Is a 4-Bit Synaptic Weight Resolution Enough? – Constraints on Enabling Spike-Timing Dependent Plasticity in Neuromorphic Hardware

PubMed Central

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

On the correlation between reservoir metrics and performance for time series classification under the influence of synaptic plasticity.

PubMed

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.

Synaptic behaviors of thin-film transistor with a Pt/HfO x /n-type indium-gallium-zinc oxide gate stack.

PubMed

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.

Synaptic behaviors of thin-film transistor with a Pt/HfO x /n-type indium–gallium–zinc oxide gate stack

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.

Supervised learning with decision margins in pools of spiking neurons.

PubMed

Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

2014-10-01

Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

Leptin gene therapy attenuates neuronal damages evoked by amyloid-β and rescues memory deficits in APP/PS1 mice.

PubMed

Pérez-González, R; Alvira-Botero, M X; Robayo, O; Antequera, D; Garzón, M; Martín-Moreno, A M; Brera, B; de Ceballos, M L; Carro, E

2014-03-01

There is growing evidence that leptin is able to ameliorate Alzheimer's disease (AD)-like pathologies, including brain amyloid-β (Aβ) burden. In order to improve the therapeutic potential for AD, we generated a lentivirus vector expressing leptin protein in a self-inactivating HIV-1 vector (HIV-leptin), and delivered this by intra-cerebroventricular administration to APP/PS1 transgenic model of AD. Three months after intra-cerebroventricular administration of HIV-leptin, brain Aβ accumulation was reduced. By electron microscopy, we found that APP/PS1 mice exhibited deficits in synaptic density, which were partially rescued by HIV-leptin treatment. Synaptic deficits in APP/PS1 mice correlated with an enhancement of caspase-3 expression, and a reduction in synaptophysin levels in synaptosome preparations. Notably, HIV-leptin therapy reverted these dysfunctions. Moreover, leptin modulated neurite outgrowth in primary neuronal cultures, and rescued them from Aβ42-induced toxicity. All the above changes suggest that leptin may affect multiple aspects of the synaptic status, and correlate with behavioral improvements. Our data suggest that leptin gene delivery has a therapeutic potential for Aβ-targeted treatment of mouse model of AD.

Hippocampal Insulin Resistance Impairs Spatial Learning and Synaptic Plasticity

PubMed Central

Piroli, Gerardo G.; Lawrence, Robert C.; Wrighten, Shayna A.; Green, Adrienne J.; Wilson, Steven P.; Sakai, Randall R.; Kelly, Sandra J.; Wilson, Marlene A.; Mott, David D.; Reagan, Lawrence P.

2015-01-01

Insulin receptors (IRs) are expressed in discrete neuronal populations in the central nervous system, including the hippocampus. To elucidate the functional role of hippocampal IRs independent of metabolic function, we generated a model of hippocampal-specific insulin resistance using a lentiviral vector expressing an IR antisense sequence (LV-IRAS). LV-IRAS effectively downregulates IR expression in the rat hippocampus without affecting body weight, adiposity, or peripheral glucose homeostasis. Nevertheless, hippocampal neuroplasticity was impaired in LV-IRAS–treated rats. High-frequency stimulation, which evoked robust long-term potentiation (LTP) in brain slices from LV control rats, failed to evoke LTP in LV-IRAS–treated rats. GluN2B subunit levels, as well as the basal level of phosphorylation of GluA1, were reduced in the hippocampus of LV-IRAS rats. Moreover, these deficits in synaptic transmission were associated with impairments in spatial learning. We suggest that alterations in the expression and phosphorylation of glutamate receptor subunits underlie the alterations in LTP and that these changes are responsible for the impairment in hippocampal-dependent learning. Importantly, these learning deficits are strikingly similar to the impairments in complex task performance observed in patients with diabetes, which strengthens the hypothesis that hippocampal insulin resistance is a key mediator of cognitive deficits independent of glycemic control. PMID:26216852

Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

PubMed Central

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

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.

Random synaptic feedback weights support error backpropagation for deep learning

PubMed Central

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

Depression-Biased Reverse Plasticity Rule Is Required for Stable Learning at Top-Down Connections

PubMed Central

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

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

PubMed

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.

Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses

PubMed Central

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

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: structuring synaptic pathways among recurrent connections.

PubMed

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.

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.

High precision computing with charge domain devices and a pseudo-spectral method therefor

NASA Technical Reports Server (NTRS)

Barhen, Jacob (Inventor); Toomarian, Nikzad (Inventor); Fijany, Amir (Inventor); Zak, Michail (Inventor)

1997-01-01

The present invention enhances the bit resolution of a CCD/CID MVM processor by storing each bit of each matrix element as a separate CCD charge packet. The bits of each input vector are separately multiplied by each bit of each matrix element in massive parallelism and the resulting products are combined appropriately to synthesize the correct product. In another aspect of the invention, such arrays are employed in a pseudo-spectral method of the invention, in which partial differential equations are solved by expressing each derivative analytically as matrices, and the state function is updated at each computation cycle by multiplying it by the matrices. The matrices are treated as synaptic arrays of a neural network and the state function vector elements are treated as neurons. In a further aspect of the invention, moving target detection is performed by driving the soliton equation with a vector of detector outputs. The neural architecture consists of two synaptic arrays corresponding to the two differential terms of the soliton-equation and an adder connected to the output thereof and to the output of the detector array to drive the soliton equation.

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks.

PubMed

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.

Pattern classification by memristive crossbar circuits using ex situ and in situ training.

PubMed

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.

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.

Text analysis devices, articles of manufacture, and text analysis methods

DOEpatents

Turner, Alan E; Hetzler, Elizabeth G; Nakamura, Grant C

2015-03-31

Text analysis devices, articles of manufacture, and text analysis methods are described according to some aspects. In one aspect, a text analysis device includes a display configured to depict visible images, and processing circuitry coupled with the display and wherein the processing circuitry is configured to access a first vector of a text item and which comprises a plurality of components, to access a second vector of the text item and which comprises a plurality of components, to weight the components of the first vector providing a plurality of weighted values, to weight the components of the second vector providing a plurality of weighted values, and to combine the weighted values of the first vector with the weighted values of the second vector to provide a third vector.

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.

A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

PubMed Central

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

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.

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.

Stability versus neuronal specialization for STDP: long-tail weight distributions solve the dilemma.

PubMed

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.

Transposon mutagenesis of Xylella fastidiosa by electroporation of Tn5 synaptic complexes.

PubMed

Guilhabert, M R; Hoffman, L M; Mills, D A; Kirkpatrick, B C

2001-06-01

Pierce's disease, a lethal disease of grapevine, is caused by Xylella fastidiosa, a gram-negative, xylem-limited bacterium that is transmitted from plant to plant by xylem-feeding insects. Strains of X. fastidiosa also have been associated with diseases that cause tremendous losses in many other economically important plants, including citrus. Although the complete genome sequence of X. fastidiosa has recently been determined, the inability to transform or produce transposon mutants of X. fastidiosa has been a major impediment to understanding pathogen-, plant-, and insect-vector interactions. We evaluated the ability of four different suicide vectors carrying either Tn5 or Tn10 transposons as well as a preformed Tn5 transposase-transposon synaptic complex (transposome) to transpose X. fastidiosa. The four suicide vectors failed to produce any detectable transposition events. Electroporation of transposomes, however, yielded 6 x 10(3) and 4 x 10(3) Tn5 mutants per microg of DNA in two different grapevine strains of X. fastidiosa. Molecular analysis showed that the transposition insertions were single, independent, stable events. Sequence analysis of the Tn5 insertion sites indicated that the transpositions occur randomly in the X. fastidiosa genome. Transposome-mediated mutagenesis should facilitate the identification of X. fastidiosa genes that mediate plant pathogenicity and insect transmission.

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.

Operational parameters of an opto-electronic neural network employing fixed planar holographic interconnects

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.

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.

A synaptic organizing principle for cortical neuronal groups

PubMed Central

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

Hippocampal Insulin Resistance Impairs Spatial Learning and Synaptic Plasticity.

PubMed

Grillo, Claudia A; Piroli, Gerardo G; Lawrence, Robert C; Wrighten, Shayna A; Green, Adrienne J; Wilson, Steven P; Sakai, Randall R; Kelly, Sandra J; Wilson, Marlene A; Mott, David D; Reagan, Lawrence P

2015-11-01

Insulin receptors (IRs) are expressed in discrete neuronal populations in the central nervous system, including the hippocampus. To elucidate the functional role of hippocampal IRs independent of metabolic function, we generated a model of hippocampal-specific insulin resistance using a lentiviral vector expressing an IR antisense sequence (LV-IRAS). LV-IRAS effectively downregulates IR expression in the rat hippocampus without affecting body weight, adiposity, or peripheral glucose homeostasis. Nevertheless, hippocampal neuroplasticity was impaired in LV-IRAS-treated rats. High-frequency stimulation, which evoked robust long-term potentiation (LTP) in brain slices from LV control rats, failed to evoke LTP in LV-IRAS-treated rats. GluN2B subunit levels, as well as the basal level of phosphorylation of GluA1, were reduced in the hippocampus of LV-IRAS rats. Moreover, these deficits in synaptic transmission were associated with impairments in spatial learning. We suggest that alterations in the expression and phosphorylation of glutamate receptor subunits underlie the alterations in LTP and that these changes are responsible for the impairment in hippocampal-dependent learning. Importantly, these learning deficits are strikingly similar to the impairments in complex task performance observed in patients with diabetes, which strengthens the hypothesis that hippocampal insulin resistance is a key mediator of cognitive deficits independent of glycemic control. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

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.

Synaptic transistor with a reversible and analog conductance modulation using a Pt/HfOx/n-IGZO memcapacitor

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.

Synaptic transistor with a reversible and analog conductance modulation using a Pt/HfOx/n-IGZO memcapacitor.

PubMed

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.

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.

Hippocampal ripples down-regulate synapses.

PubMed

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.

Optical implementation of inner product neural associative memory

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor)

1995-01-01

An optical implementation of an inner-product neural associative memory is realized with a first spatial light modulator for entering an initial two-dimensional N-tuple vector and for entering a thresholded output vector image after each iteration until convergence is reached, and a second spatial light modulator for entering M weighted vectors of inner-product scalars multiplied with each of the M stored vectors, where the inner-product scalars are produced by multiplication of the initial input vector in the first iterative cycle (and thresholded vectors in subsequent iterative cycles) with each of the M stored vectors, and the weighted vectors are produced by multiplication of the scalars with corresponding ones of the stored vectors. A Hughes liquid crystal light valve is used for the dual function of summing the weighted vectors and thresholding the sum vector. The thresholded vector is then entered through the first spatial light modulator for reiteration of the process cycle until convergence is reached.

Emergence of small-world structure in networks of spiking neurons through STDP plasticity.

PubMed

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.

An Update on Canine Adenovirus Type 2 and Its Vectors

PubMed Central

Bru, Thierry; Salinas, Sara; Kremer, Eric J.

2010-01-01

Adenovirus vectors have significant potential for long- or short-term gene transfer. Preclinical and clinical studies using human derived adenoviruses (HAd) have demonstrated the feasibility of flexible hybrid vector designs, robust expression and induction of protective immunity. However, clinical use of HAd vectors can, under some conditions, be limited by pre-existing vector immunity. Pre-existing humoral and cellular anti-capsid immunity limits the efficacy and duration of transgene expression and is poorly circumvented by injections of larger doses and immuno-suppressing drugs. This review updates canine adenovirus serotype 2 (CAV-2, also known as CAdV-2) biology and gives an overview of the generation of early region 1 (E1)-deleted to helper-dependent (HD) CAV-2 vectors. We also summarize the essential characteristics concerning their interaction with the anti-HAd memory immune responses in humans, the preferential transduction of neurons, and its high level of retrograde axonal transport in the central and peripheral nervous system. CAV-2 vectors are particularly interesting tools to study the pathophysiology and potential treatment of neurodegenerative diseases, as anti-tumoral and anti-viral vaccines, tracer of synaptic junctions, oncolytic virus and as a platform to generate chimeric vectors. PMID:21994722

Method Accelerates Training Of Some Neural Networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O.

1992-01-01

Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.

Rapid, parallel path planning by propagating wavefronts of spiking neural activity

PubMed Central

Ponulak, Filip; Hopfield, John J.

2013-01-01

Efficient path planning and navigation is critical for animals, robotics, logistics and transportation. We study a model in which spatial navigation problems can rapidly be solved in the brain by parallel mental exploration of alternative routes using propagating waves of neural activity. A wave of spiking activity propagates through a hippocampus-like network, altering the synaptic connectivity. The resulting vector field of synaptic change then guides a simulated animal to the appropriate selected target locations. We demonstrate that the navigation problem can be solved using realistic, local synaptic plasticity rules during a single passage of a wavefront. Our model can find optimal solutions for competing possible targets or learn and navigate in multiple environments. The model provides a hypothesis on the possible computational mechanisms for optimal path planning in the brain, at the same time it is useful for neuromorphic implementations, where the parallelism of information processing proposed here can fully be harnessed in hardware. PMID:23882213

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

Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems.

PubMed

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.

Effect of CDP-choline on age-dependent modifications of energy- and glutamate-linked enzyme activities in synaptic and non-synaptic mitochondria from rat cerebral cortex.

PubMed

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.

Excitement and synchronization of small-world neuronal networks with short-term synaptic plasticity.

PubMed

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.

E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.

PubMed

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.

Differential Modifications of Synaptic Weights During Odor Rule Learning: Dynamics of Interaction Between the Piriform Cortex with Lower and Higher Brain Areas

PubMed Central

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

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.

Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.

PubMed

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.

A 2D Material based Gate Tunable Memristive Device for Emulating Modulatory Input-dependent Hetero-synaptic Plasticity.

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.

New Term Weighting Formulas for the Vector Space Method in Information Retrieval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chisholm, E.; Kolda, T.G.

The goal in information retrieval is to enable users to automatically and accurately find data relevant to their queries. One possible approach to this problem i use the vector space model, which models documents and queries as vectors in the term space. The components of the vectors are determined by the term weighting scheme, a function of the frequencies of the terms in the document or query as well as throughout the collection. We discuss popular term weighting schemes and present several new schemes that offer improved performance.

Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity

PubMed Central

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

Attention Enhances Synaptic Efficacy and Signal-to-Noise in Neural Circuits

PubMed Central

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

Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations

PubMed Central

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

Selection vector filter framework

NASA Astrophysics Data System (ADS)

Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.

2003-10-01

We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.

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.

The super-Turing computational power of plastic recurrent neural networks.

PubMed

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.

Optogenetic approaches to characterize the long-range synaptic pathways from the hypothalamus to brain stem autonomic nuclei

PubMed Central

Piñol, Ramón A.; Bateman, Ryan; Mendelowitz, David

2012-01-01

Recent advances in optogenetic methods demonstrate the feasibility of selective photoactivation at the soma of neurons that express channelrhodopsin-2 (ChR2), but a comprehensive evaluation of different methods to selectively evoke transmitter release from distant synapses using optogenetic approaches is needed. Here we compared different lentiviral vectors, with sub-population-specific and strong promoters, and transgenic methods to express and photostimulate ChR2 in the long-range projections of paraventricular nucleus of the hypothalamus (PVN) neurons to brain stem cardiac vagal neurons (CVNs). Using PVN subpopulation-specific promoters for vasopressin and oxytocin, we were able to depolarize the soma of these neurons upon photostimulation, but these promoters were not strong enough to drive sufficient expression for optogenetic stimulation and synaptic release from the distal axons. However, utilizing the synapsin promoter photostimulation of distal PVN axons successfully evoked glutamatergic excitatory post-synaptic currents in CVNs. Employing the Cre/loxP system, using the Sim-1 Cre-driver mouse line, we found that the Rosa-CAG-LSL-ChR2-EYFP Cre-responder mice expressed higher levels of ChR2 than the Rosa-CAG-LSL-ChR2-tdTomato line in the PVN, judged by photo-evoked currents at the soma. However, neither was able to drive sufficient expression to observe and photostimulate the long-range projections to brainstem autonomic regions. We conclude that a viral vector approach with a strong promoter is required for successful optogenetic stimulation of distal axons to evoke transmitter release in pre-autonomic PVN neurons. This approach can be very useful to study important hypothalamus-brainstem connections, and can be easily modified to selectively activate other long-range projections within the brain. PMID:22890236

Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks

PubMed Central

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

A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners

DTIC Science & Technology

2013-08-01

best-suited for regression. Our baseline uses z-normalized shallow length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari...length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari, English and Pashto. We compare Support Vector Machines and the Margin...football, whereas they are much less common in documents about opera). We used TF -LOG weighted word frequencies on bag-of-words for each document

Differential Acute and Chronic Effects of Leptin on Hypothalamic Astrocyte Morphology and Synaptic Protein Levels

PubMed Central

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

Robust short-term memory without synaptic learning.

PubMed

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.

Large General Purpose Frame for Studying Force Vectors

ERIC Educational Resources Information Center

Heid, Christy; Rampolla, Donald

2011-01-01

Many illustrations and problems on the vector nature of forces have weights and forces in a vertical plane. One of the common devices for studying the vector nature of forces is a horizontal "force table," in which forces are produced by weights hanging vertically and transmitted to cords in a horizontal plane. Because some students have…

Parasitoid wasp sting: a cocktail of GABA, taurine, and beta-alanine opens chloride channels for central synaptic block and transient paralysis of a cockroach host.

PubMed

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.

Large developing receptive fields using a distributed and locally reprogrammable address-event receiver.

PubMed

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.

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.

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.

Effects of Lipoic Acid on High-Fat Diet-Induced Alteration of Synaptic Plasticity and Brain Glucose Metabolism: A PET/CT and 13C-NMR Study.

PubMed

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.

Glucose rapidly induces different forms of excitatory synaptic plasticity in hypothalamic POMC neurons.

PubMed

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.

Weight Vector Fluctuations in Adaptive Antenna Arrays Tuned Using the Least-Mean-Square Error Algorithm with Quadratic Constraint

NASA Astrophysics Data System (ADS)

Zimina, S. V.

2015-06-01

We present the results of statistical analysis of an adaptive antenna array tuned using the least-mean-square error algorithm with quadratic constraint on the useful-signal amplification with allowance for the weight-coefficient fluctuations. Using the perturbation theory, the expressions for the correlation function and power of the output signal of the adaptive antenna array, as well as the formula for the weight-vector covariance matrix are obtained in the first approximation. The fluctuations are shown to lead to the signal distortions at the antenna-array output. The weight-coefficient fluctuations result in the appearance of additional terms in the statistical characteristics of the antenna array. It is also shown that the weight-vector fluctuations are isotropic, i.e., identical in all directions of the weight-coefficient space.

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

Role of antioxidant enzymes in redox regulation of N-methyl-D-aspartate receptor function and memory in middle-aged rats.

PubMed

Lee, Wei-Hua; Kumar, Ashok; Rani, Asha; Foster, Thomas C

2014-06-01

Overexpression of superoxide dismutase 1 (SOD1) in the hippocampus results in age-dependent impaired cognition and altered synaptic plasticity suggesting a possible model for examining the role of oxidative stress in senescent neurophysiology. However, it is unclear if SOD1 overexpression involves an altered redox environment and a decrease in N-methyl-D-aspartate receptor (NMDAR) synaptic function reported for aging animals. Viral vectors were used to express SOD1 and green fluorescent protein (SOD1 + GFP), SOD1 and catalase (SOD1 + CAT), or GFP alone in the hippocampus of middle-aged (17 months) male Fischer 344 rats. We confirm that SOD1 + GFP and SOD1 + CAT reduced lipid peroxidation indicating superoxide metabolites were primarily responsible for lipid peroxidation. SOD1 + GFP impaired learning, decreased glutathione peroxidase activity, decreased glutathione levels, decreased NMDAR-mediated synaptic responses, and impaired long-term potentiation. Co-expression of SOD1 + CAT rescued the effects of SOD1 expression on learning, redox measures, and synaptic function suggesting the effects were mediated by excess hydrogen peroxide. Application of the reducing agent dithiolthreitol to hippocampal slices increased the NMDAR-mediated component of the synaptic response in SOD1 + GFP animals relative to animals that overexpress SOD1 + CAT indicating that the effect of antioxidant enzyme expression on NMDAR function was because of a shift in the redox environment. The results suggest that overexpression of neuronal SOD1 and CAT in middle age may provide a model for examining the role of oxidative stress in senescent physiology and the progression of age-related neurodegenerative diseases. Copyright © 2014 Elsevier Inc. All rights reserved.

