known as PROSAP2) is a postsynaptic scaffolding protein at excitatory synapses in which mutations and deletions have been implicated in patients with idiopathic autism, Phelan–McDermid (aka 22q13 microdeletion) syndrome, and other neuropsychiatric disorders. In this study, we have created a novel mouse model of human autism caused by the insertion of a single guanine nucleotide into exon 21 (Shank3G). The resulting frameshift causes a premature STOP codon and loss of major higher molecular weight Shank3 isoforms at the synapse. Shank3G/G mice exhibit deficits in hippocampus-dependent spatial learning, impaired motor coordination, altered response to novelty, and sensory processing deficits. At the cellular level, Shank3G/G mice also exhibit impaired hippocampal excitatory transmission and plasticity as well as changes in baseline NMDA receptor-mediated synaptic responses. This work identifies clear alterations in synaptic function and behavior in a novel, genetically accurate mouse model of autism mimicking an autism-associated insertion mutation. Furthermore, these findings lay the foundation for future studies aimed to validate and study region-selective and temporally selective genetic reversal studies in the Shank3G/G mouse that was engineered with such future experiments in mind. PMID:26134648
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Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.
Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios
2017-01-01
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.
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Lauretti, Elisabetta; Praticò, Domenico
2017-12-07
In recent years consumption of canola oil has increased due to lower cost compared with olive oil and the perception that it shares its health benefits. However, no data are available on the effect of canola oil intake on Alzheimer's disease (AD) pathogenesis. Herein, we investigated the effect of chronic daily consumption of canola oil on the phenotype of a mouse model of AD that develops both plaques and tangles (3xTg). To this end mice received either regular chow or a chow diet supplemented with canola oil for 6 months. At this time point we found that chronic exposure to the canola-rich diet resulted in a significant increase in body weight and impairments in their working memory together with decrease levels of post-synaptic density protein-95, a marker of synaptic integrity, and an increase in the ratio of insoluble Aβ 42/40. No significant changes were observed in tau phosphorylation and neuroinflammation. Taken together, our findings do not support a beneficial effect of chronic canola oil consumption on two important aspects of AD pathophysiology which includes memory impairments as well as synaptic integrity. While more studies are needed, our data do not justify the current trend aimed at replacing olive oil with canola oil.
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SUMO1 Affects Synaptic Function, Spine Density and Memory
Matsuzaki, Shinsuke; Lee, Linda; Knock, Erin; Srikumar, Tharan; Sakurai, Mikako; Hazrati, Lili-Naz; Katayama, Taiichi; Staniszewski, Agnieszka; Raught, Brian; Arancio, Ottavio; Fraser, Paul E.
2015-01-01
Small ubiquitin-like modifier-1 (SUMO1) plays a number of roles in cellular events and recent evidence has given momentum for its contributions to neuronal development and function. Here, we have generated a SUMO1 transgenic mouse model with exclusive overexpression in neurons in an effort to identify in vivo conjugation targets and the functional consequences of their SUMOylation. A high-expressing line was examined which displayed elevated levels of mono-SUMO1 and increased high molecular weight conjugates in all brain regions. Immunoprecipitation of SUMOylated proteins from total brain extract and proteomic analysis revealed ~95 candidate proteins from a variety of functional classes, including a number of synaptic and cytoskeletal proteins. SUMO1 modification of synaptotagmin-1 was found to be elevated as compared to non-transgenic mice. This observation was associated with an age-dependent reduction in basal synaptic transmission and impaired presynaptic function as shown by altered paired pulse facilitation, as well as a decrease in spine density. The changes in neuronal function and morphology were also associated with a specific impairment in learning and memory while other behavioral features remained unchanged. These findings point to a significant contribution of SUMO1 modification on neuronal function which may have implications for mechanisms involved in mental retardation and neurodegeneration. PMID:26022678
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Kim, Sang-Yoon; Lim, Woochang
2018-06-01
We consider an excitatory population of subthreshold Izhikevich neurons which cannot fire spontaneously without noise. As the coupling strength passes a threshold, individual neurons exhibit noise-induced burstings. This neuronal population has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). However, STDP was not considered in previous works on stochastic burst synchronization (SBS) between noise-induced burstings of sub-threshold neurons. Here, we study the effect of additive STDP on SBS by varying the noise intensity D in the Barabási-Albert scale-free network (SFN). One of our main findings is a Matthew effect in synaptic plasticity which occurs due to a positive feedback process. Good burst synchronization (with higher bursting measure) gets better via long-term potentiation (LTP) of synaptic strengths, while bad burst synchronization (with lower bursting measure) gets worse via long-term depression (LTD). Consequently, a step-like rapid transition to SBS occurs by changing D , in contrast to a relatively smooth transition in the absence of STDP. We also investigate the effects of network architecture on SBS by varying the symmetric attachment degree [Formula: see text] and the asymmetry parameter [Formula: see text] in the SFN, and Matthew effects are also found to occur by varying [Formula: see text] and [Formula: see text]. Furthermore, emergences of LTP and LTD of synaptic strengths are investigated in details via our own microscopic methods based on both the distributions of time delays between the burst onset times of the pre- and the post-synaptic neurons and the pair-correlations between the pre- and the post-synaptic instantaneous individual burst rates (IIBRs). Finally, a multiplicative STDP case (depending on states) with soft bounds is also investigated in comparison with the additive STDP case (independent of states) with hard bounds. Due to the soft bounds, a Matthew effect with some quantitative differences is also found to occur for the case of multiplicative STDP.
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Soriano, Jaymar; Kubo, Takatomi; Inoue, Takao; Kida, Hiroyuki; Yamakawa, Toshitaka; Suzuki, Michiyasu; Ikeda, Kazushi
2017-10-01
Experiments with drug-induced epilepsy in rat brains and epileptic human brain region reveal that focal cooling can suppress epileptic discharges without affecting the brain's normal neurological function. Findings suggest a viable treatment for intractable epilepsy cases via an implantable cooling device. However, precise mechanisms by which cooling suppresses epileptic discharges are still not clearly understood. Cooling experiments in vitro presented evidence of reduction in neurotransmitter release from presynaptic terminals and loss of dendritic spines at post-synaptic terminals offering a possible synaptic mechanism. We show that termination of epileptic discharges is possible by introducing a homogeneous temperature factor in a neural mass model which attenuates the post-synaptic impulse responses of the neuronal populations. This result however may be expected since such attenuation leads to reduced post-synaptic potential and when the effect on inhibitory interneurons is less than on excitatory interneurons, frequency of firing of pyramidal cells is consequently reduced. While this is observed in cooling experiments in vitro, experiments in vivo exhibit persistent discharges during cooling but suppressed in magnitude. This leads us to conjecture that reduction in the frequency of discharges may be compensated through intrinsic excitability mechanisms. Such compensatory mechanism is modelled using a reciprocal temperature factor in the firing response function in the neural mass model. We demonstrate that the complete model can reproduce attenuation of both magnitude and frequency of epileptic discharges during cooling. The compensatory mechanism suggests that cooling lowers the average and the variance of the distribution of threshold potential of firing across the population. Bifurcation study with respect to the temperature parameters of the model reveals how heterogeneous response of epileptic discharges to cooling (termination or suppression only) is exhibited. Possibility of differential temperature effects on post-synaptic potential generation of different populations is also explored.
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Inoue, Takao; Kida, Hiroyuki; Yamakawa, Toshitaka; Suzuki, Michiyasu
2017-01-01
Experiments with drug-induced epilepsy in rat brains and epileptic human brain region reveal that focal cooling can suppress epileptic discharges without affecting the brain’s normal neurological function. Findings suggest a viable treatment for intractable epilepsy cases via an implantable cooling device. However, precise mechanisms by which cooling suppresses epileptic discharges are still not clearly understood. Cooling experiments in vitro presented evidence of reduction in neurotransmitter release from presynaptic terminals and loss of dendritic spines at post-synaptic terminals offering a possible synaptic mechanism. We show that termination of epileptic discharges is possible by introducing a homogeneous temperature factor in a neural mass model which attenuates the post-synaptic impulse responses of the neuronal populations. This result however may be expected since such attenuation leads to reduced post-synaptic potential and when the effect on inhibitory interneurons is less than on excitatory interneurons, frequency of firing of pyramidal cells is consequently reduced. While this is observed in cooling experiments in vitro, experiments in vivo exhibit persistent discharges during cooling but suppressed in magnitude. This leads us to conjecture that reduction in the frequency of discharges may be compensated through intrinsic excitability mechanisms. Such compensatory mechanism is modelled using a reciprocal temperature factor in the firing response function in the neural mass model. We demonstrate that the complete model can reproduce attenuation of both magnitude and frequency of epileptic discharges during cooling. The compensatory mechanism suggests that cooling lowers the average and the variance of the distribution of threshold potential of firing across the population. Bifurcation study with respect to the temperature parameters of the model reveals how heterogeneous response of epileptic discharges to cooling (termination or suppression only) is exhibited. Possibility of differential temperature effects on post-synaptic potential generation of different populations is also explored. PMID:28981509
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NASA Astrophysics Data System (ADS)
Bukoski, Alex; Steyn-Ross, D. A.; Pickett, Ashley F.; Steyn-Ross, Moira L.
2018-06-01
The dynamics of a stochastic type-I Hodgkin-Huxley-like point neuron model exposed to inhibitory synaptic noise are investigated as a function of distance from spiking threshold and the inhibitory influence of the general anesthetic agent propofol. The model is biologically motivated and includes the effects of intrinsic ion-channel noise via a stochastic differential equation description as well as inhibitory synaptic noise modeled as multiple Poisson-distributed impulse trains with saturating response functions. The effect of propofol on these synapses is incorporated through this drug's principal influence on fast inhibitory neurotransmission mediated by γ -aminobutyric acid (GABA) type-A receptors via reduction of the synaptic response decay rate. As the neuron model approaches spiking threshold from below, we track membrane voltage fluctuation statistics of numerically simulated stochastic trajectories. We find that for a given distance from spiking threshold, increasing the magnitude of anesthetic-induced inhibition is associated with augmented signatures of critical slowing: fluctuation amplitudes and correlation times grow as spectral power is increasingly focused at 0 Hz. Furthermore, as a function of distance from threshold, anesthesia significantly modifies the power-law exponents for variance and correlation time divergences observable in stochastic trajectories. Compared to the inverse square root power-law scaling of these quantities anticipated for the saddle-node bifurcation of type-I neurons in the absence of anesthesia, increasing anesthetic-induced inhibition results in an observable exponent <-0.5 for variance and >-0.5 for correlation time divergences. However, these behaviors eventually break down as distance from threshold goes to zero with both the variance and correlation time converging to common values independent of anesthesia. Compared to the case of no synaptic input, linearization of an approximating multivariate Ornstein-Uhlenbeck model reveals these effects to be the consequence of an additional slow eigenvalue associated with synaptic activity that competes with those of the underlying point neuron in a manner that depends on distance from spiking threshold.
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Baroni, Fabiano; Burkitt, Anthony N; Grayden, David B
2014-05-01
High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of hyperpolarizing, post-inhibitory rebound is not elicited and factors i) and ii) dominate, yielding lower synchrony in GIF networks than in IF networks.
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Postsynaptic localization of PSD-95 is regulated by all three pathways downstream of TrkB signaling.
Yoshii, Akira; Constantine-Paton, Martha
2014-01-01
Brain-derived neurotrophic factor (BDNF) and its receptor TrkB regulate synaptic plasticity. TrkB triggers three downstream signaling pathways; Phosphatidylinositol 3-kinase (PI3K), Phospholipase Cγ (PLCγ) and Mitogen activated protein kinases/Extracellular signal-regulated kinases (MAPK/ERK). We previously showed two distinct mechanisms whereby BDNF-TrkB pathway controls trafficking of PSD-95, which is the major scaffold at excitatory synapses and is critical for synapse maturation. BDNF activates the PI3K-Akt pathway and regulates synaptic delivery of PSD-95 via vesicular transport (Yoshii and Constantine-Paton, 2007). BDNF-TrkB signaling also triggers PSD-95 palmitoylation and its transport to synapses through the phosphorylation of the palmitoylation enzyme ZDHHC8 by a protein kinase C (PKC; Yoshii etal., 2011). The second study used PKC inhibitors chelerythrine as well as a synthetic zeta inhibitory peptide (ZIP) which was originally designed to block the brain-specific PKC isoform protein kinase Mϖ (PKMϖ). However, recent studies raise concerns about specificity of ZIP. Here, we assessed the contribution of TrkB and its three downstream pathways to the synaptic distribution of endogenous PSD-95 in cultured neurons using chemical and genetic interventions. We confirmed that TrkB, PLC, and PI3K were critical for the postsynaptic distribution of PSD-95. Furthermore, suppression of MAPK/ERK also disrupted PSD-95 expression. Next, we examined the contribution of PKC. While both chelerythrine and ZIP suppressed the postsynaptic localization of PSD-95, RNA interference for PKMϖ did not have a significant effect. This result suggests that the ZIP peptide, widely used as the "specific" PKMϖ antagonist by many investigators may block a PKC variant other than PKMϖ such as PKCλ/ι. Our results indicate that TrkB regulates postsynaptic localization of PSD-95 through all three downstream pathways, but also recommend further work to identify other PKC variants that regulate palmitoylation and synaptic localization of PSD-95.
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Baroni, Fabiano; Burkitt, Anthony N.; Grayden, David B.
2014-01-01
High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of hyperpolarizing, post-inhibitory rebound is not elicited and factors i) and ii) dominate, yielding lower synchrony in GIF networks than in IF networks. PMID:24784237
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FIB/SEM technology and Alzheimer's disease: three-dimensional analysis of human cortical synapses.
Blazquez-Llorca, Lidia; Merchán-Pérez, Ángel; Rodríguez, José-Rodrigo; Gascón, Jorge; DeFelipe, Javier
2013-01-01
The quantification and measurement of synapses is a major goal in the study of brain organization in both health and disease. Serial section electron microscopy (EM) is the ideal method since it permits the direct quantification of crucial features such as the number of synapses per unit volume or the distribution and size of synapses. However, a major limitation is that obtaining long series of ultrathin sections is extremely time-consuming and difficult. Consequently, quantitative EM studies are scarce and the most common method employed to estimate synaptic density in the human brain is indirect, by counting at the light microscopic level immunoreactive puncta using synaptic markers. The recent development of automatic EM methods in experimental animals, such as the combination of focused ion beam milling and scanning electron microscopy (FIB/SEM), are opening new avenues. Here we explored the utility of FIB/SEM to examine the cerebral cortex of Alzheimer's disease patients. We found that FIB/SEM is an excellent tool to study in detail the ultrastructure and alterations of the synaptic organization of the human brain. Using this technology, it is possible to reconstruct different types of plaques and the surrounding neuropil to find new aspects of the pathological process associated with the disease, namely; to count the exact number and types of synapses in different regions of the plaques, to study the spatial distribution of synapses, and to analyze the morphology and nature of the various types of dystrophic neurites and amyloid deposits.
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Dynamics of Action Potential Initiation in the GABAergic Thalamic Reticular Nucleus In Vivo
Muñoz, Fabián; Fuentealba, Pablo
2012-01-01
Understanding the neural mechanisms of action potential generation is critical to establish the way neural circuits generate and coordinate activity. Accordingly, we investigated the dynamics of action potential initiation in the GABAergic thalamic reticular nucleus (TRN) using in vivo intracellular recordings in cats in order to preserve anatomically-intact axo-dendritic distributions and naturally-occurring spatiotemporal patterns of synaptic activity in this structure that regulates the thalamic relay to neocortex. We found a wide operational range of voltage thresholds for action potentials, mostly due to intrinsic voltage-gated conductances and not synaptic activity driven by network oscillations. Varying levels of synchronous synaptic inputs produced fast rates of membrane potential depolarization preceding the action potential onset that were associated with lower thresholds and increased excitability, consistent with TRN neurons performing as coincidence detectors. On the other hand the presence of action potentials preceding any given spike was associated with more depolarized thresholds. The phase-plane trajectory of the action potential showed somato-dendritic propagation, but no obvious axon initial segment component, prominent in other neuronal classes and allegedly responsible for the high onset speed. Overall, our results suggest that TRN neurons could flexibly integrate synaptic inputs to discharge action potentials over wide voltage ranges, and perform as coincidence detectors and temporal integrators, supported by a dynamic action potential threshold. PMID:22279567
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Burton, Shawn D.
2015-01-01
Granule cell-mediated inhibition is critical to patterning principal neuron activity in the olfactory bulb, and perturbation of synaptic input to granule cells significantly alters olfactory-guided behavior. Despite the critical role of granule cells in olfaction, little is known about how sensory input recruits granule cells. Here, we combined whole-cell patch-clamp electrophysiology in acute mouse olfactory bulb slices with biophysical multicompartmental modeling to investigate the synaptic basis of granule cell recruitment. Physiological activation of sensory afferents within single glomeruli evoked diverse modes of granule cell activity, including subthreshold depolarization, spikelets, and suprathreshold responses with widely distributed spike latencies. The generation of these diverse activity modes depended, in part, on the asynchronous time course of synaptic excitation onto granule cells, which lasted several hundred milliseconds. In addition to asynchronous excitation, each granule cell also received synchronous feedforward inhibition. This inhibition targeted both proximal somatodendritic and distal apical dendritic domains of granule cells, was reliably recruited across sniff rhythms, and scaled in strength with excitation as more glomeruli were activated. Feedforward inhibition onto granule cells originated from deep short-axon cells, which responded to glomerular activation with highly reliable, short-latency firing consistent with tufted cell-mediated excitation. Simulations showed that feedforward inhibition interacts with asynchronous excitation to broaden granule cell spike latency distributions and significantly attenuates granule cell depolarization within local subcellular compartments. Collectively, our results thus identify feedforward inhibition onto granule cells as a core feature of olfactory bulb circuitry and establish asynchronous excitation and feedforward inhibition as critical regulators of granule cell activity. SIGNIFICANCE STATEMENT Inhibitory granule cells are involved critically in shaping odor-evoked principal neuron activity in the mammalian olfactory bulb, yet little is known about how sensory input activates granule cells. Here, we show that sensory input to the olfactory bulb evokes a barrage of asynchronous synaptic excitation and highly reliable, short-latency synaptic inhibition onto granule cells via a disynaptic feedforward inhibitory circuit involving deep short-axon cells. Feedforward inhibition attenuates local depolarization within granule cell dendritic branches, interacts with asynchronous excitation to suppress granule cell spike-timing precision, and scales in strength with excitation across different levels of sensory input to normalize granule cell firing rates. PMID:26490853
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Synaptic Democracy and Vesicular Transport in Axons
NASA Astrophysics Data System (ADS)
Bressloff, Paul C.; Levien, Ethan
2015-04-01
Synaptic democracy concerns the general problem of how regions of an axon or dendrite far from the cell body (soma) of a neuron can play an effective role in neuronal function. For example, stimulated synapses far from the soma are unlikely to influence the firing of a neuron unless some sort of active dendritic processing occurs. Analogously, the motor-driven transport of newly synthesized proteins from the soma to presynaptic targets along the axon tends to favor the delivery of resources to proximal synapses. Both of these phenomena reflect fundamental limitations of transport processes based on a localized source. In this Letter, we show that a more democratic distribution of proteins along an axon can be achieved by making the transport process less efficient. This involves two components: bidirectional or "stop-and-go" motor transport (which can be modeled in terms of advection-diffusion), and reversible interactions between motor-cargo complexes and synaptic targets. Both of these features have recently been observed experimentally. Our model suggests that, just as in human societies, there needs to be a balance between "efficiency" and "equality".
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Partata, W A; Krepsky, A M R; Xavier, L L; Marques, M; Achaval, M
2003-04-01
Immunoreactive substance P was investigated in turtle lumbar spinal cord after sciatic nerve transection. In control animals immunoreactive fibers were densest in synaptic field Ia, where the longest axons invaded synaptic field III. Positive neuronal bodies were identified in the lateral column of the dorsal horn and substance P immunoreactive varicosities were observed in the ventral horn, in close relationship with presumed motoneurons. Other varicosities appeared in the lateral and anterior funiculi. After axotomy, substance P immunoreactive fibers were reduced slightly on the side of the lesion, which was located in long fibers that invaded synaptic field III and in the varicosities of the lateral and anterior funiculus. The changes were observed at 7 days after axonal injury and persisted at 15, 30, 60 and 90 days after the lesion. These findings show that turtles should be considered as a model to study the role of substance P in peripheral axonal injury, since the distribution and temporal changes of substance P were similar to those found in mammals.
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Recruitment of Staufen2 Enhances Dendritic Localization of an Intron-Containing CaMKIIα mRNA.
Ortiz, Raúl; Georgieva, Maya V; Gutiérrez, Sara; Pedraza, Neus; Fernández-Moya, Sandra M; Gallego, Carme
2017-07-05
Regulation of mRNA localization is a conserved cellular process observed in many types of cells and organisms. Asymmetrical mRNA distribution plays a particularly important role in the nervous system, where local translation of localized mRNA represents a key mechanism in synaptic plasticity. CaMKIIα is a very abundant mRNA detected in neurites, consistent with its crucial role at glutamatergic synapses. Here, we report the presence of CaMKIIα mRNA isoforms that contain intron i16 in dendrites, RNA granules, and synaptoneurosomes from primary neurons and brain. This subpopulation of unspliced mRNA preferentially localizes to distal dendrites in a synaptic-activity-dependent manner. Staufen2, a well-established marker of RNA transport in dendrites, interacts with intron i16 sequences and enhances its distal dendritic localization, pointing to the existence of intron-mediated mechanisms in the molecular pathways that modulate dendritic transport and localization of synaptic mRNAs. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
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Nakayama, Kei; Ohashi, Rie; Shinoda, Yo; Yamazaki, Maya; Abe, Manabu; Fujikawa, Akihiro; Shigenobu, Shuji; Futatsugi, Akira; Noda, Masaharu; Mikoshiba, Katsuhiko; Furuichi, Teiichi; Sakimura, Kenji; Shiina, Nobuyuki
2017-11-21
Local regulation of synaptic efficacy is thought to be important for proper networking of neurons and memory formation. Dysregulation of global translation influences long-term memory in mice, but the relevance of the regulation specific for local translation by RNA granules remains elusive. Here, we demonstrate roles of RNG105/caprin1 in long-term memory formation. RNG105 deletion in mice impaired synaptic strength and structural plasticity in hippocampal neurons. Furthermore, RNG105-deficient mice displayed unprecedentedly severe defects in long-term memory formation in spatial and contextual learning tasks. Genome-wide profiling of mRNA distribution in the hippocampus revealed an underlying mechanism: RNG105 deficiency impaired the asymmetric somato-dendritic localization of mRNAs. Particularly, RNG105 deficiency reduced the dendritic localization of mRNAs encoding regulators of AMPAR surface expression, which was consistent with attenuated homeostatic AMPAR scaling in dendrites and reduced synaptic strength. Thus, RNG105 has an essential role, as a key regulator of dendritic mRNA localization, in long-term memory formation.
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Bourgeois, J P; Jastreboff, P J; Rakic, P
1989-01-01
We used quantitative electron microscopy to determine the effect of precocious visual experience on the time course, magnitude, and pattern of perinatal synaptic overproduction in the primary visual cortex of the rhesus monkey. Fetuses were delivered by caesarean section 3 weeks before term, exposed to normal light intensity and day/night cycles, and killed within the first postnatal month, together with age-matched controls that were delivered at term. We found that premature visual stimulation does not affect the rate of synaptic accretion and overproduction. Both of these processes proceed in relation to the time of conception rather than to the time of delivery. In contrast, the size, type, and laminar distribution of synapses were significantly different between preterm and control infants. The changes and differences in these parameters correlate with the duration of visual stimulation and become less pronounced with age. If visual experience in infancy influences the maturation of the visual cortex, it must do so predominantly by strengthening, modifying, and/or eliminating synapses that have already been formed, rather than by regulating the rate of synapse production. Images PMID:2726773
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Mouse VAP33 is associated with the endoplasmic reticulum and microtubules
Skehel, P. A.; Fabian-Fine, R.; Kandel, E. R.
2000-01-01
VAMP/synaptobrevin is a synaptic vesicle protein that is essential for neurotransmitter release. Intracellular injection of antisera against the Aplysia californica VAMP/synaptobrevin-binding protein ApVAP33 inhibited evoked excitatory postsynaptic potentials (EPSPs) in cultured cells, suggesting that this association may regulate the function of VAMP/synaptobrevin. We have identified and characterized a mouse homologue of ApVAP33, mVAP33. The overall domain structure of the proteins is conserved, and they have similar biochemical properties. mVAP33 mRNA is detectable in all mouse tissues examined, in contrast to the more restricted expression seen in A. californica. We analyzed the cellular distribution of mVAP33 protein in brain slices and cultured cortical cells by light and electron microscopy. Although present at higher levels in neurons, immunoreactivity was detected throughout both neurons and glia in a reticular pattern similar to that of endoplasmic reticulum-resident proteins. mVAP33 does not colocalize with VAMP/synaptobrevin at synaptic structures, but expression overlaps with lower levels of VAMP/synaptobrevin in the soma. Ultrastructural analysis revealed mVAP33 associated with microtubules and intracellular vesicles of heterogeneous size. In primary neuronal cultures, large aggregates of mVAP33 are also detected in short filamentous structures, which are occasionally associated with intracellular membranes. There is no evidence for accumulation of mVAP33 on synaptic vesicles or at the plasma membrane. These data suggest that mVAP33 is an endoplasmic-reticulum–resident protein that associates with components of the cytoskeleton. Any functional interaction between mVAP33 and VAMP/synaptobrevin, therefore, most likely involves the delivery of components to synaptic terminals rather than a direct participation in synaptic vesicle exocytosis. PMID:10655491
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Shining light on neurons--elucidation of neuronal functions by photostimulation.
Eder, Matthias; Zieglgänsberger, Walter; Dodt, Hans-Ulrich
2004-01-01
Many neuronal functions can be elucidated by techniques that allow for a precise stimulation of defined regions of a neuron and its afferents. Photolytic release of neurotransmitters from 'caged' derivates in the vicinity of visualized neurons in living brain slices meets this request. This technique allows the study of the subcellular distribution and properties of functional native neurotransmitter receptors. These are prerequisites for a detailed analysis of the expression and spatial specificity of synaptic plasticity. Photostimulation can further be used to fast map the synaptic connectivity between nearby and, more importantly, distant cells in a neuronal network. Here we give a personal review of some of the technical aspects of photostimulation and recent findings, which illustrate the advantages of this technique.
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Spiking neural P systems with multiple channels.
Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian
2017-11-01
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Cascaded VLSI Chips Help Neural Network To Learn
NASA Technical Reports Server (NTRS)
Duong, Tuan A.; Daud, Taher; Thakoor, Anilkumar P.
1993-01-01
Cascading provides 12-bit resolution needed for learning. Using conventional silicon chip fabrication technology of VLSI, fully connected architecture consisting of 32 wide-range, variable gain, sigmoidal neurons along one diagonal and 7-bit resolution, electrically programmable, synaptic 32 x 31 weight matrix implemented on neuron-synapse chip. To increase weight nominally from 7 to 13 bits, synapses on chip individually cascaded with respective synapses on another 32 x 32 matrix chip with 7-bit resolution synapses only (without neurons). Cascade correlation algorithm varies number of layers effectively connected into network; adds hidden layers one at a time during learning process in such way as to optimize overall number of neurons and complexity and configuration of network.
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Short-Term Memory Trace in Rapidly Adapting Synapses of Inferior Temporal Cortex
Sugase-Miyamoto, Yasuko; Liu, Zheng; Wiener, Matthew C.; Optican, Lance M.; Richmond, Barry J.
2008-01-01
Visual short-term memory tasks depend upon both the inferior temporal cortex (ITC) and the prefrontal cortex (PFC). Activity in some neurons persists after the first (sample) stimulus is shown. This delay-period activity has been proposed as an important mechanism for working memory. In ITC neurons, intervening (nonmatching) stimuli wipe out the delay-period activity; hence, the role of ITC in memory must depend upon a different mechanism. Here, we look for a possible mechanism by contrasting memory effects in two architectonically different parts of ITC: area TE and the perirhinal cortex. We found that a large proportion (80%) of stimulus-selective neurons in area TE of macaque ITCs exhibit a memory effect during the stimulus interval. During a sequential delayed matching-to-sample task (DMS), the noise in the neuronal response to the test image was correlated with the noise in the neuronal response to the sample image. Neurons in perirhinal cortex did not show this correlation. These results led us to hypothesize that area TE contributes to short-term memory by acting as a matched filter. When the sample image appears, each TE neuron captures a static copy of its inputs by rapidly adjusting its synaptic weights to match the strength of their individual inputs. Input signals from subsequent images are multiplied by those synaptic weights, thereby computing a measure of the correlation between the past and present inputs. The total activity in area TE is sufficient to quantify the similarity between the two images. This matched filter theory provides an explanation of what is remembered, where the trace is stored, and how comparison is done across time, all without requiring delay period activity. Simulations of a matched filter model match the experimental results, suggesting that area TE neurons store a synaptic memory trace during short-term visual memory. PMID:18464917
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Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Gardner, Brian; Grüning, André
2016-01-01
Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262
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Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.