Space-Time Neural Networks

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.

Group prioritisation with unknown expert weights in incomplete linguistic context

NASA Astrophysics Data System (ADS)

Cheng, Dong; Cheng, Faxin; Zhou, Zhili; Wang, Juan

2017-09-01

In this paper, we study a group prioritisation problem in situations when the expert weights are completely unknown and their judgement preferences are linguistic and incomplete. Starting from the theory of relative entropy (RE) and multiplicative consistency, an optimisation model is provided for deriving an individual priority vector without estimating the missing value(s) of an incomplete linguistic preference relation. In order to address the unknown expert weights in the group aggregating process, we define two new kinds of expert weight indicators based on RE: proximity entropy weight and similarity entropy weight. Furthermore, a dynamic-adjusting algorithm (DAA) is proposed to obtain an objective expert weight vector and capture the dynamic properties involved in it. Unlike the extant literature of group prioritisation, the proposed RE approach does not require pre-allocation of expert weights and can solve incomplete preference relations. An interesting finding is that once all the experts express their preference relations, the final expert weight vector derived from the DAA is fixed irrespective of the initial settings of expert weights. Finally, an application example is conducted to validate the effectiveness and robustness of the RE approach.

The parallel-antiparallel signal difference in double-wave-vector diffusion-weighted MR at short mixing times: A phase evolution perspective

NASA Astrophysics Data System (ADS)

Finsterbusch, Jürgen

2011-01-01

Experiments with two diffusion weightings applied in direct succession in a single acquisition, so-called double- or two-wave-vector diffusion-weighting (DWV) experiments at short mixing times, have been shown to be a promising tool to estimate cell or compartment sizes, e.g. in living tissue. The basic theory for such experiments predicts that the signal decays for parallel and antiparallel wave vector orientations differ by a factor of three for small wave vectors. This seems to be surprising because in standard, single-wave-vector experiments the polarity of the diffusion weighting has no influence on the signal attenuation. Thus, the question how this difference can be understood more pictorially is often raised. In this rather educational manuscript, the phase evolution during a DWV experiment for simple geometries, e.g. diffusion between parallel, impermeable planes oriented perpendicular to the wave vectors, is considered step-by-step and demonstrates how the signal difference develops. Considering the populations of the phase distributions obtained, the factor of three between the signal decays which is predicted by the theory can be reproduced. Furthermore, the intermediate signal decay for orthogonal wave vector orientations can be derived when investigating diffusion in a box. Thus, the presented “phase gymnastics” approach may help to understand the signal modulation observed in DWV experiments at short mixing times.

Leptin regulation of hippocampal synaptic function in health and disease

PubMed Central

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

Cognition and Synaptic-Plasticity Related Changes in Aged Rats Supplemented with 8- and 10-Carbon Medium Chain Triglycerides.

PubMed

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.

Cognition and Synaptic-Plasticity Related Changes in Aged Rats Supplemented with 8- and 10-Carbon Medium Chain Triglycerides

PubMed Central

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

Alpha-Synuclein Produces Early Behavioral Alterations via Striatal Cholinergic Synaptic Dysfunction by Interacting With GluN2D N-Methyl-D-Aspartate Receptor Subunit.

PubMed

Tozzi, Alessandro; de Iure, Antonio; Bagetta, Vincenza; Tantucci, Michela; Durante, Valentina; Quiroga-Varela, Ana; Costa, Cinzia; Di Filippo, Massimiliano; Ghiglieri, Veronica; Latagliata, Emanuele Claudio; Wegrzynowicz, Michal; Decressac, Mickael; Giampà, Carmela; Dalley, Jeffrey W; Xia, Jing; Gardoni, Fabrizio; Mellone, Manuela; El-Agnaf, Omar Mukhtar; Ardah, Mustafa Taleb; Puglisi-Allegra, Stefano; Björklund, Anders; Spillantini, Maria Grazia; Picconi, Barbara; Calabresi, Paolo

2016-03-01

Advanced Parkinson's disease (PD) is characterized by massive degeneration of nigral dopaminergic neurons, dramatic motor and cognitive alterations, and presence of nigral Lewy bodies, whose main constituent is α-synuclein (α-syn). However, the synaptic mechanisms underlying behavioral and motor effects induced by early selective overexpression of nigral α-syn are still a matter of debate. We performed behavioral, molecular, and immunohistochemical analyses in two transgenic models of PD, mice transgenic for truncated human α-synuclein 1-120 and rats injected with the adeno-associated viral vector carrying wild-type human α-synuclein. We also investigated striatal synaptic plasticity by electrophysiological recordings from spiny projection neurons and cholinergic interneurons. We found that overexpression of truncated or wild-type human α-syn causes partial reduction of striatal dopamine levels and selectively blocks the induction of long-term potentiation in striatal cholinergic interneurons, producing early memory and motor alterations. These effects were dependent on α-syn modulation of the GluN2D-expressing N-methyl-D-aspartate receptors in cholinergic interneurons. Acute in vitro application of human α-syn oligomers mimicked the synaptic effects observed ex vivo in PD models. We suggest that striatal cholinergic dysfunction, induced by a direct interaction between α-syn and GluN2D-expressing N-methyl-D-aspartate receptors, represents a precocious biological marker of the disease. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold

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.

Neuronal uptake and propagation of a rare phosphorylated high-molecular-weight tau derived from Alzheimer's disease brain

PubMed Central

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

Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic

NASA Astrophysics Data System (ADS)

Sukono, Sidi, Pramono; Bon, Abdul Talib bin; Supian, Sudradjat

2017-03-01

The problems of investing in financial assets are to choose a combination of weighting a portfolio can be maximized return expectations and minimizing the risk. This paper discusses the modeling of Mean-VaR portfolio optimization by risk tolerance, when square-shaped utility functions. It is assumed that the asset return has a certain distribution, and the risk of the portfolio is measured using the Value-at-Risk (VaR). So, the process of optimization of the portfolio is done based on the model of Mean-VaR portfolio optimization model for the Mean-VaR done using matrix algebra approach, and the Lagrange multiplier method, as well as Khun-Tucker. The results of the modeling portfolio optimization is in the form of a weighting vector equations depends on the vector mean return vector assets, identities, and matrix covariance between return of assets, as well as a factor in risk tolerance. As an illustration of numeric, analyzed five shares traded on the stock market in Indonesia. Based on analysis of five stocks return data gained the vector of weight composition and graphics of efficient surface of portfolio. Vector composition weighting weights and efficient surface charts can be used as a guide for investors in decisions to invest.

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.

Synapsin I is associated with cholinergic nerve terminals in the electric organs of Torpedo, Electrophorus, and Malapterurus and copurifies with Torpedo synaptic vesicles.

PubMed

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.

Memristor-based cellular nonlinear/neural network: design, analysis, and applications.

PubMed

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.

Ultralow power artificial synapses using nanotextured magnetic Josephson junctions.

PubMed

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.

Ultralow power artificial synapses using nanotextured magnetic Josephson junctions

PubMed Central

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

Glucose Rapidly Induces Different Forms of Excitatory Synaptic Plasticity in Hypothalamic POMC Neurons

PubMed Central

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

Survival motor neuron protein in motor neurons determines synaptic integrity in spinal muscular atrophy.

PubMed

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.

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.

Ultrafast synaptic events in a chalcogenide memristor.

PubMed

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.

Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

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.

Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs

PubMed Central

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

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

PubMed Central

Zhu, Zhichuan; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified. PMID:29853983

Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO.

PubMed

Li, Yang; Zhu, Zhichuan; Hou, Alin; Zhao, Qingdong; Liu, Liwei; Zhang, Lijuan

2018-01-01

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Anterograde or Retrograde Transsynaptic Circuit Tracing in Vertebrates with Vesicular Stomatitis Virus Vectors

PubMed Central

Beier, Kevin T.; Mundell, Nathan A.; Pan, Y. Albert; Cepko, Constance L.

2016-01-01

Viruses have been used as transsynaptic tracers, allowing one to map the inputs and outputs of neuronal populations, due to their ability to replicate in neurons and transmit in vivo only across synaptically connected cells. To date, their use has been largely restricted to mammals. In order to explore the use of such viruses in an expanded host range, we tested the transsynaptic tracing ability of recombinant vesicular stomatitis virus (rVSV) vectors in a variety of organisms. Successful infection and gene expression were achieved in a wide range of organisms, including vertebrate and invertebrate model organisms. Moreover, rVSV enabled transsynaptic tracing of neural circuitry in predictable directions dictated by the viral envelope glycoprotein (G), derived from either VSV or rabies virus (RABV). Anterograde and retrograde labeling, from initial infection and/or viral replication and transmission, was observed in Old and New World monkeys, seahorses, jellyfish, zebrafish, chickens, and mice. These vectors are widely applicable for gene delivery, afferent tract tracing, and/or directional connectivity mapping. Here, we detail the use of these vectors and provide protocols for propagating virus, changing the surface glycoprotein, and infecting multiple organisms using several injection strategies. PMID:26729030

Anterograde or Retrograde Transsynaptic Circuit Tracing in Vertebrates with Vesicular Stomatitis Virus Vectors.

PubMed

Beier, Kevin T; Mundell, Nathan A; Pan, Y Albert; Cepko, Constance L

2016-01-04

Viruses have been used as transsynaptic tracers, allowing one to map the inputs and outputs of neuronal populations, due to their ability to replicate in neurons and transmit in vivo only across synaptically connected cells. To date, their use has been largely restricted to mammals. In order to explore the use of such viruses in an expanded host range, we tested the transsynaptic tracing ability of recombinant vesicular stomatitis virus (rVSV) vectors in a variety of organisms. Successful infection and gene expression were achieved in a wide range of organisms, including vertebrate and invertebrate model organisms. Moreover, rVSV enabled transsynaptic tracing of neural circuitry in predictable directions dictated by the viral envelope glycoprotein (G), derived from either VSV or rabies virus (RABV). Anterograde and retrograde labeling, from initial infection and/or viral replication and transmission, was observed in Old and New World monkeys, seahorses, jellyfish, zebrafish, chickens, and mice. These vectors are widely applicable for gene delivery, afferent tract tracing, and/or directional connectivity mapping. Here, we detail the use of these vectors and provide protocols for propagating virus, changing the surface glycoprotein, and infecting multiple organisms using several injection strategies. Copyright © 2016 John Wiley & Sons, Inc.

Investigation of the ferroelectric switching behavior of P(VDF-TrFE)-PMMA blended films for synaptic device applications

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.

Identification of endophilins 1 and 3 as selective binding partners for VGLUT1 and their co-localization in neocortical glutamatergic synapses: implications for vesicular glutamate transporter trafficking and excitatory vesicle formation.

PubMed

De Gois, Stephanie; Jeanclos, Elisabeth; Morris, Marie; Grewal, Sukhjeevan; Varoqui, Helene; Erickson, Jeffrey D

2006-01-01

1. Selective protein-protein interactions between neurotransmitter transporters and their synaptic targets play important roles in regulating chemical neurotransmission. We screened a yeast two-hybrid library with bait containing the C-terminal amino acids of VGLUT1 and obtained clones that encode endophilin 1 and endophilin 3, proteins considered to play an integral role in glutamatergic vesicle formation. 2. Using a modified yeast plasmid vector to enable more cost-effective screens, we analyzed the selectivity and specificity of this interaction. Endophilins 1 and 3 selectively recognize only VGLUT1 as the C-terminus of VGLUT2 and VGLUT3 do not interact with either endophilin isoform. We mutagenized four conserved stretches of primary sequence in VGLUT1 that includes two polyproline motifs (Pro1, PPAPPP, and Pro2, PPRPPPP), found only in VGLUT1, and two conserved stretches (SEEK, SYGAT), found also in VGLUT2 and VGLUT3. The absence of the VGLUT conserved regions does not affect VGLUT1-endophilin association. Of the two polyproline stretches, only one (Pro2) is required for binding specificity to both endophilin 1 and endophilin 3. 3. We also show that endophilin 1 and endophilin 3 co-localize with VGLUT1 in synaptic terminals of differentiated rat neocortical neurons in primary culture. These results indicate that VGLUT1 and both endophilins are enriched in a class of excitatory synaptic terminals in cortical neurons and there, may interact to play an important role affecting the vesicular sequestration and synaptic release of glutamate.

Chinese Text Summarization Algorithm Based on Word2vec

NASA Astrophysics Data System (ADS)

Chengzhang, Xu; Dan, Liu

2018-02-01

In order to extract some sentences that can cover the topic of a Chinese article, a Chinese text summarization algorithm based on Word2vec is used in this paper. Words in an article are represented as vectors trained by Word2vec, the weight of each word, the sentence vector and the weight of each sentence are calculated by combining word-sentence relationship with graph-based ranking model. Finally the summary is generated on the basis of the final sentence vector and the final weight of the sentence. The experimental results on real datasets show that the proposed algorithm has a better summarization quality compared with TF-IDF and TextRank.

Synaptic characteristics with strong analog potentiation, depression, and short-term to long-term memory transition in a Pt/CeO2/Pt crossbar array structure

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.

Synaptic characteristics with strong analog potentiation, depression, and short-term to long-term memory transition in a Pt/CeO2/Pt crossbar array structure.

PubMed

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.

Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change.

PubMed

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.

A cost-function approach to rival penalized competitive learning (RPCL).

PubMed

Ma, Jinwen; Wang, Taijun

2006-08-01

Rival penalized competitive learning (RPCL) has been shown to be a useful tool for clustering on a set of sample data in which the number of clusters is unknown. However, the RPCL algorithm was proposed heuristically and is still in lack of a mathematical theory to describe its convergence behavior. In order to solve the convergence problem, we investigate it via a cost-function approach. By theoretical analysis, we prove that a general form of RPCL, called distance-sensitive RPCL (DSRPCL), is associated with the minimization of a cost function on the weight vectors of a competitive learning network. As a DSRPCL process decreases the cost to a local minimum, a number of weight vectors eventually fall into a hypersphere surrounding the sample data, while the other weight vectors diverge to infinity. Moreover, it is shown by the theoretical analysis and simulation experiments that if the cost reduces into the global minimum, a correct number of weight vectors is automatically selected and located around the centers of the actual clusters, respectively. Finally, we apply the DSRPCL algorithms to unsupervised color image segmentation and classification of the wine data.

Novel strategies to construct complex synthetic vectors to produce DNA molecular weight standards.

PubMed

Chen, Zhe; Wu, Jianbing; Li, Xiaojuan; Ye, Chunjiang; Wenxing, He

2009-05-01

DNA molecular weight standards (DNA markers, nucleic acid ladders) are commonly used in molecular biology laboratories as references to estimate the size of various DNA samples in electrophoresis process. One method of DNA marker production is digestion of synthetic vectors harboring multiple DNA fragments of known sizes by restriction enzymes. In this article, we described three novel strategies-sequential DNA fragment ligation, screening of ligation products by polymerase chain reaction (PCR) with end primers, and "small fragment accumulation"-for constructing complex synthetic vectors and minimizing the mass differences between DNA fragments produced from restrictive digestion of synthetic vectors. The strategy could be applied to construct various complex synthetic vectors to produce any type of low-range DNA markers, usually available commercially. In addition, the strategy is useful for single-step ligation of multiple DNA fragments for construction of complex synthetic vectors and other applications in molecular biology field.

Attitude Determination Using Two Vector Measurements

NASA Technical Reports Server (NTRS)

Markley, F. Landis

1998-01-01

Many spacecraft attitude determination methods use exactly two vector measurements. The two vectors are typically the unit vector to the Sun and the Earth's magnetic field vector for coarse "sun-mag" attitude determination or unit vectors to two stars tracked by two star trackers for fine attitude determination. TRIAD, the earliest published algorithm for determining spacecraft attitude from two vector measurements, has been widely used in both ground-based and onboard attitude determination. Later attitude determination methods have been based on Wahba's optimality criterion for n arbitrarily weighted observations. The solution of Wahba's problem is somewhat difficult in the general case, but there is a simple closed-form solution in the two-observation case. This solution reduces to the TRIAD solution for certain choices of measurement weights. This paper presents and compares these algorithms as well as sub-optimal algorithms proposed by Bar-Itzhack, Harman, and Reynolds. Some new results will be presented, but the paper is primarily a review and tutorial.

A new simple /spl infin/OH neuron model as a biologically plausible principal component analyzer.

PubMed

Jankovic, M V

2003-01-01

A new approach to unsupervised learning in a single-layer neural network is discussed. An algorithm for unsupervised learning based upon the Hebbian learning rule is presented. A simple neuron model is analyzed. A dynamic neural model, which contains both feed-forward and feedback connections between the input and the output, has been adopted. The, proposed learning algorithm could be more correctly named self-supervised rather than unsupervised. The solution proposed here is a modified Hebbian rule, in which the modification of the synaptic strength is proportional not to pre- and postsynaptic activity, but instead to the presynaptic and averaged value of postsynaptic activity. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. Usually accepted additional decaying terms for the stabilization of the original Hebbian rule are avoided. Implementation of the basic Hebbian scheme would not lead to unrealistic growth of the synaptic strengths, thanks to the adopted network structure.

Predicting Transition from Laminar to Turbulent Flow over a Surface

NASA Technical Reports Server (NTRS)

Rajnarayan, Dev (Inventor); Sturdza, Peter (Inventor)

2016-01-01

A prediction of whether a point on a computer-generated surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For an instability mode in the plurality of instability modes, a covariance vector is determined. A predicted local instability growth rate at the point is determined using the covariance vector and the vector of regressor weights. Based on the predicted local instability growth rate, an n-factor envelope at the point is determined.

Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System

PubMed Central

Kiong, Tiong Sieh; Salem, S. Balasem; Paw, Johnny Koh Siaw; Sankar, K. Prajindra

2014-01-01

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals. PMID:25003136

Minimum variance distortionless response beamformer with enhanced nulling level control via dynamic mutated artificial immune system.

PubMed

Kiong, Tiong Sieh; Salem, S Balasem; Paw, Johnny Koh Siaw; Sankar, K Prajindra; Darzi, Soodabeh

2014-01-01

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals.

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…

Soybean isoflavone ameliorates β-amyloid 1-42-induced learning and memory deficit in rats by protecting synaptic structure and function.

PubMed

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.

Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

PubMed Central

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

A Cognitive Model Based on Neuromodulated Plasticity

PubMed Central

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

Optimal cue integration in ants.

PubMed

Wystrach, Antoine; Mangan, Michael; Webb, Barbara

2015-10-07

In situations with redundant or competing sensory information, humans have been shown to perform cue integration, weighting different cues according to their certainty in a quantifiably optimal manner. Ants have been shown to merge the directional information available from their path integration (PI) and visual memory, but as yet it is not clear that they do so in a way that reflects the relative certainty of the cues. In this study, we manipulate the variance of the PI home vector by allowing ants (Cataglyphis velox) to run different distances and testing their directional choice when the PI vector direction is put in competition with visual memory. Ants show progressively stronger weighting of their PI direction as PI length increases. The weighting is quantitatively predicted by modelling the expected directional variance of home vectors of different lengths and assuming optimal cue integration. However, a subsequent experiment suggests ants may not actually compute an internal estimate of the PI certainty, but are using the PI home vector length as a proxy. © 2015 The Author(s).