Gardner, Brian; Grüning, André
2016-01-01
Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.
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Addiction-like Synaptic Impairments in Diet-Induced Obesity.
Brown, Robyn Mary; Kupchik, Yonatan Michael; Spencer, Sade; Garcia-Keller, Constanza; Spanswick, David C; Lawrence, Andrew John; Simonds, Stephanie Elise; Schwartz, Danielle Joy; Jordan, Kelsey Ann; Jhou, Thomas Clayton; Kalivas, Peter William
2017-05-01
There is increasing evidence that the pathological overeating underlying some forms of obesity is compulsive in nature and therefore contains elements of an addictive disorder. However, direct physiological evidence linking obesity to synaptic plasticity akin to that occurring in addiction is lacking. We sought to establish whether the propensity to diet-induced obesity (DIO) is associated with addictive-like behavior, as well as synaptic impairments in the nucleus accumbens core considered hallmarks of addiction. Sprague Dawley rats were allowed free access to a palatable diet for 8 weeks then separated by weight gain into DIO-prone and DIO-resistant subgroups. Access to palatable food was then restricted to daily operant self-administration sessions using fixed ratio 1, 3, and 5 and progressive ratio schedules. Subsequently, nucleus accumbens brain slices were prepared, and we tested for changes in the ratio between α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) and N-methyl-D-aspartate currents and the ability to exhibit long-term depression. We found that propensity to develop DIO is linked to deficits in the ability to induce long-term depression in the nucleus accumbens, as well as increased potentiation at these synapses as measured by AMPA/N-methyl-D-aspartate currents. Consistent with these impairments, we observed addictive-like behavior in DIO-prone rats, including 1) heightened motivation for palatable food; 2) excessive intake; and 3) increased food seeking when food was unavailable. Our results show overlap between the propensity for DIO and the synaptic changes associated with facets of addictive behavior, supporting partial coincident neurological underpinnings for compulsive overeating and drug addiction. Copyright © 2016 Society of Biological Psychiatry. All rights reserved.
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Chen, Yi-Wen; Actor-Engel, Hannah; Sherpa, Ang Doma; Klingensmith, Lauren; Chowdhury, Tara G; Aoki, Chiye
2017-07-01
Hunger evokes foraging. This innate response can be quantified as voluntary wheel running following food restriction (FR). Paradoxically, imposing severe FR evokes voluntary FR, as some animals choose to run rather than eat, even during limited periods of food availability. This phenomenon, called activity-based anorexia (ABA), has been used to identify brain changes associated with FR and excessive exercise (EX), two core symptoms of anorexia nervosa (AN), and to explore neurobiological bases of AN vulnerability. Previously, we showed a strong positive correlation between suppression of FR-evoked hyperactivity, i.e., ABA resilience, and levels of extra-synaptic GABA receptors in stratum radiatum (SR) of hippocampal CA1. Here, we tested for the converse: whether animals with enhanced expression of NMDA receptors (NMDARs) exhibit greater levels of FR-evoked hyperactivity, i.e., ABA vulnerability. Four groups of animals were assessed for NMDAR levels at CA1 spines: (1) ABA, in which 4 days of FR was combined with wheel access to allow voluntary EX; (2) FR only; (3) EX only; and (4) control (CON) that experienced neither EX nor FR. Electron microscopy revealed that synaptic NR2A-NMDARs and NR2B-NMDARs levels are significantly elevated, relative to CONs'. Individuals' ABA severity, based on weight loss, correlated with synaptic NR2B-NMDAR levels. ABA resilience, quantified as suppression of hyperactivity, correlated strongly with reserve pools of NR2A-NMDARs in spine cytoplasm. NR2A- and NR2B-NMDAR measurements correlated with spinous prevalence of an F-actin binding protein, drebrin, suggesting that drebrin enables insertion of NR2B-NMDAR to and retention of NR2A-NMDARs away from synaptic membranes, together influencing ABA vulnerability.
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Crawford, LaTasha K; Rahman, Shumaia F; Beck, Sheryl G
2013-01-16
Anxiety disorders are among the most prevalent psychiatric disorders, yet much is unknown about the underlying mechanisms. The dorsal raphe (DR) is at the crux of the anxiety-inducing effects of uncontrollable stress, a key component of models of anxiety. Though DR serotonin (5-HT) neurons play a prominent role, anxiety-associated changes in the physiology of 5-HT neurons remain poorly understood. A 5-day social defeat model of anxiety produced a multifaceted, anxious phenotype in intruder mice that included increased avoidance behavior in the open field test, increased stress-evoked grooming, and increased bladder and heart weights when compared to control mice. Intruders were further compared to controls using electrophysiology recordings conducted in midbrain slices wherein recordings targeted 5-HT neurons of the ventromedial (vmDR) and lateral wing (lwDR) subfields of the DR. Though defining membrane characteristics of 5-HT neurons were unchanged, γ-aminobutyric-acid-mediated (GABAergic) synaptic regulation of 5-HT neurons was altered in a topographically specific way. In the vmDR of intruders, there was a decrease in the frequency and amplitude of GABAergic spontaneous inhibitory postsynaptic currents (sIPSCs). However, in the lwDR, there was an increase in the strength of inhibitory signals due to slower sIPSC kinetics. Synaptic changes were selective for GABAergic input, as glutamatergic synaptic input was unchanged in intruders. The distinct inhibitory regulation of DR subfields provides a mechanism for increased 5-HT output in vmDR target regions and decreased 5-HT output in lwDR target regions, divergent responses to uncontrollable stress that have been reported in the literature but were previously poorly understood.
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Sauer, A E; Büschges, A; Stein, W
1997-04-01
The femur-tibia (FT) joint of insects is governed by a neuronal network that controls activity in tibial motoneurons by processing sensory information about tibial position and movement provided by afferents of the femoral chordotonal organ (fCO). We show that central arborizations of fCO afferents receive presynaptic depolarizing synaptic inputs. With an average resting potential of -71.9 +/- 3.72 mV (n = 10), the reversal potential of these potentials is on average -62.8 +/- 2.3 mV (n = 5). These synaptic potentials occur either spontaneously or are related to movements at the fCO. They are thus induced by signals from other fCO afferents. Therefore, the synaptic inputs to fCO afferents are specific and depend on the sensitivity of the individual afferent affected. These potentials reduce the amplitude of concurrent afferent action potentials. Bath application of picrotoxin, a noncompetitive blocker of chloride ion channels, blocks these potentials, which indicates that they are mediated by chloride ions. From these results, it is concluded that these are inhibitory synaptic potentials generated in the central terminals of fCO afferents. Pharmacologic removal of these potentials affects the tuning of the complete FT control system. Following removal, the dependence of the FT control loop on the tibia position increases relative to the dependency on the velocity of tibia movements. This is due to changes in the relative weighting of the position and velocity signals in the parallel interneuronal pathways from the fCO onto tibial motoneurons. Consequently, the FT joint is no longer able to perform twig mimesis (i.e., catalepsy), which is known to rely on a low position compared to the high-velocity dependency of the FT control system.
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Srinivasa, Narayan; Cho, Youngkwan
2014-01-01
A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns—both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity. PMID:25566045
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Srinivasa, Narayan; Cho, Youngkwan
2014-01-01
A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.
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Escobar, Gina M.; Maffei, Arianna; Miller, Paul
2014-01-01
The computation of direction selectivity requires that a cell respond to joint spatial and temporal characteristics of the stimulus that cannot be separated into independent components. Direction selectivity in ferret visual cortex is not present at the time of eye opening but instead develops in the days and weeks following eye opening in a process that requires visual experience with moving stimuli. Classic Hebbian or spike timing-dependent modification of excitatory feed-forward synaptic inputs is unable to produce direction-selective cells from unselective or weakly directionally biased initial conditions because inputs eventually grow so strong that they can independently drive cortical neurons, violating the joint spatial-temporal activation requirement. Furthermore, without some form of synaptic competition, cells cannot develop direction selectivity in response to training with bidirectional stimulation, as cells in ferret visual cortex do. We show that imposing a maximum lateral geniculate nucleus (LGN)-to-cortex synaptic weight allows neurons to develop direction-selective responses that maintain the requirement for joint spatial and temporal activation. We demonstrate that a novel form of inhibitory plasticity, postsynaptic activity-dependent long-term potentiation of inhibition (POSD-LTPi), which operates in the developing cortex at the time of eye opening, can provide synaptic competition and enables robust development of direction-selective receptive fields with unidirectional or bidirectional stimulation. We propose a general model of the development of spatiotemporal receptive fields that consists of two phases: an experience-independent establishment of initial biases, followed by an experience-dependent amplification or modification of these biases via correlation-based plasticity of excitatory inputs that compete against gradually increasing feed-forward inhibition. PMID:24598528
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The distribution of early recombination nodules on zygotene bivalents from plants.
Anderson, L K; Hooker, K D; Stack, S M
2001-01-01
Early recombination nodules (ENs) are protein complexes approximately 100 nm in diameter that are associated with forming synaptonemal complexes (SCs) during leptotene and zygotene of meiosis. Although their functions are not yet clear, ENs may have roles in synapsis and recombination. Here we report on the frequency and distribution of ENs in zygotene SC spreads from six plant species that include one lower vascular plant, two dicots, and three monocots. For each species, the number of ENs per unit length is higher for SC segments than for (asynapsed) axial elements (AEs). In addition, EN number is strongly correlated with SC segment length. There are statistically significant differences in EN frequencies on SCs between species, but these differences are not related to genome size, number of chromosomes, or phylogenetic class. There is no difference in the frequency of ENs per unit length of SC from early to late zygotene. The distribution of distances between adjacent ENs on SC segments is random for all six species, but ENs are found at synaptic forks more often than expected for a random distribution of ENs on SCs. From these observations, we conclude that in plants: (1) some ENs bind to AEs prior to synapsis, (2) most ENs bind to forming SCs at synaptic forks, and (3) ENs do not bind to already formed SCs. PMID:11729167
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Ogundele, Olalekan M; Pardo, Joaquin; Francis, Joseph; Goya, Rodolfo G; Lee, Charles C
2018-01-01
Insulin-like growth factor 1 receptor (IGF-1R) signaling regulates the activity and phosphorylation of downstream kinases linked to inflammation, neurodevelopment, aging and synaptic function. In addition to the control of Ca 2+ currents, IGF-1R signaling modulates the activity of calcium-calmodulin-dependent kinase 2 alpha (CaMKIIα) and mitogen activated protein kinase (MAPK/ErK) through multiple signaling pathways. These proteins (CaMKIIα and MAPK) regulate Ca 2+ movement and long-term potentiation (LTP). Since IGF-1R controls the synaptic activity of Ca 2+ , CaMKIIα and MAPK signaling, the possible mechanism through which an age-dependent change in IGF-1R can alter the synaptic expression and phosphorylation of these proteins in aging needs to be investigated. In this study, we evaluated the relationship between an age-dependent change in brain IGF-1R and phosphorylation of CaMKIIα/MAPK. Furthermore, we elucidated possible mechanisms through which dysregulated CaMKIIα/MAPK interaction may be linked to a change in neurotransmitter processing and synaptic function. Male C57BL/6 VGAT-Venus mice at postnatal days 80 (P80), 365 and 730 were used to study age-related neural changes in two brain regions associated with cognitive function: hippocampus and prefrontal cortex (PFC). By means of high throughput confocal imaging and quantitative immunoblotting, we evaluated the distribution and expression of IGF-1, IGF-1R, CaMKIIα, p-CaMKIIα, MAPK and p-MAPK in whole brain lysate, hippocampus and cortex. Furthermore, we compared protein expression patterns and regional changes at P80, P365 and P730. Ultimately, we determined the relative phosphorylation pattern of CaMKIIα and MAPK through quantification of neural p-CaMKIIα and p-MAPK/ErK, and IGF-1R expression for P80, P365 and P730 brain samples. In addition to a change in synaptic function, our results show a decrease in neural IGF-1/IGF-1R expression in whole brain, hippocampus and cortex of aged mice. This was associated with a significant upregulation of phosphorylated neural MAPK (p-MAPK) and decrease in total brain CaMKIIα (i.e., CaMKIIα and p-CaMKIIα) in the aged brain. Taken together, we showed that brain aging is associated with a change in neural IGF-1/IGF-1R expression and may be linked to a change in phosphorylation of synaptic kinases (CaMKIIα and MAPK) that are involved in the modulation of LTP.
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Ohayon, Elan L; Kalitzin, Stiliyan; Suffczynski, Piotr; Jin, Frank Y; Tsang, Paul W; Borrett, Donald S; Burnham, W McIntyre; Kwan, Hon C
2004-01-01
The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 *10(26) possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. [1] First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. [2] Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. [3] The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach [1]. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space.
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Saez, Ignacio; Friedlander, Michael J
2016-01-01
Layer 4 (L4) of primary visual cortex (V1) is the main recipient of thalamocortical fibers from the dorsal lateral geniculate nucleus (LGNd). Thus, it is considered the main entry point of visual information into the neocortex and the first anatomical opportunity for intracortical visual processing before information leaves L4 and reaches supra- and infragranular cortical layers. The strength of monosynaptic connections from individual L4 excitatory cells onto adjacent L4 cells (unitary connections) is highly malleable, demonstrating that the initial stage of intracortical synaptic transmission of thalamocortical information can be altered by previous activity. However, the inhibitory network within L4 of V1 may act as an internal gate for induction of excitatory synaptic plasticity, thus providing either high fidelity throughput to supragranular layers or transmittal of a modified signal subject to recent activity-dependent plasticity. To evaluate this possibility, we compared the induction of synaptic plasticity using classical extracellular stimulation protocols that recruit a combination of excitatory and inhibitory synapses with stimulation of a single excitatory neuron onto a L4 cell. In order to induce plasticity, we paired pre- and postsynaptic activity (with the onset of postsynaptic spiking leading the presynaptic activation by 10ms) using extracellular stimulation (ECS) in acute slices of primary visual cortex and comparing the outcomes with our previously published results in which an identical protocol was used to induce synaptic plasticity between individual pre- and postsynaptic L4 excitatory neurons. Our results indicate that pairing of ECS with spiking in a L4 neuron fails to induce plasticity in L4-L4 connections if synaptic inhibition is intact. However, application of a similar pairing protocol under GABAARs inhibition by bath application of 2μM bicuculline does induce robust synaptic plasticity, long term potentiation (LTP) or long term depression (LTD), similar to our results with pairing of pre- and postsynaptic activation between individual excitatory L4 neurons in which inhibitory connections are not activated. These results are consistent with the well-established observation that inhibition limits the capacity for induction of plasticity at excitatory synapses and that pre- and postsynaptic activation at a fixed time interval can result in a variable range of plasticity outcomes. However, in the current study by virtue of having two sets of experimental data, we have provided a new insight into these processes. By randomly mixing the assorting of individual L4 neurons according to the frequency distribution of the experimentally determined plasticity outcome distribution based on the calculated convergence of multiple individual L4 neurons onto a single postsynaptic L4 neuron, we were able to compare then actual ECS plasticity outcomes to those predicted by randomly mixing individual pairs of neurons. Interestingly, the observed plasticity profiles with ECS cannot account for the random assortment of plasticity behaviors of synaptic connections between individual cell pairs. These results suggest that connections impinging onto a single postsynaptic cell may be grouped according to plasticity states.
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Ladépêche, Laurent; Planagumà, Jesús; Thakur, Shreyasi; Suárez, Irina; Hara, Makoto; Borbely, Joseph Steven; Sandoval, Angel; Laparra-Cuervo, Lara; Dalmau, Josep; Lakadamyali, Melike
2018-06-26
Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe neuropsychiatric disorder mediated by autoantibodies against the GluN1 subunit of the NMDAR. Patients' antibodies cause cross-linking and internalization of NMDAR, but the synaptic events leading to depletion of NMDAR are poorly understood. Using super-resolution microscopy, we studied the effects of the autoantibodies on the nanoscale distribution of NMDAR in cultured neurons. Our findings show that, under control conditions, NMDARs form nanosized objects and patients' antibodies increase the clustering of synaptic and extrasynaptic receptors inside the nano-objects. This clustering is subunit specific and predominantly affects GluN2B-NMDARs. Following internalization, the remaining surface NMDARs return to control clustering levels but are preferentially retained at the synapse. Monte Carlo simulations using a model in which antibodies induce NMDAR cross-linking and disruption of interactions with other proteins recapitulated these results. Finally, activation of EphB2 receptor partially antagonized the antibody-mediated disorganization of the nanoscale surface distribution of NMDARs. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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Bullen, Anwen; West, Timothy; Moores, Carolyn; Ashmore, Jonathan; Fleck, Roland A; MacLellan-Gibson, Kirsty; Forge, Andrew
2015-07-15
The ways in which cell architecture is modelled to meet cell function is a poorly understood facet of cell biology. To address this question, we have studied the cytoarchitecture of a cell with highly specialised organisation, the cochlear inner hair cell (IHC), using multiple hierarchies of three-dimensional (3D) electron microscopy analyses. We show that synaptic terminal distribution on the IHC surface correlates with cell shape, and the distribution of a highly organised network of membranes and mitochondria encompassing the infranuclear region of the cell. This network is juxtaposed to a population of small vesicles, which represents a potential new source of neurotransmitter vesicles for replenishment of the synapses. Structural linkages between organelles that underlie this organisation were identified by high-resolution imaging. Taken together, these results describe a cell-encompassing network of membranes and mitochondria present in IHCs that support efficient coding and transmission of auditory signals. Such techniques also have the potential for clarifying functionally specialised cytoarchitecture of other cell types. © 2015. Published by The Company of Biologists Ltd.
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Python scripting in the nengo simulator.
Stewart, Terrence C; Tripp, Bryan; Eliasmith, Chris
2009-01-01
Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.
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John, Rohit Abraham; Liu, Fucai; Chien, Nguyen Anh; Kulkarni, Mohit R; Zhu, Chao; Fu, Qundong; Basu, Arindam; Liu, Zheng; Mathews, Nripan
2018-06-01
Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS 2 ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Speed, Haley E; Kouser, Mehreen; Xuan, Zhong; Reimers, Jeremy M; Ochoa, Christine F; Gupta, Natasha; Liu, Shunan; Powell, Craig M
2015-07-01
SHANK3 (also known as PROSAP2) is a postsynaptic scaffolding protein at excitatory synapses in which mutations and deletions have been implicated in patients with idiopathic autism, Phelan-McDermid (aka 22q13 microdeletion) syndrome, and other neuropsychiatric disorders. In this study, we have created a novel mouse model of human autism caused by the insertion of a single guanine nucleotide into exon 21 (Shank3(G)). The resulting frameshift causes a premature STOP codon and loss of major higher molecular weight Shank3 isoforms at the synapse. Shank3(G/G) mice exhibit deficits in hippocampus-dependent spatial learning, impaired motor coordination, altered response to novelty, and sensory processing deficits. At the cellular level, Shank3(G/G) mice also exhibit impaired hippocampal excitatory transmission and plasticity as well as changes in baseline NMDA receptor-mediated synaptic responses. This work identifies clear alterations in synaptic function and behavior in a novel, genetically accurate mouse model of autism mimicking an autism-associated insertion mutation. Furthermore, these findings lay the foundation for future studies aimed to validate and study region-selective and temporally selective genetic reversal studies in the Shank3(G/G) mouse that was engineered with such future experiments in mind. Copyright © 2015 the authors 0270-6474/15/359648-18$15.00/0.
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Python Scripting in the Nengo Simulator
Stewart, Terrence C.; Tripp, Bryan; Eliasmith, Chris
2008-01-01
Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models. PMID:19352442
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Role of BMP receptor traffic in synaptic growth defects in an ALS model.
Deshpande, Mugdha; Feiger, Zachary; Shilton, Amanda K; Luo, Christina C; Silverman, Ethan; Rodal, Avital A
2016-10-01
TAR DNA-binding protein 43 (TDP-43) is genetically and functionally linked to amyotrophic lateral sclerosis (ALS) and regulates transcription, splicing, and transport of thousands of RNA targets that function in diverse cellular pathways. In ALS, pathologically altered TDP-43 is believed to lead to disease by toxic gain-of-function effects on RNA metabolism, as well as by sequestering endogenous TDP-43 and causing its loss of function. However, it is unclear which of the numerous cellular processes disrupted downstream of TDP-43 dysfunction lead to neurodegeneration. Here we found that both loss and gain of function of TDP-43 in Drosophila cause a reduction of synaptic growth-promoting bone morphogenic protein (BMP) signaling at the neuromuscular junction (NMJ). Further, we observed a shift of BMP receptors from early to recycling endosomes and increased mobility of BMP receptor-containing compartments at the NMJ. Inhibition of the recycling endosome GTPase Rab11 partially rescued TDP-43-induced defects in BMP receptor dynamics and distribution and suppressed BMP signaling, synaptic growth, and larval crawling defects. Our results indicate that defects in receptor traffic lead to neuronal dysfunction downstream of TDP-43 misregulation and that rerouting receptor traffic may be a viable strategy for rescuing neurological impairment. © 2016 Deshpande, Feiger, et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
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Yasuyama, Kouji; Meinertzhagen, Ian A
2010-02-01
Recent studies in Drosophila melanogaster indicate that the neuropeptide pigment-dispersing factor (PDF) is an important output signal from a set of major clock neurons, s-LN(v)s (small ventral lateral neurons), which transmit the circadian phase to subsets of other clock neurons, DNs (dorsal neurons). Both s-LN(v)s and DNs have fiber projections to the dorsal protocerebrum of the brain, so that this area is a conspicuous locus for coupling between different subsets of clock neurons. To unravel the neural circuits underlying the fly's circadian rhythms, we examined the detailed subcellular morphology of the PDF-positive fibers of the s-LN(v)s in the dorsal protocerebrum, focusing on their synaptic connections, using preembedding immunoelectron microscopy. To examine the distribution of synapses, we also reconstructed the three-dimensional morphology of PDF-positive varicosities from fiber profiles in the dorsal protocerebrum. The varicosities contained large dense-core vesicles (DCVs), and also numerous small clear vesicles, forming divergent output synapses onto unlabeled neurites. The DCVs apparently dock at nonsynaptic sites, suggesting their nonsynaptic release. In addition, a 3D reconstruction revealed the presence of input synapses onto the PDF-positive fibers. These were detected less frequently than output sites. These observations suggest that the PDF-positive clock neurons receive neural inputs directly through synaptic connections in the dorsal protocerebrum, in addition to supplying dual outputs, either synaptic or via paracrine release of the DCV contents, to unidentified target neurons.
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Propagating waves can explain irregular neural dynamics.
Keane, Adam; Gong, Pulin
2015-01-28
Cortical neurons in vivo fire quite irregularly. Previous studies about the origin of such irregular neural dynamics have given rise to two major models: a balanced excitation and inhibition model, and a model of highly synchronized synaptic inputs. To elucidate the network mechanisms underlying synchronized synaptic inputs and account for irregular neural dynamics, we investigate a spatially extended, conductance-based spiking neural network model. We show that propagating wave patterns with complex dynamics emerge from the network model. These waves sweep past neurons, to which they provide highly synchronized synaptic inputs. On the other hand, these patterns only emerge from the network with balanced excitation and inhibition; our model therefore reconciles the two major models of irregular neural dynamics. We further demonstrate that the collective dynamics of propagating wave patterns provides a mechanistic explanation for a range of irregular neural dynamics, including the variability of spike timing, slow firing rate fluctuations, and correlated membrane potential fluctuations. In addition, in our model, the distributions of synaptic conductance and membrane potential are non-Gaussian, consistent with recent experimental data obtained using whole-cell recordings. Our work therefore relates the propagating waves that have been widely observed in the brain to irregular neural dynamics. These results demonstrate that neural firing activity, although appearing highly disordered at the single-neuron level, can form dynamical coherent structures, such as propagating waves at the population level. Copyright © 2015 the authors 0270-6474/15/351591-15$15.00/0.
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Liu, Ji; Conde, Kristie; Zhang, Peng; Lilascharoen, Varoth; Xu, Zihui; Lim, Byung Kook; Seeley, Randy J.; Zhu, Julius J.; Scott, Michael M.; Pang, Zhiping P.
2017-01-01
SUMMARY Glucagon Like Peptide 1 (GLP-1)-expressing neurons in the hindbrain send robust projections to the paraventricular nucleus of the hypothalamus (PVN), which is involved in the regulation of food intake. Here, we describe that stimulation of GLP-1 afferent fibers within the PVN is sufficient to suppress food intake independent of glutamate release. We also show that GLP-1 receptor (GLP-1R) activation augments excitatory synaptic strength in PVN corticotropin-releasing hormone (CRH) neurons, with GLP-1R activation promoting a protein kinase A (PKA) dependent signaling cascade leading to phosphorylation of serine S845 on GluA1 AMPA receptors and their trafficking to the plasma membrane. Finally, we show that postnatal depletion of GLP-1R in the PVN increases food intake and causes obesity. This study provides a comprehensive multi-level (circuit, synaptic, and molecular) explanation of how food intake behavior and body weight are regulated by endogenous central GLP-1. PMID:29056294
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Estimating neuronal connectivity from axonal and dendritic density fields
van Pelt, Jaap; van Ooyen, Arjen
2013-01-01
Neurons innervate space by extending axonal and dendritic arborizations. When axons and dendrites come in close proximity of each other, synapses between neurons can be formed. Neurons vary greatly in their morphologies and synaptic connections with other neurons. The size and shape of the arborizations determine the way neurons innervate space. A neuron may therefore be characterized by the spatial distribution of its axonal and dendritic “mass.” A population mean “mass” density field of a particular neuron type can be obtained by averaging over the individual variations in neuron geometries. Connectivity in terms of candidate synaptic contacts between neurons can be determined directly on the basis of their arborizations but also indirectly on the basis of their density fields. To decide when a candidate synapse can be formed, we previously developed a criterion defining that axonal and dendritic line pieces should cross in 3D and have an orthogonal distance less than a threshold value. In this paper, we developed new methodology for applying this criterion to density fields. We show that estimates of the number of contacts between neuron pairs calculated from their density fields are fully consistent with the number of contacts calculated from the actual arborizations. However, the estimation of the connection probability and the expected number of contacts per connection cannot be calculated directly from density fields, because density fields do not carry anymore the correlative structure in the spatial distribution of synaptic contacts. Alternatively, these two connectivity measures can be estimated from the expected number of contacts by using empirical mapping functions. The neurons used for the validation studies were generated by our neuron simulator NETMORPH. An example is given of the estimation of average connectivity and Euclidean pre- and postsynaptic distance distributions in a network of neurons represented by their population mean density fields. PMID:24324430
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Influence of ionotropic receptor location on their dynamics at glutamatergic synapses.
Allam, Sushmita L; Bouteiller, Jean-Marie C; Hu, Eric; Greget, Renaud; Ambert, Nicolas; Bischoff, Serge; Baudry, Michel; Berger, Theodore W
2012-01-01
In this paper we study the effects of the location of ionotropic receptors, especially AMPA and NMDA receptors, on their function at excitatory glutamatergic synapses. As few computational models only allow to evaluate the influence of receptor location on state transition and receptor dynamics, we present an elaborate computational model of a glutamatergic synapse that takes into account detailed parametric models of ionotropic receptors along with glutamate diffusion within the synaptic cleft. Our simulation results underscore the importance of the wide spread distribution of AMPA receptors which is required to avoid massive desensitization of these receptors following a single glutamate release event while NMDA receptor location is potentially optimal relative to the glutamate release site thus, emphasizing the contribution of location dependent effects of the two major ionotropic receptors to synaptic efficacy.
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Synaptic electronics: materials, devices and applications.
Kuzum, Duygu; Yu, Shimeng; Wong, H-S Philip
2013-09-27
In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.
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Hardware friendly probabilistic spiking neural network with long-term and short-term plasticity.
Hsieh, Hung-Yi; Tang, Kea-Tiong
2013-12-01
This paper proposes a probabilistic spiking neural network (PSNN) with unimodal weight distribution, possessing long- and short-term plasticity. The proposed algorithm is derived by both the arithmetic gradient decent calculation and bioinspired algorithms. The algorithm is benchmarked by the Iris and Wisconsin breast cancer (WBC) data sets. The network features fast convergence speed and high accuracy. In the experiment, the PSNN took not more than 40 epochs for convergence. The average testing accuracy for Iris and WBC data is 96.7% and 97.2%, respectively. To test the usefulness of the PSNN to real world application, the PSNN was also tested with the odor data, which was collected by our self-developed electronic nose (e-nose). Compared with the algorithm (K-nearest neighbor) that has the highest classification accuracy in the e-nose for the same odor data, the classification accuracy of the PSNN is only 1.3% less but the memory requirement can be reduced at least 40%. All the experiments suggest that the PSNN is hardware friendly. First, it requires only nine-bits weight resolution for training and testing. Second, the PSNN can learn complex data sets with a little number of neurons that in turn reduce the cost of VLSI implementation. In addition, the algorithm is insensitive to synaptic noise and the parameter variation induced by the VLSI fabrication. Therefore, the algorithm can be implemented by either software or hardware, making it suitable for wider application.