Origin of the computational hardness for learning with binary synapses.

PubMed

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.

VLSI implementation of a bio-inspired olfactory spiking neural network.

PubMed

Hsieh, Hung-Yi; Tang, Kea-Tiong

2012-07-01

This paper presents a low-power, neuromorphic spiking neural network (SNN) chip that can be integrated in an electronic nose system to classify odor. The proposed SNN takes advantage of sub-threshold oscillation and onset-latency representation to reduce power consumption and chip area, providing a more distinct output for each odor input. The synaptic weights between the mitral and cortical cells are modified according to an spike-timing-dependent plasticity learning rule. During the experiment, the odor data are sampled by a commercial electronic nose (Cyranose 320) and are normalized before training and testing to ensure that the classification result is only caused by learning. Measurement results show that the circuit only consumed an average power of approximately 3.6 μW with a 1-V power supply to discriminate odor data. The SNN has either a high or low output response for a given input odor, making it easy to determine whether the circuit has made the correct decision. The measurement result of the SNN chip and some well-known algorithms (support vector machine and the K-nearest neighbor program) is compared to demonstrate the classification performance of the proposed SNN chip.The mean testing accuracy is 87.59% for the data used in this paper.

Integrative proteomics to understand the transmission mechanism of Barley yellow dwarf virus-GPV by its insect vector Rhopalosiphum padi

PubMed Central

Wang, Hui; Wu, Keke; Liu, Yan; Wu, Yunfeng; Wang, Xifeng

2015-01-01

Barley yellow dwarf virus-GPV (BYDV-GPV) is transmitted by Rhopalosiphum padi and Schizaphis graminum in a persistent nonpropagative manner. To improve our understanding of its transmission mechanism by aphid vectors, we used two approaches, isobaric tags for relative and absolute quantitation (iTRAQ) and yeast two-hybrid (YTH) system, to identify proteins in R. padi that may interact with or direct the spread of BYDV-GPV along the circulative transmission pathway. Thirty-three differential aphid proteins in viruliferous and nonviruliferous insects were identified using iTRAQ coupled to 2DLC-MS/MS. With the yeast two-hybrid system, 25 prey proteins were identified as interacting with the readthrough protein (RTP) and eight with the coat protein (CP), which are encoded by BYDV-GPV. Among the aphid proteins identified, most were involved in primary energy metabolism, synaptic vesicle cycle, the proteasome pathway and the cell cytoskeleton organization pathway. In a systematic comparison of the two methods, we found that the information generated by the two methods was complementary. Taken together, our findings provide useful information on the interactions between BYDV-GPV and its vector R. padi to further our understanding of the mechanisms regulating circulative transmission in aphid vectors. PMID:26161807

Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

PubMed

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.

Speeding Up Non-Parametric Bootstrap Computations for Statistics Based on Sample Moments in Small/Moderate Sample Size Applications

PubMed Central

Chaibub Neto, Elias

2015-01-01

In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson’s sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling. PMID:26125965

A New Model of Progressive Visceral Leishmaniasis in Hamsters by Natural Transmission via Bites of Vector Sand Flies

PubMed Central

Aslan, Hamide; Dey, Ranadhir; Meneses, Claudio; Castrovinci, Philip; Jeronimo, Selma Maria Bezerra; Oliva, Gætano; Fischer, Laurent; Duncan, Robert C.; Nakhasi, Hira L.; Valenzuela, Jesus G.; Kamhawi, Shaden

2013-01-01

Background. Visceral leishmaniasis (VL) is transmitted by sand flies. Protection of needle-challenged vaccinated mice was abrogated in vector-initiated cutaneous leishmaniasis, highlighting the importance of developing natural transmission models for VL. Methods. We used Lutzomyia longipalpis to transmit Leishmania infantum or Leishmania donovani to hamsters. Vector-initiated infections were monitored and compared with intracardiac infections. Body weights were recorded weekly. Organ parasite loads and parasite pick-up by flies were assessed in sick hamsters. Results. Vector-transmitted L. infantum and L. donovani caused ≥5-fold increase in spleen weight compared with uninfected organs and had geometric mean parasite loads (GMPL) comparable to intracardiac inoculation of 107–108 parasites, although vector-initiated disease progression was slower and weight loss was greater. Only vector-initiated L. infantum infections caused cutaneous lesions at transmission and distal sites. Importantly, 45.6%, 50.0%, and 33.3% of sand flies feeding on ear, mouth, and testicular lesions, respectively, were parasite-positive. Successful transmission was associated with a high mean percent of metacyclics (66%–82%) rather than total GMPL (2.0 × 104–8.0 × 104) per midgut. Conclusions. This model provides an improved platform to study initial immune events at the bite site, parasite tropism, and pathogenesis and to test drugs and vaccines against naturally acquired VL. PMID:23288926

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,…

Theta Coordinated Error Driven Learning in the Hippocampus (Open Access, Publisher’s Version)

DTIC Science & Technology

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

Literature-based concept profiles for gene annotation: the issue of weighting.

PubMed

Jelier, Rob; Schuemie, Martijn J; Roes, Peter-Jan; van Mulligen, Erik M; Kors, Jan A

2008-05-01

Text-mining has been used to link biomedical concepts, such as genes or biological processes, to each other for annotation purposes or the generation of new hypotheses. To relate two concepts to each other several authors have used the vector space model, as vectors can be compared efficiently and transparently. Using this model, a concept is characterized by a list of associated concepts, together with weights that indicate the strength of the association. The associated concepts in the vectors and their weights are derived from a set of documents linked to the concept of interest. An important issue with this approach is the determination of the weights of the associated concepts. Various schemes have been proposed to determine these weights, but no comparative studies of the different approaches are available. Here we compare several weighting approaches in a large scale classification experiment. Three different techniques were evaluated: (1) weighting based on averaging, an empirical approach; (2) the log likelihood ratio, a test-based measure; (3) the uncertainty coefficient, an information-theory based measure. The weighting schemes were applied in a system that annotates genes with Gene Ontology codes. As the gold standard for our study we used the annotations provided by the Gene Ontology Annotation project. Classification performance was evaluated by means of the receiver operating characteristics (ROC) curve using the area under the curve (AUC) as the measure of performance. All methods performed well with median AUC scores greater than 0.84, and scored considerably higher than a binary approach without any weighting. Especially for the more specific Gene Ontology codes excellent performance was observed. The differences between the methods were small when considering the whole experiment. However, the number of documents that were linked to a concept proved to be an important variable. When larger amounts of texts were available for the generation of the concepts' vectors, the performance of the methods diverged considerably, with the uncertainty coefficient then outperforming the two other methods.

Method and system of filtering and recommending documents

DOEpatents

Patton, Robert M.; Potok, Thomas E.

2016-02-09

Disclosed is a method and system for discovering documents using a computer and providing a small set of the most relevant documents to the attention of a human observer. Using the method, the computer obtains a seed document from the user and generates a seed document vector using term frequency-inverse corpus frequency weighting. A keyword index for a plurality of source documents can be compared with the weighted terms of the seed document vector. The comparison is then filtered to reduce the number of documents, which define an initial subset of the source documents. Initial subset vectors are generated and compared to the seed document vector to obtain a similarity value for each comparison. Based on the similarity value, the method then recommends one or more of the source documents.

A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits

PubMed Central

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

Elements of the quality management in the materials' industry

NASA Astrophysics Data System (ADS)

Ioana, Adrian; Semenescu, Augustin; Costoiu, Mihnea; Marcu, Dragoş

2017-12-01

The criteria function concept consists of transforming the criteria function (CF) in a quality-economical matrix math MQE. The levels of prescribing the criteria function was obtained by using a composition algorithm for three vectors: T¯ vector - technical parameters' vector (ti); Ē vector - economical parameters' vector (ej) and P¯ vector - weight vector (p1). For each product or service, the area of the circle represents the value of its sales. The BCG Matrix thus offers a very useful map of the organization's service strengths and weaknesses, at least in terms of current profitability, as well as the likely cash flows.

Numerical simulations of short-mixing-time double-wave-vector diffusion-weighting experiments with multiple concatenations on whole-body MR systems

NASA Astrophysics Data System (ADS)

Finsterbusch, Jürgen

2010-12-01

Double- or two-wave-vector diffusion-weighting experiments with short mixing times in which two diffusion-weighting periods are applied in direct succession, are a promising tool to estimate cell sizes in the living tissue. However, the underlying effect, a signal difference between parallel and antiparallel wave vector orientations, is considerably reduced for the long gradient pulses required on whole-body MR systems. Recently, it has been shown that multiple concatenations of the two wave vectors in a single acquisition can double the modulation amplitude if short gradient pulses are used. In this study, numerical simulations of such experiments were performed with parameters achievable with whole-body MR systems. It is shown that the theoretical model yields a good approximation of the signal behavior if an additional term describing free diffusion is included. More importantly, it is demonstrated that the shorter gradient pulses sufficient to achieve the desired diffusion weighting for multiple concatenations, increase the signal modulation considerably, e.g. by a factor of about five for five concatenations. Even at identical echo times, achieved by a shortened diffusion time, a moderate number of concatenations significantly improves the signal modulation. Thus, experiments on whole-body MR systems may benefit from multiple concatenations.

Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State.

PubMed

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.

Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State

PubMed Central

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

Locomotor Adaptation to an Asymmetric Force on the Human Pelvis Directed Along the Right Leg.

PubMed

Vashista, Vineet; Martelli, Dario; Agrawal, Sunil

2015-09-11

In this work, we study locomotor adaptation in healthy adults when an asymmetric force vector is applied to the pelvis directed along the right leg. A cable-driven Active Tethered Pelvic Assist Device (A-TPAD) is used to apply an external force on the pelvis, specific to a subject's gait pattern. The force vector is intended to provide external weight bearing during walking and modify the durations of limb supports. The motivation is to use this paradigm to improve weight bearing and stance phase symmetry in individuals with hemiparesis. An experiment with nine healthy subjects was conducted. The results show significant changes in the gait kinematics and kinetics while the healthy subjects developed temporal and spatial asymmetry in gait pattern in response to the applied force vector. This was followed by aftereffects once the applied force vector was removed. The adaptation to the applied force resulted in asymmetry in stance phase timing and lower limb muscle activity. We believe this paradigm, when extended to individuals with hemiparesis, can show improvements in weight bearing capability with positive effects on gait symmetry and walking speed.

Effects of OCR Errors on Ranking and Feedback Using the Vector Space Model.

ERIC Educational Resources Information Center

Taghva, Kazem; And Others

1996-01-01

Reports on the performance of the vector space model in the presence of OCR (optical character recognition) errors in information retrieval. Highlights include precision and recall, a full-text test collection, smart vector representation, impact of weighting parameters, ranking variability, and the effect of relevance feedback. (Author/LRW)

The role of ionotropic glutamate receptors in childhood neurodevelopmental disorders: autism spectrum disorders and fragile x syndrome.

PubMed

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.

Evaluation of the specificity and sensitivity of ferritin as an MRI reporter gene in the mouse brain using lentiviral and adeno-associated viral vectors.

PubMed

Vande Velde, G; Rangarajan, J R; Toelen, J; Dresselaers, T; Ibrahimi, A; Krylychkina, O; Vreys, R; Van der Linden, A; Maes, F; Debyser, Z; Himmelreich, U; Baekelandt, V

2011-06-01

The development of in vivo imaging protocols to reliably track transplanted cells or to report on gene expression is critical for treatment monitoring in (pre)clinical cell and gene therapy protocols. Therefore, we evaluated the potential of lentiviral vectors (LVs) and adeno-associated viral vectors (AAVs) to express the magnetic resonance imaging (MRI) reporter gene ferritin in the rodent brain. First, we compared the induction of background MRI contrast for both vector systems in immune-deficient and immune-competent mice. LV injection resulted in hypointense (that is, dark) changes of T(2)/T(2)(*) (spin-spin relaxation time)-weighted MRI contrast at the injection site, which can be partially explained by an inflammatory response against the vector injection. In contrast to LVs, AAV injection resulted in reduced background contrast. Moreover, AAV-mediated ferritin overexpression resulted in significantly enhanced contrast to background on T(2)(*)-weighted MRI. Although sensitivity associated with the ferritin reporter remains modest, AAVs seem to be the most promising vector system for in vivo MRI reporter gene imaging.

Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

DTIC Science & Technology

1999-05-17

Experimental Results In this section, we compare kNN -mut which uses the weight vector obtained using mutual information as the fi- nal weight vector and...WAKNN against kNN , C4.5 [Qui93], RIPPER [Coh95], PEBLS [CS93], Rainbow [McC96], VSM [Low95] on several synthetic and real data sets. VSM is another k...obtained without this option. 3 C4.5 RIPPER PEBLS Rainbow kNN WAKNN Syn-1 100.0 100.0 100.0 100.0 77.3 100.0 Syn-2 67.5 69.5 62.0 50.0 66.0 68.8 Syn

The problem of gestalt in neurobiology.

PubMed

Sokolov, E N

1997-01-01

The question of gestalts is discussed within the framework of its neuronal mechanisms. Two basic hypotheses are considered: 1) that of gestalts as a result of the hierarchical organization of neurons (gnostic units), and 2) that of gestalts as a result of the synchronization of neurons of a given level. Analysis of published data led to the conclusion that gestalts result from vector coding in the hierarchical organization of neurons. High-frequency oscillations in the gamma range (40-200 Hz) are of endogenous origin, and their function is to reinforce the synaptic inputs to those neurons which are involved in the synthesis of a gestalt.

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

PubMed Central

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

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

PubMed

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.

Biologically plausible learning in neural networks: a lesson from bacterial chemotaxis.

PubMed

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.

Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses

PubMed Central

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

Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses.

PubMed

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.

Efficient Transmission of Subthreshold Signals in Complex Networks of Spiking Neurons

PubMed Central

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

On vector-valued Poincaré series of weight 2

NASA Astrophysics Data System (ADS)

Meneses, Claudio

2017-10-01

Given a pair (Γ , ρ) of a Fuchsian group of the first kind, and a unitary representation ρ of Γ of arbitrary rank, the problem of construction of vector-valued Poincaré series of weight 2 is considered. Implications in the theory of parabolic bundles are discussed. When the genus of the group is zero, it is shown how an explicit basis for the space of these functions can be constructed.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2017-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2016-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered-reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Combinatorial vector fields and the valley structure of fitness landscapes.

PubMed

Stadler, Bärbel M R; Stadler, Peter F

2010-12-01

Adaptive (downhill) walks are a computationally convenient way of analyzing the geometric structure of fitness landscapes. Their inherently stochastic nature has limited their mathematical analysis, however. Here we develop a framework that interprets adaptive walks as deterministic trajectories in combinatorial vector fields and in return associate these combinatorial vector fields with weights that measure their steepness across the landscape. We show that the combinatorial vector fields and their weights have a product structure that is governed by the neutrality of the landscape. This product structure makes practical computations feasible. The framework presented here also provides an alternative, and mathematically more convenient, way of defining notions of valleys, saddle points, and barriers in landscape. As an application, we propose a refined approximation for transition rates between macrostates that are associated with the valleys of the landscape.

System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

DOEpatents

Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.

2014-06-17

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

Biochemistry of snake venom neurotoxins and their application to the study of synapse. [Neurotoxins isolated from venom of the Formosan banded krait

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

RNA interference targeting the ACE gene reduced blood pressure and improved myocardial remodelling in SHRs.

PubMed

He, Junhua; Bian, Yunfei; Gao, Fen; Li, Maolian; Qiu, Ling; Wu, Weidong; Zhou, Hua; Liu, Gaizhen; Xiao, Chuanshi

2009-02-01

The purpose of the present study was to investigate the effects on blood pressure and myocardial hypertrophy in SHRs (spontaneously hypertensive rats) of RNAi (RNA interference) targeting ACE (angiotensin-converting enzyme). SHRs were treated with normal saline as vehicle controls, with Ad5-EGFP as vector controls, and with recombinant adenoviral vectors Ad5-EGFP-ACE-shRNA, carrying shRNA (small hairpin RNA) for ACE as ACE-RNAi. WKY (Wistar-Kyoto) rats were used as normotensive controls treated with normal saline. The systolic blood pressure of the caudal artery was recorded. Serum levels of ACE and AngII (angiotensin II) were determined using ELISA. ACE mRNA and protein levels were determined in aorta, myocardium, kidney and lung. On day 32 of the experiment, the heart was pathologically examined. The ratios of heart weight/body weight and left ventricular weight/body weight were calculated. The serum concentration of ACE was lower in ACE-RNAi rats (16.37+/-3.90 ng/ml) compared with vehicle controls and vector controls (48.26+/-1.50 ng/ml and 46.67+/-2.82 ng/ml respectively; both P<0.05), but comparable between ACE-RNAi rats and WKY rats (14.88+/-3.15 ng/ml; P>0.05). The serum concentration of AngII was also significantly lower in ACE-RNAi rats (18.24+/-3.69 pg/ml) compared with vehicle controls and vector controls (46.21+/-5.06 pg/ml and 44.93+/-4.12 pg/ml respectively; both P<0.05), but comparable between ACE-RNAi rats and WKY rats (16.06+/-3.11 pg/ml; P>0.05). The expression of ACE mRNA and ACE protein were significantly reduced in the myocardium, aorta, kidney and lung in ACE-RNAi rats compared with that in vehicle controls and in vector controls (all P<0.05). ACE-RNAi treatment resulted in a reduction in systolic blood pressure by 22+/-3 mmHg and the ACE-RNAi-induced reduction lasted for more than 14 days. In contrast, blood pressure was continuously increased in the vehicle controls as well as in the vector controls. The ratios of heart weight/body weight and left ventricular weight/body weight were significantly lower in ACE-RNAi rats (3.12+/-0.23 mg/g and 2.24+/-0.19 mg/g) compared with the vehicle controls (4.29+/-0.24 mg/g and 3.21+/-0.13 mg/g; P<0.05) and the vector controls (4.43+/-0.19 mg/g and 3.13+/-0.12 mg/g; P<0.05). The conclusion of the present study is that ACE-silencing had significant antihypertensive effects and reversed hypertensive-induced cardiac hypertrophy in SHRs, and therefore RNAi might be a new strategy in controlling hypertension.

Poly(ethylene glycol) analogs grafted with low molecular weight poly(ethylene imine) as non-viral gene vectors.

PubMed

Zhang, Zhenfang; Yang, Cuihong; Duan, Yajun; Wang, Yanming; Liu, Jianfeng; Wang, Lianyong; Kong, Deling

2010-07-01

A novel class of non-viral gene vectors consisting of low molecular weight poly(ethylene imine) (PEI) (molecular weight 800 Da) grafted onto degradable linear poly(ethylene glycol) (PEG) analogs was synthesized. First, a Michael addition reaction between poly(ethylene glycol) diacrylates (PEGDA) (molecular weight 258 Da) and d,l-dithiothreitol (DTT) was carried out to generate a linear polymer (PEG-DTT) having a terminal thiol, methacrylate and pendant hydroxyl functional groups. Five PEG-DTT analogs were synthesized by varying the molar ratio of diacrylates to thiols from 1.2:1 to 1:1.2. Then PEI (800 Da) was grafted onto the main chain of the PEG-DTTs using 1,1'-carbonyldiimidazole as the linker. The above reaction gave rise to a new class of non-viral gene vectors, (PEG-DTT)-g-PEI copolymers, which can effectively complex DNA to form nanoparticles. The molecular weights and structures of the copolymers were characterized by gel permeation chromatography, (1)H nuclear magnetic resonance and Fourier transform infrared spectroscopy. The size of the nanoparticles was<200 nm and the surface charge of the nanoparticles, expressed as the zeta potential, was between+20 and+40 mV. Cytotoxicity assays showed that the copolymers exhibited much lower cytotoxicities than high molecular weight PEI (25 kDa). Transfection was performed in cultured HeLa, HepG2, MCF-7 and COS-7 cells. The copolymers showed higher transfection efficiencies than PEI (25 kDa) tested in four cell lines. The presence of serum (up to 30%) had no inhibitory effect on the transfection efficiency. These results indicate that this new class of non-viral gene vectors may be a promising gene carrier that is worth further investigation. Copyright 2010 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex.

PubMed

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.

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.

Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators.

PubMed

Karayiannis, N B

2000-01-01

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.

An adaptive evolutionary multi-objective approach based on simulated annealing.

PubMed

Li, H; Landa-Silva, D

2011-01-01

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

PubMed Central

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

Nicotine Significantly Improves Chronic Stress-Induced Impairments of Cognition and Synaptic Plasticity in Mice.

PubMed

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.

Low molecular weight polyethylenimine cross-linked by 2-hydroxypropyl-gamma-cyclodextrin coupled to peptide targeting HER2 as a gene delivery vector.

PubMed

Huang, Hongliang; Yu, Hai; Tang, Guping; Wang, Qingqing; Li, Jun

2010-03-01

Gene delivery is one of the critical steps for gene therapy. Non-viral vectors have many advantages but suffered from low gene transfection efficiency. Here, in order to develop new polymeric gene vectors with low cytotoxicity and high gene transfection efficiency, we synthesized a cationic polymer composed of low molecular weight polyethylenimine (PEI) of molecular weight of 600 Da cross-linked by 2-hydroxypropyl-gamma-cyclodextrin (HP gamma-CD) and then coupled to MC-10 oligopeptide containing a sequence of Met-Ala-Arg-Ala-Lys-Glu. The oligopeptide can target to HER2, the human epidermal growth factor receptor 2, which is often over expressed in many breast and ovary cancers. The new gene vector was expected to be able to target delivery of genes to HER2 positive cancer cells for gene therapy. The new gene vector was composed of chemically bonded HP gamma-CD, PEI (600 Da), and MC-10 peptide at a molar ratio of 1:3.3:1.2. The gene vector could condense plasmid DNA at an N/P ratio of 6 or above. The particle size of HP gamma-CD-PEI-P/DNA complexes at N/P ratios 40 was around 170-200 nm, with zeta potential of about 20 mV. The gene vector showed very low cytotoxicity, strong targeting specificity to HER2 receptor, and high efficiency of delivering DNA to target cells in vitro and in vivo with the reporter genes. The delivery of therapeutic IFN-alpha gene mediated by the new gene vector and the therapeutic efficiency were also studied in mice animal model. The animal study results showed that the new gene vector HP gamma-CD-PEI-P significantly enhanced the anti-tumor effect on tumor-bearing nude mice as compared to PEI (25 kDa), HP gamma-CD-PEI, and other controls, indicating that this new polymeric gene vector is a potential candidate for cancer gene therapy. (c) 2009 Elsevier Ltd. All rights reserved.

Predicting Transition from Laminar to Turbulent Flow over a Surface

NASA Technical Reports Server (NTRS)

Sturdza, Peter (Inventor); Rajnarayan, Dev (Inventor)

2013-01-01

A prediction of whether a point on a computer-generated surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of boundary-layer properties at the point are obtained from a steady-state solution of a fluid flow in a region adjacent to the point. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For each instability mode in the plurality of instability modes, a covariance vector is determined, which is the covariance of a predicted local growth rate with the known instability growth rates. Each covariance vector is used with the vector of regressor weights to determine a predicted local growth rate at the point. Based on the predicted local growth rates, an n-factor envelope at the point is determined.

Influence of molecular weight upon mannosylated bio-synthetic hybrids for targeted antigen presenting cell gene delivery

PubMed Central

Jones, Charles H.; Gollakota, Akhila; Chen, Mingfu; Chung, Tai-Chun; Ravikrishnan, Anitha; Zhang, Guojian; Pfeifer, Blaine A.

2015-01-01

Given the rise of antibiotic resistant microbes, genetic vaccination is a promising prophylactic strategy that enables rapid design and manufacture. Facilitating this process is the choice of vector, which is often situationally-specific and limited in engineering capacity. Furthermore, these shortcomings are usually tied to an incomplete understanding of the structure-function relationships driving vector-mediated gene delivery. Building upon our initial report of a hybrid bacterial-biomaterial gene delivery vector, a comprehensive structure-function assessment was completed using a class of mannosylated poly(beta-amino esters). Through a top-down screening methodology, an ideal polymer was selected on the basis of gene delivery efficacy and then used for the synthesis of a stratified molecular weight polymer library. By eliminating contributions of polymer chemical background, we were able to complete an in-depth assessment of gene delivery as a function of (1) polymer molecular weight, (2) relative mannose content, (3) polymer-membrane biophysical properties, (4) APC uptake specificity, and (5) serum inhibition. In summary, the flexibility and potential of the hybrid design featured in this work highlights the ability to systematically probe vector-associated properties for the development of translational gene delivery candidates. PMID:25941787

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

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.

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…

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

PubMed Central

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

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

PubMed Central

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

Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.

PubMed

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.

Synchrony detection and amplification by silicon neurons with STDP synapses.

PubMed

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.

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.

The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables.

PubMed

Yin, Kedong; Wang, Pengyu; Li, Xuemei

2017-12-13

With respect to multi-attribute group decision-making (MAGDM) problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs) and the weights (including expert and attribute weight) are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV.

Singular vectors for the WN algebras

NASA Astrophysics Data System (ADS)

Ridout, David; Siu, Steve; Wood, Simon

2018-03-01

In this paper, we use free field realisations of the A-type principal, or Casimir, WN algebras to derive explicit formulae for singular vectors in Fock modules. These singular vectors are constructed by applying screening operators to Fock module highest weight vectors. The action of the screening operators is then explicitly evaluated in terms of Jack symmetric functions and their skew analogues. The resulting formulae depend on sequences of pairs of integers that completely determine the Fock module as well as the Jack symmetric functions.

Single-Sided Noinvasive Inspection of Multielement Sample Using Fan-Beam Multiplexed Compton Scatter Tomography

DTIC Science & Technology

2000-05-01

a vector , ρ "# represents the set of voxel densities sorted into a vector , and ( )A ρ $# "# represents a 8 mapping of the voxel densities to...density vector in equation (4) suggests that solving for ρ "# by direct inversion is not possible, calling for an iterative technique beginning with...the vector of measured spectra, and D is the diagonal matrix of the inverse of the variances. The diagonal matrix provides weighting terms, which

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

Measuring Symmetry, Asymmetry and Randomness in Neural Network Connectivity

PubMed Central

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

Measuring symmetry, asymmetry and randomness in neural network connectivity.

PubMed

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.

AHaH computing-from metastable switches to attractors to machine learning.

PubMed

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.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks

PubMed Central

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

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

PubMed

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.

AHaH Computing–From Metastable Switches to Attractors to Machine Learning

PubMed Central

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

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Generalized sidelobe canceller beamforming method for ultrasound imaging.

PubMed

Wang, Ping; Li, Na; Luo, Han-Wu; Zhu, Yong-Kun; Cui, Shi-Gang

2017-03-01

A modified generalized sidelobe canceller (IGSC) algorithm is proposed to enhance the resolution and robustness against the noise of the traditional generalized sidelobe canceller (GSC) and coherence factor combined method (GSC-CF). In the GSC algorithm, weighting vector is divided into adaptive and non-adaptive parts, while the non-adaptive part does not block all the desired signal. A modified steer vector of the IGSC algorithm is generated by the projection of the non-adaptive vector on the signal space constructed by the covariance matrix of received data. The blocking matrix is generated based on the orthogonal complementary space of the modified steer vector and the weighting vector is updated subsequently. The performance of IGSC was investigated by simulations and experiments. Through simulations, IGSC outperformed GSC-CF in terms of spatial resolution by 0.1 mm regardless there is noise or not, as well as the contrast ratio respect. The proposed IGSC can be further improved by combining with CF. The experimental results also validated the effectiveness of the proposed algorithm with dataset provided by the University of Michigan.

Errors in the estimation of the variance: implications for multiple-probability fluctuation analysis.

PubMed

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.

Forebrain glutamatergic neurons mediate leptin action on depression-like behaviors and synaptic depression

PubMed Central

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

Unbiased View of Synaptic and Neuronal Gene Complement in Ctenophores: Are There Pan-neuronal and Pan-synaptic Genes across Metazoa?

PubMed

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.

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film.

PubMed

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.

The Role of Ionotropic Glutamate Receptors in Childhood Neurodevelopmental Disorders: Autism Spectrum Disorders and Fragile X Syndrome

PubMed Central

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

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

PubMed Central

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

Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film

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.

Ensemble stacking mitigates biases in inference of synaptic connectivity.

PubMed

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.

Fast Quaternion Attitude Estimation from Two Vector Measurements

NASA Technical Reports Server (NTRS)

Markley, F. Landis; Bauer, Frank H. (Technical Monitor)

2001-01-01

Many spacecraft attitude determination methods use exactly two vector measurements. The two vectors are typically the unit vector to the Sun and the Earth's magnetic field vector for coarse "sun-mag" attitude determination or unit vectors to two stars tracked by two star trackers for fine attitude determination. Existing closed-form attitude estimates based on Wahba's optimality criterion for two arbitrarily weighted observations are somewhat slow to evaluate. This paper presents two new fast quaternion attitude estimation algorithms using two vector observations, one optimal and one suboptimal. The suboptimal method gives the same estimate as the TRIAD algorithm, at reduced computational cost. Simulations show that the TRIAD estimate is almost as accurate as the optimal estimate in representative test scenarios.

Competitive STDP Learning of Overlapping Spatial Patterns.

PubMed

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.

Learning Reward Uncertainty in the Basal Ganglia

PubMed Central

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

Light sleep versus slow wave sleep in memory consolidation: a question of global versus local processes?

PubMed

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.

Improved Autoassociative Neural Networks

NASA Technical Reports Server (NTRS)

Hand, Charles

2003-01-01

Improved autoassociative neural networks, denoted nexi, have been proposed for use in controlling autonomous robots, including mobile exploratory robots of the biomorphic type. In comparison with conventional autoassociative neural networks, nexi would be more complex but more capable in that they could be trained to do more complex tasks. A nexus would use bit weights and simple arithmetic in a manner that would enable training and operation without a central processing unit, programs, weight registers, or large amounts of memory. Only a relatively small amount of memory (to hold the bit weights) and a simple logic application- specific integrated circuit would be needed. A description of autoassociative neural networks is prerequisite to a meaningful description of a nexus. An autoassociative network is a set of neurons that are completely connected in the sense that each neuron receives input from, and sends output to, all the other neurons. (In some instantiations, a neuron could also send output back to its own input terminal.) The state of a neuron is completely determined by the inner product of its inputs with weights associated with its input channel. Setting the weights sets the behavior of the network. The neurons of an autoassociative network are usually regarded as comprising a row or vector. Time is a quantized phenomenon for most autoassociative networks in the sense that time proceeds in discrete steps. At each time step, the row of neurons forms a pattern: some neurons are firing, some are not. Hence, the current state of an autoassociative network can be described with a single binary vector. As time goes by, the network changes the vector. Autoassociative networks move vectors over hyperspace landscapes of possibilities.

Focal expression of mutated tau in entorhinal cortex neurons of rats impairs spatial working memory.

PubMed

Ramirez, Julio J; Poulton, Winona E; Knelson, Erik; Barton, Cole; King, Michael A; Klein, Ronald L

2011-01-01

Entorhinal cortex neuropathology begins very early in Alzheimer's disease (AD), a disorder characterized by severe memory disruption. Indeed, loss of entorhinal volume is predictive of AD and two of the hallmark neuroanatomical markers of AD, amyloid plaques and neurofibrillary tangles (NFTs), are particularly prevalent in the entorhinal area of AD-afflicted brains. Gene transfer techniques were used to create a model neurofibrillary tauopathy by injecting a recombinant adeno-associated viral vector with a mutated human tau gene (P301L) into the entorhinal cortex of adult rats. The objective of the present investigation was to determine whether adult onset, spatially restricted tauopathy could be sufficient to reproduce progressive deficits in mnemonic function. Spatial memory on a Y-maze was tested for approximately 3 months post-surgery. Upon completion of behavioral testing the brains were assessed for expression of human tau and evidence of tauopathy. Rats injected with the tau vector became persistently impaired on the task after about 6 weeks of postoperative testing, whereas the control rats injected with a green fluorescent protein vector performed at criterion levels during that period. Histological analysis confirmed the presence of hyperphosphorylated tau and NFTs in the entorhinal cortex and neighboring retrohippocampal areas as well as limited synaptic degeneration of the perforant path. Thus, highly restricted vector-induced tauopathy in retrohippocampal areas is sufficient for producing progressive impairment in mnemonic ability in rats, successfully mimicking a key aspect of tauopathies such as AD. Copyright © 2010 Elsevier B.V. All rights reserved.

Ultramicroscopy as a novel tool to unravel the tropism of AAV gene therapy vectors in the brain.

PubMed

Alves, Sandro; Bode, Julia; Bemelmans, Alexis-Pierre; von Kalle, Christof; Cartier, Nathalie; Tews, Björn

2016-06-20

Recombinant adeno-associated viral (AAV) vectors have advanced to the vanguard of gene therapy. Numerous naturally occurring serotypes have been used to target cells in various tissues. There is a strong need for fast and dynamic methods which efficiently unravel viral tropism in whole organs. Ultramicroscopy (UM) is a novel fluorescence microscopy technique that images optically cleared undissected specimens, achieving good resolutions at high penetration depths while being non-destructive. UM was applied to obtain high-resolution 3D analysis of AAV transduction in adult mouse brains, especially in the hippocampus, a region of interest for Alzheimer's disease therapy. We separately or simultaneously compared transduction efficacies for commonly used serotypes (AAV9 and AAVrh10) using fluorescent reporter expression. We provide a detailed comparative and quantitative analysis of the transduction profiles. UM allowed a rapid analysis of marker fluorescence expression in neurons with intact projections deep inside the brain, in defined anatomical structures. Major hippocampal neuronal transduction was observed with both vectors, with slightly better efficacy for AAV9 in UM. Glial response and synaptic marker expression did not change post transduction.We propose UM as a novel valuable complementary tool to efficiently and simultaneously unravel tropism of different viruses in a single non-dissected adult rodent brain.

General Quantum Meet-in-the-Middle Search Algorithm Based on Target Solution of Fixed Weight

NASA Astrophysics Data System (ADS)

Fu, Xiang-Qun; Bao, Wan-Su; Wang, Xiang; Shi, Jian-Hong

2016-10-01

Similar to the classical meet-in-the-middle algorithm, the storage and computation complexity are the key factors that decide the efficiency of the quantum meet-in-the-middle algorithm. Aiming at the target vector of fixed weight, based on the quantum meet-in-the-middle algorithm, the algorithm for searching all n-product vectors with the same weight is presented, whose complexity is better than the exhaustive search algorithm. And the algorithm can reduce the storage complexity of the quantum meet-in-the-middle search algorithm. Then based on the algorithm and the knapsack vector of the Chor-Rivest public-key crypto of fixed weight d, we present a general quantum meet-in-the-middle search algorithm based on the target solution of fixed weight, whose computational complexity is \\sumj = 0d {(O(\\sqrt {Cn - k + 1d - j }) + O(C_kj log C_k^j))} with Σd i =0 Ck i memory cost. And the optimal value of k is given. Compared to the quantum meet-in-the-middle search algorithm for knapsack problem and the quantum algorithm for searching a target solution of fixed weight, the computational complexity of the algorithm is lower. And its storage complexity is smaller than the quantum meet-in-the-middle-algorithm. Supported by the National Basic Research Program of China under Grant No. 2013CB338002 and the National Natural Science Foundation of China under Grant No. 61502526

Influence of molecular weight upon mannosylated bio-synthetic hybrids for targeted antigen presenting cell gene delivery.

PubMed

Jones, Charles H; Gollakota, Akhila; Chen, Mingfu; Chung, Tai-Chun; Ravikrishnan, Anitha; Zhang, Guojian; Pfeifer, Blaine A

2015-07-01

Given the rise of antibiotic resistant microbes, genetic vaccination is a promising prophylactic strategy that enables rapid design and manufacture. Facilitating this process is the choice of vector, which is often situationally-specific and limited in engineering capacity. Furthermore, these shortcomings are usually tied to an incomplete understanding of the structure-function relationships driving vector-mediated gene delivery. Building upon our initial report of a hybrid bacterial-biomaterial gene delivery vector, a comprehensive structure-function assessment was completed using a class of mannosylated poly(beta-amino esters). Through a top-down screening methodology, an ideal polymer was selected on the basis of gene delivery efficacy and then used for the synthesis of a stratified molecular weight polymer library. By eliminating contributions of polymer chemical background, we were able to complete an in-depth assessment of gene delivery as a function of (1) polymer molecular weight, (2) relative mannose content, (3) polymer-membrane biophysical properties, (4) APC uptake specificity, and (5) serum inhibition. In summary, the flexibility and potential of the hybrid design featured in this work highlights the ability to systematically probe vector-associated properties for the development of translational gene delivery candidates. Copyright © 2015 Elsevier Ltd. All rights reserved.

Advanced Techniques for Scene Analysis

DTIC Science & Technology

2010-06-01

robustness prefers a bigger intergration window to handle larger motions. The advantage of pyramidal implementation is that, while each motion vector dL...labeled SAR images. Now the previous algorithm leads to a more dedicated classifier for the particular target; however, our algorithm trades generality for...accuracy is traded for generality. 7.3.2 I-RELIEF Feature weighting transforms the original feature vector x into a new feature vector x′ by assigning each

Associative memory - An optimum binary neuron representation

NASA Technical Reports Server (NTRS)

Awwal, A. A.; Karim, M. A.; Liu, H. K.

1989-01-01

Convergence mechanism of vectors in the Hopfield's neural network is studied in terms of both weights (i.e., inner products) and Hamming distance. It is shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, weights (which in turn depend on the neuron representation) are found to play a more dominant role in the convergence mechanism. Consequently, a new binary neuron representation for associative memory is proposed. With the new neuron representation, the associative memory responds unambiguously to the partial input in retrieving the stored information.

AGT, N-Burge partitions and {{W}}_N minimal models

NASA Astrophysics Data System (ADS)

Belavin, Vladimir; Foda, Omar; Santachiara, Raoul

2015-10-01

Let {B}_{N,n}^{p,p', H} be a conformal block, with n consecutive channels χ ι , ι = 1, ⋯ n, in the conformal field theory {M}_N^{p,p'× {M}^{H} , where {M}_N^{p,p' } is a {W}_N minimal model, generated by chiral spin-2, ⋯ spin- N currents, and labeled by two co-prime integers p and p', 1 < p < p', while {M}^{H} is a free boson conformal field theory. {B}_{N,n}^{p,p', H} is the expectation value of vertex operators between an initial and a final state. Each vertex operator is labelled by a charge vector that lives in the weight lattice of the Lie algebra A N - 1, spanned by weight vectors {overrightarrow{ω}}_1,\\cdots, {overrightarrow{ω}}_{N-1} . We restrict our attention to conformal blocks with vertex operators whose charge vectors point along {overrightarrow{ω}}_1 . The charge vectors that label the initial and final states can point in any direction.