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Calibration of neural networks using genetic algorithms, with application to optimal path planning
NASA Technical Reports Server (NTRS)
Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel
1987-01-01
Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.
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NASA Astrophysics Data System (ADS)
Takiguchi, Yu; Toyoda, Haruyoshi
2017-11-01
We report here an algorithm for calculating a hologram to be employed in a high-access speed microscope for observing sensory-driven synaptic activity across all inputs to single living neurons in an intact cerebral cortex. The system is based on holographic multi-beam generation using a two-dimensional phase-only spatial light modulator to excite multiple locations in three dimensions with a single hologram. The hologram was calculated with a three-dimensional weighted iterative Fourier transform method using the Ewald sphere restriction to increase the calculation speed. Our algorithm achieved good uniformity of three dimensionally generated excitation spots; the standard deviation of the spot intensities was reduced by a factor of two compared with a conventional algorithm.
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NASA Astrophysics Data System (ADS)
Takiguchi, Yu; Toyoda, Haruyoshi
2018-06-01
We report here an algorithm for calculating a hologram to be employed in a high-access speed microscope for observing sensory-driven synaptic activity across all inputs to single living neurons in an intact cerebral cortex. The system is based on holographic multi-beam generation using a two-dimensional phase-only spatial light modulator to excite multiple locations in three dimensions with a single hologram. The hologram was calculated with a three-dimensional weighted iterative Fourier transform method using the Ewald sphere restriction to increase the calculation speed. Our algorithm achieved good uniformity of three dimensionally generated excitation spots; the standard deviation of the spot intensities was reduced by a factor of two compared with a conventional algorithm.
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USAF 1990 Research Initiation Program. Volume 3
1992-06-25
the reinforcement outward, both on the entrance and exit sides. The reinforcement was bent outward like a membrane under internal prressure. This led...potential of a neuron. C: cell capacitance. R: membrane resistance. w: synaptic weight. h(x): sigmoidal function describing the firing rate. y: external...Proof: The uncertain closed loop plant may be written as x(t)=(A+AA)x(t)+(Ao+ AAo )x(t-T) (B+AB)F(C AC)x(t) =(A+BFC)x(t)+(AA+ABFC+BFAC+ABFAC)x(t)+(Ao+Ao)X(t
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Bit-serial neuroprocessor architecture
NASA Technical Reports Server (NTRS)
Tawel, Raoul (Inventor)
2001-01-01
A neuroprocessor architecture employs a combination of bit-serial and serial-parallel techniques for implementing the neurons of the neuroprocessor. The neuroprocessor architecture includes a neural module containing a pool of neurons, a global controller, a sigmoid activation ROM look-up-table, a plurality of neuron state registers, and a synaptic weight RAM. The neuroprocessor reduces the number of neurons required to perform the task by time multiplexing groups of neurons from a fixed pool of neurons to achieve the successive hidden layers of a recurrent network topology.
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Higher-order neural networks, Polyà polynomials, and Fermi cluster diagrams
NASA Astrophysics Data System (ADS)
Kürten, K. E.; Clark, J. W.
2003-09-01
The problem of controlling higher-order interactions in neural networks is addressed with techniques commonly applied in the cluster analysis of quantum many-particle systems. For multineuron synaptic weights chosen according to a straightforward extension of the standard Hebbian learning rule, we show that higher-order contributions to the stimulus felt by a given neuron can be readily evaluated via Polyà’s combinatoric group-theoretical approach or equivalently by exploiting a precise formal analogy with fermion diagrammatics.
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Synaptic Plasticity In Mammalian Gravity Sensors: Preliminary Results From SLS-2
NASA Technical Reports Server (NTRS)
Ross, M. D.; Hargens, Alan R. (Technical Monitor)
1996-01-01
Sensory conflict is the prevalent theoretical explanation for space adaptation syndrome. This ultrastructural study tests the hypothesis that peripheral gravity sensors (maculae) play a role. Results were obtained from the medial part of utricular maculae of adult rats exposed to microgravity for 14 days, and from controls. Means and statistical significance of synapse counts were calculated using SUPERANOVA(Trademark) and Scheffe's procedure for post-hoc comparisons. Preliminary findings are from 2 sets of 100 serial sections for each dataset. Synapses were doubled numerically in type II hair cells of utricular maculae collected on day 13 inflight compared to controls (11.4 +/- 7.1 vs. 5.3 +/- 3.8; p < 0.0001). Flight mean synaptic number declined rapidly postflight and became comparable to means of controls. Synapses also increased numerically in type I cells inflight (2.4 +/- 1.6 vs. 1.7 +/- 1.0; p < 0.0341). Postflight there were no significant differences in counts. Results concerning shifts in ribbon type and distribution are also largely replicating previous findings from flight studies. Results indicate that mammalian maculae are adaptive endorgans that retain the property of synaptic plasticity into the adult stage. Macular plasticity has clinical implications for balance disorders of peripheral origin.
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Flexible Proton-Gated Oxide Synaptic Transistors on Si Membrane.
Zhu, Li Qiang; Wan, Chang Jin; Gao, Ping Qi; Liu, Yang Hui; Xiao, Hui; Ye, Ji Chun; Wan, Qing
2016-08-24
Ion-conducting materials have received considerable attention for their applications in fuel cells, electrochemical devices, and sensors. Here, flexible indium zinc oxide (InZnO) synaptic transistors with multiple presynaptic inputs gated by proton-conducting phosphorosilicate glass-based electrolyte films are fabricated on ultrathin Si membranes. Transient characteristics of the proton gated InZnO synaptic transistors are investigated, indicating stable proton-gating behaviors. Short-term synaptic plasticities are mimicked on the proposed proton-gated synaptic transistors. Furthermore, synaptic integration regulations are mimicked on the proposed synaptic transistor networks. Spiking logic modulations are realized based on the transition between superlinear and sublinear synaptic integration. The multigates coupled flexible proton-gated oxide synaptic transistors may be interesting for neuroinspired platforms with sophisticated spatiotemporal information processing.
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Cycles of insanity and creativity within contemplative neural systems.
Thaler, Stephen L
2016-09-01
Random connection weight disturbances within an assembly of artificial neural networks (ANN) drive a progression of activation patterns that are tantamount to the memories and ideas nucleating within the brain's cortex. The numerical evaluation of these pattern-based notions by another, more placid system of ANNs governs the magnitude of weight disturbances administered to the former assembly, that perturbative intensity in turn controlling the novelty of the resulting ideational stream as well as the retention of newly formed concepts. In search of solution patterns to posed problems, such collaborating neural systems autonomously cycle between two extremes in mean synaptic perturbation level. The higher limit, characterized by chaos and inattentiveness to exogenous input patterns, is the regime in which ideas first form and incubate. The lower bound, marked by relative synaptic tranquility, is favorable to the reactivation and reinforcement of concepts first seeded during heightened perturbation. When considering this synthetic neural architecture as a cognitive model, the proposed source of such synaptic fluctuations is volume neurotransmitter release within cortex where both ideational and critic nets are commingled. As a result of their overlap, not only are the generative cortical networks suffused with neurotransmitters, but also those functioning in a critic role, leading to altered 'opinions' about the perturbation-driven stream of consciousness that then govern the injection of neurotransmitters into cortex. The likely effect of such chemical feedback is that the brain constantly cycles between states of idea generating chaos and perception stabilizing tranquility in much the same way that creative artificial neural systems do. Postulating that ideas are potentially useful or interesting false memories born within such turmoil, creativity appears to take place through a cyclic process consisting of alternating phases of (1) cognitive incapacitation, during which confabulatory notions incubate, and (2) synaptic calm when these incubated thoughts reemerge and reinforce themselves as they are then recognized for their value by a lucid perceptual apparatus. Extremes in such cycling, especially within the former dysfunctional phase, would be problematic from a mental health perspective. Whereas the literature is replete with findings linking creativity and various psychopathologies, the main hypothesis advanced herein is that the neurodynamics of both phenomena are the same. If vindicated, this theory may lead to advanced treatments that could potentially boost creativity as well as safeguard against the associated cognitive and psychological disorders, all through control of just one parameter, the difference between cortical concentrations of excitatory and inhibitory neurotransmitters. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.
Ly, Cheng; Marsat, Gary
2018-02-01
Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.
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Development of Ca2+ hotspots between Lymnaea neurons during synaptogenesis
Feng, Zhong-Ping; Grigoriev, Nikita; Munno, David; Lukowiak, Ken; MacVicar, Brian A; Goldberg, Jeffrey I; Syed, Naweed I
2002-01-01
Calcium (Ca2+) channel clustering at specific presynaptic sites is a hallmark of mature synapses. However, the spatial distribution patterns of Ca2+ channels at newly formed synapses have not yet been demonstrated. Similarly, it is unclear whether Ca2+ ‘hotspots’ often observed at the presynaptic sites are indeed target cell contact specific and represent a specialized mechanism by which Ca2+ channels are targeted to select synaptic sites. Utilizing both soma–soma paired (synapsed) and single neurons from the mollusk Lymnaea, we have tested the hypothesis that differential gradients of voltage-dependent Ca2+ signals develop in presynaptic neuron at its contact point with the postsynaptic neuron; and that these Ca2+ hotspots are target cell contact specific. Fura-2 imaging, or two-photon laser scanning microscopy of Calcium Green, was coupled with electrophysiological techniques to demonstrate that voltage-induced Ca2+ gradients (hotspots) develop in the presynaptic cell at its contact point with the postsynaptic neuron, but not in unpaired single cells. The incidence of Ca2+ hotspots coincided with the appearance of synaptic transmission between the paired cells, and these gradients were target cell contact specific. In contrast, the voltage-induced Ca2+ signal in unpaired neurons was uniformly distributed throughout the somata; a similar pattern of Ca2+ gradient was observed in the presynaptic neuron when it was soma–soma paired with a non-synaptic partner cell. Moreover, voltage clamp recording techniques, in conjunction with a fast, optical differential perfusion system, were used to demonstrate that the total whole-cell Ca2+ (or Ba2+) current density in single and paired cells was not significantly different. However, the amplitude of Ba2+ current was significantly higher in the presynaptic cell at its contact side with the postsynaptic neurons, compared with non-contacted regions. In summary, this study demonstrates that voltage-induced Ca2+ hotspots develop in the presynaptic cell, concomitant with the appearance of synaptic transmission between the soma–soma paired cells. The appearance of Ca2+ gradients in presynaptic neurons is target cell contact specific and is probably due to a spatial redistribution of existing channels during synaptogenesis. PMID:11850501
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2013-01-01
Background Previous work showed differences in the polysynaptic activation of GABAergic synapses during corticostriatal suprathreshold responses in direct and indirect striatal projection neurons (dSPNs and iSPNs). Here, we now show differences and similarities in the polysynaptic activation of cortical glutamatergic synapses on the same responses. Corticostriatal contacts have been extensively studied. However, several questions remain unanswered, e.g.: what are the differences and similarities in the responses to glutamate in dSPNs and iSPNs? Does glutamatergic synaptic activation exhibits a distribution of latencies over time in vitro? That would be a strong suggestion of polysynaptic cortical convergence. What is the role of kainate receptors in corticostriatal transmission? Current-clamp recordings were used to answer these questions. One hypothesis was: if prolonged synaptic activation distributed along time was present, then it would be mainly generated from the cortex, and not from the striatum. Results By isolating responses from AMPA-receptors out of the complex suprathreshold response of SPNs, it is shown that a single cortical stimulus induces early and late synaptic activation lasting hundreds of milliseconds. Prolonged responses depended on cortical stimulation because they could not be elicited using intrastriatal stimulation, even if GABAergic transmission was blocked. Thus, the results are not explained by differences in evoked inhibition. Moreover, inhibitory participation was larger after cortical than after intrastriatal stimulation. A strong activation of interneurons was obtained from the cortex, demonstrating that polysynaptic activation includes the striatum. Prolonged kainate (KA) receptor responses were also elicited from the cortex. Responses of dSPNs and iSPNs did not depend on the cortical area stimulated. In contrast to AMPA-receptors, responses from NMDA- and KA-receptors do not exhibit early and late responses, but generate slow responses that contribute to plateau depolarizations. Conclusions As it has been established in previous physiological studies in vivo, synaptic invasion over different latencies, spanning hundreds of milliseconds after a single stimulus strongly indicates convergent polysynaptic activation. Interconnected cortical neurons converging on the same SPNs may explain prolonged corticostriatal responses. Glutamate receptors participation in these responses is described as well as differences and similarities between dSPNs and iSPNs. PMID:23782743
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Del Prete, Dolores; Lombino, Franco; Liu, Xinran; D'Adamio, Luciano
2014-01-01
Amyloid Precursor Protein (APP) is a type I membrane protein that undergoes extensive processing by secretases, including BACE1. Although mutations in APP and genes that regulate processing of APP, such as PSENs and BRI2/ITM2B, cause dementias, the normal function of APP in synaptic transmission, synaptic plasticity and memory formation is poorly understood. To grasp the biochemical mechanisms underlying the function of APP in the central nervous system, it is important to first define the sub-cellular localization of APP in synapses and the synaptic interactome of APP. Using biochemical and electron microscopy approaches, we have found that APP is localized in pre-synaptic vesicles, where it is processed by Bace1. By means of a proteomic approach, we have characterized the synaptic interactome of the APP intracellular domain. We focused on this region of APP because in vivo data underline the central functional and pathological role of the intracellular domain of APP. Consistent with the expression of APP in pre-synaptic vesicles, the synaptic APP intracellular domain interactome is predominantly constituted by pre-synaptic, rather than post-synaptic, proteins. This pre-synaptic interactome of the APP intracellular domain includes proteins expressed on pre-synaptic vesicles such as the vesicular SNARE Vamp2/Vamp1 and the Ca2+ sensors Synaptotagmin-1/Synaptotagmin-2, and non-vesicular pre-synaptic proteins that regulate exocytosis, endocytosis and recycling of pre-synaptic vesicles, such as target-membrane-SNAREs (Syntaxin-1b, Syntaxin-1a, Snap25 and Snap47), Munc-18, Nsf, α/β/γ-Snaps and complexin. These data are consistent with a functional role for APP, via its carboxyl-terminal domain, in exocytosis, endocytosis and/or recycling of pre-synaptic vesicles.
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Orlando, Marta; Ravasenga, Tiziana; Petrini, Enrica Maria; Falqui, Andrea; Marotta, Roberto; Barberis, Andrea
2017-10-23
Both excitatory and inhibitory synaptic contacts display activity dependent dynamic changes in their efficacy that are globally termed synaptic plasticity. Although the molecular mechanisms underlying glutamatergic synaptic plasticity have been extensively investigated and described, those responsible for inhibitory synaptic plasticity are only beginning to be unveiled. In this framework, the ultrastructural changes of the inhibitory synapses during plasticity have been poorly investigated. Here we combined confocal fluorescence microscopy (CFM) with high resolution scanning electron microscopy (HRSEM) to characterize the fine structural rearrangements of post-synaptic GABA A Receptors (GABA A Rs) at the nanometric scale during the induction of inhibitory long-term potentiation (iLTP). Additional electron tomography (ET) experiments on immunolabelled hippocampal neurons allowed the visualization of synaptic contacts and confirmed the reorganization of post-synaptic GABA A R clusters in response to chemical iLTP inducing protocol. Altogether, these approaches revealed that, following the induction of inhibitory synaptic potentiation, GABA A R clusters increase in size and number at the post-synaptic membrane with no other major structural changes of the pre- and post-synaptic elements.
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Stauch, Kelly L; Purnell, Phillip R; Fox, Howard S
2014-05-02
Synaptic mitochondria are essential for maintaining calcium homeostasis and producing ATP, processes vital for neuronal integrity and synaptic transmission. Synaptic mitochondria exhibit increased oxidative damage during aging and are more vulnerable to calcium insult than nonsynaptic mitochondria. Why synaptic mitochondria are specifically more susceptible to cumulative damage remains to be determined. In this study, the generation of a super-SILAC mix that served as an appropriate internal standard for mouse brain mitochondria mass spectrometry based analysis allowed for the quantification of the proteomic differences between synaptic and nonsynaptic mitochondria isolated from 10-month-old mice. We identified a total of 2260 common proteins between synaptic and nonsynaptic mitochondria of which 1629 were annotated as mitochondrial. Quantitative proteomic analysis of the proteins common between synaptic and nonsynaptic mitochondria revealed significant differential expression of 522 proteins involved in several pathways including oxidative phosphorylation, mitochondrial fission/fusion, calcium transport, and mitochondrial DNA replication and maintenance. In comparison to nonsynaptic mitochondria, synaptic mitochondria exhibited increased age-associated mitochondrial DNA deletions and decreased bioenergetic function. These findings provide insights into synaptic mitochondrial susceptibility to damage.
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2015-01-01
Synaptic mitochondria are essential for maintaining calcium homeostasis and producing ATP, processes vital for neuronal integrity and synaptic transmission. Synaptic mitochondria exhibit increased oxidative damage during aging and are more vulnerable to calcium insult than nonsynaptic mitochondria. Why synaptic mitochondria are specifically more susceptible to cumulative damage remains to be determined. In this study, the generation of a super-SILAC mix that served as an appropriate internal standard for mouse brain mitochondria mass spectrometry based analysis allowed for the quantification of the proteomic differences between synaptic and nonsynaptic mitochondria isolated from 10-month-old mice. We identified a total of 2260 common proteins between synaptic and nonsynaptic mitochondria of which 1629 were annotated as mitochondrial. Quantitative proteomic analysis of the proteins common between synaptic and nonsynaptic mitochondria revealed significant differential expression of 522 proteins involved in several pathways including oxidative phosphorylation, mitochondrial fission/fusion, calcium transport, and mitochondrial DNA replication and maintenance. In comparison to nonsynaptic mitochondria, synaptic mitochondria exhibited increased age-associated mitochondrial DNA deletions and decreased bioenergetic function. These findings provide insights into synaptic mitochondrial susceptibility to damage. PMID:24708184
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Alcohol induces synaptotagmin 1 expression in neurons via activation of heat shock factor 1.
Varodayan, F P; Pignataro, L; Harrison, N L
2011-10-13
Many synapses within the central nervous system are sensitive to ethanol. Although alcohol is known to affect the probability of neurotransmitter release in specific brain regions, the effects of alcohol on the underlying synaptic vesicle fusion machinery have been little studied. To identify a potential pathway by which ethanol can regulate neurotransmitter release, we investigated the effects of acute alcohol exposure (1-24 h) on the expression of the gene encoding synaptotagmin 1 (Syt1), a synaptic protein that binds calcium to directly trigger vesicle fusion. Syt1 was identified in a microarray screen as a gene that may be sensitive to alcohol and heat shock. We found that Syt1 mRNA and protein expression are rapidly and robustly up-regulated by ethanol in mouse cortical neurons, and that the distribution of Syt1 protein along neuronal processes is also altered. Syt1 mRNA up-regulation is dependent on the activation of the transcription factor heat shock factor 1 (HSF1). The transfection of a constitutively active Hsf1 construct into neurons stimulates Syt1 transcription, while transfection of Hsf1 small interfering RNA (siRNA) or a constitutively inactive Hsf1 construct into neurons attenuates the induction of Syt1 by ethanol. This suggests that the activation of HSF1 can induce Syt1 expression and that this may be a mechanism by which alcohol regulates neurotransmitter release during brief exposures. Further analysis revealed that a subset of the genes encoding the core synaptic vesicle fusion (soluble NSF (N-ethylmaleimide-sensitive factor) attachment protein receptor; SNARE) proteins share this property of induction by ethanol, suggesting that alcohol may trigger a specific coordinated adaptation in synaptic function. This molecular mechanism could explain some of the changes in synaptic function that occur following alcohol administration and may be an important step in the process of neuronal adaptation to alcohol. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.
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Fuzzy logic and neural networks in artificial intelligence and pattern recognition
NASA Astrophysics Data System (ADS)
Sanchez, Elie
1991-10-01
With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.
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Oxide-based synaptic transistors gated by solution-processed gelatin electrolytes
NASA Astrophysics Data System (ADS)
He, Yinke; Sun, Jia; Qian, Chuan; Kong, Ling-An; Gou, Guangyang; Li, Hongjian
2017-04-01
In human brain, a large number of neurons are connected via synapses. Simulation of the synaptic behaviors using electronic devices is the most important step for neuromorphic systems. In this paper, proton conducting gelatin electrolyte-gated oxide field-effect transistors (FETs) were used for emulating synaptic functions, in which the gate electrode is regarded as pre-synaptic neuron and the channel layer as the post-synaptic neuron. In analogy to the biological synapse, a potential spike can be applied at the gate electrode and trigger ionic motion in the gelatin electrolyte, which in turn generates excitatory post-synaptic current (EPSC) in the channel layer. Basic synaptic behaviors including spike time-dependent EPSC, paired-pulse facilitation (PPF), self-adaptation, and frequency-dependent synaptic transmission were successfully mimicked. Such ionic/electronic hybrid devices are beneficial for synaptic electronics and brain-inspired neuromorphic systems.
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Liu, Jackie J; Grace, Kevin P; Horner, Richard L; Cortez, Miguel A; Shao, Yiwen; Jia, Zhengping
2017-04-07
Human studies demonstrate that sleep impairment is a concurrent comorbidity of autism spectrum disorders (ASD), but its etiology remains largely uncertain. One of the prominent theories of ASD suggests that an imbalance in synaptic excitation/inhibition may contribute to various aspects of ASD, including sleep impairments. Following the identification of Nlgn3 R451C mutation in patients with ASD, its effects on synaptic transmission and social behaviours have been examined extensively in the mouse model. However, the contributory role of this mutation to sleep impairments in ASD remains unknown. In this study, we showed that Nlgn3 R451C knock-in mice, an established genetic model for ASD, exhibited normal duration and distribution of sleep/wake states but significantly altered electroencephalography (EEG) power spectral profiles for wake and sleep.
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Three-Dimensional Analysis of Spiny Dendrites Using Straightening and Unrolling Transforms
Morales, Juan; Benavides-Piccione, Ruth; Pastor, Luis; Yuste, Rafael; DeFelipe, Javier
2014-01-01
Current understanding of the synaptic organization of the brain depends to a large extent on knowledge about the synaptic inputs to the neurons. Indeed, the dendritic surfaces of pyramidal cells (the most common neuron in the cerebral cortex) are covered by thin protrusions named dendritic spines. These represent the targets of most excitatory synapses in the cerebral cortex and therefore, dendritic spines prove critical in learning, memory and cognition. This paper presents a new method that facilitates the analysis of the 3D structure of spine insertions in dendrites, providing insight on spine distribution patterns. This method is based both on the implementation of straightening and unrolling transformations to move the analysis process to a planar, unfolded arrangement, and on the design of DISPINE, an interactive environment that supports the visual analysis of 3D patterns. PMID:22644869
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Arc expression identifies the lateral amygdala fear memory trace
Gouty-Colomer, L A; Hosseini, B; Marcelo, I M; Schreiber, J; Slump, D E; Yamaguchi, S; Houweling, A R; Jaarsma, D; Elgersma, Y; Kushner, S A
2016-01-01
Memories are encoded within sparsely distributed neuronal ensembles. However, the defining cellular properties of neurons within a memory trace remain incompletely understood. Using a fluorescence-based Arc reporter, we were able to visually identify the distinct subset of lateral amygdala (LA) neurons activated during auditory fear conditioning. We found that Arc-expressing neurons have enhanced intrinsic excitability and are preferentially recruited into newly encoded memory traces. Furthermore, synaptic potentiation of thalamic inputs to the LA during fear conditioning is learning-specific, postsynaptically mediated and highly localized to Arc-expressing neurons. Taken together, our findings validate the immediate-early gene Arc as a molecular marker for the LA neuronal ensemble recruited during fear learning. Moreover, these results establish a model of fear memory formation in which intrinsic excitability determines neuronal selection, whereas learning-related encoding is governed by synaptic plasticity. PMID:25802982
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Anstötz, Max; Lee, Sun Kyong; Maccaferri, Gianmaria
2018-05-28
By taking advantage of calcium imaging and electrophysiology, we provide direct pharmacological evidence for the functional expression of TRPV1 channels in hippocampal Cajal-Retzius cells. Application of the TRPV1 activator capsaicin powerfully enhances spontaneous synaptic transmission in the hippocampal layers that are innervated by the axons of Cajal-Retzius cells. Capsaicin-triggered calcium responses and membrane currents in Cajal-Retzius cells, as well as layer-specific modulation of spontaneous synaptic transmission, are absent when the drug is applied to slices prepared from TRPV1 - / - animals. We discuss the implications of the functional expression of TRPV1 channels in Cajal-Retzius cells and of the observed TRPV1-dependent layer-specific modulation of synaptic transmission for physiological and pathological network processing. The vanilloid receptor TRPV1 forms complex polymodal channels that are expressed by sensory neurons and play a critical role in nociception. Their distribution pattern and functions in cortical circuits are, however, much less understood. Although TRPV1 reporter mice have suggested that, in the hippocampus, TRPV1 is predominantly expressed by Cajal-Retzius cells (CRs), direct functional evidence is missing. As CRs powerfully excite GABAergic interneurons of the molecular layers, TRPV1 could play important roles in the regulation of layer-specific processing. Here, we have taken advantage of calcium imaging with the genetically encoded indicator GCaMP6s and patch-clamp techniques to study the responses of hippocampal CRs to the activation of TRPV1 by capsaicin, and have compared the effect of TRPV1 stimulation on synaptic transmission in layers innervated or non-innervated by CRs. Capsaicin induced both calcium responses and membrane currents in ∼50% of the cell tested. Neither increases of intracellular calcium nor whole-cell currents were observed in the presence of the TRPV1 antagonists capsazepine/Ruthenium Red or in slices prepared from TRPV1 knockout mice. We also report a powerful TRPV1-dependent enhancement of spontaneous synaptic transmission onto interneurons with dendritic trees confined to the layers innervated by CRs. In conclusion, our work establishes that functional TRPV1 is expressed by a significant fraction of CRs and we propose that TRPV1 activity may regulate layer-specific synaptic transmission in the hippocampus. Lastly, as CR density decreases during postnatal development, we also propose that functional TRPV1 receptors may be related to mechanisms involved in CR progressive reduction by calcium-dependent toxicity/apoptosis. © 2018 The Authors. The Journal of Physiology © 2018 The Physiological Society.
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Hayakawa, Tetsu; Takanaga, Akinori; Tanaka, Koichi; Maeda, Seishi; Seki, Makoto
2004-04-23
Almost all parasympathetic preganglionic motor neurons contain acetylcholine, whereas quite a few motor neurons in the dorsal motor nucleus of the vagus (DMV) contain dopamine. We determined the distribution and ultrastructure of these dopaminergic neurons with double-labeling immunohistochemistry for tyrosine hydroxylase (TH) and the retrograde tracer cholera toxin subunit b (CTb) following its injection into the stomach. A few TH-immunoreactive (TH-ir) neurons were found in the rostral half of the DMV, while a moderate number of these neurons were found in the caudal half. Most of the TH-ir neurons (78.4%) were double-labeled for CTb in the half of the DMV caudal to the area postrema, but only a few TH-ir neurons (5.5%) were double-labeled in the rostral half. About 20% of gastric motor neurons showed TH-immunoreactivity in the caudal half of the DMV, but only 0.3% were TH-ir in the rostral half. In all gastric motor neurons, 8.1% were double-labeled for TH. The ultrastructure of the TH-ir neurons in the caudal DMV was determined with immuno-gold-silver labeling. The TH-ir neurons were small (20.4 x 12.4 microm), round or oval, and contained numerous mitochondria, many free ribosomes, several Golgi apparatuses, a round nucleus and a few Nissl bodies. The average number of axosomatic terminals per section was 4.0. More than half of them contained round synaptic vesicles and made asymmetric synaptic contacts (Gray's type I). Most of the axodendritic terminals contacting TH-ir dendrites were Gray's type I (90%), but a few contained pleomorphic vesicles and made symmetric synaptic contacts (Gray's type II).
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Learning rules for spike timing-dependent plasticity depend on dendritic synapse location.