NUDTSNA at TREC 2015 Microblog Track: A Live Retrieval System Framework for Social Network based on Semantic Expansion and Quality Model

DTIC Science & Technology

2015-11-20

between tweets and profiles as follow, • TFIDF Score, which calculates the cosine similarity between a tweet and a profile in vector space model with...TFIDF weight of terms. Vector space model is a model which represents a document as a vector. Tweets and profiles can be expressed as vectors, ~ T = (t...gain(Tr i ) (13) where Tr is the returned tweet sets, gain() is the score func- tion for a tweet. Not interesting, spam/ junk tweets receive a gain of 0

Investigation of propagation dynamics of truncated vector vortex beams.

PubMed

Srinivas, P; Perumangatt, C; Lal, Nijil; Singh, R P; Srinivasan, B

2018-06-01

In this Letter, we experimentally investigate the propagation dynamics of truncated vector vortex beams generated using a Sagnac interferometer. Upon focusing, the truncated vector vortex beam is found to regain its original intensity structure within the Rayleigh range. In order to explain such behavior, the propagation dynamics of a truncated vector vortex beam is simulated by decomposing it into the sum of integral charge beams with associated complex weights. We also show that the polarization of the truncated composite vector vortex beam is preserved all along the propagation axis. The experimental observations are consistent with theoretical predictions based on previous literature and are in good agreement with our simulation results. The results hold importance as vector vortex modes are eigenmodes of the optical fiber.

Self-entanglement of long linear DNA vectors using transient non-B-DNA attachment points: a new concept for improvement of non-viral therapeutic gene delivery.

PubMed

Tolmachov, Oleg E

2012-05-01

The cell-specific and long-term expression of therapeutic transgenes often requires a full array of native gene control elements including distal enhancers, regulatory introns and chromatin organisation sequences. The delivery of such extended gene expression modules to human cells can be accomplished with non-viral high-molecular-weight DNA vectors, in particular with several classes of linear DNA vectors. All high-molecular-weight DNA vectors are susceptible to damage by shear stress, and while for some of the vectors the harmful impact of shear stress can be minimised through the transformation of the vectors to compact topological configurations by supercoiling and/or knotting, linear DNA vectors with terminal loops or covalently attached terminal proteins cannot be self-compacted in this way. In this case, the only available self-compacting option is self-entangling, which can be defined as the folding of single DNA molecules into a configuration with mutual restriction of molecular motion by the individual segments of bent DNA. A negatively charged phosphate backbone makes DNA self-repulsive, so it is reasonable to assume that a certain number of 'sticky points' dispersed within DNA could facilitate the entangling by bringing DNA segments into proximity and by interfering with the DNA slipping away from the entanglement. I propose that the spontaneous entanglement of vector DNA can be enhanced by the interlacing of the DNA with sites capable of mutual transient attachment through the formation of non-B-DNA forms, such as interacting cruciform structures, inter-segment triplexes, slipped-strand DNA, left-handed duplexes (Z-forms) or G-quadruplexes. It is expected that the non-B-DNA based entanglement of the linear DNA vectors would consist of the initial transient and co-operative non-B-DNA mediated binding events followed by tight self-ensnarement of the vector DNA. Once in the nucleoplasm of the target human cells, the DNA can be disentangled by type II topoisomerases. The technology for such self-entanglement can be an avenue for the improvement of gene delivery with high-molecular-weight naked DNA using therapeutically important methods associated with considerable shear stress. Priority applications include in vivo muscle electroporation and sonoporation for Duchenne muscular dystrophy patients, aerosol inhalation to reach the target lung cells of cystic fibrosis patients and bio-ballistic delivery to skin melanomas with the vector DNA adsorbed on gold or tungsten projectiles. Copyright © 2012 Elsevier Ltd. All rights reserved.

Addiction-like synaptic impairments in diet-induced obesity

PubMed Central

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

Learning and memory disabilities in IUGR babies: Functional and molecular analysis in a rat model.

PubMed

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.

A novel three-stage distance-based consensus ranking method

NASA Astrophysics Data System (ADS)

Aghayi, Nazila; Tavana, Madjid

2018-05-01

In this study, we propose a three-stage weighted sum method for identifying the group ranks of alternatives. In the first stage, a rank matrix, similar to the cross-efficiency matrix, is obtained by computing the individual rank position of each alternative based on importance weights. In the second stage, a secondary goal is defined to limit the vector of weights since the vector of weights obtained in the first stage is not unique. Finally, in the third stage, the group rank position of alternatives is obtained based on a distance of individual rank positions. The third stage determines a consensus solution for the group so that the ranks obtained have a minimum distance from the ranks acquired by each alternative in the previous stage. A numerical example is presented to demonstrate the applicability and exhibit the efficacy of the proposed method and algorithms.

Asymmetric Operation of the Locomotor Central Pattern Generator in the Neonatal Mouse Spinal Cord

PubMed Central

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

The morphological characteristics of corticostriatal and thalamostriatal neurons and their intrastriatal terminals in rats.

PubMed

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.

On-chip photonic synapse.

PubMed

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.

Myostatin-like proteins regulate synaptic function and neuronal morphology.

PubMed

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.

A Mathematical Model for Storage and Recall of Images using Targeted Synchronization of Coupled Maps.

PubMed

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.

The modeling and simulation of visuospatial working memory

PubMed Central

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

On-chip photonic synapse

PubMed Central

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

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

2013-12-15

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

PubMed

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.

The cortical structure of consolidated memory: a hypothesis on the role of the cingulate-entorhinal cortical connection.

PubMed

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.

Nilpotent representations of classical quantum groups at roots of unity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abe, Yuuki; Nakashima, Toshiki

2005-11-01

Properly specializing the parameters in 'Schnizer modules', for types A,B,C, and D, we get its unique primitive vector. Then we show that the module generated by the primitive vector is an irreducible highest weight module of finite dimensional classical quantum groups at roots of unity.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

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

Regulation of Local Ambient GABA Levels via Transporter-Mediated GABA Import and Export for Subliminal Learning.

PubMed

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.

Autism-Associated Insertion Mutation (InsG) of Shank3 Exon 21 Causes Impaired Synaptic Transmission and Behavioral Deficits

PubMed Central

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

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

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.

Effect of canola oil consumption on memory, synapse and neuropathology in the triple transgenic mouse model of Alzheimer's disease.

PubMed

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.

SUMO1 Affects Synaptic Function, Spine Density and Memory

PubMed Central

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

Music Signal Processing Using Vector Product Neural Networks

NASA Astrophysics Data System (ADS)

Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

2017-05-01

We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

Walsh-Hadamard transform kernel-based feature vector for shot boundary detection.

PubMed

Lakshmi, Priya G G; Domnic, S

2014-12-01

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.

Real weights, bound states and duality orbits

NASA Astrophysics Data System (ADS)

Marrani, Alessio; Riccioni, Fabio; Romano, Luca

2016-01-01

We show that the duality orbits of extremal black holes in supergravity theories with symmetric scalar manifolds can be derived by studying the stabilizing subalgebras of suitable representatives, realized as bound states of specific weight vectors of the corresponding representation of the duality symmetry group. The weight vectors always correspond to weights that are real, where the reality properties are derived from the Tits-Satake diagram that identifies the real form of the Lie algebra of the duality symmetry group. Both 𝒩 = 2 magic Maxwell-Einstein supergravities and the semisimple infinite sequences of 𝒩 = 2 and 𝒩 = 4 theories in D = 4 and 5 are considered, and various results, obtained over the years in the literature using different methods, are retrieved. In particular, we show that the stratification of the orbits of these theories occurs because of very specific properties of the representations: in the case of the theory based on the real numbers, whose symmetry group is maximally noncompact and therefore all the weights are real, the stratification is due to the presence of weights of different lengths, while in the other cases it is due to the presence of complex weights.

A study on the performance comparison of metaheuristic algorithms on the learning of neural networks

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2017-08-01

The learning or training process of neural networks entails the task of finding the most optimal set of parameters, which includes translation vectors, dilation parameter, synaptic weights, and bias terms. Apart from the traditional gradient descent-based methods, metaheuristic methods can also be used for this learning purpose. Since the inception of genetic algorithm half a century ago, the last decade witnessed the explosion of a variety of novel metaheuristic algorithms, such as harmony search algorithm, bat algorithm, and whale optimization algorithm. Despite the proof of the no free lunch theorem in the discipline of optimization, a survey in the literature of machine learning gives contrasting results. Some researchers report that certain metaheuristic algorithms are superior to the others, whereas some others argue that different metaheuristic algorithms give comparable performance. As such, this paper aims to investigate if a certain metaheuristic algorithm will outperform the other algorithms. In this work, three metaheuristic algorithms, namely genetic algorithms, particle swarm optimization, and harmony search algorithm are considered. The algorithms are incorporated in the learning of neural networks and their classification results on the benchmark UCI machine learning data sets are compared. It is found that all three metaheuristic algorithms give similar and comparable performance, as captured in the average overall classification accuracy. The results corroborate the findings reported in the works done by previous researchers. Several recommendations are given, which include the need of statistical analysis to verify the results and further theoretical works to support the obtained empirical results.

Summary of Fluidic Thrust Vectoring Research Conducted at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Deere, Karen A.

2003-01-01

Interest in low-observable aircraft and in lowering an aircraft's exhaust system weight sparked decades of research for fixed geometry exhaust nozzles. The desire for such integrated exhaust nozzles was the catalyst for new fluidic control techniques; including throat area control, expansion control, and thrust-vector angle control. This paper summarizes a variety of fluidic thrust vectoring concepts that have been tested both experimentally and computationally at NASA Langley Research Center. The nozzle concepts are divided into three categories according to the method used for fluidic thrust vectoring: the shock vector control method, the throat shifting method, and the counterflow method. This paper explains the thrust vectoring mechanism for each fluidic method, provides examples of configurations tested for each method, and discusses the advantages and disadvantages of each method.

Polar decomposition for attitude determination from vector observations

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.

1993-01-01

This work treats the problem of weighted least squares fitting of a 3D Euclidean-coordinate transformation matrix to a set of unit vectors measured in the reference and transformed coordinates. A closed-form analytic solution to the problem is re-derived. The fact that the solution is the closest orthogonal matrix to some matrix defined on the measured vectors and their weights is clearly demonstrated. Several known algorithms for computing the analytic closed form solution are considered. An algorithm is discussed which is based on the polar decomposition of matrices into the closest unitary matrix to the decomposed matrix and a Hermitian matrix. A somewhat longer improved algorithm is suggested too. A comparison of several algorithms is carried out using simulated data as well as real data from the Upper Atmosphere Research Satellite. The comparison is based on accuracy and time consumption. It is concluded that the algorithms based on polar decomposition yield a simple although somewhat less accurate solution. The precision of the latter algorithms increase with the number of the measured vectors and with the accuracy of their measurement.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

The Weighted Burgers Vector: a new quantity for constraining dislocation densities and types using electron backscatter diffraction on 2D sections through crystalline materials.

PubMed

Wheeler, J; Mariani, E; Piazolo, S; Prior, D J; Trimby, P; Drury, M R

2009-03-01

The Weighted Burgers Vector (WBV) is defined here as the sum, over all types of dislocations, of [(density of intersections of dislocation lines with a map) x (Burgers vector)]. Here we show that it can be calculated, for any crystal system, solely from orientation gradients in a map view, unlike the full dislocation density tensor, which requires gradients in the third dimension. No assumption is made about gradients in the third dimension and they may be non-zero. The only assumption involved is that elastic strains are small so the lattice distortion is entirely due to dislocations. Orientation gradients can be estimated from gridded orientation measurements obtained by EBSD mapping, so the WBV can be calculated as a vector field on an EBSD map. The magnitude of the WBV gives a lower bound on the magnitude of the dislocation density tensor when that magnitude is defined in a coordinate invariant way. The direction of the WBV can constrain the types of Burgers vectors of geometrically necessary dislocations present in the microstructure, most clearly when it is broken down in terms of lattice vectors. The WBV has three advantages over other measures of local lattice distortion: it is a vector and hence carries more information than a scalar quantity, it has an explicit mathematical link to the individual Burgers vectors of dislocations and, since it is derived via tensor calculus, it is not dependent on the map coordinate system. If a sub-grain wall is included in the WBV calculation, the magnitude of the WBV becomes dependent on the step size but its direction still carries information on the Burgers vectors in the wall. The net Burgers vector content of dislocations intersecting an area of a map can be simply calculated by an integration round the edge of that area, a method which is fast and complements point-by-point WBV calculations.

Internal performance characteristics of vectored axisymmetric ejector nozzles

NASA Technical Reports Server (NTRS)

Lamb, Milton

1993-01-01

A series of vectoring axisymmetric ejector nozzles were designed and experimentally tested for internal performance and pumping characteristics at NASA-Langley Research Center. These ejector nozzles used convergent-divergent nozzles as the primary nozzles. The model geometric variables investigated were primary nozzle throat area, primary nozzle expansion ratio, effective ejector expansion ratio (ratio of shroud exit area to primary nozzle throat area), ratio of minimum ejector area to primary nozzle throat area, ratio of ejector upper slot height to lower slot height (measured on the vertical centerline), and thrust vector angle. The primary nozzle pressure ratio was varied from 2.0 to 10.0 depending upon primary nozzle throat area. The corrected ejector-to-primary nozzle weight-flow ratio was varied from 0 (no secondary flow) to approximately 0.21 (21 percent of primary weight-flow rate) depending on ejector nozzle configuration. In addition to the internal performance and pumping characteristics, static pressures were obtained on the shroud walls.

Thrust vector control using electric actuation

NASA Astrophysics Data System (ADS)

Bechtel, Robert T.; Hall, David K.

1995-01-01

Presently, gimbaling of launch vehicle engines for thrust vector control is generally accomplished using a hydraulic system. In the case of the space shuttle solid rocket boosters and main engines, these systems are powered by hydrazine auxiliary power units. Use of electromechanical actuators would provide significant advantages in cost and maintenance. However, present energy source technologies such as batteries are heavy to the point of causing significant weight penalties. Utilizing capacitor technology developed by the Auburn University Space Power Institute in collaboration with the Auburn CCDS, Marshall Space Flight Center (MSFC) and Auburn are developing EMA system components with emphasis on high discharge rate energy sources compatible with space shuttle type thrust vector control requirements. Testing has been done at MSFC as part of EMA system tests with loads up to 66000 newtons for pulse times of several seconds. Results show such an approach to be feasible providing a potential for reduced weight and operations costs for new launch vehicles.

Proinsulin slows retinal degeneration and vision loss in the P23H rat model of retinitis pigmentosa.

PubMed

Fernández-Sánchez, Laura; Lax, Pedro; Isiegas, Carolina; Ayuso, Eduard; Ruiz, José M; de la Villa, Pedro; Bosch, Fatima; de la Rosa, Enrique J; Cuenca, Nicolás

2012-12-01

Proinsulin has been characterized as a neuroprotective molecule. In this work we assess the therapeutic potential of proinsulin on photoreceptor degeneration, synaptic connectivity, and functional activity of the retina in the transgenic P23H rat, an animal model of autosomal dominant retinitis pigmentosa (RP). P23H homozygous rats received an intramuscular injection of an adeno-associated viral vector serotype 1 (AAV1) expressing human proinsulin (hPi+) or AAV1-null vector (hPi-) at P20. Levels of hPi in serum were determined by enzyme-linked immunosorbent assay (ELISA), and visual function was evaluated by electroretinographic (ERG) recording at P30, P60, P90, and P120. Preservation of retinal structure was assessed by immunohistochemistry at P120. Human proinsulin was detected in serum from rats injected with hPi+ at all times tested, with average hPi levels ranging from 1.1 nM (P30) to 1.4 nM (P120). ERG recordings showed an amelioration of vision loss in hPi+ animals. The scotopic b-waves were significantly higher in hPi+ animals than in control rats at P90 and P120. This attenuation of visual deterioration correlated with a delay in photoreceptor degeneration and the preservation of retinal cytoarchitecture. hPi+ animals had 48.7% more photoreceptors than control animals. Presynaptic and postsynaptic elements, as well as the synaptic contacts between photoreceptors and bipolar or horizontal cells, were preserved in hPi+ P23H rats. Furthermore, in hPi+ rat retinas the number of rod bipolar cell bodies was greater than in control rats. Our data demonstrate that hPi expression preserves cone and rod structure and function, together with their contacts with postsynaptic neurons, in the P23H rat. These data strongly support the further development of proinsulin-based therapy to counteract retinitis pigmentosa.

Memory in aged mice is rescued by enhanced expression of the GluN2B subunit of the NMDA receptor

PubMed Central

Brim, B. L.; Haskell, R.; Awedikian, R.; Ellinwood, N.M.; Jin, L.; Kumar, A.; Foster, T.C.; Magnusson, K.

2012-01-01

The GluN2B subunit of the N-methyl-D-aspartate (NMDA) receptor shows age-related declines in expression across the frontal cortex and hippocampus. This decline is strongly correlated to age-related memory declines. This study was designed to determine if increasing GluN2B subunit expression in the frontal lobe or hippocampus would improve memory in aged mice. Mice were injected bilaterally with either the GluN2B vector, containing cDNA specific for the GluN2B subunit and enhanced Green Fluorescent Protein (eGFP); a control vector or vehicle. Spatial memory, cognitive flexibility, and associative memory were assessed using the Morris water maze. Aged mice, with increased GluN2B subunit expression, exhibited improved long-term spatial memory, comparable to young mice. However, memory was rescued on different days in the Morris water maze; early for hippocampal GluN2B subunit enrichment and later for the frontal lobe. A higher concentration of the GluN2B antagonist, Ro 25-6981, was required to impair long-term spatial memory in aged mice with enhanced GluN2B expression, as compared to aged controls, suggesting there was an increase in the number of GluN2B-containing NMDA receptors. In addition, hippocampal slices from aged mice with increased GluN2B subunit expression exhibited enhanced NMDA receptor-mediated excitatory post-synaptic potentials (EPSP). Treatment with Ro 25-6981 showed that a greater proportion of the NMDA receptor-mediated EPSP was due to the GluN2B subunit in these animals, as compared to aged controls. These results suggest that increasing the production of the GluN2B subunit in aged animals enhances memory and synaptic transmission. Therapies that enhance GluN2B subunit expression within the aged brain may be useful for ameliorating age-related memory declines. PMID:23103326

Global rotational motion and displacement estimation of digital image stabilization based on the oblique vectors matching algorithm

NASA Astrophysics Data System (ADS)

Yu, Fei; Hui, Mei; Zhao, Yue-jin

2009-08-01

The image block matching algorithm based on motion vectors of correlative pixels in oblique direction is presented for digital image stabilization. The digital image stabilization is a new generation of image stabilization technique which can obtains the information of relative motion among frames of dynamic image sequences by the method of digital image processing. In this method the matching parameters are calculated from the vectors projected in the oblique direction. The matching parameters based on the vectors contain the information of vectors in transverse and vertical direction in the image blocks at the same time. So the better matching information can be obtained after making correlative operation in the oblique direction. And an iterative weighted least square method is used to eliminate the error of block matching. The weights are related with the pixels' rotational angle. The center of rotation and the global emotion estimation of the shaking image can be obtained by the weighted least square from the estimation of each block chosen evenly from the image. Then, the shaking image can be stabilized with the center of rotation and the global emotion estimation. Also, the algorithm can run at real time by the method of simulated annealing in searching method of block matching. An image processing system based on DSP was used to exam this algorithm. The core processor in the DSP system is TMS320C6416 of TI, and the CCD camera with definition of 720×576 pixels was chosen as the input video signal. Experimental results show that the algorithm can be performed at the real time processing system and have an accurate matching precision.

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.

Short-Term Memory Trace in Rapidly Adapting Synapses of Inferior Temporal Cortex

PubMed Central

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

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

PubMed Central

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

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

PubMed

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.

CNTNAP2 is a direct FoxP2 target in vitro and in vivo in zebra finches: complex regulation by age and activity.

PubMed

Adam, I; Mendoza, E; Kobalz, U; Wohlgemuth, S; Scharff, C

2017-07-01

Mutations of FOXP2 are associated with altered brain structure, including the striatal part of the basal ganglia, and cause a severe speech and language disorder. Songbirds serve as a tractable neurobiological model for speech and language research. Experimental downregulation of FoxP2 in zebra finch Area X, a nucleus of the striatal song control circuitry, affects synaptic transmission and spine densities. It also renders song learning and production inaccurate and imprecise, similar to the speech impairment of patients carrying FOXP2 mutations. Here we show that experimental downregulation of FoxP2 in Area X using lentiviral vectors leads to reduced expression of CNTNAP2, a FOXP2 target gene in humans. In addition, natural downregulation of FoxP2 by age or by singing also downregulated CNTNAP2 expression. Furthermore, we report that FoxP2 binds to and activates the avian CNTNAP2 promoter in vitro. Taken together these data establish CNTNAP2 as a direct FoxP2 target gene in songbirds, likely affecting synaptic function relevant for song learning and song maintenance. © 2017 The Authors. Genes, Brain and Behavior published by International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd.

Addiction-like Synaptic Impairments in Diet-Induced Obesity.

PubMed

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.

NR2A- and NR2B-NMDA receptors and drebrin within postsynaptic spines of the hippocampus correlate with hunger-evoked exercise.

PubMed

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.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

PubMed

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.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

PubMed Central

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

Social stress alters inhibitory synaptic input to distinct subpopulations of raphe serotonin neurons.

PubMed

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.

Role of presynaptic inputs to proprioceptive afferents in tuning sensorimotor pathways of an insect joint control network.

PubMed

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.

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity

PubMed Central

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

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

PubMed

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.

Emerging feed-forward inhibition allows the robust formation of direction selectivity in the developing ferret visual cortex

PubMed Central

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

Weighted optimization of irradiance for photodynamic therapy of port wine stains

NASA Astrophysics Data System (ADS)

He, Linhuan; Zhou, Ya; Hu, Xiaoming

2016-10-01

Planning of irradiance distribution (PID) is one of the foremost factors for on-demand treatment of port wine stains (PWS) with photodynamic therapy (PDT). A weighted optimization method for PID was proposed according to the grading of PWS with a three dimensional digital illumination instrument. Firstly, the point clouds of lesions were filtered to remove the error or redundant points, the triangulation was carried out and the lesion was divided into small triangular patches. Secondly, the parameters such as area, normal vector and orthocenter for optimization of each triangular patch were calculated, and the weighted coefficients were determined by the erythema indexes and areas of patches. Then, the optimization initial point was calculated based on the normal vectors and orthocenters to optimize the light direction. In the end, the irradiation can be optimized according to cosine values of irradiance angles and weighted coefficients. Comparing the irradiance distribution before and after optimization, the proposed weighted optimization method can make the irradiance distribution match better with the characteristics of lesions, and has the potential to improve the therapeutic efficacy.

PubMed-supported clinical term weighting approach for improving inter-patient similarity measure in diagnosis prediction.

PubMed

Chan, Lawrence Wc; Liu, Ying; Chan, Tao; Law, Helen Kw; Wong, S C Cesar; Yeung, Andy Ph; Lo, K F; Yeung, S W; Kwok, K Y; Chan, William Yl; Lau, Thomas Yh; Shyu, Chi-Ren

2015-06-02

Similarity-based retrieval of Electronic Health Records (EHRs) from large clinical information systems provides physicians the evidence support in making diagnoses or referring examinations for the suspected cases. Clinical Terms in EHRs represent high-level conceptual information and the similarity measure established based on these terms reflects the chance of inter-patient disease co-occurrence. The assumption that clinical terms are equally relevant to a disease is unrealistic, reducing the prediction accuracy. Here we propose a term weighting approach supported by PubMed search engine to address this issue. We collected and studied 112 abdominal computed tomography imaging examination reports from four hospitals in Hong Kong. Clinical terms, which are the image findings related to hepatocellular carcinoma (HCC), were extracted from the reports. Through two systematic PubMed search methods, the generic and specific term weightings were established by estimating the conditional probabilities of clinical terms given HCC. Each report was characterized by an ontological feature vector and there were totally 6216 vector pairs. We optimized the modified direction cosine (mDC) with respect to a regularization constant embedded into the feature vector. Equal, generic and specific term weighting approaches were applied to measure the similarity of each pair and their performances for predicting inter-patient co-occurrence of HCC diagnoses were compared by using Receiver Operating Characteristics (ROC) analysis. The Areas under the curves (AUROCs) of similarity scores based on equal, generic and specific term weighting approaches were 0.735, 0.728 and 0.743 respectively (p < 0.01). In comparison with equal term weighting, the performance was significantly improved by specific term weighting (p < 0.01) but not by generic term weighting. The clinical terms "Dysplastic nodule", "nodule of liver" and "equal density (isodense) lesion" were found the top three image findings associated with HCC in PubMed. Our findings suggest that the optimized similarity measure with specific term weighting to EHRs can improve significantly the accuracy for predicting the inter-patient co-occurrence of diagnosis when compared with equal and generic term weighting approaches.

A feedforward artificial neural network based on quantum effect vector-matrix multipliers.

PubMed

Levy, H J; McGill, T C

1993-01-01

The vector-matrix multiplier is the engine of many artificial neural network implementations because it can simulate the way in which neurons collect weighted input signals from a dendritic arbor. A new technology for building analog weighting elements that is theoretically capable of densities and speeds far beyond anything that conventional VLSI in silicon could ever offer is presented. To illustrate the feasibility of such a technology, a small three-layer feedforward prototype network with five binary neurons and six tri-state synapses was built and used to perform all of the fundamental logic functions: XOR, AND, OR, and NOT.

Iterative Minimum Variance Beamformer with Low Complexity for Medical Ultrasound Imaging.

PubMed

Deylami, Ali Mohades; Asl, Babak Mohammadzadeh

2018-06-04

Minimum variance beamformer (MVB) improves the resolution and contrast of medical ultrasound images compared with delay and sum (DAS) beamformer. The weight vector of this beamformer should be calculated for each imaging point independently, with a cost of increasing computational complexity. The large number of necessary calculations limits this beamformer to application in real-time systems. A beamformer is proposed based on the MVB with lower computational complexity while preserving its advantages. This beamformer avoids matrix inversion, which is the most complex part of the MVB, by solving the optimization problem iteratively. The received signals from two imaging points close together do not vary much in medical ultrasound imaging. Therefore, using the previously optimized weight vector for one point as initial weight vector for the new neighboring point can improve the convergence speed and decrease the computational complexity. The proposed method was applied on several data sets, and it has been shown that the method can regenerate the results obtained by the MVB while the order of complexity is decreased from O(L 3 ) to O(L 2 ). Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

Balanced Centrality of Networks.

PubMed

Debono, Mark; Lauri, Josef; Sciriha, Irene

2014-01-01

There is an age-old question in all branches of network analysis. What makes an actor in a network important, courted, or sought? Both Crossley and Bonacich contend that rather than its intrinsic wealth or value, an actor's status lies in the structures of its interactions with other actors. Since pairwise relation data in a network can be stored in a two-dimensional array or matrix, graph theory and linear algebra lend themselves as great tools to gauge the centrality (interpreted as importance, power, or popularity, depending on the purpose of the network) of each actor. We express known and new centralities in terms of only two matrices associated with the network. We show that derivations of these expressions can be handled exclusively through the main eigenvectors (not orthogonal to the all-one vector) associated with the adjacency matrix. We also propose a centrality vector (SWIPD) which is a linear combination of the square, walk, power, and degree centrality vectors with weightings of the various centralities depending on the purpose of the network. By comparing actors' scores for various weightings, a clear understanding of which actors are most central is obtained. Moreover, for threshold networks, the (SWIPD) measure turns out to be independent of the weightings.

Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation

ERIC Educational Resources Information Center

Hinton, Geoffrey; Osindero, Simon; Welling, Max; Teh, Yee-Whye

2006-01-01

We describe a way of modeling high-dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron-like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of…

Boosting specificity of MEG artifact removal by weighted support vector machine.

PubMed

Duan, Fang; Phothisonothai, Montri; Kikuchi, Mitsuru; Yoshimura, Yuko; Minabe, Yoshio; Watanabe, Kastumi; Aihara, Kazuyuki

2013-01-01

An automatic artifact removal method of magnetoencephalogram (MEG) was presented in this paper. The method proposed is based on independent components analysis (ICA) and support vector machine (SVM). However, different from the previous studies, in this paper we consider two factors which would influence the performance. First, the imbalance factor of independent components (ICs) of MEG is handled by weighted SVM. Second, instead of simply setting a fixed weight to each class, a re-weighting scheme is used for the preservation of useful MEG ICs. Experimental results on manually marked MEG dataset showed that the method proposed could correctly distinguish the artifacts from the MEG ICs. Meanwhile, 99.72% ± 0.67 of MEG ICs were preserved. The classification accuracy was 97.91% ± 1.39. In addition, it was found that this method was not sensitive to individual differences. The cross validation (leave-one-subject-out) results showed an averaged accuracy of 97.41% ± 2.14.

Nodal distances for rooted phylogenetic trees.

PubMed

Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel

2010-08-01

Dissimilarity measures for (possibly weighted) phylogenetic trees based on the comparison of their vectors of path lengths between pairs of taxa, have been present in the systematics literature since the early seventies. For rooted phylogenetic trees, however, these vectors can only separate non-weighted binary trees, and therefore these dissimilarity measures are metrics only on this class of rooted phylogenetic trees. In this paper we overcome this problem, by splitting in a suitable way each path length between two taxa into two lengths. We prove that the resulting splitted path lengths matrices single out arbitrary rooted phylogenetic trees with nested taxa and arcs weighted in the set of positive real numbers. This allows the definition of metrics on this general class of rooted phylogenetic trees by comparing these matrices through metrics in spaces M(n)(R) of real-valued n x n matrices. We conclude this paper by establishing some basic facts about the metrics for non-weighted phylogenetic trees defined in this way using L(p) metrics on M(n)(R), with p [epsilon] R(>0).

Python scripting in the nengo simulator.

PubMed

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.

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity.

PubMed

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.

Autism-Associated Insertion Mutation (InsG) of Shank3 Exon 21 Causes Impaired Synaptic Transmission and Behavioral Deficits.

PubMed

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.

Python Scripting in the Nengo Simulator

PubMed Central

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

Gating of Long-Term Potentiation by Nicotinic Acetylcholine Receptors at the Cerebellum Input Stage

PubMed Central

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

The Coordinate Orthogonality Check (corthog)

NASA Astrophysics Data System (ADS)

Avitabile, P.; Pechinsky, F.

1998-05-01

A new technique referred to as the coordinate orthogonality check (CORTHOG) helps to identify how each physical degree of freedom contributes to the overall orthogonality relationship between analytical and experimental modal vectors on a mass-weighted basis. Using the CORTHOG technique together with the pseudo-orthogonality check (POC) clarifies where potential discrepancies exist between the analytical and experimental modal vectors. CORTHOG improves the understanding of the correlation (or lack of correlation) that exists between modal vectors. The CORTHOG theory is presented along with the evaluation of several cases to show the use of the technique.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Link-Based Similarity Measures Using Reachability Vectors

PubMed Central

Yoon, Seok-Ho; Kim, Ji-Soo; Ryu, Minsoo; Choi, Ho-Jin

2014-01-01

We present a novel approach for computing link-based similarities among objects accurately by utilizing the link information pertaining to the objects involved. We discuss the problems with previous link-based similarity measures and propose a novel approach for computing link based similarities that does not suffer from these problems. In the proposed approach each target object is represented by a vector. Each element of the vector corresponds to all the objects in the given data, and the value of each element denotes the weight for the corresponding object. As for this weight value, we propose to utilize the probability of reaching from the target object to the specific object, computed using the “Random Walk with Restart” strategy. Then, we define the similarity between two objects as the cosine similarity of the two vectors. In this paper, we provide examples to show that our approach does not suffer from the aforementioned problems. We also evaluate the performance of the proposed methods in comparison with existing link-based measures, qualitatively and quantitatively, with respect to two kinds of data sets, scientific papers and Web documents. Our experimental results indicate that the proposed methods significantly outperform the existing measures. PMID:24701188

Enhanced AMPA receptor trafficking mediates the anorexigenic effect of endogenous glucagon like peptide-1 in the paraventricular hypothalamus

PubMed Central

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

Synaptic Scaling in Combination with Many Generic Plasticity Mechanisms Stabilizes Circuit Connectivity

PubMed Central

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

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

A novel dynamical community detection algorithm based on weighting scheme

NASA Astrophysics Data System (ADS)

Li, Ju; Yu, Kai; Hu, Ke

2015-12-01

Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

Synaptic electronics: materials, devices and applications.

PubMed

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.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study

PubMed Central

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

Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study.

PubMed

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.

Three-dimensional weight-accumulation algorithm for generating multiple excitation spots in fast optical stimulation

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.

Three-dimensional weight-accumulation algorithm for generating multiple excitation spots in fast optical stimulation

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.

USAF 1990 Research Initiation Program. Volume 3

DTIC Science & Technology

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

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.

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.

Photoalignment and resulting holographic vector grating formation in composites of low molecular weight liquid crystals and photoreactive liquid crystalline polymers

NASA Astrophysics Data System (ADS)

Sasaki, Tomoyuki; Shoho, Takashi; Goto, Kohei; Noda, Kohei; Kawatsuki, Nobuhiro; Ono, Hiroshi

2015-08-01

Polarization holographic gratings were formed in liquid crystal (LC) cells fabricated from a mixture of low molecular weight nematic LC and a photoreactive liquid crystalline polymer (PLCP) with 4-(4-methoxycinnamoyloxy)biphenyl side groups. The diffraction properties of the gratings were analyzed using theoretical models which were determined based on the polarization patterns of the polarization holography. The results demonstrated that vector gratings comprised of periodic orientation distributions of the LC molecule were induced in the cells based on the axis-selective photoreaction of the PLCP. The vector gratings were erased by applying a sufficiently high voltage to the cells and then were reformed with no hysteresis after the voltage was removed. This phenomenon suggested that the PLCP molecules were stabilized based on the axis-selective photocrosslink reaction and that the LC molecules were aligned by the photocrosslinked PLCP. This LC composite with axis-selective photoreactivity is useful for various optical applications, because of their stability, transparency, and response to applied voltage.

An artificial neural network model for periodic trajectory generation

NASA Astrophysics Data System (ADS)

Shankar, S.; Gander, R. E.; Wood, H. C.

A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

Flexible Proton-Gated Oxide Synaptic Transistors on Si Membrane.

PubMed

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.

Point-based warping with optimized weighting factors of displacement vectors

NASA Astrophysics Data System (ADS)

Pielot, Ranier; Scholz, Michael; Obermayer, Klaus; Gundelfinger, Eckart D.; Hess, Andreas

2000-06-01

The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non-metabolized radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark-generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark- specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset.

Low molecular weight chitosan conjugated with folate for siRNA delivery in vitro: optimization studies

PubMed Central

Fernandes, Julio C; Qiu, Xingping; Winnik, Francoise M; Benderdour, Mohamed; Zhang, Xiaoling; Dai, Kerong; Shi, Qin

2012-01-01

The low transfection efficiency of chitosan is one of its drawbacks as a gene delivery carrier. Low molecular weight chitosan may help to form small-sized polymer-DNA or small interfering RNA (siRNA) complexes. Folate conjugation may improve gene transfection efficiency because of the promoted uptake of folate receptor-bearing cells. In the present study, chitosan was conjugated with folate and investigated for its efficacy as a delivery vector for siRNA in vitro. We demonstrate that the molecular weight of chitosan has a major influence on its biological and physicochemical properties, and very low molecular weight chitosan (below 10 kDa) has difficulty in forming stable complexes with siRNA. In this study, chitosan 25 kDa and 50 kDa completely absorbed siRNA and formed nanoparticles (≤220 nm) at a chitosan to siRNA weight ratio of 50:1. The introduction of a folate ligand onto chitosan decreased nanoparticle toxicity. Compared with chitosan-siRNA, folate-chitosan-siRNA nanoparticles improved gene silencing transfection efficiency. Therefore, folate-chitosan shows potential as a viable candidate vector for safe and efficient siRNA delivery. PMID:23209368

Cycles of insanity and creativity within contemplative neural systems.

PubMed

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.

Bioelectrical impedance vector distribution in the first year of life.

PubMed

Savino, Francesco; Grasso, Giulia; Cresi, Francesco; Oggero, Roberto; Silvestro, Leandra

2003-06-01

We assessed the bioelectrical impedance vector distribution in a sample of healthy infants in the first year of life, which is not available in literature. The study was conducted as a cross-sectional study in 153 healthy Caucasian infants (90 male and 63 female) younger than 1 y, born at full term, adequate for gestational age, free from chronic diseases or growth problems, and not feverish. Z scores for weight, length, cranial circumference, and body mass index for the study population were within the range of +/-1.5 standard deviations according to the Euro-Growth Study references. Concurrent anthropometrics (weight, length, and cranial circumference), body mass index, and bioelectrical impedance (resistance and reactance) measurements were made by the same operator. Whole-body (hand to foot) tetrapolar measurements were performed with a single-frequency (50 kHz), phase-sensitive impedance analyzer. The study population was subdivided into three classes of age for statistical analysis: 0 to 3.99 mo, 4 to 7.99 mo, and 8 to 11.99 mo. Using the bivariate normal distribution of resistance and reactance components standardized by the infant's length, the bivariate 95% confidence limits for the mean impedance vector separated by sex and age groups were calculated and plotted. Further, the bivariate 95%, 75%, and 50% tolerance intervals for individual vector measurements in the first year of life were plotted. Resistance and reactance values often fluctuated during the first year of life, particularly as raw measurements (without normalization by subject's length). However, 95% confidence ellipses of mean vectors from the three age groups overlapped each other, as did confidence ellipses by sex for each age class, indicating no significant vector migration during the first year of life. We obtained an estimate of mean impedance vector in a sample of healthy infants in the first year of life and calculated the bivariate values for an individual vector (95%, 75%, and 50% tolerance ellipses).

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

APP is cleaved by Bace1 in pre-synaptic vesicles and establishes a pre-synaptic interactome, via its intracellular domain, with molecular complexes that regulate pre-synaptic vesicles functions.

PubMed

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.

CURRENT CONDITIONS AND RESIDENCE PREFERENCES OR CITIZENS' PERCEPTIONS ON NONCONVENTIONAL WATER RESOURCES

NASA Astrophysics Data System (ADS)

Tsuzuki, Yoshiaki; Aramaki, Toshiya

Preferences or perceptions of ordinary citizens on three kinds of nonconventional water resources including rainwater, permissible groundwater exuding to underground railway stations and tunnels and reclaimed wastewater were investigated by use of the Internet survey method. The survey results were analysed with analytical hierar chal process (AHP) and willingness to pay (WTP). Weight vectors of natural environment and people's lives were found larger than other three first order evaluation conditions, society, economics and technology. The order of the weight vector values for the three water resources were rainwater, reclaimed wastewater and permissible groundwater. That for the five water usages were agricultural and horticulture water, water storage in preparation for disaster, toilet flushing water, environment water and sprinkler water for washing road and cooling atmosphere temperature. The difference between toilet flushing water and environment water was not significant by 5% significance. The analyzed data showed that differences between the weight vector values of the alternatives (water resources and their usages) became small by increasing the number of the evaluation conditions, which would be a topic to be resolved for AHP application to actual public projects. For water resources, WTP with public budgets was Japanese Yen (JY) 53,100-55,100 person-1 year-1, and WTP with private finances was JY 19,100-20,800 person-1 year-1. For water usages, public WTP was JY 20,400-47,200 person-1 year-1 and private WTP was JY 8,400-16,000 person-1 year-1. The orders of WTP values were similar to the orders of the weight vector values for both water resources and their usages obtained by the AHP analysis. Effective dissemination subjects and objects of the nonconventional water resources and their usages were extracted by the analysis for attributes including sex, age, living area, occupation and education.