Letzkus, Johannes J; Kampa, Björn M; Stuart, Greg J
2006-10-11
Previous studies focusing on the temporal rules governing changes in synaptic strength during spike timing-dependent synaptic plasticity (STDP) have paid little attention to the fact that synaptic inputs are distributed across complex dendritic trees. During STDP, propagation of action potentials (APs) back to the site of synaptic input is thought to trigger plasticity. However, in pyramidal neurons, backpropagation of single APs is decremental, whereas high-frequency bursts lead to generation of distal dendritic calcium spikes. This raises the question whether STDP learning rules depend on synapse location and firing mode. Here, we investigate this issue at synapses between layer 2/3 and layer 5 pyramidal neurons in somatosensory cortex. We find that low-frequency pairing of single APs at positive times leads to a distance-dependent shift to long-term depression (LTD) at distal inputs. At proximal sites, this LTD could be converted to long-term potentiation (LTP) by dendritic depolarizations suprathreshold for BAC-firing or by high-frequency AP bursts. During AP bursts, we observed a progressive, distance-dependent shift in the timing requirements for induction of LTP and LTD, such that distal synapses display novel timing rules: they potentiate when inputs are activated after burst onset (negative timing) but depress when activated before burst onset (positive timing). These findings could be explained by distance-dependent differences in the underlying dendritic voltage waveforms driving NMDA receptor activation during STDP induction. Our results suggest that synapse location within the dendritic tree is a crucial determinant of STDP, and that synapses undergo plasticity according to local rather than global learning rules.
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Knoblauch, Andreas; Körner, Edgar; Körner, Ursula; Sommer, Friedrich T.
2014-01-01
Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are “potential synapses” defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the “effectual network connectivity”, that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect. PMID:24858841
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Yu, Mei; Zhang, Yuan; Chen, Xiaoyu; Zhang, Tao
2016-01-01
The aim of this study was to examine whether amantadine (AMA), as a low-affinity noncompetitive N-methyl-d-aspartate (NMDA) receptor antagonist, is able to improve cognitive deficits caused by chronic stress in rats. Male Wistar rats were divided into four groups: control, control + AMA, stress and stress + AMA groups. The chronic stress model combined chronic unpredictable stress (CUS) with isolated feeding. Animals were exposed to CUS continued for 21 days. AMA (25 mg/kg) was administrated p.o. for 20 days from the 4th day of CUS to the 23rd. Weight and sucrose consumption were measured during model establishing period. Spatial memory was evaluated using the Morris water maze (MWM) test. Following MWM testing, both long-term potentiation (LTP) and depotentiation were recorded in the hippocampal CA1 region. NR2B and postsynaptic density protein 95 (PSD-95) proteins were measured by Western-blot analysis. AMA increased weight and sucrose consumption of stressed rats. Spatial memory and reversal learning in stressed rats were impaired relative to controls, whereas AMA significantly attenuated cognitive impairment. AMA also mitigated the chronic stress-induced impairment of hippocampal synaptic plasticity, in which both the LTP and depotentiation were significantly inhibited in stressed rats. Moreover, AMA enhanced the expression of hippocampal NR2B and PSD-95 in stressed rats. The data suggest that AMA may be an effective therapeutic agent for depression-like symptoms and associated cognitive disturbances.
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Chrol-Cannon, Joseph; Jin, Yaochu
2014-01-01
Reservoir computing provides a simpler paradigm of training recurrent networks by initialising and adapting the recurrent connections separately to a supervised linear readout. This creates a problem, though. As the recurrent weights and topology are now separated from adapting to the task, there is a burden on the reservoir designer to construct an effective network that happens to produce state vectors that can be mapped linearly into the desired outputs. Guidance in forming a reservoir can be through the use of some established metrics which link a number of theoretical properties of the reservoir computing paradigm to quantitative measures that can be used to evaluate the effectiveness of a given design. We provide a comprehensive empirical study of four metrics; class separation, kernel quality, Lyapunov's exponent and spectral radius. These metrics are each compared over a number of repeated runs, for different reservoir computing set-ups that include three types of network topology and three mechanisms of weight adaptation through synaptic plasticity. Each combination of these methods is tested on two time-series classification problems. We find that the two metrics that correlate most strongly with the classification performance are Lyapunov's exponent and kernel quality. It is also evident in the comparisons that these two metrics both measure a similar property of the reservoir dynamics. We also find that class separation and spectral radius are both less reliable and less effective in predicting performance.
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14 CFR 25.1519 - Weight, center of gravity, and weight distribution.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Weight, center of gravity, and weight... Information Operating Limitations § 25.1519 Weight, center of gravity, and weight distribution. The airplane weight, center of gravity, and weight distribution limitations determined under §§ 25.23 through 25.27...
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14 CFR 25.1519 - Weight, center of gravity, and weight distribution.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Weight, center of gravity, and weight... Information Operating Limitations § 25.1519 Weight, center of gravity, and weight distribution. The airplane weight, center of gravity, and weight distribution limitations determined under §§ 25.23 through 25.27...
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14 CFR 25.1519 - Weight, center of gravity, and weight distribution.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 14 Aeronautics and Space 1 2012-01-01 2012-01-01 false Weight, center of gravity, and weight... Information Operating Limitations § 25.1519 Weight, center of gravity, and weight distribution. The airplane weight, center of gravity, and weight distribution limitations determined under §§ 25.23 through 25.27...
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14 CFR 25.1519 - Weight, center of gravity, and weight distribution.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 14 Aeronautics and Space 1 2013-01-01 2013-01-01 false Weight, center of gravity, and weight... Information Operating Limitations § 25.1519 Weight, center of gravity, and weight distribution. The airplane weight, center of gravity, and weight distribution limitations determined under §§ 25.23 through 25.27...
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14 CFR 25.1519 - Weight, center of gravity, and weight distribution.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 14 Aeronautics and Space 1 2014-01-01 2014-01-01 false Weight, center of gravity, and weight... Information Operating Limitations § 25.1519 Weight, center of gravity, and weight distribution. The airplane weight, center of gravity, and weight distribution limitations determined under §§ 25.23 through 25.27...
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A Model of Bidirectional Synaptic Plasticity: From Signaling Network to Channel Conductance
ERIC Educational Resources Information Center
Castellani, Gastone C.; Quinlan, Elizabeth M.; Bersani, Ferdinando; Cooper, Leon N.; Shouval, Harel Z.
2005-01-01
In many regions of the brain, including the mammalian cortex, the strength of synaptic transmission can be bidirectionally regulated by cortical activity (synaptic plasticity). One line of evidence indicates that long-term synaptic potentiation (LTP) and long-term synaptic depression (LTD), correlate with the phosphorylation/dephosphorylation of…
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On the Dynamics of the Spontaneous Activity in Neuronal Networks
Bonifazi, Paolo; Ruaro, Maria Elisabetta; Torre, Vincent
2007-01-01
Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABAA receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics. PMID:17502919
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Aguayo, Felipe I; Pacheco, Aníbal A; García-Rojo, Gonzalo J; Pizarro-Bauerle, Javier A; Doberti, Ana V; Tejos, Macarena; García-Pérez, María A; Rojas, Paulina S; Fiedler, Jenny L
2018-05-16
A single stress exposure facilitates memory formation through neuroplastic processes that reshape excitatory synapses in the hippocampus, probably requiring changes in extracellular matrix components. We tested the hypothesis that matrix metalloproteinase 9 (MMP-9), an enzyme that degrades components of extracellular matrix and synaptic proteins such as β-dystroglycan (β-DG 43 ), changes their activity and distribution in rat hippocampus during the acute stress response. After 2.5 h of restraint stress, we found (i) increased MMP-9 levels and potential activity in whole hippocampal extracts, accompanied by β-DG 43 cleavage, and (ii) a significant enhancement of MMP-9 immunoreactivity in dendritic fields such as stratum radiatum and the molecular layer of hippocampus. After 24 h of stress, we found that (i) MMP-9 net activity rises at somatic field, i.e., stratum pyramidale and granule cell layers, and also at synaptic field, mainly stratum radiatum and the molecular layer of hippocampus, and (ii) hippocampal synaptoneurosome fractions are enriched with MMP-9, without variation of its potential enzymatic activity, in accordance with the constant level of cleaved β-DG 43 . These findings indicate that stress triggers a peculiar timing response in the MMP-9 levels, net activity, and subcellular distribution in the hippocampus, suggesting its involvement in the processing of substrates during the stress response.
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Ca2+ Dependence of Synaptic Vesicle Endocytosis.
Leitz, Jeremy; Kavalali, Ege T
2016-10-01
Ca(2+)-dependent synaptic vesicle recycling is essential for structural homeostasis of synapses and maintenance of neurotransmission. Although, the executive role of intrasynaptic Ca(2+) transients in synaptic vesicle exocytosis is well established, identifying the exact role of Ca(2+) in endocytosis has been difficult. In some studies, Ca(2+) has been suggested as an essential trigger required to initiate synaptic vesicle retrieval, whereas others manipulating synaptic Ca(2+) concentrations reported a modulatory role for Ca(2+) leading to inhibition or acceleration of endocytosis. Molecular studies of synaptic vesicle endocytosis, on the other hand, have consistently focused on the roles of Ca(2+)-calmodulin dependent phosphatase calcineurin and synaptic vesicle protein synaptotagmin as potential Ca(2+) sensors for endocytosis. Most studies probing the role of Ca(2+) in endocytosis have relied on measurements of synaptic vesicle retrieval after strong stimulation. Strong stimulation paradigms elicit fusion and retrieval of multiple synaptic vesicles and therefore can be affected by several factors besides the kinetics and duration of Ca(2+) signals that include the number of exocytosed vesicles and accumulation of released neurotransmitters thus altering fusion and retrieval processes indirectly via retrograde signaling. Studies monitoring single synaptic vesicle endocytosis may help resolve this conundrum as in these settings the impact of Ca(2+) on synaptic fusion probability can be uncoupled from its putative role on synaptic vesicle retrieval. Future experiments using these single vesicle approaches will help dissect the specific role(s) of Ca(2+) and its sensors in synaptic vesicle endocytosis. © The Author(s) 2015.
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NASA Astrophysics Data System (ADS)
Li, Qiang; Wang, Zhi; Le, Yansi; Sun, Chonghui; Song, Xiaojia; Wu, Chongqing
2016-10-01
Neuromorphic engineering has a wide range of applications in the fields of machine learning, pattern recognition, adaptive control, etc. Photonics, characterized by its high speed, wide bandwidth, low power consumption and massive parallelism, is an ideal way to realize ultrafast spiking neural networks (SNNs). Synaptic plasticity is believed to be critical for learning, memory and development in neural circuits. Experimental results have shown that changes of synapse are highly dependent on the relative timing of pre- and postsynaptic spikes. Synaptic plasticity in which presynaptic spikes preceding postsynaptic spikes results in strengthening, while the opposite timing results in weakening is called antisymmetric spike-timing-dependent plasticity (STDP) learning rule. And synaptic plasticity has the opposite effect under the same conditions is called antisymmetric anti-STDP learning rule. We proposed and experimentally demonstrated an optical implementation of neural learning algorithms, which can achieve both of antisymmetric STDP and anti-STDP learning rule, based on the cross-gain modulation (XGM) within a single semiconductor optical amplifier (SOA). The weight and height of the potentitation and depression window can be controlled by adjusting the injection current of the SOA, to mimic the biological antisymmetric STDP and anti-STDP learning rule more realistically. As the injection current increases, the width of depression and potentitation window decreases and height increases, due to the decreasing of recovery time and increasing of gain under a stronger injection current. Based on the demonstrated optical STDP circuit, ultrafast learning in optical SNNs can be realized.
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TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.
Mareschal, Denis; French, Robert M
2017-01-05
Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).
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TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning
French, Robert M.
2017-01-01
Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872375
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Peng, X-X; Lister, A; Rabinowitsch, A; Kolaric, R; Cabeza de Vaca, S; Ziff, E B; Carr, K D
2015-06-04
Weight-loss dieting often leads to loss of control, rebound weight gain, and is a risk factor for binge pathology. Based on findings that food restriction (FR) upregulates sucrose-induced trafficking of glutamatergic AMPA receptors to the nucleus accumbens (NAc) postsynaptic density (PSD), this study was an initial test of the hypothesis that episodic "breakthrough" intake of forbidden food during dieting interacts with upregulated mechanisms of synaptic plasticity to increase reward-driven feeding. Ad libitum (AL) fed and FR subjects consumed a limited amount of 10% sucrose, or had access to water, every other day for 10 occasions. Beginning three weeks after return of FR rats to AL feeding, when 24-h chow intake and rate of body weight gain had normalized, subjects with a history of sucrose intake during FR consumed more sucrose during a four week intermittent access protocol than the two AL groups and the group that had access to water during FR. In an experiment that substituted noncontingent administration of d-amphetamine for sucrose, FR subjects displayed an enhanced locomotor response during active FR but a blunted response, relative to AL subjects, during recovery from FR. This result suggests that the enduring increase in sucrose consumption is unlikely to be explained by residual enhancing effects of FR on dopamine signaling. In a biochemical experiment which paralleled the sucrose behavioral experiment, rats with a history of sucrose intake during FR displayed increased abundance of pSer845-GluA1, GluA2, and GluA3 in the NAc PSD relative to rats with a history of FR without sucrose access and rats that had been AL throughout, whether they had a history of episodic sucrose intake or not. A history of FR, with or without a history of sucrose intake, was associated with increased abundance of GluA1. A terminal 15-min bout of sucrose intake produced a further increase in pSer845-GluA1 and GluA2 in subjects with a history of sucrose intake during FR. Generally, neither a history of sucrose intake nor a terminal bout of sucrose intake affected AMPA receptor abundance in the NAc PSD of AL subjects. Together, these results are consistent with the hypothesis, but the functional contribution of increased synaptic incorporation of AMPA receptors remains to be established. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.
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Trueta, Citlali; De-Miguel, Francisco F
2012-01-01
We review the evidence of exocytosis from extrasynaptic sites in the soma, dendrites, and axonal varicosities of central and peripheral neurons of vertebrates and invertebrates, with emphasis on somatic exocytosis, and how it contributes to signaling in the nervous system. The finding of secretory vesicles in extrasynaptic sites of neurons, the presence of signaling molecules (namely transmitters or peptides) in the extracellular space outside synaptic clefts, and the mismatch between exocytosis sites and the location of receptors for these molecules in neurons and glial cells, have long suggested that in addition to synaptic communication, transmitters are released, and act extrasynaptically. The catalog of these molecules includes low molecular weight transmitters such as monoamines, acetylcholine, glutamate, gama-aminobutiric acid (GABA), adenosine-5-triphosphate (ATP), and a list of peptides including substance P, brain-derived neurotrophic factor (BDNF), and oxytocin. By comparing the mechanisms of extrasynaptic exocytosis of different signaling molecules by various neuron types we show that it is a widespread mechanism for communication in the nervous system that uses certain common mechanisms, which are different from those of synaptic exocytosis but similar to those of exocytosis from excitable endocrine cells. Somatic exocytosis has been measured directly in different neuron types. It starts after high-frequency electrical activity or long experimental depolarizations and may continue for several minutes after the end of stimulation. Activation of L-type calcium channels, calcium release from intracellular stores and vesicle transport towards the plasma membrane couple excitation and exocytosis from small clear or large dense core vesicles in release sites lacking postsynaptic counterparts. The presence of synaptic and extrasynaptic exocytosis endows individual neurons with a wide variety of time- and space-dependent communication possibilities. Extrasynaptic exocytosis may be the major source of signaling molecules producing volume transmission and by doing so may be part of a long duration signaling mode in the nervous system.
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Prenatal caffeine intake differently affects synaptic proteins during fetal brain development.
Mioranzza, Sabrina; Nunes, Fernanda; Marques, Daniela M; Fioreze, Gabriela T; Rocha, Andréia S; Botton, Paulo Henrique S; Costa, Marcelo S; Porciúncula, Lisiane O
2014-08-01
Caffeine is the psychostimulant most consumed worldwide. However, little is known about its effects during fetal brain development. In this study, adult female Wistar rats received caffeine in drinking water (0.1, 0.3 and 1.0 g/L) during the active cycle in weekdays, two weeks before mating and throughout pregnancy. Cerebral cortex and hippocampus from embryonic stages 18 or 20 (E18 or E20, respectively) were collected for immunodetection of the following synaptic proteins: brain-derived neurotrophic factor (BDNF), TrkB receptor, Sonic Hedgehog (Shh), Growth Associated Protein 43 (GAP-43) and Synaptosomal-associated Protein 25 (SNAP-25). Besides, the estimation of NeuN-stained nuclei (mature neurons) and non-neuronal nuclei was verified in both brain regions and embryonic periods. Caffeine (1.0 g/L) decreased the body weight of embryos at E20. Cortical BDNF at E18 was decreased by caffeine (1.0 g/L), while it increased at E20, with no major effects on TrkB receptors. In the hippocampus, caffeine decreased TrkB receptor only at E18, with no effects on BDNF. Moderate and high doses of caffeine promoted an increase in Shh in both brain regions at E18, and in the hippocampus at E20. Caffeine (0.3g/L) decreased GAP-43 only in the hippocampus at E18. The NeuN-stained nuclei increased in the cortex at E20 by lower dose and in the hippocampus at E18 by moderate dose. Our data revealed that caffeine transitorily affect synaptic proteins during fetal brain development. The increased number of NeuN-stained nuclei by prenatal caffeine suggests a possible acceleration of the telencephalon maturation. Although some modifications in the synaptic proteins were transient, our data suggest that caffeine even in lower doses may alter the fetal brain development. Copyright © 2014 ISDN. Published by Elsevier Ltd. All rights reserved.
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Bailey, Jason A.; Ray, Balmiki; Greig, Nigel H.; Lahiri, Debomoy K.
2011-01-01
Overproduction of amyloid-β (Aβ) protein in the brain has been hypothesized as the primary toxic insult that, via numerous mechanisms, produces cognitive deficits in Alzheimer's disease (AD). Cholinesterase inhibition is a primary strategy for treatment of AD, and specific compounds of this class have previously been demonstrated to influence Aβ precursor protein (APP) processing and Aβ production. However, little information is available on the effects of rivastigmine, a dual acetylcholinesterase and butyrylcholinesterase inhibitor, on APP processing. As this drug is currently used to treat AD, characterization of its various activities is important to optimize its clinical utility. We have previously shown that rivastigmine can preserve or enhance neuronal and synaptic terminal markers in degenerating primary embryonic cerebrocortical cultures. Given previous reports on the effects of APP and Aβ on synapses, regulation of APP processing represents a plausible mechanism for the synaptic effects of rivastigmine. To test this hypothesis, we treated degenerating primary cultures with rivastigmine and measured secreted APP (sAPP) and Aβ. Rivastigmine treatment increased metabolic activity in these cultured cells, and elevated APP secretion. Analysis of the two major forms of APP secreted by these cultures, attributed to neurons or glia based on molecular weight showed that rivastigmine treatment significantly increased neuronal relative to glial secreted APP. Furthermore, rivastigmine treatment increased α-secretase cleaved sAPPα and decreased Aβ secretion, suggesting a therapeutic mechanism wherein rivastigmine alters the relative activities of the secretase pathways. Assessment of sAPP levels in rodent CSF following once daily rivastigmine administration for 21 days confirmed that elevated levels of APP in cell culture translated in vivo. Taken together, rivastigmine treatment enhances neuronal sAPP and shifts APP processing toward the α-secretase pathway in degenerating neuronal cultures, which mirrors the trend of synaptic proteins, and metabolic activity. PMID:21799757
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Bermejo, Marie Kristel; Milenkovic, Marija; Salahpour, Ali; Ramsey, Amy J
2014-09-03
Neuronal subcellular fractionation techniques allow the quantification of proteins that are trafficked to and from the synapse. As originally described in the late 1960's, proteins associated with the synaptic plasma membrane can be isolated by ultracentrifugation on a sucrose density gradient. Once synaptic membranes are isolated, the macromolecular complex known as the post-synaptic density can be subsequently isolated due to its detergent insolubility. The techniques used to isolate synaptic plasma membranes and post-synaptic density proteins remain essentially the same after 40 years, and are widely used in current neuroscience research. This article details the fractionation of proteins associated with the synaptic plasma membrane and post-synaptic density using a discontinuous sucrose gradient. Resulting protein preparations are suitable for western blotting or 2D DIGE analysis.
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Flores-Otero, Jacqueline; Davis, Robin L.
2011-01-01
Inherent in the design of the mammalian auditory system is the precision necessary to transduce complex sounds and transmit the resulting electrical signals to higher neural centers. Unique specializations in the organ of Corti are required to make this conversion, such that mechanical and electrical properties of hair cell receptors are tailored to their specific role in signal coding. Electrophysiological and immunocytochemical characterizations have shown that this principle also applies to neurons of the spiral ganglion, as evidenced by distinctly different firing features and synaptic protein distributions of neurons that innervate high- and low-frequency regions of the cochlea. However, understanding the fine structure of how these properties are distributed along the cochlear partition and within the type I and type II classes of spiral ganglion neurons is necessary to appreciate their functional significance fully. To address this issue, we assessed the localization of the postsynaptic AMPA receptor subunits GluR2 and GluR3 and the presynaptic protein synaptophysin by using immunocytochemical labeling in both postnatal and adult tissue. We report that these presynaptic and postsynaptic proteins are distributed oppositely in relation to the tonotopic map and that they are equally distributed in each neuronal class, thus having an overall gradation from one end of the cochlea to the other. For synaptophysin, an additional layer of heterogeneity was superimposed orthogonal to the tonotopic axis. The highest anti-synaptophysin antibody levels were observed within neurons located close to the scala tympani compared with those located close to the scala vestibuli. Furthermore, we noted that the protein distribution patterns observed in postnatal preparations were largely retained in adult tissue sections, indicating that these features characterize spiral ganglion neurons in the fully developed ear. PMID:21452215
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Diversity of neuropsin (KLK8)-dependent synaptic associativity in the hippocampal pyramidal neuron
Ishikawa, Yasuyuki; Tamura, Hideki; Shiosaka, Sadao
2011-01-01
Abstract Hippocampal early (E-) long-term potentiation (LTP) and long-term depression (LTD) elicited by a weak stimulus normally fades within 90 min. Late (L-) LTP and LTD elicited by strong stimuli continue for >180 min and require new protein synthesis to persist. If a strong tetanus is applied once to synaptic inputs, even a weak tetanus applied to another synaptic input can evoke persistent LTP. A synaptic tag is hypothesized to enable the capture of newly synthesized synaptic molecules. This process, referred to as synaptic tagging, is found between not only the same processes (i.e. E- and L-LTP; E- and L-LTD) but also between different processes (i.e. E-LTP and L-LTD; E-LTD and L-LTP) induced at two independent synaptic inputs (cross-tagging). However, the mechanisms of synaptic tag setting remain unclear. In our previous study, we found that synaptic associativity in the hippocampal Schaffer collateral pathway depended on neuropsin (kallikrein-related peptidase 8 or KLK8), a plasticity-related extracellular protease. In the present study, we investigated how neuropsin participates in synaptic tagging and cross-tagging. We report that neuropsin is involved in synaptic tagging during LTP at basal and apical dendritic inputs. Moreover, neuropsin is involved in synaptic tagging and cross-tagging during LTP at apical dendritic inputs via integrin β1 and calcium/calmodulin-dependent protein kinase II signalling. Thus, neuropsin is a candidate molecule for the LTP-specific tag setting and regulates the transformation of E- to L-LTP during both synaptic tagging and cross-tagging. PMID:21646406
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Meditations on birth weight: is it better to reduce the variance or increase the mean?
Haig, David
2003-07-01
A conceptual model is presented here in which the birth weight distribution is decomposed into a distribution of target weights and a distribution of perturbations from the target. The target weight is the adaptive goal of fetal development. In the simplest model, perinatal mortality is independent of variation in target weight and determined solely by the magnitude of the perturbation of birth weight from the target. In this model, mortality risk is concentrated in the tails of the birth weight distribution. A difference between populations in their distributions of target weights will be associated with a corresponding shift in their curves of weight-specific risk, without any difference between the populations in overall risk. In this model, risk would be reduced by decreasing the variance of the distribution of perturbations. The model is discussed in the context of the so-called "paradoxes of low birth weight."
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Non-synaptic receptors and transporters involved in brain functions and targets of drug treatment.
Vizi, E S; Fekete, A; Karoly, R; Mike, A
2010-06-01
Beyond direct synaptic communication, neurons are able to talk to each other without making synapses. They are able to send chemical messages by means of diffusion to target cells via the extracellular space, provided that the target neurons are equipped with high-affinity receptors. While synaptic transmission is responsible for the 'what' of brain function, the 'how' of brain function (mood, attention, level of arousal, general excitability, etc.) is mainly controlled non-synaptically using the extracellular space as communication channel. It is principally the 'how' that can be modulated by medicine. In this paper, we discuss different forms of non-synaptic transmission, localized spillover of synaptic transmitters, local presynaptic modulation and tonic influence of ambient transmitter levels on the activity of vast neuronal populations. We consider different aspects of non-synaptic transmission, such as synaptic-extrasynaptic receptor trafficking, neuron-glia communication and retrograde signalling. We review structural and functional aspects of non-synaptic transmission, including (i) anatomical arrangement of non-synaptic release sites, receptors and transporters, (ii) intravesicular, intra- and extracellular concentrations of neurotransmitters, as well as the spatiotemporal pattern of transmitter diffusion. We propose that an effective general strategy for efficient pharmacological intervention could include the identification of specific non-synaptic targets and the subsequent development of selective pharmacological tools to influence them.
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Sailamul, Pachaya; Jang, Jaeson; Paik, Se-Bum
2017-12-01
Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.
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Cell-specific gain modulation by synaptically released zinc in cortical circuits of audition.
Anderson, Charles T; Kumar, Manoj; Xiong, Shanshan; Tzounopoulos, Thanos
2017-09-09
In many excitatory synapses, mobile zinc is found within glutamatergic vesicles and is coreleased with glutamate. Ex vivo studies established that synaptically released (synaptic) zinc inhibits excitatory neurotransmission at lower frequencies of synaptic activity but enhances steady state synaptic responses during higher frequencies of activity. However, it remains unknown how synaptic zinc affects neuronal processing in vivo. Here, we imaged the sound-evoked neuronal activity of the primary auditory cortex in awake mice. We discovered that synaptic zinc enhanced the gain of sound-evoked responses in CaMKII-expressing principal neurons, but it reduced the gain of parvalbumin- and somatostatin-expressing interneurons. This modulation was sound intensity-dependent and, in part, NMDA receptor-independent. By establishing a previously unknown link between synaptic zinc and gain control of auditory cortical processing, our findings advance understanding about cortical synaptic mechanisms and create a new framework for approaching and interpreting the role of the auditory cortex in sound processing.
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Ribbon Synaptic Plasticity in Gravity Sensors of Rats Flown on Neurolab
NASA Technical Reports Server (NTRS)
Ross, Muriel D.; Varelas, Joseph
2003-01-01
Previous spaceflight experiments (Space Life Sciences-1 and -2 (SLS-1 and SLS-2)) first demonstrated the extraordinary ability of gravity sensor hair cells to change the number, kind, and distribution of connections (synapses) they make to other cells while in weightlessness. The number of synapses in hair cells in one part of the inner ear (the utricle) was markedly elevated on flight day 13 (FD13) of SLS-2. Unanswered questions, however, were whether these increases in synapses occur rapidly and whether they remain stable in weightlessness. The answers have implications for long-duration human space travel. If gravity sensors can adapt quickly, crews may be able to move easily between different gravity levels, since the sensors will adapt rapidly to weightlessness on the spacecraft and then back to Earth's gravity when the mission ends. This ability to adapt is also important for recovery from balance disorders. To further our understanding of this adaptive potential (a property called neuronal synaptic plasticity), the present Neurolab research was undertaken. Our experiment examined whether: (a) increases in synapses would remain stable throughout the flight, (b) changes in the number of synapses were uniform across different portions of the gravity sensors (the utricle and saccule), and (c) synaptic changes were similar for the different types of hair cells (Type I and Type II). Utricular and saccular maculae (the gravity-sensing portions of the inner ear) were collected in flight from rats on FD2 and FD14. Samples were also collected from control rats on the ground. Tissues were prepared for ultrastructural study. Hair cells and their ribbon synapses were examined in a transmission electron microscope. Synapses were counted in all hair cells in 50 consecutive sections that crossed the striolar zone. Results indicate that utricular hair cell synapses initially increased significantly in number in both types of hair cells by FD2. Counts declined by FD14, but the mean number of synapses in utricular Type II cells remained significantly higher than in the ground control rats. For saccular samples, synaptic number in Type I and Type II cells declined on FD2, but returned to near-baseline values by FD14. These findings indicate that: (a) synaptic plasticity occurs rapidly in weightlessness, and (b) synaptic changes are not identical for the two types of hair cells or for the two maculae.