Prediction of subcellular localization of eukaryotic proteins using position-specific profiles and neural network with weighted inputs.

PubMed

Zou, Lingyun; Wang, Zhengzhi; Huang, Jiaomin

2007-12-01

Subcellular location is one of the key biological characteristics of proteins. Position-specific profiles (PSP) have been introduced as important characteristics of proteins in this article. In this study, to obtain position-specific profiles, the Position Specific Iterative-Basic Local Alignment Search Tool (PSI-BLAST) has been used to search for protein sequences in a database. Position-specific scoring matrices are extracted from the profiles as one class of characteristics. Four-part amino acid compositions and 1st-7th order dipeptide compositions have also been calculated as the other two classes of characteristics. Therefore, twelve characteristic vectors are extracted from each of the protein sequences. Next, the characteristic vectors are weighed by a simple weighing function and inputted into a BP neural network predictor named PSP-Weighted Neural Network (PSP-WNN). The Levenberg-Marquardt algorithm is employed to adjust the weight matrices and thresholds during the network training instead of the error back propagation algorithm. With a jackknife test on the RH2427 dataset, PSP-WNN has achieved a higher overall prediction accuracy of 88.4% rather than the prediction results by the general BP neural network, Markov model, and fuzzy k-nearest neighbors algorithm on this dataset. In addition, the prediction performance of PSP-WNN has been evaluated with a five-fold cross validation test on the PK7579 dataset and the prediction results have been consistently better than those of the previous method on the basis of several support vector machines, using compositions of both amino acids and amino acid pairs. These results indicate that PSP-WNN is a powerful tool for subcellular localization prediction. At the end of the article, influences on prediction accuracy using different weighting proportions among three characteristic vector categories have been discussed. An appropriate proportion is considered by increasing the prediction accuracy.

Correlating Fluorescence and High-Resolution Scanning Electron Microscopy (HRSEM) for the study of GABAA receptor clustering induced by inhibitory synaptic plasticity.

PubMed

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.

Quantitative proteomics of synaptic and nonsynaptic mitochondria: insights for synaptic mitochondrial vulnerability.

PubMed

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.

Quantitative Proteomics of Synaptic and Nonsynaptic Mitochondria: Insights for Synaptic Mitochondrial Vulnerability

PubMed Central

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

Unbiased View of Synaptic and Neuronal Gene Complement in Ctenophores: Are There Pan-neuronal and Pan-synaptic Genes across Metazoa?

PubMed Central

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

Synaptic plasticity and gravity: Ultrastructural, biochemical and physico-chemical fundamentals

NASA Astrophysics Data System (ADS)

Rahmann, H.; Slenzka, K.; Körtje, K. H.; Hilbig, R.

On the basis of quantitative disturbances of the swimming behaviour of aquatic vertebrates (``loop-swimming'' in fish and frog larvae) following long-term hyper-g-exposure the question was raised whether or not and to what extent changes in the gravitational vector might influence the CNS at the cellular level. Therefore, by means of histological, histochemical and biochemical analyses the effect of 2-4 x g for 9 days on the gross morphology of the fish brain, and on different neuronal enzymes was investigated. In order to enable a more precise analysis in future-μg-experiments of any gravity-related effects on the neuronal synapses within the gravity-perceptive integration centers differentiated electron-microscopical and electronspectroscopical techniques have been developed to accomplish an ultrastructural localization of calcium, a high-affinity Ca2+-ATPase, creatine kinase and cytochrome oxidase. In hyper-g animals vs. 1-g controls, a reduction of total brain volume (15 %), a decrease in creatine kinase activity (20 %), a local increase in cytochrome oxidase activity, but no differences in Ca2+/Mg2+-ATPase activities were observed. Ultrastructural peculiarities of synaptic contact formation in gravity-related integration centers (Nucleus magnocellularis) were found. These results are discussed on the basis of a direct effect of hyper-gravity not only on the gravity-sensitive neuronal integration centers but possibly also on the physico-chemical properties of the lipid bilayer of neuronal membranes in general.

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.

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.

The method for froth floatation condition recognition based on adaptive feature weighted

NASA Astrophysics Data System (ADS)

Wang, Jieran; Zhang, Jun; Tian, Jinwen; Zhang, Daimeng; Liu, Xiaomao

2018-03-01

The fusion of foam characteristics can play a complementary role in expressing the content of foam image. The weight of foam characteristics is the key to make full use of the relationship between the different features. In this paper, an Adaptive Feature Weighted Method For Froth Floatation Condition Recognition is proposed. Foam features without and with weights are both classified by using support vector machine (SVM).The classification accuracy and optimal equaling algorithm under the each ore grade are regarded as the result of the adaptive feature weighting algorithm. At the same time the effectiveness of adaptive weighted method is demonstrated.

Automatic EEG artifact removal: a weighted support vector machine approach with error correction.

PubMed

Shao, Shi-Yun; Shen, Kai-Quan; Ong, Chong Jin; Wilder-Smith, Einar P V; Li, Xiao-Ping

2009-02-01

An automatic electroencephalogram (EEG) artifact removal method is presented in this paper. Compared to past methods, it has two unique features: 1) a weighted version of support vector machine formulation that handles the inherent unbalanced nature of component classification and 2) the ability to accommodate structural information typically found in component classification. The advantages of the proposed method are demonstrated on real-life EEG recordings with comparisons made to several benchmark methods. Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities. Qualitative evaluation of the reconstructed EEG epochs also demonstrates that after artifact removal inherent brain activities are largely preserved.

Noise generated by a flight weight, air flow control valve in a vertical takeoff and landing aircraft thrust vectoring system

NASA Technical Reports Server (NTRS)

Huff, Ronald G.

1989-01-01

Tests were conducted in the NASA Lewis Research Center's Powered Lift Facility to experimentally evaluate the noise generated by a flight weight, 12 in. butterfly valve installed in a proposed vertical takeoff and landing thrust vectoring system. Fluctuating pressure measurements were made in the circular duct upstream and downstream of the valve. This data report presents the results of these tests. The maximum overall sound pressure level is generated in the duct downstream of the valve and reached a value of 180 dB at a valve pressure ratio of 2.8. At the higher valve pressure ratios the spectra downstream of the valve is broad banded with its maximum at 1000 Hz.

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect

PubMed Central

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

Antidepressant-like effects and possible mechanisms of amantadine on cognitive and synaptic deficits in a rat model of chronic stress.

PubMed

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.

Application of optimal control theory to the design of the NASA/JPL 70-meter antenna servos

NASA Technical Reports Server (NTRS)

Alvarez, L. S.; Nickerson, J.

1989-01-01

The application of Linear Quadratic Gaussian (LQG) techniques to the design of the 70-m axis servos is described. Linear quadratic optimal control and Kalman filter theory are reviewed, and model development and verification are discussed. Families of optimal controller and Kalman filter gain vectors were generated by varying weight parameters. Performance specifications were used to select final gain vectors.

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…

Ca2+ Dependence of Synaptic Vesicle Endocytosis.

PubMed

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.

Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier

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.

Low-rate image coding using vector quantization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makur, A.

1990-01-01

This thesis deals with the development and analysis of a computationally simple vector quantization image compression system for coding monochrome images at low bit rate. Vector quantization has been known to be an effective compression scheme when a low bit rate is desirable, but the intensive computation required in a vector quantization encoder has been a handicap in using it for low rate image coding. The present work shows that, without substantially increasing the coder complexity, it is indeed possible to achieve acceptable picture quality while attaining a high compression ratio. Several modifications to the conventional vector quantization coder aremore » proposed in the thesis. These modifications are shown to offer better subjective quality when compared to the basic coder. Distributed blocks are used instead of spatial blocks to construct the input vectors. A class of input-dependent weighted distortion functions is used to incorporate psychovisual characteristics in the distortion measure. Computationally simple filtering techniques are applied to further improve the decoded image quality. Finally, unique designs of the vector quantization coder using electronic neural networks are described, so that the coding delay is reduced considerably.« less

Weighted polygamy inequalities of multiparty entanglement in arbitrary-dimensional quantum systems

NASA Astrophysics Data System (ADS)

Kim, Jeong San

2018-04-01

We provide a generalization for the polygamy constraint of multiparty entanglement in arbitrary-dimensional quantum systems. By using the β th power of entanglement of assistance for 0 ≤β ≤1 and the Hamming weight of the binary vector related with the distribution of subsystems, we establish a class of weighted polygamy inequalities of multiparty entanglement in arbitrary-dimensional quantum systems. We further show that our class of weighted polygamy inequalities can even be improved to be tighter inequalities with some conditions on the assisted entanglement of bipartite subsystems.

Episodic sucrose intake during food restriction increases synaptic abundance of AMPA receptors in nucleus accumbens and augments intake of sucrose following restoration of ad libitum feeding.

PubMed

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.

Extrasynaptic exocytosis and its mechanisms: a source of molecules mediating volume transmission in the nervous system.

PubMed

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.

Prenatal caffeine intake differently affects synaptic proteins during fetal brain development.

PubMed

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.

Rivastigmine Lowers Aβ and Increases sAPPα Levels, Which Parallel Elevated Synaptic Markers and Metabolic Activity in Degenerating Primary Rat Neurons

PubMed Central

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

Preparation of synaptic plasma membrane and postsynaptic density proteins using a discontinuous sucrose gradient.

PubMed

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.

Diversity of neuropsin (KLK8)-dependent synaptic associativity in the hippocampal pyramidal neuron

PubMed Central

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

Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

PubMed

Lao, Zhiqiang; Shen, Dinggang; Liu, Dengfeng; Jawad, Abbas F; Melhem, Elias R; Launer, Lenore J; Bryan, R Nick; Davatzikos, Christos

2008-03-01

Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans. Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set. Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.

Non-synaptic receptors and transporters involved in brain functions and targets of drug treatment.

PubMed

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.

Semi-automatic feedback using concurrence between mixture vectors for general databases

NASA Astrophysics Data System (ADS)

Larabi, Mohamed-Chaker; Richard, Noel; Colot, Olivier; Fernandez-Maloigne, Christine

2001-12-01

This paper describes how a query system can exploit the basic knowledge by employing semi-automatic relevance feedback to refine queries and runtimes. For general databases, it is often useless to call complex attributes, because we have not sufficient information about images in the database. Moreover, these images can be topologically very different from one to each other and an attribute that is powerful for a database category may be very powerless for the other categories. The idea is to use very simple features, such as color histogram, correlograms, Color Coherence Vectors (CCV), to fill out the signature vector. Then, a number of mixture vectors is prepared depending on the number of very distinctive categories in the database. Knowing that a mixture vector is a vector containing the weight of each attribute that will be used to compute a similarity distance. We post a query in the database using successively all the mixture vectors defined previously. We retain then the N first images for each vector in order to make a mapping using the following information: Is image I present in several mixture vectors results? What is its rank in the results? These informations allow us to switch the system on an unsupervised relevance feedback or user's feedback (supervised feedback).

Spatial distribution of an infectious disease in a small mammal community

NASA Astrophysics Data System (ADS)

Correa, Juana P.; Bacigalupo, Antonella; Fontúrbel, Francisco E.; Oda, Esteban; Cattan, Pedro E.; Solari, Aldo; Botto-Mahan, Carezza

2015-10-01

Chagas disease is a zoonosis caused by the parasite Trypanosoma cruzi and transmitted by insect vectors to several mammals, but little is known about its spatial epidemiology. We assessed the spatial distribution of T. cruzi infection in vectors and small mammals to test if mammal infection status is related to the proximity to vector colonies. During four consecutive years we captured and georeferenced the locations of mammal species and colonies of Mepraia spinolai, a restricted-movement vector. Infection status on mammals and vectors was evaluated by molecular techniques. To examine the effect of vector colonies on mammal infection status, we constructed an infection distance index using the distance between the location of each captured mammal to each vector colony and the average T. cruzi prevalence of each vector colony, weighted by the number of colonies assessed. We collected and evaluated T. cruzi infection in 944 mammals and 1976 M. spinolai. We found a significant effect of the infection distance index in explaining their infection status, when considering all mammal species together. By examining the most abundant species separately, we found this effect only for the diurnal and gregarious rodent Octodon degus. Spatially explicit models involving the prevalence and location of infected vectors and hosts had not been reported previously for a wild disease.

Bioreducible Zinc(II)-Coordinative Polyethylenimine with Low Molecular Weight for Robust Gene Delivery of Primary and Stem Cells.

PubMed

Liu, Shuai; Zhou, Dezhong; Yang, Jixiang; Zhou, Hao; Chen, Jiatong; Guo, Tianying

2017-03-30

To transform common low-molecular-weight (LMW) cationic polymers, such as polyethylenimine (PEI), to highly efficient gene vectors would be of great significance but remains challenging. Because LMW cationic polymers perform far less efficiently than their high-molecular-weight counterparts, mainly due to weaker nucleic acid encapsulation, herein we report the design and synthesis of a dipicolylamine-based disulfide-containing zinc(II) coordinative module (Zn-DDAC), which is used to functionalize LMW PEI (M w ≈ 1800 Da) to give a non-viral vector (Zn-PD) with high efficiency and safety in primary and stem cells. Given its high phosphate binding affinity, Zn-DDAC can significantly promote the DNA packaging functionality of PEI 1.8k and improve the cellular uptake of formulated polyplexes, which is particularly critical for hard-to-transfect cell types. Furthermore, Zn-PD polymer can be cleaved by glutathione in cytoplasm to facilitate DNA release post internalization and diminish the cytotoxicity. Consequently, the optimal Zn-PD mediates 1-2 orders of magnitude higher gluciferase activity than commercial transfection reagents, Xfect and PEI 25k , across diverse cell types, including primary and stem cells. Our findings provide a valuable insight into the exploitation of LMW cationic polymers for gene delivery and demonstrate great promise for the development of next-generation non-viral vectors for clinically viable gene therapy.

Cell-specific gain modulation by synaptically released zinc in cortical circuits of audition.

PubMed

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.

The network organisation of consumer complaints

NASA Astrophysics Data System (ADS)

Rocha, L. E. C.; Holme, P.

2010-07-01

Interaction between consumers and companies can create conflict. When a consensus is unreachable there are legal authorities to resolve the case. This letter is a study of data from the Brazilian Department of Justice from which we build a bipartite network of categories of complaints linked to the companies receiving those complaints. We find the complaint categories organised in an hierarchical way where companies only get complaints of lower degree if they already got complaints of higher degree. The fraction of resolved complaints for a company appears to be nearly independent of the equity of the company but is positively correlated with the total number of complaints received. We construct feature vectors based on the edge-weight —the weight of an edge represents the times complaints of a category have been filed against that company— and use these vectors to study the similarity between the categories of complaints. From this analysis, we obtain trees mapping the hierarchical organisation of the complaints. We also apply principal component analysis to the set of feature vectors concluding that a reduction of the dimensionality of these from 8827 to 27 gives an optimal hierarchical representation.

Different types of degradable vectors from low-molecular-weight polycation-functionalized poly(aspartic acid) for efficient gene delivery.

PubMed

Dou, X B; Hu, Y; Zhao, N N; Xu, F J

2014-03-01

Poly(aspartic acid) (PAsp) has been employed as the potential backbone for the preparation of efficient gene carriers, due to its low cytotoxicity, good biodegradability and excellent biocompatibility. In this work, the degradable linear or star-shaped PBLA was first prepared via ring-opining polymerization of β-benzyl-L-aspartate N-carboxy anhydride (BLA-NCA) initiated by ethylenediamine (ED) or ED-functionalized cyclodextrin cores. Then, PBLA was functionalized via aminolysis reaction with low-molecular-weight poly(2-(dimethylamino)ethyl methacrylate) with one terminal primary amine group (PDMAEMA-NH2), followed by addition of excess ED or ethanolamine (EA) to complete the aminolysis process. The obtained different types of cationic PAsp-based vectors including linear or star PAsp-PDM-NH2 and PAsp-PDM-OH exhibited good condensation capability and degradability, benefiting gene delivery process. In comparison with gold standard polyethylenimine (PEI, ∼ 25 kDa), the cationic PAsp-based vectors, particularly star-shaped ones, exhibited much better transfection performances. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

Enhanced AMPA Receptor Trafficking Mediates the Anorexigenic Effect of Endogenous Glucagon-like Peptide-1 in the Paraventricular Hypothalamus.

PubMed

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.

Development of a Highly Automated and Multiplexed Targeted Proteome Pipeline and Assay for 112 Rat Brain Synaptic Proteins

PubMed Central

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

Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations

PubMed Central

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

An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table.

PubMed

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.

Genetic removal of p70 S6 kinase 1 corrects molecular, synaptic, and behavioral phenotypes in fragile X syndrome mice.

PubMed

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.

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

PubMed Central

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

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.

Presynaptic establishment of the synaptic cleft extracellular matrix is required for post-synaptic differentiation

PubMed Central

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

Activity-Induced Synaptic Structural Modifications by an Activator of Integrin Signaling at the Drosophila Neuromuscular Junction.

PubMed

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.

PSD-95 and PSD-93 Play Critical but Distinct Roles in Synaptic Scaling Up and Down

PubMed Central

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

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

PubMed

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.

Concerning an application of the method of least squares with a variable weight matrix

NASA Technical Reports Server (NTRS)

Sukhanov, A. A.

1979-01-01

An estimate of a state vector for a physical system when the weight matrix in the method of least squares is a function of this vector is considered. An iterative procedure is proposed for calculating the desired estimate. Conditions for the existence and uniqueness of the limit of this procedure are obtained, and a domain is found which contains the limit estimate. A second method for calculating the desired estimate which reduces to the solution of a system of algebraic equations is proposed. The question of applying Newton's method of tangents to solving the given system of algebraic equations is considered and conditions for the convergence of the modified Newton's method are obtained. Certain properties of the estimate obtained are presented together with an example.

Multiple vesicle recycling pathways in central synapses and their impact on neurotransmission

PubMed Central

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

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

PubMed

Wu, Chung Kit; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei

2016-05-09

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human's biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

Feasibility study of new energy projects on three-level indicator system

NASA Astrophysics Data System (ADS)

Zhan, Zhigang

2018-06-01

With the rapid development of new energy industry, many new energy development projects are being carried out all over the world. To analyze the feasibility of the project. we build feasibility of new energy projects assessment model, based on the gathered abundant data about progress in new energy projects.12 indicators are selected by principal component analysis(PCA). Then we construct a new three-level indicator system, where the first level has 1 indicator, the second level has 5 indicators and the third level has 12 indicators to evaluate. Moreover, we use the entropy weight method (EWM) to get weight vector of the indicators in the third level and the multivariate statistical analysis(MVA)to get the weight vector of indicators in the second-class. We use this evaluation model to evaluate the feasibility of the new energy project and make a reference for the subsequent new energy investment. This could be a contribution to the world's low-carbon and green development by investing in sustainable new energy projects. We will introduce new variables and improve the weight model in the future. We also conduct a sensitivity analysis of the model and illustrate the strengths and weaknesses.

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.