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Bellucci, Arianna; Navarria, Laura; Falarti, Elisa; Zaltieri, Michela; Bono, Federica; Collo, Ginetta; Grazia, Maria; Missale, Cristina; Spano, PierFranco
2011-01-01
Alpha-synuclein, the major component of Lewy bodies, is thought to play a central role in the onset of synaptic dysfunctions in Parkinson's disease (PD). In particular, α-synuclein may affect dopaminergic neuron function as it interacts with a key protein modulating dopamine (DA) content at the synapse: the DA transporter (DAT). Indeed, recent evidence from our “in vitro” studies showed that α-synuclein aggregation decreases the expression and membrane trafficking of the DAT as the DAT is retained into α-synuclein-immunopositive inclusions. This notwithstanding, “in vivo” studies on PD animal models investigating whether DAT distribution is altered by the pathological overexpression and aggregation of α-synuclein are missing. By using the proximity ligation assay, a technique which allows the “in situ” visualization of protein-protein interactions, we studied the occurrence of alterations in the distribution of DAT/α-synuclein complexes in the SYN120 transgenic mouse model, showing insoluble α-synuclein aggregates into dopaminergic neurons of the nigrostriatal system, reduced striatal DA levels and an altered distribution of synaptic proteins in the striatum. We found that DAT/α-synuclein complexes were markedly redistributed in the striatum and substantia nigra of SYN120 mice. These alterations were accompanied by a significant increase of DAT striatal levels in transgenic animals when compared to wild type littermates. Our data indicate that, in the early pathogenesis of PD, α-synuclein acts as a fine modulator of the dopaminergic synapse by regulating the subcellular distribution of key proteins such as the DAT. PMID:22163275
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Contribution of sublinear and supralinear dendritic integration to neuronal computations
Tran-Van-Minh, Alexandra; Cazé, Romain D.; Abrahamsson, Therése; Cathala, Laurence; Gutkin, Boris S.; DiGregorio, David A.
2015-01-01
Nonlinear dendritic integration is thought to increase the computational ability of neurons. Most studies focus on how supralinear summation of excitatory synaptic responses arising from clustered inputs within single dendrites result in the enhancement of neuronal firing, enabling simple computations such as feature detection. Recent reports have shown that sublinear summation is also a prominent dendritic operation, extending the range of subthreshold input-output (sI/O) transformations conferred by dendrites. Like supralinear operations, sublinear dendritic operations also increase the repertoire of neuronal computations, but feature extraction requires different synaptic connectivity strategies for each of these operations. In this article we will review the experimental and theoretical findings describing the biophysical determinants of the three primary classes of dendritic operations: linear, sublinear, and supralinear. We then review a Boolean algebra-based analysis of simplified neuron models, which provides insight into how dendritic operations influence neuronal computations. We highlight how neuronal computations are critically dependent on the interplay of dendritic properties (morphology and voltage-gated channel expression), spiking threshold and distribution of synaptic inputs carrying particular sensory features. Finally, we describe how global (scattered) and local (clustered) integration strategies permit the implementation of similar classes of computations, one example being the object feature binding problem. PMID:25852470
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Liu, Ji; Conde, Kristie; Zhang, Peng; Lilascharoen, Varoth; Xu, Zihui; Lim, Byung Kook; Seeley, Randy J; Zhu, J Julius; Scott, Michael M; Pang, Zhiping P
2017-11-15
Glucagon-like Peptide 1 (GLP-1)-expressing neurons in the hindbrain send robust projections to the paraventricular nucleus of the hypothalamus (PVN), which is involved in the regulation of food intake. Here, we describe that stimulation of GLP-1 afferent fibers within the PVN is sufficient to suppress food intake independent of glutamate release. We also show that GLP-1 receptor (GLP-1R) activation augments excitatory synaptic strength in PVN corticotropin-releasing hormone (CRH) neurons, with GLP-1R activation promoting a protein kinase A (PKA)-dependent signaling cascade leading to phosphorylation of serine S845 on GluA1 AMPA receptors and their trafficking to the plasma membrane. Finally, we show that postnatal depletion of GLP-1R in the PVN increases food intake and causes obesity. This study provides a comprehensive multi-level (circuit, synaptic, and molecular) explanation of how food intake behavior and body weight are regulated by endogenous central GLP-1. VIDEO ABSTRACT. Copyright © 2017 Elsevier Inc. All rights reserved.
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Colangelo, Christopher M.; Ivosev, Gordana; Chung, Lisa; Abbott, Thomas; Shifman, Mark; Sakaue, Fumika; Cox, David; Kitchen, Rob R.; Burton, Lyle; Tate, Stephen A; Gulcicek, Erol; Bonner, Ron; Rinehart, Jesse; Nairn, Angus C.; Williams, Kenneth R.
2015-01-01
We present a comprehensive workflow for large scale (>1000 transitions/run) label-free LC-MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling (xMRM) that improves Signal/Noise by >2-fold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, Normalized Group Area Ratio (NGAR), MLR normalization, weighted regression analysis, and data dissemination through the Yale Protein Expression Database. As a proof of principle we developed a robust 90 minute LC-MRM assay for Mouse/Rat Post-Synaptic Density (PSD) fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label-free and SIS analyses. Overall, our first method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC-MRM assay. PMID:25476245
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Cho, Hwasuk; Son, Hyunwoo; Seong, Kihwan; Kim, Byungsub; Park, Hong-June; Sim, Jae-Yoon
2018-02-01
This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder-decoder pair for training and recovery of images with two-dimensional pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.
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Bhattacharya, Aditi; Kaphzan, Hanoch; Alvarez-Dieppa, Amanda C; Murphy, Jaclyn P; Pierre, Philippe; Klann, Eric
2012-10-18
Fragile X syndrome (FXS) is the leading inherited cause of autism and intellectual disability. Aberrant synaptic translation has been implicated in the etiology of FXS, but most lines of research on therapeutic strategies have targeted protein synthesis indirectly, far upstream of the translation machinery. We sought to perturb p70 ribosomal S6 kinase 1 (S6K1), a key translation initiation and elongation regulator, in FXS model mice. We found that genetic reduction of S6K1 prevented elevated phosphorylation of translational control molecules, exaggerated protein synthesis, enhanced mGluR-dependent long-term depression (LTD), weight gain, and macro-orchidism in FXS model mice. In addition, S6K1 deletion prevented immature dendritic spine morphology and multiple behavioral phenotypes, including social interaction deficits, impaired novel object recognition, and behavioral inflexibility. Our results support the model that dysregulated protein synthesis is the key causal factor in FXS and that restoration of normal translation can stabilize peripheral and neurological function in FXS. Copyright © 2012 Elsevier Inc. All rights reserved.
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Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity
Abbott, L. F.; Sompolinsky, Haim
2017-01-01
Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory tasks. We find that robustness to output noise requires synaptic connections to be in a balanced regime in which excitation and inhibition are strong and largely cancel each other. We evaluate the conditions required for this regime to exist and determine the properties of networks operating within it. A plausible synaptic plasticity rule for learning that balances weight configurations is presented. Our theory predicts an optimal ratio of the number of excitatory and inhibitory synapses for maximizing the encoding capacity of balanced networks for given statistics of afferent activations. Previous work has shown that balanced networks amplify spatiotemporal variability and account for observed asynchronous irregular states. Here we present a distinct type of balanced network that amplifies small changes in the impinging signals and emerges automatically from learning to perform neuronal and network functions robustly. PMID:29042519
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Mixed protonic and electronic conductors hybrid oxide synaptic transistors
NASA Astrophysics Data System (ADS)
Fu, Yang Ming; Zhu, Li Qiang; Wen, Juan; Xiao, Hui; Liu, Rui
2017-05-01
Mixed ionic and electronic conductor hybrid devices have attracted widespread attention in the field of brain-inspired neuromorphic systems. Here, mixed protonic and electronic conductor (MPEC) hybrid indium-tungsten-oxide (IWO) synaptic transistors gated by nanogranular phosphorosilicate glass (PSG) based electrolytes were obtained. Unique field-configurable proton self-modulation behaviors were observed on the MPEC hybrid transistor with extremely strong interfacial electric-double-layer effects. Temporally coupled synaptic plasticities were demonstrated on the MPEC hybrid IWO synaptic transistor, including depolarization/hyperpolarization, synaptic facilitation and depression, facilitation-stead/depression-stead behaviors, spiking rate dependent plasticity, and high-pass/low-pass synaptic filtering behaviors. MPEC hybrid synaptic transistors may find potential applications in neuron-inspired platforms.
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Rohrbough, Jeffrey; Rushton, Emma; Woodruff, Elvin; Fergestad, Tim; Vigneswaran, Krishanthan; Broadie, Kendal
2007-01-01
Formation and regulation of excitatory glutamatergic synapses is essential for shaping neural circuits throughout development. In a Drosophila genetic screen for synaptogenesis mutants, we identified mind the gap (mtg), which encodes a secreted, extracellular N-glycosaminoglycan-binding protein. MTG is expressed neuronally and detected in the synaptic cleft, and is required to form the specialized transsynaptic matrix that links the presynaptic active zone with the post-synaptic glutamate receptor (GluR) domain. Null mtg embryonic mutant synapses exhibit greatly reduced GluR function, and a corresponding loss of localized GluR domains. All known post-synaptic signaling/scaffold proteins functioning upstream of GluR localization are also grossly reduced or mislocalized in mtg mutants, including the dPix–dPak–Dock cascade and the Dlg/PSD-95 scaffold. Ubiquitous or neuronally targeted mtg RNA interference (RNAi) similarly reduce post-synaptic assembly, whereas post-synaptically targeted RNAi has no effect, indicating that presynaptic MTG induces and maintains the post-synaptic pathways driving GluR domain formation. These findings suggest that MTG is secreted from the presynaptic terminal to shape the extracellular synaptic cleft domain, and that the cleft domain functions to mediate transsynaptic signals required for post-synaptic development. PMID:17901219
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Lee, Joo Yeun; Geng, Junhua; Lee, Juhyun; Wang, Andrew R; Chang, Karen T
2017-03-22
Activity-induced synaptic structural modification is crucial for neural development and synaptic plasticity, but the molecular players involved in this process are not well defined. Here, we report that a protein named Shriveled (Shv) regulates synaptic growth and activity-dependent synaptic remodeling at the Drosophila neuromuscular junction. Depletion of Shv causes synaptic overgrowth and an accumulation of immature boutons. We find that Shv physically and genetically interacts with βPS integrin. Furthermore, Shv is secreted during intense, but not mild, neuronal activity to acutely activate integrin signaling, induce synaptic bouton enlargement, and increase postsynaptic glutamate receptor abundance. Consequently, loss of Shv prevents activity-induced synapse maturation and abolishes post-tetanic potentiation, a form of synaptic plasticity. Our data identify Shv as a novel trans-synaptic signal secreted upon intense neuronal activity to promote synapse remodeling through integrin receptor signaling. SIGNIFICANCE STATEMENT The ability of neurons to rapidly modify synaptic structure in response to neuronal activity, a process called activity-induced structural remodeling, is crucial for neuronal development and complex brain functions. The molecular players that are important for this fundamental biological process are not well understood. Here we show that the Shriveled (Shv) protein is required during development to maintain normal synaptic growth. We further demonstrate that Shv is selectively released during intense neuronal activity, but not mild neuronal activity, to acutely activate integrin signaling and trigger structural modifications at the Drosophila neuromuscular junction. This work identifies Shv as a key modulator of activity-induced structural remodeling and suggests that neurons use distinct molecular cues to differentially modulate synaptic growth and remodeling to meet synaptic demand. Copyright © 2017 the authors 0270-6474/17/373246-18$15.00/0.
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PSD-95 and PSD-93 Play Critical but Distinct Roles in Synaptic Scaling Up and Down
Sun, Qian; Turrigiano, Gina G.
2011-01-01
Synaptic scaling stabilizes neuronal firing through the homeostatic regulation of postsynaptic strength, but the mechanisms by which chronic changes in activity lead to bidirectional adjustments in synaptic AMPAR abundance are incompletely understood. Further, it remains unclear to what extent scaling up and scaling down utilize distinct molecular machinery. PSD-95 is a scaffold protein proposed to serve as a binding “slot” that determines synaptic AMPAR content, and synaptic PSD-95 abundance is regulated by activity, raising the possibility that activity-dependent changes in the synaptic abundance of PSD-95 or other MAGUKs drives the bidirectional changes in AMPAR accumulation during synaptic scaling. We found that synaptic PSD-95 and SAP102 (but not PSD-93) abundance were bidirectionally regulated by activity, but these changes were not sufficient to drive homeostatic changes in synaptic strength. Although not sufficient, the PSD-95-MAGUKs were necessary for synaptic scaling, but scaling up and down were differentially dependent on PSD-95 and PSD-93. Scaling down was completely blocked by reduced or enhanced PSD-95, through a mechanism that depended on the PDZ1/2 domains. In contrast scaling up could be supported by either PSD-95 or PSD-93 in a manner that depended on neuronal age, and was unaffected by a superabundance of PSD-95. Taken together, our data suggest that scaling up and down of quantal amplitude is not driven by changes in synaptic abundance of PSD-95-MAGUKs, but rather that the PSD-95 MAGUKs serve as critical synaptic organizers that utilize distinct protein-protein interactions to mediate homeostatic accumulation and loss of synaptic AMPAR. PMID:21543610
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Watabe, Tadashi; Naka, Sadahiro; Ikeda, Hayato; Horitsugi, Genki; Kanai, Yasukazu; Isohashi, Kayako; Ishibashi, Mana; Kato, Hiroki; Shimosegawa, Eku; Watabe, Hiroshi; Hatazawa, Jun
2014-01-01
Acetylcholinesterase (AChE) inhibitors have been used for patients with Alzheimer's disease. However, its pharmacokinetics in non-target organs other than the brain has not been clarified yet. The purpose of this study was to evaluate the relationship between the whole-body distribution of intravenously administered (11)C-Donepezil (DNP) and the AChE activity in the normal rat, with special focus on the adrenal glands. The distribution of (11)C-DNP was investigated by PET/CT in 6 normal male Wistar rats (8 weeks old, body weight = 220 ± 8.9 g). A 30-min dynamic scan was started simultaneously with an intravenous bolus injection of (11)C-DNP (45.0 ± 10.7 MBq). The whole-body distribution of the (11)C-DNP PET was evaluated based on the Vt (total distribution volume) by Logan-plot analysis. A fluorometric assay was performed to quantify the AChE activity in homogenized tissue solutions of the major organs. The PET analysis using Vt showed that the adrenal glands had the 2nd highest level of (11)C-DNP in the body (following the liver) (13.33 ± 1.08 and 19.43 ± 1.29 ml/cm(3), respectively), indicating that the distribution of (11)C-DNP was the highest in the adrenal glands, except for that in the excretory organs. The AChE activity was the third highest in the adrenal glands (following the small intestine and the stomach) (24.9 ± 1.6, 83.1 ± 3.0, and 38.5 ± 8.1 mU/mg, respectively), indicating high activity of AChE in the adrenal glands. We demonstrated the whole-body distribution of (11)C-DNP by PET and the AChE activity in the major organs by fluorometric assay in the normal rat. High accumulation of (11)C-DNP was observed in the adrenal glands, which suggested the risk of enhanced cholinergic synaptic transmission by the use of AChE inhibitors.
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Application of the weibull distribution function to the molecular weight distribution of cellulose
A. Broido; Hsiukang Yow
1977-01-01
The molecular weight distribution of a linear homologous polymer is usually obtained empirically for any particular sample. Sample-to-sample comparisons are made in terms of the weight- or number-average molecular weights and graphic displays of the distribution curves. Such treatment generally precludes data interpretations in which a distribution can be described in...
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DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.
Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P
2015-12-01
Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.
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Clustered Dynamics of Inhibitory Synapses and Dendritic Spines in the Adult Neocortex
Chen, Jerry L.; Villa, Katherine L; Cha, Jae Won; So, Peter T.C.; Kubota, Yoshiyuki; Nedivi, Elly
2012-01-01
A key feature of the mammalian brain is its capacity to adapt in response to experience, in part by remodeling of synaptic connections between neurons. Excitatory synapse rearrangements have been monitored in vivo by observation of dendritic spine dynamics, but lack of a vital marker for inhibitory synapses has precluded their observation. Here, we simultaneously monitor in vivo inhibitory synapse and dendritic spine dynamics across the entire dendritic arbor of pyramidal neurons in the adult mammalian cortex using large volume high-resolution dual color two-photon microscopy. We find that inhibitory synapses on dendritic shafts and spines differ in their distribution across the arbor and in their remodeling kinetics during normal and altered sensory experience. Further, we find inhibitory synapse and dendritic spine remodeling to be spatially clustered, and that clustering is influenced by sensory input. Our findings provide in vivo evidence for local coordination of inhibitory and excitatory synaptic rearrangements. PMID:22542188
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Kurz, Jonathan E; Rana, Annu; Parsons, J Travis; Churn, Severn B
2003-12-01
This study was performed to determine the effect of prolonged status epilepticus on the activity and subcellular location of a neuronally enriched, calcium-regulated enzyme, calcineurin. Brain fractions isolated from control animals and rats subjected to pilocarpine-induced status epilepticus were subjected to differential centrifugation. Specific subcellular fractions were tested for both calcineurin activity and enzyme content. Significant, status epilepticus-induced increases in calcineurin activity were found in homogenates, nuclear fractions, and crude synaptic membrane-enriched fractions isolated from both cortex and hippocampus. Additionally, significant increases in enzyme levels were observed in crude synaptic fractions as measured by Western analysis. Immunohistochemical studies revealed a status epilepticus-induced increase in calcineurin immunoreactivity in dendritic structures of pyramidal neurons of the hippocampus. The data demonstrate a status epilepticus-induced increase in calcineurin activity and concentration in the postsynaptic region of forebrain pyramidal neurons.
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Multiple vesicle recycling pathways in central synapses and their impact on neurotransmission
Kavalali, Ege T
2007-01-01
Short-term synaptic depression during repetitive activity is a common property of most synapses. Multiple mechanisms contribute to this rapid depression in neurotransmission including a decrease in vesicle fusion probability, inactivation of voltage-gated Ca2+ channels or use-dependent inhibition of release machinery by presynaptic receptors. In addition, synaptic depression can arise from a rapid reduction in the number of vesicles available for release. This reduction can be countered by two sources. One source is replenishment from a set of reserve vesicles. The second source is the reuse of vesicles that have undergone exocytosis and endocytosis. If the synaptic vesicle reuse is fast enough then it can replenish vesicles during a brief burst of action potentials and play a substantial role in regulating the rate of synaptic depression. In the last 5 years, we have examined the impact of synaptic vesicle reuse on neurotransmission using fluorescence imaging of synaptic vesicle trafficking in combination with electrophysiological detection of short-term synaptic plasticity. These studies have revealed that synaptic vesicle reuse shapes the kinetics of short-term synaptic depression in a frequency-dependent manner. In addition, synaptic vesicle recycling helps maintain the level of neurotransmission at steady state. Moreover, our studies showed that synaptic vesicle reuse is a highly plastic process as it varies widely among synapses and can adapt to changes in chronic activity levels. PMID:17690145
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Influence of Synaptic Depression on Memory Storage Capacity
NASA Astrophysics Data System (ADS)
Otsubo, Yosuke; Nagata, Kenji; Oizumi, Masafumi; Okada, Masato
2011-08-01
Synaptic efficacy between neurons is known to change within a short time scale dynamically. Neurophysiological experiments show that high-frequency presynaptic inputs decrease synaptic efficacy between neurons. This phenomenon is called synaptic depression, a short term synaptic plasticity. Many researchers have investigated how the synaptic depression affects the memory storage capacity. However, the noise has not been taken into consideration in their analysis. By introducing ``temperature'', which controls the level of the noise, into an update rule of neurons, we investigate the effects of synaptic depression on the memory storage capacity in the presence of the noise. We analytically compute the storage capacity by using a statistical mechanics technique called Self Consistent Signal to Noise Analysis (SCSNA). We find that the synaptic depression decreases the storage capacity in the case of finite temperature in contrast to the case of the low temperature limit, where the storage capacity does not change.
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Saheki, Yasunori; De Camilli, Pietro
2012-01-01
Neurons can sustain high rates of synaptic transmission without exhausting their supply of synaptic vesicles. This property relies on a highly efficient local endocytic recycling of synaptic vesicle membranes, which can be reused for hundreds, possibly thousands, of exo-endocytic cycles. Morphological, physiological, molecular, and genetic studies over the last four decades have provided insight into the membrane traffic reactions that govern this recycling and its regulation. These studies have shown that synaptic vesicle endocytosis capitalizes on fundamental and general endocytic mechanisms but also involves neuron-specific adaptations of such mechanisms. Thus, investigations of these processes have advanced not only the field of synaptic transmission but also, more generally, the field of endocytosis. This article summarizes current information on synaptic vesicle endocytosis with an emphasis on the underlying molecular mechanisms and with a special focus on clathrin-mediated endocytosis, the predominant pathway of synaptic vesicle protein internalization. PMID:22763746
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Two-Photon Scanning Photochemical Microscopy: Mapping Ligand-Gated Ion Channel Distributions
NASA Astrophysics Data System (ADS)
Denk, Winfried
1994-07-01
The locations and densities of ionotropic membrane receptors, which are responsible for receiving synaptic transmission throughout the nervous system, are of prime importance in understanding the function of neural circuits. It is shown that the highly localized liberation of "caged" neurotransmitters by two-photon absorption-mediated photoactivation can be used in conjunction with recording the induced whole-cell current to determine the distribution of ligand-gated ion channels. The technique is potentially sensitive enough to detect individual channels with diffraction-limited spatial resolution. Images of the distribution of nicotinic acetylcholine receptors on cultured BC3H1 cells were obtained using a photoactivatable precursor of the nicotinic agonist carbamoylcholine.
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Role of DHA in aging-related changes in mouse brain synaptic plasma membrane proteome.
Sidhu, Vishaldeep K; Huang, Bill X; Desai, Abhishek; Kevala, Karl; Kim, Hee-Yong
2016-05-01
Aging has been related to diminished cognitive function, which could be a result of ineffective synaptic function. We have previously shown that synaptic plasma membrane proteins supporting synaptic integrity and neurotransmission were downregulated in docosahexaenoic acid (DHA)-deprived brains, suggesting an important role of DHA in synaptic function. In this study, we demonstrate aging-induced synaptic proteome changes and DHA-dependent mitigation of such changes using mass spectrometry-based protein quantitation combined with western blot or messenger RNA analysis. We found significant reduction of 15 synaptic plasma membrane proteins in aging brains including fodrin-α, synaptopodin, postsynaptic density protein 95, synaptic vesicle glycoprotein 2B, synaptosomal-associated protein 25, synaptosomal-associated protein-α, N-methyl-D-aspartate receptor subunit epsilon-2 precursor, AMPA2, AP2, VGluT1, munc18-1, dynamin-1, vesicle-associated membrane protein 2, rab3A, and EAAT1, most of which are involved in synaptic transmission. Notably, the first 9 proteins were further reduced when brain DHA was depleted by diet, indicating that DHA plays an important role in sustaining these synaptic proteins downregulated during aging. Reduction of 2 of these proteins was reversed by raising the brain DHA level by supplementing aged animals with an omega-3 fatty acid sufficient diet for 2 months. The recognition memory compromised in DHA-depleted animals was also improved. Our results suggest a potential role of DHA in alleviating aging-associated cognitive decline by offsetting the loss of neurotransmission-regulating synaptic proteins involved in synaptic function. Published by Elsevier Inc.
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2014-10-01
Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Autism and autism spectrum disorders (ASD) are complex neurodevelopmental ...1. INTRODUCTION: Autism and autism spectrum disorders (ASD) are complex neurodevelopmental diseases that affect about 1% of children in the...and neurons. 2. KEYWORDS: Autism spectrum disorder, ASD, neurodevelopmental disease, disease modeling, induced pluripotent stem cell, iPS
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Staufen 2 Regulates mGluR Long-Term Depression and Map1b mRNA Distribution in Hippocampal Neurons
ERIC Educational Resources Information Center
Lebeau, Genevieve; Miller, Linda C.; Tartas, Maylis; McAdam, Robyn; Laplante, Isabel; Badeaux, Frederique; DesGroseillers, Luc; Sossin, Wayne S.; Lacaille, Jean-Claude
2011-01-01
The two members of the Staufen family of RNA-binding proteins, Stau1 and Stau2, are present in distinct ribonucleoprotein complexes and associate with different mRNAs. Stau1 is required for protein synthesis-dependent long-term potentiation (L-LTP) in hippocampal pyramidal cells. However, the role of Stau2 in synaptic plasticity remains…
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Heuser, J E; Reese, T S
1973-05-01
When the nerves of isolated frog sartorius muscles were stimulated at 10 Hz, synaptic vesicles in the motor nerve terminals became transiently depleted. This depletion apparently resulted from a redistribution rather than disappearance of synaptic vesicle membrane, since the total amount of membrane comprising these nerve terminals remained constant during stimulation. At 1 min of stimulation, the 30% depletion in synaptic vesicle membrane was nearly balanced by an increase in plasma membrane, suggesting that vesicle membrane rapidly moved to the surface as it might if vesicles released their content of transmitter by exocytosis. After 15 min of stimulation, the 60% depletion of synaptic vesicle membrane was largely balanced by the appearance of numerous irregular membrane-walled cisternae inside the terminals, suggesting that vesicle membrane was retrieved from the surface as cisternae. When muscles were rested after 15 min of stimulation, cisternae disappeared and synaptic vesicles reappeared, suggesting that cisternae divided to form new synaptic vesicles so that the original vesicle membrane was now recycled into new synaptic vesicles. When muscles were soaked in horseradish peroxidase (HRP), this tracerfirst entered the cisternae which formed during stimulation and then entered a large proportion of the synaptic vesicles which reappeared during rest, strengthening the idea that synaptic vesicle membrane added to the surface was retrieved as cisternae which subsequently divided to form new vesicles. When muscles containing HRP in synaptic vesicles were washed to remove extracellular HRP and restimulated, HRP disappeared from vesicles without appearing in the new cisternae formed during the second stimulation, confirming that a one-way recycling of synaptic membrane, from the surface through cisternae to new vesicles, was occurring. Coated vesicles apparently represented the actual mechanism for retrieval of synaptic vesicle membrane from the plasma membrane, because during nerve stimulation they proliferated at regions of the nerve terminals covered by Schwann processes, took up peroxidase, and appeared in various stages of coalescence with cisternae. In contrast, synaptic vesicles did not appear to return directly from the surface to form cisternae, and cisternae themselves never appeared directly connected to the surface. Thus, during stimulation the intracellular compartments of this synapse change shape and take up extracellular protein in a manner which indicates that synaptic vesicle membrane added to the surface during exocytosis is retrieved by coated vesicles and recycled into new synaptic vesicles by way of intermediate cisternae.
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Heuser, J. E.; Reese, T. S.
1973-01-01
When the nerves of isolated frog sartorius muscles were stimulated at 10 Hz, synaptic vesicles in the motor nerve terminals became transiently depleted. This depletion apparently resulted from a redistribution rather than disappearance of synaptic vesicle membrane, since the total amount of membrane comprising these nerve terminals remained constant during stimulation. At 1 min of stimulation, the 30% depletion in synaptic vesicle membrane was nearly balanced by an increase in plasma membrane, suggesting that vesicle membrane rapidly moved to the surface as it might if vesicles released their content of transmitter by exocytosis. After 15 min of stimulation, the 60% depletion of synaptic vesicle membrane was largely balanced by the appearance of numerous irregular membrane-walled cisternae inside the terminals, suggesting that vesicle membrane was retrieved from the surface as cisternae. When muscles were rested after 15 min of stimulation, cisternae disappeared and synaptic vesicles reappeared, suggesting that cisternae divided to form new synaptic vesicles so that the original vesicle membrane was now recycled into new synaptic vesicles. When muscles were soaked in horseradish peroxidase (HRP), this tracerfirst entered the cisternae which formed during stimulation and then entered a large proportion of the synaptic vesicles which reappeared during rest, strengthening the idea that synaptic vesicle membrane added to the surface was retrieved as cisternae which subsequently divided to form new vesicles. When muscles containing HRP in synaptic vesicles were washed to remove extracellular HRP and restimulated, HRP disappeared from vesicles without appearing in the new cisternae formed during the second stimulation, confirming that a one-way recycling of synaptic membrane, from the surface through cisternae to new vesicles, was occurring. Coated vesicles apparently represented the actual mechanism for retrieval of synaptic vesicle membrane from the plasma membrane, because during nerve stimulation they proliferated at regions of the nerve terminals covered by Schwann processes, took up peroxidase, and appeared in various stages of coalescence with cisternae. In contrast, synaptic vesicles did not appear to return directly from the surface to form cisternae, and cisternae themselves never appeared directly connected to the surface. Thus, during stimulation the intracellular compartments of this synapse change shape and take up extracellular protein in a manner which indicates that synaptic vesicle membrane added to the surface during exocytosis is retrieved by coated vesicles and recycled into new synaptic vesicles by way of intermediate cisternae. PMID:4348786
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Powers, Randall K.; Türker, Kemal S.
2010-01-01
The amplitude and time course of synaptic potentials in human motoneurons can be estimated in tonically discharging motor units by measuring stimulus-evoked changes in the rate and probability of motor unit action potentials. However, in spite of the fact that some of these techniques have been used for over thirty years, there is still no consensus on the best way to estimate the characteristics of synaptic potentials or on the accuracy of these estimates. In this review, we compare different techniques for estimating synaptic potentials from human motor unit discharge and also discuss relevant animal models in which estimated synaptic potentials can be compared to those directly measured from intracellular recordings. We also review the experimental evidence on how synaptic noise and intrinsic motoneuron properties influence their responses to synaptic inputs. Finally, we consider to what extent recordings of single motor unit discharge in humans can be used to distinguish the contribution of changes in synaptic inputs versus changes in intrinsic motoneuron properties to altered motoneuron responses following CNS injury. PMID:20427230
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Forced neuronal interactions cause poor communication.
Krzisch, Marine; Toni, Nicolas
2017-01-01
Post-natal hippocampal neurogenesis plays a role in hippocampal function, and neurons born post-natally participate to spatial memory and mood control. However, a great proportion of granule neurons generated in the post-natal hippocampus are eliminated during the first 3 weeks of their maturation, a mechanism that depends on their synaptic integration. In a recent study, we examined the possibility of enhancing the synaptic integration of neurons born post-natally, by specifically overexpressing synaptic cell adhesion molecules in these cells. Synaptic cell adhesion molecules are transmembrane proteins mediating the physical connection between pre- and post-synaptic neurons at the synapse, and their overexpression enhances synapse formation. Accordingly, we found that overexpressing synaptic adhesion molecules increased the synaptic integration and survival of newborn neurons. Surprisingly, the synaptic adhesion molecule with the strongest effect on new neurons' survival, Neuroligin-2A, decreased memory performances in a water maze task. We present here hypotheses explaining these surprising results, in the light of the current knowledge of the mechanisms of synaptic integration of new neurons in the post-natal hippocampus.
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González-Rueda, Ana; Pedrosa, Victor; Feord, Rachael C; Clopath, Claudia; Paulsen, Ole
2018-03-21
Activity-dependent synaptic plasticity is critical for cortical circuit refinement. The synaptic homeostasis hypothesis suggests that synaptic connections are strengthened during wake and downscaled during sleep; however, it is not obvious how the same plasticity rules could explain both outcomes. Using whole-cell recordings and optogenetic stimulation of presynaptic input in urethane-anesthetized mice, which exhibit slow-wave-sleep (SWS)-like activity, we show that synaptic plasticity rules are gated by cortical dynamics in vivo. While Down states support conventional spike timing-dependent plasticity, Up states are biased toward depression such that presynaptic stimulation alone leads to synaptic depression, while connections contributing to postsynaptic spiking are protected against this synaptic weakening. We find that this novel activity-dependent and input-specific downscaling mechanism has two important computational advantages: (1) improved signal-to-noise ratio, and (2) preservation of previously stored information. Thus, these synaptic plasticity rules provide an attractive mechanism for SWS-related synaptic downscaling and circuit refinement. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
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Oginsky, Max F; Rodgers, Edmund W; Clark, Merry C; Simmons, Robert; Krenz, Wulf-Dieter C; Baro, Deborah J
2010-02-01
Dopamine (DA) modulates motor systems in phyla as diverse as nematodes and arthropods up through chordates. A comparison of dopaminergic systems across a broad phylogenetic range should reveal shared organizing principles. The pyloric network, located in the stomatogastric ganglion (STG), is an important model for neuromodulation of motor networks. The effects of DA on this network have been well characterized at the circuit and cellular levels in the spiny lobster, Panulirus interruptus. Here we provide the first data about the physical organization of the DA signaling system in the STG and the function of D(2) receptors in pyloric neurons. Previous studies showed that DA altered intrinsic firing properties and synaptic output in the pyloric dilator (PD) neuron, in part by reducing calcium currents and increasing outward potassium currents. We performed single cell reverse transcriptase-polymerase chain reaction (RT-PCR) experiments to show that PD neurons exclusively expressed a type 2 (D(2alphaPan)) DA receptor. This was confirmed by using confocal microscopy in conjunction with immunohistochemistry (IHC) on STG whole-mount preparations containing dye-filled PD neurons. Immunogold electron microscopy showed that surface receptors were concentrated in fine neurites/terminal swellings and vesicle-laden varicosities in the synaptic neuropil. Double-label IHC experiments with tyrosine hydroxylase antiserum suggested that the D(2alphaPan) receptors received volume neurotransmissions. Receptors were further mapped onto three-dimensional models of PD neurons built from Neurolucida tracings of confocal stacks from the IHC experiments. The data showed that D(2alphaPan) receptors were selectively targeted to approximately 40% of synaptic structures in any given PD neuron, and were nonuniformly distributed among neurites.
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Toutounji, Hazem; Pasemann, Frank
2014-01-01
The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot.
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Hrynevich, Sviatlana V; Pekun, Tatyana G; Waseem, Tatyana V; Fedorovich, Sergei V
2015-06-01
Hypoglycemia can cause neuronal cell death similar to that of glutamate-induced cell death. In the present paper, we investigated the effect of glucose removal from incubation medium on changes of mitochondrial and plasma membrane potentials in rat brain synaptosomes using the fluorescent dyes DiSC3(5) and JC-1. We also monitored pH gradients in synaptic vesicles and their recycling by the fluorescent dye acridine orange. Glucose deprivation was found to cause an inhibition of K(+)-induced Ca(2+)-dependent exocytosis and a shift of mitochondrial and plasma membrane potentials to more positive values. The sensitivity of these parameters to the energy deficit caused by the removal of glucose showed the following order: mitochondrial membrane potential > plasma membrane potential > pH gradient in synaptic vesicles. The latter was almost unaffected by deprivation compared with the control. The pH-dependent dye acridine orange was used to investigate synaptic vesicle recycling. However, the compound's fluorescence was shown to be enhanced also by the mixture of mitochondrial toxins rotenone (10 µM) and oligomycin (5 µg/mL). This means that acridine orange can presumably be partially distributed in the intermembrane space of mitochondria. Glucose removal from the incubation medium resulted in a 3.7-fold raise of acridine orange response to rotenone + oligomycin suggesting a dramatic increase in the mitochondrial pH gradient. Our results suggest that the biophysical characteristics of neuronal presynaptic endings do not favor excessive non-controlled neurotransmitter release in case of hypoglycemia. The inhibition of exocytosis and the increase of the mitochondrial pH gradient, while preserving the vesicular pH gradient, are proposed as compensatory mechanisms.
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Liu, Xiaoni; Kerov, Vasily; Haeseleer, Françoise; Majumder, Anurima; Artemyev, Nikolai; Baker, Sheila A; Lee, Amy
2013-01-01
Mutations in the gene encoding Cav 1.4, CACNA1F, are associated with visual disorders including X-linked incomplete congenital stationary night blindness type 2 (CSNB2). In mice lacking Cav 1.4 channels, there are defects in the development of "ribbon" synapses formed between photoreceptors (PRs) and second-order neurons. However, many CSNB2 mutations disrupt the function rather than expression of Cav 1.4 channels. Whether defects in PR synapse development due to altered Cav 1.4 function are common features contributing to the pathogenesis of CSNB2 is unknown. To resolve this issue, we profiled changes in the subcellular distribution of Cav 1.4 channels and synapse morphology during development in wild-type (WT) mice and mouse models of CSNB2. Using Cav 1.4-selective antibodies, we found that Cav 1.4 channels associate with ribbon precursors early in development and are concentrated at both rod and cone PR synapses in the mature retina. In mouse models of CSNB2 in which the voltage-dependence of Cav 1.4 activation is either enhanced (Cav 1.4I756T) or inhibited (CaBP4 KO), the initial stages of PR synaptic ribbon formation are largely unaffected. However, after postnatal day 13, many PR ribbons retain the immature morphology. This synaptic abnormality corresponds in severity to the defect in synaptic transmission in the adult mutant mice, suggesting that lack of sufficient mature synapses contributes to vision impairment in Cav 1.4I756T and CaBP4 KO mice. Our results demonstrate the importance of proper Cav 1.4 function for efficient PR synapse maturation, and that dysregulation of Cav 1.4 channels in CSNB2 may have synaptopathic consequences.
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Liu, Mengying; Huangfu, Xuhong; Zhao, Yangang; Zhang, Dongmei; Zhang, Jiqiang
2015-11-01
Hippocampus local estrogen which is converted from androgen that catalyzed by aromatase has been shown to play important roles in the regulation of learning and memory as well as cognition through action on synaptic plasticity, but the underlying mechanisms are poorly understood. Steroid receptor coactivator-1 (SRC-1) is one of the coactivators of steroid nuclear receptors; it is widely distributed in brain areas that related to learning and memory, reproductive regulation, sensory and motor information integration. Previous studies have revealed high levels of SRC-1 immunoreactivities in the hippocampus; it is closely related to the levels of synaptic proteins such as PSD-95 under normal development or gonadectomy, but its exact roles in the regulation of these proteins remains unclear. In this study, we used aromatase inhibitor letrozole in vivo and SRC-1 RNA interference in vitro to investigate whether SRC-1 mediated endogenous estrogen regulation of hippocampal PSD-95. The results revealed that letrozole injection synchronously decreased hippocampal SRC-1 and PSD-95 in a dose-dependant manner. Furthermore, when SRC-1 specific shRNA pool was applied to block the expression of SRC-1 in the primary hippocampal neuron culture, both immunocytochemistry and Western blot revealed that levels of PSD-95 were also decreased significantly. Taking together, these results provided the first evidence that SRC-1 mediated endogenous estrogen regulation of hippocampal synaptic plasticity by targeting the expression of synaptic protein PSD-95. Additionally, since letrozole is frequently used to treat estrogen-sensitive breast cancer, the above results also indicate its potential side effects in clinical administration. Copyright © 2015 Elsevier Ltd. All rights reserved.
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A framework for plasticity implementation on the SpiNNaker neural architecture.
Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B
2014-01-01
Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.
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A framework for plasticity implementation on the SpiNNaker neural architecture
Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A.; Furber, Steve B.; Benosman, Ryad B.
2015-01-01
Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system. PMID:25653580
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Massart, R; Freyburger, M; Suderman, M; Paquet, J; El Helou, J; Belanger-Nelson, E; Rachalski, A; Koumar, O C; Carrier, J; Szyf, M; Mongrain, V
2014-01-21
Sleep is critical for normal brain function and mental health. However, the molecular mechanisms mediating the impact of sleep loss on both cognition and the sleep electroencephalogram remain mostly unknown. Acute sleep loss impacts brain gene expression broadly. These data contributed to current hypotheses regarding the role for sleep in metabolism, synaptic plasticity and neuroprotection. These changes in gene expression likely underlie increased sleep intensity following sleep deprivation (SD). Here we tested the hypothesis that epigenetic mechanisms coordinate the gene expression response driven by SD. We found that SD altered the cortical genome-wide distribution of two major epigenetic marks: DNA methylation and hydroxymethylation. DNA methylation differences were enriched in gene pathways involved in neuritogenesis and synaptic plasticity, whereas large changes (>4000 sites) in hydroxymethylation where observed in genes linked to cytoskeleton, signaling and neurotransmission, which closely matches SD-dependent changes in the transcriptome. Moreover, this epigenetic remodeling applied to elements previously linked to sleep need (for example, Arc and Egr1) and synaptic partners of Neuroligin-1 (Nlgn1; for example, Dlg4, Nrxn1 and Nlgn3), which we recently identified as a regulator of sleep intensity following SD. We show here that Nlgn1 mutant mice display an enhanced slow-wave slope during non-rapid eye movement sleep following SD but this mutation does not affect SD-dependent changes in gene expression, suggesting that the Nlgn pathway acts downstream to mechanisms triggering gene expression changes in SD. These data reveal that acute SD reprograms the epigenetic landscape, providing a unique molecular route by which sleep can impact brain function and health.
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Toutounji, Hazem; Pasemann, Frank
2014-01-01
The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot. PMID:24904403
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Oginsky, Max F.; Rodgers, Edmund W.; Clark, Merry C.; Simmons, Robert; Krenz, Wulf-Dieter C.; Baro, Deborah J.
2014-01-01
Dopamine (DA) modulates motor systems in phyla as diverse as nematodes and arthropods up through chordates. A comparison of dopaminergic systems across a broad phylogenetic range should reveal shared organizing principles. The pyloric network, located in the stomatogastric ganglion (STG), is an important model for neuromodulation of motor networks. The effects of DA on this network have been well characterized at the circuit and cellular levels in the spiny lobster, Panulirus interruptus. Here we provide the first data about the physical organization of the DA signaling system in the STG and the function of D2 receptors in pyloric neurons. Previous studies showed that DA altered intrinsic firing properties and synaptic output in the pyloric dilator (PD) neuron, in part by reducing calcium currents and increasing outward potassium currents. We performed single cell reverse transcriptase-polymerase chain reaction (RT-PCR) experiments to show that PD neurons exclusively expressed a type 2 (D2αPan) DA receptor. This was confirmed by using confocal microscopy in conjunction with immunohistochemistry (IHC) on STG whole-mount preparations containing dye-filled PD neurons. Immunogold electron microscopy showed that surface receptors were concentrated in fine neurites/terminal swellings and vesicle-laden varicosities in the synaptic neuropil. Double-label IHC experiments with tyrosine hydroxylase antiserum suggested that the D2αPan receptors received volume neurotransmissions. Receptors were further mapped onto three-dimensional models of PD neurons built from Neurolucida tracings of confocal stacks from the IHC experiments. The data showed that D2αPan receptors were selectively targeted to approximately 40% of synaptic structures in any given PD neuron, and were nonuniformly distributed among neurites. PMID:19941347
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High affinity kainate receptor subunits are necessary for ionotropic but not metabotropic signaling
Fernandes, Herman B.; Catches, Justin S.; Petralia, Ronald S.; Copits, Bryan A.; Xu, Jian; Russell, Theron A.; Swanson, Geoffrey T.; Contractor, Anis
2009-01-01
Summary Kainate receptors are atypical members of the glutamate receptor family which are able to signal through both ionotropic and metabotropic pathways. Of the five individual kainate receptor subunits the high-affinity subunits, GluK4 (KA1) and GluK5 (KA2), are unique in that they do not form functional homomeric receptors in recombinant expression systems, but combine with the primary subunits GluK1-3 (GluR5-7) to form heteromeric assemblies. Here we generated a GluK4 mutant mouse by disrupting the Grik4 gene locus. We found that loss of the GluK4 subunit leads to a significant reduction in synaptic kainate receptor currents. Moreover, ablation of both high-affinity subunits in GluK4/GluK5 double knockout mice leads to a complete loss of pre- and postsynaptic ionotropic function of synaptic kainate receptors. The principal subunits remain at the synaptic plasma membrane, but are distributed away from postsynaptic densities and presynaptic active zones. There is also an alteration in the properties of the remaining kainate receptors, as kainic acid application fails to elicit responses in GluK4/GluK5 knockout neurons. Despite the lack of detectable ionotropic synaptic receptors, the kainate receptor-mediated inhibition of the slow afterhyperpolarization current (IsAHP), which is dependent on metabotropic pathways, was intact in GluK4/GluK5 knockout mice. These results uncover a previously unknown critical role for the high-affinity kainate receptor subunits as obligatory components of ionotropic kainate receptor function, and further, demonstrate that kainate receptor participation in metabotropic signaling pathways does not require their classic role as ion channels. PMID:19778510
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Diaz-Ruiz, Oscar; Zhang, Yajun; Shan, Lufei; Malik, Nasir; Hoffman, Alexander F; Ladenheim, Bruce; Cadet, Jean Lud; Lupica, Carl R; Tagliaferro, Adriana; Brusco, Alicia; Bäckman, Cristina M
2012-07-20
In the present study, we analyzed mice with a targeted deletion of β-catenin in DA neurons (DA-βcat KO mice) to address the functional significance of this molecule in the shaping of synaptic responses associated with motor learning and following exposure to drugs of abuse. Relative to controls, DA-βcat KO mice showed significant deficits in their ability to form long-term memories and displayed reduced expression of methamphetamine-induced behavioral sensitization after subsequent challenge doses with this drug, suggesting that motor learning and drug-induced learning plasticity are altered in these mice. Morphological analyses showed no changes in the number or distribution of tyrosine hydroxylase-labeled neurons in the ventral midbrain. While electrochemical measurements in the striatum determined no changes in acute DA release and uptake, a small but significant decrease in DA release was detected in mutant animals after prolonged repetitive stimulation, suggesting a possible deficit in the DA neurotransmitter vesicle reserve pool. However, electron microscopy analyses did not reveal significant differences in the content of synaptic vesicles per terminal, and striatal DA levels were unchanged in DA-βcat KO animals. In contrast, striatal mRNA levels for several markers known to regulate synaptic plasticity and DA neurotransmission were altered in DA-βcat KO mice. This study demonstrates that ablation of β-catenin in DA neurons leads to alterations of motor and reward-associated memories and to adaptations of the DA neurotransmitter system and suggests that β-catenin signaling in DA neurons is required to facilitate the synaptic remodeling underlying the consolidation of long-term memories.
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Rohrbough, Jeffrey; Broadie, Kendal
2010-10-01
Bidirectional trans-synaptic signals induce synaptogenesis and regulate subsequent synaptic maturation. Presynaptically secreted Mind the gap (Mtg) molds the synaptic cleft extracellular matrix, leading us to hypothesize that Mtg functions to generate the intercellular environment required for efficient signaling. We show in Drosophila that secreted Jelly belly (Jeb) and its receptor tyrosine kinase Anaplastic lymphoma kinase (Alk) are localized to developing synapses. Jeb localizes to punctate aggregates in central synaptic neuropil and neuromuscular junction (NMJ) presynaptic terminals. Secreted Jeb and Mtg accumulate and colocalize extracellularly in surrounding synaptic boutons. Alk concentrates in postsynaptic domains, consistent with an anterograde, trans-synaptic Jeb-Alk signaling pathway at developing synapses. Jeb synaptic expression is increased in Alk mutants, consistent with a requirement for Alk receptor function in Jeb uptake. In mtg null mutants, Alk NMJ synaptic levels are reduced and Jeb expression is dramatically increased. NMJ synapse morphology and molecular assembly appear largely normal in jeb and Alk mutants, but larvae exhibit greatly reduced movement, suggesting impaired functional synaptic development. jeb mutant movement is significantly rescued by neuronal Jeb expression. jeb and Alk mutants display normal NMJ postsynaptic responses, but a near loss of patterned, activity-dependent NMJ transmission driven by central excitatory output. We conclude that Jeb-Alk expression and anterograde trans-synaptic signaling are modulated by Mtg and play a key role in establishing functional synaptic connectivity in the developing motor circuit.
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Meredith, Rhiannon M.; van Ooyen, Arjen
2012-01-01
CA1 pyramidal neurons receive hundreds of synaptic inputs at different distances from the soma. Distance-dependent synaptic scaling enables distal and proximal synapses to influence the somatic membrane equally, a phenomenon called “synaptic democracy”. How this is established is unclear. The backpropagating action potential (BAP) is hypothesised to provide distance-dependent information to synapses, allowing synaptic strengths to scale accordingly. Experimental measurements show that a BAP evoked by current injection at the soma causes calcium currents in the apical shaft whose amplitudes decay with distance from the soma. However, in vivo action potentials are not induced by somatic current injection but by synaptic inputs along the dendrites, which creates a different excitable state of the dendrites. Due to technical limitations, it is not possible to study experimentally whether distance information can also be provided by synaptically-evoked BAPs. Therefore we adapted a realistic morphological and electrophysiological model to measure BAP-induced voltage and calcium signals in spines after Schaffer collateral synapse stimulation. We show that peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. Peak calcium levels also predicted the attenuation of the EPSP across the dendritic tree. Furthermore, we show that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value. We conclude that information derived from synaptically-generated BAPs can indicate synapse location and can subsequently be utilised to implement a synaptic democracy. PMID:22719238
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Comparing development of synaptic proteins in rat visual, somatosensory, and frontal cortex.
Pinto, Joshua G A; Jones, David G; Murphy, Kathryn M
2013-01-01
Two theories have influenced our understanding of cortical development: the integrated network theory, where synaptic development is coordinated across areas; and the cascade theory, where the cortex develops in a wave-like manner from sensory to non-sensory areas. These different views on cortical development raise challenges for current studies aimed at comparing detailed maturation of the connectome among cortical areas. We have taken a different approach to compare synaptic development in rat visual, somatosensory, and frontal cortex by measuring expression of pre-synaptic (synapsin and synaptophysin) proteins that regulate vesicle cycling, and post-synaptic density (PSD-95 and Gephyrin) proteins that anchor excitatory or inhibitory (E-I) receptors. We also compared development of the balances between the pairs of pre- or post-synaptic proteins, and the overall pre- to post-synaptic balance, to address functional maturation and emergence of the E-I balance. We found that development of the individual proteins and the post-synaptic index overlapped among the three cortical areas, but the pre-synaptic index matured later in frontal cortex. Finally, we applied a neuroinformatics approach using principal component analysis and found that three components captured development of the synaptic proteins. The first component accounted for 64% of the variance in protein expression and reflected total protein expression, which overlapped among the three cortical areas. The second component was gephyrin and the E-I balance, it emerged as sequential waves starting in somatosensory, then frontal, and finally visual cortex. The third component was the balance between pre- and post-synaptic proteins, and this followed a different developmental trajectory in somatosensory cortex. Together, these results give the most support to an integrated network of synaptic development, but also highlight more complex patterns of development that vary in timing and end point among the cortical areas.
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Wang, Xiao-Dong; Chen, Yuncai; Wolf, Miriam; Wagner, Klaus V.; Liebl, Claudia; Scharf, Sebastian H.; Harbich, Daniela; Mayer, Bianca; Wurst, Wolfgang; Holsboer, Florian; Deussing, Jan M.; Baram, Tallie Z.; Müller, Marianne B.; Schmidt, Mathias V.
2011-01-01
Chronic stress evokes profound structural and molecular changes in the hippocampus, which may underlie spatial memory deficits. Corticotropin-releasing hormone (CRH) and CRH receptor 1 (CRHR1) mediate some of the rapid effects of stress on dendritic spine morphology and modulate learning and memory, thus providing a potential molecular basis for impaired synaptic plasticity and spatial memory by repeated stress exposure. Using adult male mice with CRHR1 conditionally inactivated in the forebrain regions, we investigated the role of CRH-CRHR1 signaling in the effects of chronic social defeat stress on spatial memory, the dendritic morphology of hippocampal CA3 pyramidal neurons, and the hippocampal expression of nectin-3, a synaptic cell adhesion molecule important in synaptic remodeling. In chronically stressed wild-type mice, spatial memory was disrupted, and the complexity of apical dendrites of CA3 neurons reduced. In contrast, stressed mice with forebrain CRHR1 deficiency exhibited normal dendritic morphology of CA3 neurons and mild impairments in spatial memory. Additionally, we showed that the expression of nectin-3 in the CA3 area was regulated by chronic stress in a CRHR1-dependent fashion and associated with spatial memory and dendritic complexity. Moreover, forebrain CRHR1 deficiency prevented the down-regulation of hippocampal glucocorticoid receptor expression by chronic stress but induced increased body weight gain during persistent stress exposure. These findings underscore the important role of forebrain CRH-CRHR1 signaling in modulating chronic stress-induced cognitive, structural and molecular adaptations, with implications for stress-related psychiatric disorders. PMID:21296667
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Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.
Walter, Florian; Röhrbein, Florian; Knoll, Alois
2015-12-01
The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Christie, Louisa A.; Russell, Theron A.; Xu, Jian; Wood, Lydia; Shepherd, Gordon M. G.; Contractor, Anis
2010-01-01
AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole-propionate) recep-tors desensitize rapidly and completely in the continued presence of their endogenous ligand glutamate; however, it is not clear what role AMPA receptor desensitization plays in the brain. We generated a knock-in mouse in which a single amino acid residue, which controls desensitization, was mutated in the GluA2 (GluR2) receptor subunit (GluA2L483Y). This mutation was homozygous lethal. However, mice carrying a single mutated allele, GluA2L483Y/wt, survived past birth, but displayed severe and progressive neurological deficits including seizures and, ultimately, increased mortality. The expression of the AMPA receptor subunits GluA1 and GluA2 was decreased, whereas NMDA receptor protein expression was increased in GluA2L483Y/wt mice. Despite this, basal synaptic transmission and plasticity in the hippocampus were largely unaffected, suggesting that neurons preferentially target receptors to synapses to normalize synaptic weight. We found no gross neuroanatomical alterations in GluA2L483Y/wt mice. Moreover, there was no accumulation of AMPA receptor subunits in intracellular compartments, suggesting that folding and assembly of AMPA receptors are not affected by this mutation. Interestingly, EPSC paired pulse ratios in the CA1 were enhanced without a change in synaptic release probability, demonstrating that postsynaptic receptor properties can contribute to facilitation. The dramatic phenotype observed in this study by the introduction of a single amino acid change demonstrates an essential role in vivo for AMPA receptor desensitization. PMID:20439731
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An, Lei; Zhang, Tao
2013-11-01
Prenatal ethanol exposure can lead to long-lasting impairments in the ability of rats to process spatial information, as well as produce long-lasting deficits in long-term potentiation (LTP), a biological model of learning and memory processing. The present study aimed to examine the sexually dimorphic effects of chronic prenatal ethanol exposure (CPEE) on behavior cognition and synaptic plasticity balance (SPB), and tried to understand a possible mechanism by evaluating the alternation of SPB. The animal model was produced by ethanol exposure throughout gestational period with 4 g/kg bodyweight. Offspring of both male and female were selected and studied on postnatal days 36. Subsequently, the data showed that chronic ethanol exposure resulted in birth weight reduction, losing bodyweight gain, microcephaly and hippocampus weight retardation. In Morris water maze (MWM) test, escape latencies were significantly higher in CPEE-treated rats than that in control ones. They also spent much less time in the target quadrant compared to that of control animals in the probe phase. In addition, it was found that there was a more severe impairment in females than that in males after CPEE treatment. Electrophysiological studies showed that CPEE considerably inhibited hippocampal LTP and facilitated depotentiation in males, while significantly enhanced LTP and suppressed depotentiation in females. A novel index, developed by us, showed that the action of CPEE on SPB was more sensitive in females than that in males, suggesting that it might be an effective index to distinguish the difference of SPB impairment between males and females. Copyright © 2013 Elsevier B.V. All rights reserved.
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Hinahon, Erika; Estrada, Christina; Tong, Lin; Won, Deborah S; de Leon, Ray D
2017-08-01
The application of resistive forces has been used during body weight-supported treadmill training (BWSTT) to improve walking function after spinal cord injury (SCI). Whether this form of training actually augments the effects of BWSTT is not yet known. To determine if robotic-applied resistance augments the effects of BWSTT using a controlled experimental design in a rodent model of SCI. Spinally contused rats were treadmill trained using robotic resistance against horizontal (n = 9) or vertical (n = 8) hind limb movements. Hind limb stepping was tested before and after 6 weeks of training. Two control groups, one receiving standard training (ie, without resistance; n = 9) and one untrained (n = 8), were also tested. At the terminal experiment, the spinal cords were prepared for immunohistochemical analysis of synaptophysin. Six weeks of training with horizontal resistance increased step length, whereas training with vertical resistance enhanced step height and movement velocity. None of these changes occurred in the group that received standard (ie, no resistance) training or in the untrained group. Only standard training increased the number of step cycles and shortened cycle period toward normal values. Synaptophysin expression in the ventral horn was highest in rats trained with horizontal resistance and in untrained rats and was positively correlated with step length. Adding robotic-applied resistance to BWSTT produced gains in locomotor function over BWSTT alone. The impact of resistive forces on spinal connections may depend on the nature of the resistive forces and the synaptic milieu that is present after SCI.
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Xiang, Bo; Yu, Minglan; Liang, Xuemei; Lei, Wei; Huang, Chaohua; Chen, Jing; He, Wenying; Zhang, Tao; Li, Tao; Liu, Kezhi
2017-12-10
To explore common biological pathways for attention deficit hyperactivity disorder (ADHD) and low birth weight (LBW). Thei-Gsea4GwasV2 software was used to analyze the result of genome-wide association analysis (GWAS) for LBW (pathways were derived from Reactome), and nominally significant (P< 0.05, FDR< 0.25) pathways were tested for replication in ADHD.Significant pathways were analyzed with DAPPLE and Reatome FI software to identify genes involved in such pathways, with each cluster enriched with the gene ontology (GO). The Centiscape2.0 software was used to calculate the degree of genetic networks and the betweenness value to explore the core node (gene). Weighed gene co-expression network analysis (WGCNA) was then used to explore the co-expression of genes in these pathways.With gene expression data derived from BrainSpan, GO enrichment was carried out for each gene module. Eleven significant biological pathways was identified in association with LBW, among which two (Selenoamino acid metabolism and Diseases associated with glycosaminoglycan metabolism) were replicated during subsequent ADHD analysis. Network analysis of 130 genes in these pathways revealed that some of the sub-networksare related with morphology of cerebellum, development of hippocampus, and plasticity of synaptic structure. Upon co-expression network analysis, 120 genes passed the quality control and were found to express in 3 gene modules. These modules are mainly related to the regulation of synaptic structure and activity regulation. ADHD and LBW share some biological regulation processes. Anomalies of such proces sesmay predispose to ADHD.
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Cascade Back-Propagation Learning in Neural Networks
NASA Technical Reports Server (NTRS)
Duong, Tuan A.
2003-01-01
The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.
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The interplay between neuronal activity and actin dynamics mimic the setting of an LTD synaptic tag
Szabó, Eszter C.; Manguinhas, Rita; Fonseca, Rosalina
2016-01-01
Persistent forms of plasticity, such as long-term depression (LTD), are dependent on the interplay between activity-dependent synaptic tags and the capture of plasticity-related proteins. We propose that the synaptic tag represents a structural alteration that turns synapses permissive to change. We found that modulation of actin dynamics has different roles in the induction and maintenance of LTD. Inhibition of either actin depolymerisation or polymerization blocks LTD induction whereas only the inhibition of actin depolymerisation blocks LTD maintenance. Interestingly, we found that actin depolymerisation and CaMKII activation are involved in LTD synaptic-tagging and capture. Moreover, inhibition of actin polymerisation mimics the setting of a synaptic tag, in an activity-dependent manner, allowing the expression of LTD in non-stimulated synapses. Suspending synaptic activation also restricts the time window of synaptic capture, which can be restored by inhibiting actin polymerization. Our results support our hypothesis that modulation of the actin cytoskeleton provides an input-specific signal for synaptic protein capture. PMID:27650071
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Crabtree, Gregg W.; Gogos, Joseph A.
2014-01-01
Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations. PMID:25505409
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Non-synaptic receptors and transporters involved in brain functions and targets of drug treatment
Vizi, ES; Fekete, A; Karoly, R; Mike, A
2010-01-01
Beyond direct synaptic communication, neurons are able to talk to each other without making synapses. They are able to send chemical messages by means of diffusion to target cells via the extracellular space, provided that the target neurons are equipped with high-affinity receptors. While synaptic transmission is responsible for the ‘what’ of brain function, the ‘how’ of brain function (mood, attention, level of arousal, general excitability, etc.) is mainly controlled non-synaptically using the extracellular space as communication channel. It is principally the ‘how’ that can be modulated by medicine. In this paper, we discuss different forms of non-synaptic transmission, localized spillover of synaptic transmitters, local presynaptic modulation and tonic influence of ambient transmitter levels on the activity of vast neuronal populations. We consider different aspects of non-synaptic transmission, such as synaptic–extrasynaptic receptor trafficking, neuron–glia communication and retrograde signalling. We review structural and functional aspects of non-synaptic transmission, including (i) anatomical arrangement of non-synaptic release sites, receptors and transporters, (ii) intravesicular, intra- and extracellular concentrations of neurotransmitters, as well as the spatiotemporal pattern of transmitter diffusion. We propose that an effective general strategy for efficient pharmacological intervention could include the identification of specific non-synaptic targets and the subsequent development of selective pharmacological tools to influence them. PMID:20136842
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Spaceflight-induced synaptic modifications within hair cells of the mammalian utricle.
Sultemeier, David R; Choy, Kristel R; Schweizer, Felix E; Hoffman, Larry F
2017-06-01
Exposure to the microgravity conditions of spaceflight alleviates the load normally imposed by the Earth's gravitational field on the inner ear utricular epithelia. Previous ultrastructural investigations have shown that spaceflight induces an increase in synapse density within hair cells of the rat utricle. However, the utricle exhibits broad physiological heterogeneity across different epithelial regions, and it is unknown whether capabilities for synaptic plasticity generalize to hair cells across its topography. To achieve systematic and broader sampling of the epithelium than was previously conducted, we used immunohistochemistry and volumetric image analyses to quantify synapse distributions across representative utricular regions in specimens from mice exposed to spaceflight (a 15-day mission of the space shuttle Discovery). These measures were compared with similarly sampled Earth-bound controls. Following paraformaldehyde fixation and microdissection, immunohistochemistry was performed on intact specimens to label presynaptic ribbons (anti-CtBP2) and postsynaptic receptor complexes (anti-Shank1A). Synapses were identified as closely apposed pre- and postsynaptic puncta. Epithelia from horizontal semicircular canal cristae served as "within-specimen" controls, whereas utricles and cristae from Earth-bound cohorts served as experimental controls. We found that synapse densities decreased in the medial extrastriolae of microgravity specimens compared with experimental controls, whereas they were unchanged in the striolae and horizontal cristae from the two conditions. These data demonstrate that structural plasticity was topographically localized to the utricular region that encodes very low frequency and static changes in linear acceleration, and illuminates the remarkable capabilities of utricular hair cells for synaptic plasticity in adapting to novel gravitational environments. NEW & NOTEWORTHY Spaceflight imposes a radically different sensory environment from that in which the inner ear utricle normally operates. We investigated synaptic modifications in utricles from mice flown aboard a space shuttle mission. Structural synaptic plasticity was detected in the medial extrastriola, a region associated with encoding static head position, as decreased synapse density. These results are remarkably congruent with a recent report of decreased utricular function in astronauts immediately after returning from the International Space Station. Copyright © 2017 the American Physiological Society.
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Effects of chronic weight perturbation on energy homeostasis and brain structure in mice
Ravussin, Y.; Gutman, R.; Diano, S.; Shanabrough, M.; Borok, E.; Sarman, B.; Lehmann, A.; LeDuc, C. A.; Rosenbaum, M.; Horvath, T. L.
2011-01-01
Maintenance of reduced body weight in lean and obese human subjects results in the persistent decrease in energy expenditure below what can be accounted for by changes in body mass and composition. Genetic and developmental factors may determine a central nervous system (CNS)-mediated minimum threshold of somatic energy stores below which behavioral and metabolic compensations for weight loss are invoked. A critical question is whether this threshold can be altered by environmental influences and by what mechanisms such alterations might be achieved. We examined the bioenergetic, behavioral, and CNS structural responses to weight reduction of diet-induced obese (DIO) and never-obese (CON) C57BL/6J male mice. We found that weight-reduced (WR) DIO-WR and CON-WR animals showed reductions in energy expenditure, adjusted for body mass and composition, comparable (−10–15%) to those seen in human subjects. The proportion of excitatory synapses on arcuate nucleus proopiomelanocortin neurons was decreased by ∼50% in both DIO-WR and CON-WR mice. These data suggest that prolonged maintenance of an elevated body weight (fat) alters energy homeostatic systems to defend a higher level of body fat. The synaptic changes could provide a neural substrate for the disproportionate decline in energy expenditure in weight-reduced individuals. This response to chronic weight elevation may also occur in humans. The mouse model described here could help to identify the molecular/cellular mechanisms underlying both the defense mechanisms against sustained weight loss and the upward resetting of those mechanisms following sustained weight gain. PMID:21411766
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Stochastic lattice model of synaptic membrane protein domains.
Li, Yiwei; Kahraman, Osman; Haselwandter, Christoph A
2017-05-01
Neurotransmitter receptor molecules, concentrated in synaptic membrane domains along with scaffolds and other kinds of proteins, are crucial for signal transmission across chemical synapses. In common with other membrane protein domains, synaptic domains are characterized by low protein copy numbers and protein crowding, with rapid stochastic turnover of individual molecules. We study here in detail a stochastic lattice model of the receptor-scaffold reaction-diffusion dynamics at synaptic domains that was found previously to capture, at the mean-field level, the self-assembly, stability, and characteristic size of synaptic domains observed in experiments. We show that our stochastic lattice model yields quantitative agreement with mean-field models of nonlinear diffusion in crowded membranes. Through a combination of analytic and numerical solutions of the master equation governing the reaction dynamics at synaptic domains, together with kinetic Monte Carlo simulations, we find substantial discrepancies between mean-field and stochastic models for the reaction dynamics at synaptic domains. Based on the reaction and diffusion properties of synaptic receptors and scaffolds suggested by previous experiments and mean-field calculations, we show that the stochastic reaction-diffusion dynamics of synaptic receptors and scaffolds provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the observed single-molecule trajectories, and spatial heterogeneity in the effective rates at which receptors and scaffolds are recycled at the cell membrane. Our work sheds light on the physical mechanisms and principles linking the collective properties of membrane protein domains to the stochastic dynamics that rule their molecular components.
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A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.
Mostafa, Hesham; Cauwenberghs, Gert
2018-06-01
Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.
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Musical representation of dendritic spine distribution: a new exploratory tool.
Toharia, Pablo; Morales, Juan; de Juan, Octavio; Fernaud, Isabel; Rodríguez, Angel; DeFelipe, Javier
2014-04-01
Dendritic spines are small protrusions along the dendrites of many types of neurons in the central nervous system and represent the major target of excitatory synapses. For this reason, numerous anatomical, physiological and computational studies have focused on these structures. In the cerebral cortex the most abundant and characteristic neuronal type are pyramidal cells (about 85 % of all neurons) and their dendritic spines are the main postsynaptic target of excitatory glutamatergic synapses. Thus, our understanding of the synaptic organization of the cerebral cortex largely depends on the knowledge regarding synaptic inputs to dendritic spines of pyramidal cells. Much of the structural data on dendritic spines produced by modern neuroscience involves the quantitative analysis of image stacks from light and electron microscopy, using standard statistical and mathematical tools and software developed to this end. Here, we present a new method with musical feedback for exploring dendritic spine morphology and distribution patterns in pyramidal neurons. We demonstrate that audio analysis of spiny dendrites with apparently similar morphology may "sound" quite different, revealing anatomical substrates that are not apparent from simple visual inspection. These morphological/music translations may serve as a guide for further mathematical analysis of the design of the pyramidal neurons and of spiny dendrites in general.
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Nonvolatile programmable neural network synaptic array
NASA Technical Reports Server (NTRS)
Tawel, Raoul (Inventor)
1994-01-01
A floating-gate metal oxide semiconductor (MOS) transistor is implemented for use as a nonvolatile analog storage element of a synaptic cell used to implement an array of processing synaptic cells. These cells are based on a four-quadrant analog multiplier requiring both X and Y differential inputs, where one Y input is UV programmable. These nonvolatile synaptic cells are disclosed fully connected in a 32 x 32 synaptic cell array using standard very large scale integration (VLSI) complementary MOS (CMOS) technology.
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Sleep and protein synthesis-dependent synaptic plasticity: impacts of sleep loss and stress
Grønli, Janne; Soulé, Jonathan; Bramham, Clive R.
2014-01-01
Sleep has been ascribed a critical role in cognitive functioning. Several lines of evidence implicate sleep in the consolidation of synaptic plasticity and long-term memory. Stress disrupts sleep while impairing synaptic plasticity and cognitive performance. Here, we discuss evidence linking sleep to mechanisms of protein synthesis-dependent synaptic plasticity and synaptic scaling. We then consider how disruption of sleep by acute and chronic stress may impair these mechanisms and degrade sleep function. PMID:24478645
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Ivannikov, Maxim V.; Sugimori, Mutsuyuki; Llinás, Rodolfo R.
2012-01-01
Synaptic plasticity in many regions of the central nervous system leads to the continuous adjustment of synaptic strength, which is essential for learning and memory. In this study, we show by visualizing synaptic vesicle release in mouse hippocampal synaptosomes that presynaptic mitochondria and specifically, their capacities for ATP production are essential determinants of synaptic vesicle exocytosis and its magnitude. Total internal reflection microscopy of FM1-43 loaded hippocampal synaptosomes showed that inhibition of mitochondrial oxidative phosphorylation reduces evoked synaptic release. This reduction was accompanied by a substantial drop in synaptosomal ATP levels. However, cytosolic calcium influx was not affected. Structural characterization of stimulated hippocampal synaptosomes revealed that higher total presynaptic mitochondrial volumes were consistently associated with higher levels of exocytosis. Thus, synaptic vesicle release is linked to the presynaptic ability to regenerate ATP, which itself is a utility of mitochondrial density and activity. PMID:22772899
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On the Teneurin track: a new synaptic organization molecule emerges
Mosca, Timothy J.
2015-01-01
To achieve proper synaptic development and function, coordinated signals must pass between the pre- and postsynaptic membranes. Such transsynaptic signals can be comprised of receptors and secreted ligands, membrane associated receptors, and also pairs of synaptic cell adhesion molecules. A critical open question bridging neuroscience, developmental biology, and cell biology involves identifying those signals and elucidating how they function. Recent work in Drosophila and vertebrate systems has implicated a family of proteins, the Teneurins, as a new transsynaptic signal in both the peripheral and central nervous systems. The Teneurins have established roles in neuronal wiring, but studies now show their involvement in regulating synaptic connections between neurons and bridging the synaptic membrane and the cytoskeleton. This review will examine the Teneurins as synaptic cell adhesion molecules, explore how they regulate synaptic organization, and consider how some consequences of human Teneurin mutations may have synaptopathic origins. PMID:26074772
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Interneuron- and GABAA receptor-specific inhibitory synaptic plasticity in cerebellar Purkinje cells
NASA Astrophysics Data System (ADS)
He, Qionger; Duguid, Ian; Clark, Beverley; Panzanelli, Patrizia; Patel, Bijal; Thomas, Philip; Fritschy, Jean-Marc; Smart, Trevor G.
2015-07-01
Inhibitory synaptic plasticity is important for shaping both neuronal excitability and network activity. Here we investigate the input and GABAA receptor subunit specificity of inhibitory synaptic plasticity by studying cerebellar interneuron-Purkinje cell (PC) synapses. Depolarizing PCs initiated a long-lasting increase in GABA-mediated synaptic currents. By stimulating individual interneurons, this plasticity was observed at somatodendritic basket cell synapses, but not at distal dendritic stellate cell synapses. Basket cell synapses predominantly express β2-subunit-containing GABAA receptors; deletion of the β2-subunit ablates this plasticity, demonstrating its reliance on GABAA receptor subunit composition. The increase in synaptic currents is dependent upon an increase in newly synthesized cell surface synaptic GABAA receptors and is abolished by preventing CaMKII phosphorylation of GABAA receptors. Our results reveal a novel GABAA receptor subunit- and input-specific form of inhibitory synaptic plasticity that regulates the temporal firing pattern of the principal output cells of the cerebellum.
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Archaerhodopsin Selectively and Reversibly Silences Synaptic Transmission through Altered pH.
El-Gaby, Mohamady; Zhang, Yu; Wolf, Konstantin; Schwiening, Christof J; Paulsen, Ole; Shipton, Olivia A
2016-08-23
Tools that allow acute and selective silencing of synaptic transmission in vivo would be invaluable for understanding the synaptic basis of specific behaviors. Here, we show that presynaptic expression of the proton pump archaerhodopsin enables robust, selective, and reversible optogenetic synaptic silencing with rapid onset and offset. Two-photon fluorescence imaging revealed that this effect is accompanied by a transient increase in pH restricted to archaerhodopsin-expressing boutons. Crucially, clamping intracellular pH abolished synaptic silencing without affecting the archaerhodopsin-mediated hyperpolarizing current, indicating that changes in pH mediate the synaptic silencing effect. To verify the utility of this technique, we used trial-limited, archaerhodopsin-mediated silencing to uncover a requirement for CA3-CA1 synapses whose afferents originate from the left CA3, but not those from the right CA3, for performance on a long-term memory task. These results highlight optogenetic, pH-mediated silencing of synaptic transmission as a spatiotemporally selective approach to dissecting synaptic function in behaving animals. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.
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Synaptic vesicle recycling: steps and principles.
Rizzoli, Silvio O
2014-04-16
Synaptic vesicle recycling is one of the best-studied cellular pathways. Many of the proteins involved are known, and their interactions are becoming increasingly clear. However, as for many other pathways, it is still difficult to understand synaptic vesicle recycling as a whole. While it is generally possible to point out how synaptic reactions take place, it is not always easy to understand what triggers or controls them. Also, it is often difficult to understand how the availability of the reaction partners is controlled: how the reaction partners manage to find each other in the right place, at the right time. I present here an overview of synaptic vesicle recycling, discussing the mechanisms that trigger different reactions, and those that ensure the availability of reaction partners. A central argument is that synaptic vesicles bind soluble cofactor proteins, with low affinity, and thus control their availability in the synapse, forming a buffer for cofactor proteins. The availability of cofactor proteins, in turn, regulates the different synaptic reactions. Similar mechanisms, in which one of the reaction partners buffers another, may apply to many other processes, from the biogenesis to the degradation of the synaptic vesicle.
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Sharing is Caring: The Role of Actin/Myosin-V in Synaptic Vesicle Transport between Synapses in vivo
NASA Astrophysics Data System (ADS)
Gramlich, Michael
Inter-synaptic vesicle sharing is an important but not well understood process of pre-synaptic function. Further, the molecular mechanisms that underlie this inter-synaptic exchange are not well known, and whether this inter-synaptic vesicle sharing is regulated by neural activity remains largely unexplored. I address these questions by studying CA1/CA3 Hippocampal neurons at the single synaptic vesicle level. Using high-resolution tracking of individual vesicles that have recently undergone endocytosis, I observe long-distance axonal transport of synaptic vesicles is partly mediated by the actin network. Further, the actin-dependent transport is predominantly carried out by Myosin-V. I develop a correlated-motion analysis to characterize the mechanics of how actin and Myosin-V affect vesicle transport. Lastly, I also observe that vesicle exit rates from the synapse to the axon and long-distance vesicle transport are both regulated by activity, but Myosin-V does not appear to mediate the activity dependence. These observations highlight the roles of the axonal actin network, and Myosin-V in particular, in regulating inter-synaptic vesicle exchange.
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Bu, Yunfei; Wang, Ning; Wang, Shaoli; Sheng, Tao; Tian, Tian; Chen, Linlin; Pan, Weiwei; Zhu, Minsheng; Luo, Jianhong; Lu, Wei
2015-10-16
N-Methyl-d-aspartate receptor (NMDAR) synaptic incorporation changes the number of NMDARs at synapses and is thus critical to various NMDAR-dependent brain functions. To date, the molecules involved in NMDAR trafficking and the underlying mechanisms are poorly understood. Here, we report that myosin IIb is an essential molecule in NMDAR synaptic incorporation during PKC- or θ burst stimulation-induced synaptic plasticity. Moreover, we demonstrate that myosin light chain kinase (MLCK)-dependent actin reorganization contributes to NMDAR trafficking. The findings from additional mutual occlusion experiments demonstrate that PKC and MLCK share a common signaling pathway in NMDAR-mediated synaptic regulation. Because myosin IIb is the primary substrate of MLCK and can regulate actin dynamics during synaptic plasticity, we propose that the MLCK- and myosin IIb-dependent regulation of actin dynamics is required for NMDAR trafficking during synaptic plasticity. This study provides important insights into a mechanical framework for understanding NMDAR trafficking associated with synaptic plasticity. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.
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Reddy, P. Hemachandra; Tripathy, Raghav; Troung, Quang; Thirumala, Karuna; Reddy, Tejaswini P.; Anekonda, Vishwanath; Shirendeb, Ulziibat P.; Calkins, Marcus J.; Reddy, Arubala P.; Mao, Peizhong; Manczak, Maria
2011-01-01
Synaptic pathology and mitochondrial oxidative damage are early events in Alzheimer’s disease (AD) progression. Loss of synapses and synaptic damage are the best correlate of cognitive deficits found in AD patients. Recent research on amyloid bet (Aβ) and mitochondria in AD revealed that Aβ accumulates in synapses and synaptic mitochondria, leading to abnormal mitochondrial dynamics and synaptic degeneration in AD neurons. Further, recent studies using live-cell imaging and primary neurons from amyloid beta precursor protein (AβPP) transgenic mice revealed that reduced mitochondrial mass, defective axonal transport of mitochondria and synaptic degeneration, indicating that Aβ is responsible for mitochondrial and synaptic deficiencies. Tremendous progress has been made in studying antioxidant approaches in mouse models of AD and clinical trials of AD patients. This article highlights the recent developments made in Aβ-induced abnormal mitochondrial dynamics, defective mitochondrial biogenesis, impaired axonal transport and synaptic deficiencies in AD. This article also focuses on mitochondrial approaches in treating AD, and also discusses latest research on mitochondria-targeted antioxidants in AD. PMID:22037588
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Asghari Adib, Elham; Stanchev, Doychin T; Xiong, Xin; Klinedinst, Susan; Soppina, Pushpanjali; Jahn, Thomas Robert; Hume, Richard I
2017-01-01
The kinesin-3 family member Unc-104/KIF1A is required for axonal transport of many presynaptic components to synapses, and mutation of this gene results in synaptic dysfunction in mice, flies and worms. Our studies at the Drosophila neuromuscular junction indicate that many synaptic defects in unc-104-null mutants are mediated independently of Unc-104’s transport function, via the Wallenda (Wnd)/DLK MAP kinase axonal damage signaling pathway. Wnd signaling becomes activated when Unc-104’s function is disrupted, and leads to impairment of synaptic structure and function by restraining the expression level of active zone (AZ) and synaptic vesicle (SV) components. This action concomitantly suppresses the buildup of synaptic proteins in neuronal cell bodies, hence may play an adaptive role to stresses that impair axonal transport. Wnd signaling also becomes activated when pre-synaptic proteins are over-expressed, suggesting the existence of a feedback circuit to match synaptic protein levels to the transport capacity of the axon. PMID:28925357
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Glutamatergic synaptic plasticity in the mesocorticolimbic system in addiction
van Huijstee, Aile N.; Mansvelder, Huibert D.
2015-01-01
Addictive drugs remodel the brain’s reward circuitry, the mesocorticolimbic dopamine (DA) system, by inducing widespread adaptations of glutamatergic synapses. This drug-induced synaptic plasticity is thought to contribute to both the development and the persistence of addiction. This review highlights the synaptic modifications that are induced by in vivo exposure to addictive drugs and describes how these drug-induced synaptic changes may contribute to the different components of addictive behavior, such as compulsive drug use despite negative consequences and relapse. Initially, exposure to an addictive drug induces synaptic changes in the ventral tegmental area (VTA). This drug-induced synaptic potentiation in the VTA subsequently triggers synaptic changes in downstream areas of the mesocorticolimbic system, such as the nucleus accumbens (NAc) and the prefrontal cortex (PFC), with further drug exposure. These glutamatergic synaptic alterations are then thought to mediate many of the behavioral symptoms that characterize addiction. The later stages of glutamatergic synaptic plasticity in the NAc and in particular in the PFC play a role in maintaining addiction and drive relapse to drug-taking induced by drug-associated cues. Remodeling of PFC glutamatergic circuits can persist into adulthood, causing a lasting vulnerability to relapse. We will discuss how these neurobiological changes produced by drugs of abuse may provide novel targets for potential treatment strategies for addiction. PMID:25653591
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Neurogranin restores amyloid β-mediated synaptic transmission and long-term potentiation deficits.
Kaleka, Kanwardeep Singh; Gerges, Nashaat Z
2016-03-01
Amyloid β (Aβ) is widely considered one of the early causes of cognitive deficits observed in Alzheimer's disease. Many of the deficits caused by Aβ are attributed to its disruption of synaptic function represented by its blockade of long-term potentiation (LTP) and its induction of synaptic depression. Identifying pathways that reverse these synaptic deficits may open the door to new therapeutic targets. In this study, we explored the possibility that Neurogranin (Ng)-a postsynaptic calmodulin (CaM) targeting protein that enhances synaptic function-may rescue Aβ-mediated deficits in synaptic function. Our results show that Ng is able to reverse synaptic depression and LTP deficits induced by Aβ. Furthermore, Ng's restoration of synaptic transmission is through the insertion of GluA1-containing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid glutamate receptors (AMPARs). These restorative effects of Ng are dependent on the interaction of Ng and CaM and CaM-dependent activation of CaMKII. Overall, this study identifies a novel mechanism to rescue synaptic deficits induced by Aβ oligomers. It also suggests Ng and CaM signaling as potential therapeutic targets for Alzheimer's disease as well as important tools to further explore the pathophysiology underlying the disease. Copyright © 2015 Elsevier Inc. All rights reserved.
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Daniels, Richard W; Collins, Catherine A; Gelfand, Maria V; Dant, Jaime; Brooks, Elizabeth S; Krantz, David E; DiAntonio, Aaron
2004-11-17
Quantal size is a fundamental parameter controlling the strength of synaptic transmission. The transmitter content of synaptic vesicles is one mechanism that can affect the physiological response to the release of a single vesicle. At glutamatergic synapses, vesicular glutamate transporters (VGLUTs) are responsible for filling synaptic vesicles with glutamate. To investigate how VGLUT expression can regulate synaptic strength in vivo, we have identified the Drosophila vesicular glutamate transporter, which we name DVGLUT. DVGLUT mRNA is expressed in glutamatergic motoneurons and a large number of interneurons in the Drosophila CNS. DVGLUT protein resides on synaptic vesicles and localizes to the presynaptic terminals of all known glutamatergic neuromuscular junctions as well as to synapses throughout the CNS neuropil. Increasing the expression of DVGLUT in motoneurons leads to an increase in quantal size that is accompanied by an increase in synaptic vesicle volume. At synapses confronted with increased glutamate release from each vesicle, there is a compensatory decrease in the number of synaptic vesicles released that maintains normal levels of synaptic excitation. These results demonstrate that (1) expression of DVGLUT determines the size and glutamate content of synaptic vesicles and (2) homeostatic mechanisms exist to attenuate the excitatory effects of excess glutamate release.
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Patty, Philipus J; Frisken, Barbara J
2006-04-01
We compare results for the number-weighted mean radius and polydispersity obtained either by directly fitting number distributions to dynamic light-scattering data or by converting results obtained by fitting intensity-weighted distributions. We find that results from fits using number distributions are angle independent and that converting intensity-weighted distributions is not always reliable, especially when the polydispersity of the sample is large. We compare the results of fitting symmetric and asymmetric distributions, as represented by Gaussian and Schulz distributions, respectively, to data for extruded vesicles and find that the Schulz distribution provides a better estimate of the size distribution for these samples.
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Synaptic Plasticity and Memory: New Insights from Hippocampal Left-Right Asymmetries.
El-Gaby, Mohamady; Shipton, Olivia A; Paulsen, Ole
2015-10-01
All synapses are not the same. They differ in their morphology, molecular constituents, and malleability. A striking left-right asymmetry in the distribution of different types of synapse was recently uncovered at the CA3-CA1 projection in the mouse hippocampus, whereby afferents from the CA3 in the left hemisphere innervate small, highly plastic synapses on the apical dendrites of CA1 pyramidal neurons, whereas those originating from the right CA3 target larger, more stable synapses. Activity-dependent modification of these synapses is thought to participate in circuit formation and remodeling during development, and further plastic changes may support memory encoding in adulthood. Therefore, exploiting the CA3-CA1 asymmetry provides a promising opportunity to investigate the roles that different types of synapse play in these fundamental properties of the CNS. Here we describe the discovery of these segregated synaptic populations in the mouse hippocampus, and discuss what we have already learnt about synaptic plasticity from this asymmetric arrangement. We then propose models for how the asymmetry could be generated during development, and how the adult hippocampus might use these distinct populations of synapses differentially during learning and memory. Finally, we outline the potential implications of this left-right asymmetry for human hippocampal function, as well as dysfunction in memory disorders such as Alzheimer's disease. © The Author(s) 2014.
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Active zone density is conserved during synaptic growth but impaired in aged mice
Chen, Jie; Mizushige, Takafumi; Nishimune, Hiroshi
2013-01-01
Presynaptic active zones are essential structures for synaptic vesicle release, but the developmental regulation of their number and maintenance during aging at mammalian neuromuscular junctions (NMJs) remains unknown. Here, we analyzed the distribution of active zones in developing, mature, and aged mouse NMJs by immunohistochemical detection of the active zone-specific protein Bassoon. Bassoon is a cytosolic scaffolding protein essential for the active zone assembly in ribbon synapses and some brain synapses. Bassoon staining showed a punctate pattern in nerve terminals and axons at the nascent NMJ on embryonic days 16.5–18.5. Three-dimensional reconstruction of NMJs revealed that the majority of Bassoon puncta within an NMJ were attached to the presynaptic membrane from postnatal day 0 to adulthood, and colocalized with another active zone protein Piccolo. During postnatal development, the number of Bassoon puncta increased as the size of the synapses increased. Importantly, the density of Bassoon puncta remained relatively constant from postnatal day 0 to 54 at 2.3 puncta/μm2, while the synapse size increased 3.3-fold. However, Bassoon puncta density and signal intensity were significantly attenuated at the NMJs of 27-month-old aged mice. These results suggest that synapses maintain the density of synaptic vesicle release sites while the synapse size changes, but this density becomes impaired during aging. PMID:21935939
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Liprin-α3 controls vesicle docking and exocytosis at the active zone of hippocampal synapses.
Wong, Man Yan; Liu, Changliang; Wang, Shan Shan H; Roquas, Aram C F; Fowler, Stephen C; Kaeser, Pascal S
2018-02-27
The presynaptic active zone provides sites for vesicle docking and release at central nervous synapses and is essential for speed and accuracy of synaptic transmission. Liprin-α binds to several active zone proteins, and loss-of-function studies in invertebrates established important roles for Liprin-α in neurodevelopment and active zone assembly. However, Liprin-α localization and functions in vertebrates have remained unclear. We used stimulated emission depletion superresolution microscopy to systematically determine the localization of Liprin-α2 and Liprin-α3, the two predominant Liprin-α proteins in the vertebrate brain, relative to other active-zone proteins. Both proteins were widely distributed in hippocampal nerve terminals, and Liprin-α3, but not Liprin-α2, had a prominent component that colocalized with the active-zone proteins Bassoon, RIM, Munc13, RIM-BP, and ELKS. To assess Liprin-α3 functions, we generated Liprin-α3-KO mice by using CRISPR/Cas9 gene editing. We found reduced synaptic vesicle tethering and docking in hippocampal neurons of Liprin-α3-KO mice, and synaptic vesicle exocytosis was impaired. Liprin-α3 KO also led to mild alterations in active zone structure, accompanied by translocation of Liprin-α2 to active zones. These findings establish important roles for Liprin-α3 in active-zone assembly and function, and suggest that interplay between various Liprin-α proteins controls their active-zone localization.
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Partanen, Sanna; Haapanen, Aleksi; Kielar, Catherine; Pontikis, Charles; Alexander, Noreen; Inkinen, Teija; Saftig, Paul; Gillingwater, Thomas H; Cooper, Jonathan D; Tyynelä, Jaana
2008-01-01
Cathepsin D (CTSD; EC 3.4.23.5) is a lysosomal aspartic protease, the deficiency of which causes early-onset and particularly aggressive forms of neuronal ceroid-lipofuscinosis in infants, sheep, and mice. Cathepsin D deficiencies are characterized by severe neurodegeneration, but the molecular mechanisms behind the neuronal death remain poorly understood. In this study, we have systematically mapped the distribution of neuropathologic changes in CTSD-deficient mouse brains by stereologic, immunologic, and electron microscopic methods. We report highly accentuated neuropathologic changes within the ventral posterior nucleus (ventral posteromedial [VPM]/ventral posterolateral [VPL]) of thalamus and in neuronal laminae IV and VI of the somatosensory cortex (S1BF), which receive and send information to the thalamic VPM/VPL. These changes included pronounced astrocytosis and microglial activation that begin in the VPM/VPL thalamic nucleus of CTSD-deficient mice and are associated with reduced neuronal number and redistribution of presynaptic markers. In addition, loss of synapses, axonal pathology, and aggregation of synaptophysin and synaptobrevin were observed in the VPM/VPL. These synaptic alterations are accompanied by changes in the amount of synaptophysin/synaptobrevin heterodimer, which regulates formation of the SNARE complex at the synapse. Taken together, these data reveal the somatosensory thalamocortical circuitry as a particular focus of pathologic changes and provide the first evidence for synaptic alterations at the molecular and ultrastructural levels in CTSD deficiency.
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Dunn, Amy R; Stout, Kristen A; Ozawa, Minagi; Lohr, Kelly M; Hoffman, Carlie A; Bernstein, Alison I; Li, Yingjie; Wang, Minzheng; Sgobio, Carmelo; Sastry, Namratha; Cai, Huaibin; Caudle, W Michael; Miller, Gary W
2017-03-14
Members of the synaptic vesicle glycoprotein 2 (SV2) family of proteins are involved in synaptic function throughout the brain. The ubiquitously expressed SV2A has been widely implicated in epilepsy, although SV2C with its restricted basal ganglia distribution is poorly characterized. SV2C is emerging as a potentially relevant protein in Parkinson disease (PD), because it is a genetic modifier of sensitivity to l-DOPA and of nicotine neuroprotection in PD. Here we identify SV2C as a mediator of dopamine homeostasis and report that disrupted expression of SV2C within the basal ganglia is a pathological feature of PD. Genetic deletion of SV2C leads to reduced dopamine release in the dorsal striatum as measured by fast-scan cyclic voltammetry, reduced striatal dopamine content, disrupted α-synuclein expression, deficits in motor function, and alterations in neurochemical effects of nicotine. Furthermore, SV2C expression is dramatically altered in postmortem brain tissue from PD cases but not in Alzheimer disease, progressive supranuclear palsy, or multiple system atrophy. This disruption was paralleled in mice overexpressing mutated α-synuclein. These data establish SV2C as a mediator of dopamine neuron function and suggest that SV2C disruption is a unique feature of PD that likely contributes to dopaminergic dysfunction.
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Robustness effect of gap junctions between Golgi cells on cerebellar cortex oscillations
2011-01-01
Background Previous one-dimensional network modeling of the cerebellar granular layer has been successfully linked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap junctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how gap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a novel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions between GoCs. Results Isolated GoCs coupled by gap junctions had a strong tendency to generate spontaneous oscillations without affecting their mean firing frequencies in response to distributed mossy fiber input. Conversely, when GoCs were synaptically connected in the granular layer, gap junctions increased the power of the oscillations, but the oscillations were primarily driven by the synaptic feedback loop between GoCs and GCs, and the gap junctions did not change oscillation frequency or the mean firing rate of either GoCs or GCs. Conclusion Our modeling results suggest that gap junctions between GoCs increase the robustness of cerebellar cortex oscillations that are primarily driven by the feedback loop between GoCs and GCs. The robustness effect of gap junctions on synaptically driven oscillations observed in our model may be a general mechanism, also present in other regions of the brain. PMID:22330240
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Bengtson, C Peter; Kaiser, Martin; Obermayer, Joshua; Bading, Hilmar
2013-07-01
Both synaptic N-methyl-d-aspartate (NMDA) receptors and voltage-operated calcium channels (VOCCs) have been shown to be critical for nuclear calcium signals associated with transcriptional responses to bursts of synaptic input. However the direct contribution to nuclear calcium signals from calcium influx through NMDA receptors and VOCCs has been obscured by their concurrent roles in action potential generation and synaptic transmission. Here we compare calcium responses to synaptically induced bursts of action potentials with identical bursts devoid of any synaptic contribution generated using the pre-recorded burst as the voltage clamp command input to replay the burst in the presence of blockers of action potentials or ionotropic glutamate receptors. Synapse independent replays of bursts produced nuclear calcium responses with amplitudes around 70% of their original synaptically generated signals and were abolished by the L-type VOCC blocker, verapamil. These results identify a major direct source of nuclear calcium from local L-type VOCCs whose activation is boosted by NMDA receptor dependent depolarization. The residual component of synaptically induced nuclear calcium signals which was both VOCC independent and NMDA receptor dependent showed delayed kinetics consistent with a more distal source such as synaptic NMDA receptors or internal stores. The dual requirement of NMDA receptors and L-type VOCCs for synaptic activity-induced nuclear calcium dependent transcriptional responses most likely reflects a direct somatic calcium influx from VOCCs whose activation is amplified by synaptic NMDA receptor-mediated depolarization and whose calcium signal is boosted by a delayed input from distal calcium sources mostly likely entry through NMDA receptors and release from internal stores. This article is part of a Special Issue entitled: 12th European Symposium on Calcium. Copyright © 2013 Elsevier B.V. All rights reserved.
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Alteration of synaptic connectivity of oligodendrocyte precursor cells following demyelination
Sahel, Aurélia; Ortiz, Fernando C.; Kerninon, Christophe; Maldonado, Paloma P.; Angulo, María Cecilia; Nait-Oumesmar, Brahim
2015-01-01
Oligodendrocyte precursor cells (OPCs) are a major source of remyelinating oligodendrocytes in demyelinating diseases such as Multiple Sclerosis (MS). While OPCs are innervated by unmyelinated axons in the normal brain, the fate of such synaptic contacts after demyelination is still unclear. By combining electrophysiology and immunostainings in different transgenic mice expressing fluorescent reporters, we studied the synaptic innervation of OPCs in the model of lysolecithin (LPC)-induced demyelination of corpus callosum. Synaptic innervation of reactivated OPCs in the lesion was revealed by the presence of AMPA receptor-mediated synaptic currents, VGluT1+ axon-OPC contacts in 3D confocal reconstructions and synaptic junctions observed by electron microscopy. Moreover, 3D confocal reconstructions of VGluT1 and NG2 immunolabeling showed the existence of glutamatergic axon-OPC contacts in post-mortem MS lesions. Interestingly, patch-clamp recordings in LPC-induced lesions demonstrated a drastic decrease in spontaneous synaptic activity of OPCs early after demyelination that was not caused by an impaired conduction of compound action potentials. A reduction in synaptic connectivity was confirmed by the lack of VGluT1+ axon-OPC contacts in virtually all rapidly proliferating OPCs stained with EdU (50-ethynyl-20-deoxyuridine). At the end of the massive proliferation phase in lesions, the proportion of innervated OPCs rapidly recovers, although the frequency of spontaneous synaptic currents did not reach control levels. In conclusion, our results demonstrate that newly-generated OPCs do not receive synaptic inputs during their active proliferation after demyelination, but gain synapses during the remyelination process. Hence, glutamatergic synaptic inputs may contribute to inhibit OPC proliferation and might have a physiopathological relevance in demyelinating disorders. PMID:25852473
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Pinto, Joshua G. A.; Jones, David G.; Williams, C. Kate; Murphy, Kathryn M.
2015-01-01
Although many potential neuroplasticity based therapies have been developed in the lab, few have translated into established clinical treatments for human neurologic or neuropsychiatric diseases. Animal models, especially of the visual system, have shaped our understanding of neuroplasticity by characterizing the mechanisms that promote neural changes and defining timing of the sensitive period. The lack of knowledge about development of synaptic plasticity mechanisms in human cortex, and about alignment of synaptic age between animals and humans, has limited translation of neuroplasticity therapies. In this study, we quantified expression of a set of highly conserved pre- and post-synaptic proteins (Synapsin, Synaptophysin, PSD-95, Gephyrin) and found that synaptic development in human primary visual cortex (V1) continues into late childhood. Indeed, this is many years longer than suggested by neuroanatomical studies and points to a prolonged sensitive period for plasticity in human sensory cortex. In addition, during childhood we found waves of inter-individual variability that are different for the four proteins and include a stage during early development (<1 year) when only Gephyrin has high inter-individual variability. We also found that pre- and post-synaptic protein balances develop quickly, suggesting that maturation of certain synaptic functions happens within the 1 year or 2 of life. A multidimensional analysis (principle component analysis) showed that most of the variance was captured by the sum of the four synaptic proteins. We used that sum to compare development of human and rat visual cortex and identified a simple linear equation that provides robust alignment of synaptic age between humans and rats. Alignment of synaptic ages is important for age-appropriate targeting and effective translation of neuroplasticity therapies from the lab to the clinic. PMID:25729353
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Friedl, W; Lentes, K U; Schmitz, E; Propping, P; Hebebrand, J
1988-12-01
Peptide mapping can be used to elucidate further the structural similarities of the benzodiazepine binding proteins in different vertebrate species. Crude synaptic membrane preparations were photoaffinity-labeled with [3H]flunitrazepam and subsequently degraded with various concentrations of trypsin. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by fluorography allowed a comparison of the molecular weights of photolabeled peptides in different species. Tryptic degradation led to a common peptide of 40K in all species investigated, a finding indicating that the benzodiazepine binding proteins are structurally homologous in higher bony fishes and tetrapods.
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Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo
2013-12-01
How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.
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Disorder generated by interacting neural networks: application to econophysics and cryptography
NASA Astrophysics Data System (ADS)
Kinzel, Wolfgang; Kanter, Ido
2003-10-01
When neural networks are trained on their own output signals they generate disordered time series. In particular, when two neural networks are trained on their mutual output they can synchronize; they relax to a time-dependent state with identical synaptic weights. Two applications of this phenomenon are discussed for (a) econophysics and (b) cryptography. (a) When agents competing in a closed market (minority game) are using neural networks to make their decisions, the total system relaxes to a state of good performance. (b) Two partners communicating over a public channel can find a common secret key.
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Distribution of Structural Weight of Wing Along the Span
NASA Technical Reports Server (NTRS)
Savelyev, V. V.
1946-01-01
In the present report the true weight distribution law of the wing structure along the span is investigated. It is shown that the triangular distribution and that based on the proportionality to the chords do not correspond to the actual weight distribution, On the basis of extensive data on wings of the CAHI type airplane formulas are obtained from which it is possible to determine the true diagram of the structural weight distribution along the span from a knowledge of only the geometrical dimensions of the wing. At the end of the paper data are presented showing how the structural weight is distributed between the straight center portion and the tapered portion as a function of their areas.
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Epidemic spreading in weighted networks: an edge-based mean-field solution.
Yang, Zimo; Zhou, Tao
2012-05-01
Weight distribution greatly impacts the epidemic spreading taking place on top of networks. This paper presents a study of a susceptible-infected-susceptible model on regular random networks with different kinds of weight distributions. Simulation results show that the more homogeneous weight distribution leads to higher epidemic prevalence, which, unfortunately, could not be captured by the traditional mean-field approximation. This paper gives an edge-based mean-field solution for general weight distribution, which can quantitatively reproduce the simulation results. This method could be applied to characterize the nonequilibrium steady states of dynamical processes on weighted networks.
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Hertäg, Loreen; Durstewitz, Daniel; Brunel, Nicolas
2014-01-01
Computational models offer a unique tool for understanding the network-dynamical mechanisms which mediate between physiological and biophysical properties, and behavioral function. A traditional challenge in computational neuroscience is, however, that simple neuronal models which can be studied analytically fail to reproduce the diversity of electrophysiological behaviors seen in real neurons, while detailed neuronal models which do reproduce such diversity are intractable analytically and computationally expensive. A number of intermediate models have been proposed whose aim is to capture the diversity of firing behaviors and spike times of real neurons while entailing the simplest possible mathematical description. One such model is the exponential integrate-and-fire neuron with spike rate adaptation (aEIF) which consists of two differential equations for the membrane potential (V) and an adaptation current (w). Despite its simplicity, it can reproduce a wide variety of physiologically observed spiking patterns, can be fit to physiological recordings quantitatively, and, once done so, is able to predict spike times on traces not used for model fitting. Here we compute the steady-state firing rate of aEIF in the presence of Gaussian synaptic noise, using two approaches. The first approach is based on the 2-dimensional Fokker-Planck equation that describes the (V,w)-probability distribution, which is solved using an expansion in the ratio between the time constants of the two variables. The second is based on the firing rate of the EIF model, which is averaged over the distribution of the w variable. These analytically derived closed-form expressions were tested on simulations from a large variety of model cells quantitatively fitted to in vitro electrophysiological recordings from pyramidal cells and interneurons. Theoretical predictions closely agreed with the firing rate of the simulated cells fed with in-vivo-like synaptic noise.
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Temperature manipulation of neuronal dynamics in a forebrain motor control nucleus
Mindlin, Gabriel B.
2017-01-01
Different neuronal types within brain motor areas contribute to the generation of complex motor behaviors. A widely studied songbird forebrain nucleus (HVC) has been recognized as fundamental in shaping the precise timing characteristics of birdsong. This is based, among other evidence, on the stretching and the “breaking” of song structure when HVC is cooled. However, little is known about the temperature effects that take place in its neurons. To address this, we investigated the dynamics of HVC both experimentally and computationally. We developed a technique where simultaneous electrophysiological recordings were performed during temperature manipulation of HVC. We recorded spontaneous activity and found three effects: widening of the spike shape, decrease of the firing rate and change in the interspike interval distribution. All these effects could be explained with a detailed conductance based model of all the neurons present in HVC. Temperature dependence of the ionic channel time constants explained the first effect, while the second was based in the changes of the maximal conductance using single synaptic excitatory inputs. The last phenomenon, only emerged after introducing a more realistic synaptic input to the inhibitory interneurons. Two timescales were present in the interspike distributions. The behavior of one timescale was reproduced with different input balances received form the excitatory neurons, whereas the other, which disappears with cooling, could not be found assuming poissonian synaptic inputs. Furthermore, the computational model shows that the bursting of the excitatory neurons arises naturally at normal brain temperature and that they have an intrinsic delay at low temperatures. The same effect occurs at single synapses, which may explain song stretching. These findings shed light on the temperature dependence of neuronal dynamics and present a comprehensive framework to study neuronal connectivity. This study, which is based on intrinsic neuronal characteristics, may help to understand emergent behavioral changes. PMID:28829769
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Glavinovíc, M I
1999-02-01
The release of vesicular glutamate, spatiotemporal changes in glutamate concentration in the synaptic cleft and the subsequent generation of fast excitatory postsynaptic currents at a hippocampal synapse were modeled using the Monte Carlo method. It is assumed that glutamate is released from a spherical vesicle through a cylindrical fusion pore into the synaptic cleft and that S-alpha-amino-3-hydroxy -5-methyl-4-isoxazolepropionic acid (AMPA) receptors are uniformly distributed postsynaptically. The time course of change in vesicular concentration can be described by a single exponential, but a slow tail is also observed though only following the release of most of the glutamate. The time constant of decay increases with vesicular size and a lower diffusion constant, and is independent of the initial concentration, becoming markedly shorter for wider fusion pores. The cleft concentration at the fusion pore mouth is not negligible compared to vesicular concentration, especially for wider fusion pores. Lateral equilibration of glutamate is rapid, and within approximately 50 micros all AMPA receptors on average see the same concentration of glutamate. Nevertheless the single-channel current and the number of channels estimated from mean-variance plots are unreliable and different when estimated from rise- and decay-current segments. Greater saturation of AMPA receptor channels provides higher but not more accurate estimates. Two factors contribute to the variability of postsynaptic currents and render the mean-variance nonstationary analysis unreliable, even when all receptors see on average the same glutamate concentration. Firstly, the variability of the instantaneous cleft concentration of glutamate, unlike the mean concentration, first rapidly decreases before slowly increasing; the variability is greater for fewer molecules in the cleft and is spatially nonuniform. Secondly, the efficacy with which glutamate produces a response changes with time. Understanding the factors that determine the time course of vesicular content release as well as the spatiotemporal changes of glutamate concentration in the cleft is crucial for understanding the mechanism that generates postsynaptic currents.
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Firing rate of noisy integrate-and-fire neurons with synaptic current dynamics
DOE Office of Scientific and Technical Information (OSTI.GOV)
Andrieux, David; Monnai, Takaaki; Department of Applied Physics, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555
2009-08-15
We derive analytical formulas for the firing rate of integrate-and-fire neurons endowed with realistic synaptic dynamics. In particular, we include the possibility of multiple synaptic inputs as well as the effect of an absolute refractory period into the description. The latter affects the firing rate through its interaction with the synaptic dynamics.
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The Role of Short Term Synaptic Plasticity in Temporal Coding of Neuronal Networks
ERIC Educational Resources Information Center
Chandrasekaran, Lakshmi
2008-01-01
Short term synaptic plasticity is a phenomenon which is commonly found in the central nervous system. It could contribute to functions of signal processing namely, temporal integration and coincidence detection by modulating the input synaptic strength. This dissertation has two parts. First, we study the effects of short term synaptic plasticity…
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The DcpS inhibitor RG3039 improves motor function in SMA mice
Van Meerbeke, James P.; Gibbs, Rebecca M.; Plasterer, Heather L.; Miao, Wenyan; Feng, Zhihua; Lin, Ming-Yi; Rucki, Agnieszka A.; Wee, Claribel D.; Xia, Bing; Sharma, Shefali; Jacques, Vincent; Li, Darrick K.; Pellizzoni, Livio; Rusche, James R.; Ko, Chien-Ping; Sumner, Charlotte J.
2013-01-01
Spinal muscular atrophy (SMA) is caused by mutations of the survival motor neuron 1 (SMN1) gene, retention of the survival motor neuron 2 (SMN2) gene and insufficient expression of full-length survival motor neuron (SMN) protein. Quinazolines increase SMN2 promoter activity and inhibit the ribonucleic acid scavenger enzyme DcpS. The quinazoline derivative RG3039 has advanced to early phase clinical trials. In preparation for efficacy studies in SMA patients, we investigated the effects of RG3039 in severe SMA mice. Here, we show that RG3039 distributed to central nervous system tissues where it robustly inhibited DcpS enzyme activity, but minimally activated SMN expression or the assembly of small nuclear ribonucleoproteins. Nonetheless, treated SMA mice showed a dose-dependent increase in survival, weight and motor function. This was associated with improved motor neuron somal and neuromuscular junction synaptic innervation and function and increased muscle size. RG3039 also enhanced survival of conditional SMA mice in which SMN had been genetically restored to motor neurons. As this systemically delivered drug may have therapeutic benefits that extend beyond motor neurons, it could act additively with SMN-restoring therapies delivered directly to the central nervous system such as antisense oligonucleotides or gene therapy. PMID:23727836
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Mesulam, M M
1998-06-01
Sensory information undergoes extensive associative elaboration and attentional modulation as it becomes incorporated into the texture of cognition. This process occurs along a core synaptic hierarchy which includes the primary sensory, upstream unimodal, downstream unimodal, heteromodal, paralimbic and limbic zones of the cerebral cortex. Connections from one zone to another are reciprocal and allow higher synaptic levels to exert a feedback (top-down) influence upon earlier levels of processing. Each cortical area provides a nexus for the convergence of afferents and divergence of efferents. The resultant synaptic organization supports parallel as well as serial processing, and allows each sensory event to initiate multiple cognitive and behavioural outcomes. Upstream sectors of unimodal association areas encode basic features of sensation such as colour, motion, form and pitch. More complex contents of sensory experience such as objects, faces, word-forms, spatial locations and sound sequences become encoded within downstream sectors of unimodal areas by groups of coarsely tuned neurons. The highest synaptic levels of sensory-fugal processing are occupied by heteromodal, paralimbic and limbic cortices, collectively known as transmodal areas. The unique role of these areas is to bind multiple unimodal and other transmodal areas into distributed but integrated multimodal representations. Transmodal areas in the midtemporal cortex, Wernicke's area, the hippocampal-entorhinal complex and the posterior parietal cortex provide critical gateways for transforming perception into recognition, word-forms into meaning, scenes and events into experiences, and spatial locations into targets for exploration. All cognitive processes arise from analogous associative transformations of similar sets of sensory inputs. The differences in the resultant cognitive operation are determined by the anatomical and physiological properties of the transmodal node that acts as the critical gateway for the dominant transformation. Interconnected sets of transmodal nodes provide anatomical and computational epicentres for large-scale neurocognitive networks. In keeping with the principles of selectively distributed processing, each epicentre of a large-scale network displays a relative specialization for a specific behavioural component of its principal neurospychological domain. The destruction of transmodal epicentres causes global impairments such as multimodal anomia, neglect and amnesia, whereas their selective disconnection from relevant unimodal areas elicits modality-specific impairments such as prosopagnosia, pure word blindness and category-specific anomias. The human brain contains at least five anatomically distinct networks. The network for spatial awareness is based on transmodal epicentres in the posterior parietal cortex and the frontal eye fields; the language network on epicentres in Wernicke's and Broca's areas; the explicit memory/emotion network on epicentres in the hippocampal-entorhinal complex and the amygdala; the face-object recognition network on epicentres in the midtemporal and temporopolar cortices; and the working memory-executive function network on epicentres in the lateral prefrontal cortex and perhaps the posterior parietal cortex. Individual sensory modalities give rise to streams of processing directed to transmodal nodes belonging to each of these networks. The fidelity of sensory channels is actively protected through approximately four synaptic levels of sensory-fugal processing. The modality-specific cortices at these four synaptic levels encode the most veridical representations of experience. Attentional, motivational and emotional modulations, including those related to working memory, novelty-seeking and mental imagery, become increasingly more pronounced within downstream components of unimodal areas, where they help to create a highly edited subjective version of the world. (ABSTRACT TRUNCATED)
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Neuronal modelling of baroreflex response to orthostatic stress
NASA Astrophysics Data System (ADS)
Samin, Azfar
The accelerations experienced in aerial combat can cause pilot loss of consciousness (GLOC) due to a critical reduction in cerebral blood circulation. The development of smart protective equipment requires understanding of how the brain processes blood pressure (BP) information in response to acceleration. We present a biologically plausible model of the Baroreflex to investigate the neural correlates of short-term BP control under acceleration or orthostatic stress. The neuronal network model, which employs an integrate-and-fire representation of a biological neuron, comprises the sensory, motor, and the central neural processing areas that form the Baroreflex. Our modelling strategy is to test hypotheses relating to the encoding mechanisms of multiple sensory inputs to the nucleus tractus solitarius (NTS), the site of central neural processing. The goal is to run simulations and reproduce model responses that are consistent with the variety of available experimental data. Model construction and connectivity are inspired by the available anatomical and neurophysiological evidence that points to a barotopic organization in the NTS, and the presence of frequency-dependent synaptic depression, which provides a mechanism for generating non-linear local responses in NTS neurons that result in quantifiable dynamic global baroreflex responses. The entire physiological range of BP and rate of change of BP variables is encoded in a palisade of NTS neurons in that the spike responses approximate Gaussian 'tuning' curves. An adapting weighted-average decoding scheme computes the motor responses and a compensatory signal regulates the heart rate (HR). Model simulations suggest that: (1) the NTS neurons can encode the hydrostatic pressure difference between two vertically separated sensory receptor regions at +Gz, and use changes in that difference for the regulation of HR; (2) even though NTS neurons do not fire with a cardiac rhythm seen in the afferents, pulse-rhythmic activity is regained downstream provided the input phase information in preserved centrally; (3) frequency-dependent synaptic depression, which causes temporal variations in synaptic strength due to changes in input frequency, is a possible mechanism of non-linear dynamic baroreflex gain control. Synaptic depression enables the NTS neuron to encode dBP/dt but to lose information about the steady state firing of the afferents.
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Tewari, H B; Tyagi, H R
1977-01-01
The present study incorporates the details of distribution of adenosine triphosphatase amongst the various constituents of retinae of Passer, Psittacula, Streptopelia and Athene. The outer segments in all the cases are intensely positive for the enzyme. This is the part where the light strikes first and initiates the visual processes. The nuclear layers are also positive for the enzyme activity. It is interesting to note that inner plexiform layers show clear-out demarcations of various sub-synaptic layers in all the birds except Psittacula. The ganglion cells and optic nerve fibres are also positive for the enzyme.
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Face classification using electronic synapses
NASA Astrophysics Data System (ADS)
Yao, Peng; Wu, Huaqiang; Gao, Bin; Eryilmaz, Sukru Burc; Huang, Xueyao; Zhang, Wenqiang; Zhang, Qingtian; Deng, Ning; Shi, Luping; Wong, H.-S. Philip; Qian, He
2017-05-01
Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.
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Face classification using electronic synapses.
Yao, Peng; Wu, Huaqiang; Gao, Bin; Eryilmaz, Sukru Burc; Huang, Xueyao; Zhang, Wenqiang; Zhang, Qingtian; Deng, Ning; Shi, Luping; Wong, H-S Philip; Qian, He
2017-05-12
Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.
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Brenachot, Xavier; Rigault, Caroline; Nédélec, Emmanuelle; Laderrière, Amélie; Khanam, Tasneem; Gouazé, Alexandra; Chaudy, Sylvie; Lemoine, Aleth; Datiche, Frédérique; Gascuel, Jean; Pénicaud, Luc; Benani, Alexandre
2014-01-01
Overfeeding causes rapid synaptic remodeling in hypothalamus feeding circuits. Polysialylation of cell surface molecules is a key step in this neuronal rewiring and allows normalization of food intake. Here we examined the role of hypothalamic polysialylation in the long-term maintenance of body weight, and deciphered the molecular sequence underlying its nutritional regulation. We found that upon high fat diet (HFD), reduced hypothalamic polysialylation exacerbated the diet-induced obese phenotype in mice. Upon HFD, the histone acetyltransferase MOF was rapidly recruited on the St8sia4 polysialyltransferase-encoding gene. Mof silencing in the mediobasal hypothalamus of adult mice prevented activation of the St8sia4 gene transcription, reduced polysialylation, altered the acute homeostatic feeding response to HFD and increased the body weight gain. These findings indicate that impaired hypothalamic polysialylation contribute to the development of obesity, and establish a role for MOF in the brain control of energy balance. PMID:25161885
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Adaptive WTA with an analog VLSI neuromorphic learning chip.
Häfliger, Philipp
2007-03-01
In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.