Synaptic Vesicle Endocytosis

PubMed Central

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

Role of DHA in aging-related changes in mouse brain synaptic plasma membrane proteome.

PubMed

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.

Evidence for recycling of synaptic vesicle membrane during transmitter release at the frog neuromuscular junction.

PubMed

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.

EVIDENCE FOR RECYCLING OF SYNAPTIC VESICLE MEMBRANE DURING TRANSMITTER RELEASE AT THE FROG NEUROMUSCULAR JUNCTION

PubMed Central

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

Deciphering the contribution of intrinsic and synaptic currents to the effects of transient synaptic inputs on human motor unit discharge

PubMed Central

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

Forced neuronal interactions cause poor communication.

PubMed

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.

Activity-Dependent Downscaling of Subthreshold Synaptic Inputs during Slow-Wave-Sleep-like Activity In Vivo.

PubMed

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.

New syndrome decoder for (n, 1) convolutional codes

NASA Technical Reports Server (NTRS)

Reed, I. S.; Truong, T. K.

1983-01-01

The letter presents a new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck. The new technique uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). A recursive, Viterbi-like, algorithm is developed to find the minimum weight error vector E(D). An example is given for the binary nonsystematic (2, 1) CC.

Ellipsoidal fuzzy learning for smart car platoons

NASA Astrophysics Data System (ADS)

Dickerson, Julie A.; Kosko, Bart

1993-12-01

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Anterograde Jelly belly ligand to Alk receptor signaling at developing synapses is regulated by Mind the gap.

PubMed

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.

Spine Calcium Transients Induced by Synaptically-Evoked Action Potentials Can Predict Synapse Location and Establish Synaptic Democracy

PubMed Central

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

Metabolic Turnover of Synaptic Proteins: Kinetics, Interdependencies and Implications for Synaptic Maintenance

PubMed Central

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

Comparing development of synaptic proteins in rat visual, somatosensory, and frontal cortex.

PubMed

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.

Spontaneously emerging direction selectivity maps in visual cortex through STDP.

PubMed

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.

Forebrain CRHR1 deficiency attenuates chronic stress-induced cognitive deficits and dendritic remodeling

PubMed Central

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

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

PubMed

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.

AMPA receptor desensitization mutation results in severe developmental phenotypes and early postnatal lethality

PubMed Central

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

Spatial cognition and sexually dimorphic synaptic plasticity balance impairment in rats with chronic prenatal ethanol exposure.

PubMed

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.

Robot-Applied Resistance Augments the Effects of Body Weight-Supported Treadmill Training on Stepping and Synaptic Plasticity in a Rodent Model of Spinal Cord Injury.

PubMed

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.

[Exploration of common biological pathways for attention deficit hyperactivity disorder and low birth weight].

PubMed

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.

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.

Multiple sensor fault diagnosis for dynamic processes.

PubMed

Li, Cheng-Chih; Jeng, Jyh-Cheng

2010-10-01

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

The interplay between neuronal activity and actin dynamics mimic the setting of an LTD synaptic tag

PubMed Central

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

An Adaptive Spectrally Weighted Structure Tensor Applied to Tensor Anisotropic Nonlinear Diffusion for Hyperspectral Images

ERIC Educational Resources Information Center

Marin Quintero, Maider J.

2013-01-01

The structure tensor for vector valued images is most often defined as the average of the scalar structure tensors in each band. The problem with this definition is the assumption that all bands provide the same amount of edge information giving them the same weights. As a result non-edge pixels can be reinforced and edges can be weakened…

Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia

PubMed Central

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

Non-synaptic receptors and transporters involved in brain functions and targets of drug treatment

PubMed Central

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

Effects of chronic weight perturbation on energy homeostasis and brain structure in mice

PubMed Central

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

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

PubMed

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

NASA Astrophysics Data System (ADS)

Yu, Lean; Wang, Shouyang; Lai, K. K.

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

Stochastic lattice model of synaptic membrane protein domains.

PubMed

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.

Silk-Elastinlike Copolymers for Breast Cancer Gene Therapy

DTIC Science & Technology

2005-05-01

linearized expression vector were mixed hydrolysis of the sample in 6 N HCI at 110 ’C for 20 h. at a high monomer-to-vector molar ratio in the presence of...measurements were taken for each sample GAGTA(GA(GTGC(GGTGTA(GAGTTCCTGCGATrT’GC( for calculation of q. TAcCAC(;AGTA(;(;CGTACCGCGAGG’AGGAGTGCC(;,GGTC The...P (Pro), proline ; Q (Gln), steps are to examine the influence of polymer molecular glutamine; Q, weight equilibrium swelling ratios of hydrogels

Preliminary performance of a vertical-attitude takeoff and landing, supersonic cruise aircraft concept having thrust vectoring integrated into the flight control system

NASA Technical Reports Server (NTRS)

Robins, A. W.; Beissner, F. L., Jr.; Domack, C. S.; Swanson, E. E.

1985-01-01

A performance study was made of a vertical attitude takeoff and landing (VATOL), supersonic cruise aircraft concept having thrust vectoring integrated into the flight control system. Those characteristics considered were aerodynamics, weight, balance, and performance. Preliminary results indicate that high levels of supersonic aerodynamic performance can be achieved. Further, with the assumption of an advanced (1985 technology readiness) low bypass ratio turbofan engine and advanced structures, excellent mission performance capability is indicated.

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.

Sleep and protein synthesis-dependent synaptic plasticity: impacts of sleep loss and stress

PubMed Central

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

[Cloning and gene expression in lactic acid bacteria].

PubMed

Bondarenko, V M; Beliavskaia, V A

2000-01-01

The possibility of using the genera Lactobacillus and Lactococcus as vector representatives is widely discussed at present. The prospects of the construction of recombinant bacteria are closely connected with the solution of a number of problems: the level of the transcription of cloned genes, the effectiveness of the translation of heterologous mRNA, the stability of protein with respect to bacterial intracellular proteases, the method by protein molecules leave the cell (by secretion or as the result of lysis). To prevent segregation instability, the construction of vector molecules on the basis of stable cryptic plasmids found in wild strains of lactic acid bacteria was proposed. High copying plasmids with low molecular weight were detected in L. plantarum and L. pentosus strains. Several plasmids with molecular weights of 1.7, 1.8 and 2.3 kb were isolated from bacterial cells to be used as the basis for the construction of vector molecules. Genes of chloramphenicol- and erythromycin-resistance from Staphylococcus aureus plasmids were used as marker genes ensuring cell transformation. The vector plasmids thus constructed exhibited high transformation activity in the electroporation of different strains, including L. casei, L. plantarum, L. acidophilus, L. fermentum and L. brevis which could be classified with the replicons of a wide circle of hosts. But the use of these plasmids was limited due to the risk of the uncontrolled dissemination of recombinant plasmids. L. acidophilus were also found to have strictly specific plasmids with good prospects of being used as the basis for the creation of vectors, incapable of dissemination. In addition to the search of strain-specific plasmids, incapable of uncontrolled gene transmission, the use of chromosome-integrated heterologous genes is recommended in cloning to ensure the maximum safety.

Adaptive vector validation in image velocimetry to minimise the influence of outlier clusters

NASA Astrophysics Data System (ADS)

Masullo, Alessandro; Theunissen, Raf

2016-03-01

The universal outlier detection scheme (Westerweel and Scarano in Exp Fluids 39:1096-1100, 2005) and the distance-weighted universal outlier detection scheme for unstructured data (Duncan et al. in Meas Sci Technol 21:057002, 2010) are the most common PIV data validation routines. However, such techniques rely on a spatial comparison of each vector with those in a fixed-size neighbourhood and their performance subsequently suffers in the presence of clusters of outliers. This paper proposes an advancement to render outlier detection more robust while reducing the probability of mistakenly invalidating correct vectors. Velocity fields undergo a preliminary evaluation in terms of local coherency, which parametrises the extent of the neighbourhood with which each vector will be compared subsequently. Such adaptivity is shown to reduce the number of undetected outliers, even when implemented in the afore validation schemes. In addition, the authors present an alternative residual definition considering vector magnitude and angle adopting a modified Gaussian-weighted distance-based averaging median. This procedure is able to adapt the degree of acceptable background fluctuations in velocity to the local displacement magnitude. The traditional, extended and recommended validation methods are numerically assessed on the basis of flow fields from an isolated vortex, a turbulent channel flow and a DNS simulation of forced isotropic turbulence. The resulting validation method is adaptive, requires no user-defined parameters and is demonstrated to yield the best performances in terms of outlier under- and over-detection. Finally, the novel validation routine is applied to the PIV analysis of experimental studies focused on the near wake behind a porous disc and on a supersonic jet, illustrating the potential gains in spatial resolution and accuracy.

Synaptic vesicle exocytosis in hippocampal synaptosomes correlates directly with total mitochondrial volume

PubMed Central

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

On the Teneurin track: a new synaptic organization molecule emerges

PubMed Central

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

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.

Analysis of structural response data using discrete modal filters. M.S. Thesis

NASA Technical Reports Server (NTRS)

Freudinger, Lawrence C.

1991-01-01

The application of reciprocal modal vectors to the analysis of structural response data is described. Reciprocal modal vectors are constructed using an existing experimental modal model and an existing frequency response matrix of a structure, and can be assembled into a matrix that effectively transforms the data from the physical space to a modal space within a particular frequency range. In other words, the weighting matrix necessary for modal vector orthogonality (typically the mass matrix) is contained within the reciprocal model matrix. The underlying goal of this work is mostly directed toward observing the modal state responses in the presence of unknown, possibly closed loop forcing functions, thus having an impact on both operating data analysis techniques and independent modal space control techniques. This study investigates the behavior of reciprocol modal vectors as modal filters with respect to certain calculation parameters and their performance with perturbed system frequency response data.

Boosting with Averaged Weight Vectors

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

2002-01-01

AdaBoost is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the previous base model in the sequence. The idea is to make the next base model's errors uncorrelated with those of the previous model. Some researchers have pointed out the intuition that it is probably better to construct a distribution that is orthogonal to the mistake vectors of all the previous base models, but that this is not always possible. We present an algorithm that attempts to come as close as possible to this goal in an efficient manner. We present experimental results demonstrating significant improvement over AdaBoost and the Totally Corrective boosting algorithm, which also attempts to satisfy this goal.

A VLSI chip set for real time vector quantization of image sequences

NASA Technical Reports Server (NTRS)

Baker, Richard L.

1989-01-01

The architecture and implementation of a VLSI chip set that vector quantizes (VQ) image sequences in real time is described. The chip set forms a programmable Single-Instruction, Multiple-Data (SIMD) machine which can implement various vector quantization encoding structures. Its VQ codebook may contain unlimited number of codevectors, N, having dimension up to K = 64. Under a weighted least squared error criterion, the engine locates at video rates the best code vector in full-searched or large tree searched VQ codebooks. The ability to manipulate tree structured codebooks, coupled with parallelism and pipelining, permits searches in as short as O (log N) cycles. A full codebook search results in O(N) performance, compared to O(KN) for a Single-Instruction, Single-Data (SISD) machine. With this VLSI chip set, an entire video code can be built on a single board that permits realtime experimentation with very large codebooks.

Archaerhodopsin Selectively and Reversibly Silences Synaptic Transmission through Altered pH.

PubMed

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.

Synaptic vesicle recycling: steps and principles.

PubMed

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.

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.

Myosin IIb-dependent Regulation of Actin Dynamics Is Required for N-Methyl-D-aspartate Receptor Trafficking during Synaptic Plasticity.

PubMed

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.

Abnormal Mitochondrial Dynamics and Synaptic Degeneration as Early Events in Alzheimer’s Disease: Implications to Mitochondria-Targeted Antioxidant Therapeutics

PubMed Central

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

Restraint of presynaptic protein levels by Wnd/DLK signaling mediates synaptic defects associated with the kinesin-3 motor Unc-104

PubMed Central

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

Glutamatergic synaptic plasticity in the mesocorticolimbic system in addiction

PubMed Central

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

Neurogranin restores amyloid β-mediated synaptic transmission and long-term potentiation deficits.

PubMed

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.

Increased expression of the Drosophila vesicular glutamate transporter leads to excess glutamate release and a compensatory decrease in quantal content.

PubMed

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.

On Kedlaya-type inequalities for weighted means.

PubMed

Páles, Zsolt; Pasteczka, Paweł

2018-01-01

In 2016 we proved that for every symmetric, repetition invariant and Jensen concave mean [Formula: see text] the Kedlaya-type inequality [Formula: see text] holds for an arbitrary [Formula: see text] ([Formula: see text] stands for the arithmetic mean). We are going to prove the weighted counterpart of this inequality. More precisely, if [Formula: see text] is a vector with corresponding (non-normalized) weights [Formula: see text] and [Formula: see text] denotes the weighted mean then, under analogous conditions on [Formula: see text], the inequality [Formula: see text] holds for every [Formula: see text] and [Formula: see text] such that the sequence [Formula: see text] is decreasing.

Calcium responses to synaptically activated bursts of action potentials and their synapse-independent replay in cultured networks of hippocampal neurons.

PubMed

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.

Alteration of synaptic connectivity of oligodendrocyte precursor cells following demyelination

PubMed Central

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

Characterizing synaptic protein development in human visual cortex enables alignment of synaptic age with rat visual cortex

PubMed Central

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

Limited tryptic proteolysis of the benzodiazepine binding proteins in different species reveals structural homologies.

PubMed

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.

Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

PubMed

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.

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.

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-01-01

This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

Current status of non-viral gene therapy for CNS disorders

PubMed Central

Jayant, Rahul Dev; Sosa, Daniela; Kaushik, Ajeet; Atluri, Venkata; Vashist, Arti; Tomitaka, Asahi; Nair, Madhavan

2017-01-01

Introduction Viral and non-viral vectors have been used as methods of delivery in gene therapy for many CNS diseases. Currently, viral vectors such as adeno-associated viruses (AAV), retroviruses, lentiviruses, adenoviruses and herpes simplex viruses (HHV) are being used as successful vectors in gene therapy at clinical trial levels. However, many disadvantages have risen from their usage. Non-viral vectors like cationic polymers, cationic lipids, engineered polymers, nanoparticles, and naked DNA offer a much safer option and can therefore be explored for therapeutic purposes. Areas covered This review discusses different types of viral and non-viral vectors for gene therapy and explores clinical trials for CNS diseases that have used these types of vectors for gene delivery. Highlights include non-viral gene delivery and its challenges, possible strategies to improve transfection, regulatory issues concerning vector usage, and future prospects for clinical applications. Expert opinion Transfection efficiency of cationic lipids and polymers can be improved through manipulation of molecules used. Efficacy of cationic lipids is dependent on cationic charge, saturation levels, and stability of linkers. Factors determining efficacy of cationic polymers are total charge density, molecular weights, and complexity of molecule. All of the above mentioned parameters must be taken care for efficient gene delivery. PMID:27249310

Intramammary expression and therapeutic effect of a human lysozyme-expressing vector for treating bovine mastitis*

PubMed Central

Sun, Huai-Chang; Xue, Fang-Ming; Qian, Ke; Fang, Hao-Xia; Qiu, Hua-Lei; Zhang, Xin-Yu; Yin, Zhao-Hua

2006-01-01

To develop a gene therapy strategy for treating bovine mastitis, a new mammary-specific vector containing human lysozyme (hLYZ) cDNA and kanamycin resistance gene was constructed for intramammary expression and clinical studies. After one time acupuncture or intracisternal infusion of healthy cows with 400 μg of the p215C3LYZ vector, over 2.0 μg/ml of rhLYZ could be detected by enzymatic assay for about 3 weeks in the milk samples. Western blotting showed that rhLYZ secreted into milk samples from the vector-injected cows had molecular weight similar to that of the natural hLYZ in human colostrums. Twenty days after the primary injection, the quarters were re-injected with the same vector by quarter acupuncture and even higher concentrations of rhLYZ could be detected. Indirect competitive ELISA of milk samples showed that the vector injection did not induce detectable humoral immune response against hLYZ. Clinical studies showed that twice acupuncture of quarters with the p215C3LYZ vector had overt therapeutic effect on clinical and subclinical mastitis previously treated with antibiotics, including disappearance of clinical symptoms and relatively high microbiological cure rates. These data provide a solid rationale for using the vector to develop gene therapy for treating bovine mastitis. PMID:16532537

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.

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…

Safety mechanism assisted by the repressor of tetracycline (SMART) vaccinia virus vectors for vaccines and therapeutics.

PubMed

Grigg, Patricia; Titong, Allison; Jones, Leslie A; Yilma, Tilahun D; Verardi, Paulo H

2013-09-17

Replication-competent viruses, such as Vaccinia virus (VACV), are powerful tools for the development of oncolytic viral therapies and elicit superior immune responses when used as vaccine and immunotherapeutic vectors. However, severe complications from uncontrolled viral replication can occur, particularly in immunocompromised individuals or in those with other predisposing conditions. VACVs constitutively expressing interferon-γ (IFN-γ) replicate in cell culture indistinguishably from control viruses; however, they replicate in vivo to low or undetectable levels, and are rapidly cleared even in immunodeficient animals. In an effort to develop safe and highly effective replication-competent VACV vectors, we established a system to inducibly express IFN-γ. Our SMART (safety mechanism assisted by the repressor of tetracycline) vectors are designed to express the tetracycline repressor under a constitutive VACV promoter and IFN-γ under engineered tetracycline-inducible promoters. Immunodeficient SCID mice inoculated with VACVs not expressing IFN-γ demonstrated severe weight loss, whereas those given VACVs expressing IFN-γ under constitutive VACV promoters showed no signs of infection. Most importantly, mice inoculated with a VACV expressing the IFN-γ gene under an inducible promoter remained healthy in the presence of doxycycline, but exhibited severe weight loss in the absence of doxycycline. In this study, we developed a safety mechanism for VACV based on the conditional expression of IFN-γ under a tightly controlled tetracycline-inducible VACV promoter for use in vaccines and oncolytic cancer therapies.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition

PubMed Central

Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods. PMID:27807443

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.

A ‘tool box’ for deciphering neuronal circuits in the developing chick spinal cord

PubMed Central

Hadas, Yoav; Etlin, Alex; Falk, Haya; Avraham, Oshri; Kobiler, Oren; Panet, Amos; Lev-Tov, Aharon; Klar, Avihu

2014-01-01

The genetic dissection of spinal circuits is an essential new means for understanding the neural basis of mammalian behavior. Molecular targeting of specific neuronal populations, a key instrument in the genetic dissection of neuronal circuits in the mouse model, is a complex and time-demanding process. Here we present a circuit-deciphering ‘tool box’ for fast, reliable and cheap genetic targeting of neuronal circuits in the developing spinal cord of the chick. We demonstrate targeting of motoneurons and spinal interneurons, mapping of axonal trajectories and synaptic targeting in both single and populations of spinal interneurons, and viral vector-mediated labeling of pre-motoneurons. We also demonstrate fluorescent imaging of the activity pattern of defined spinal neurons during rhythmic motor behavior, and assess the role of channel rhodopsin-targeted population of interneurons in rhythmic behavior using specific photoactivation. PMID:25147209

Noise focusing in neuronal tissues: Symmetry breaking and localization in excitable networks with quenched disorder

NASA Astrophysics Data System (ADS)

Orlandi, Javier G.; Casademunt, Jaume

2017-05-01

We introduce a coarse-grained stochastic model for the spontaneous activity of neuronal cultures to explain the phenomenon of noise focusing, which entails localization of the noise activity in excitable networks with metric correlations. The system is modeled as a continuum excitable medium with a state-dependent spatial coupling that accounts for the dynamics of synaptic connections. The most salient feature is the emergence at the mesoscale of a vector field V (r ) , which acts as an advective carrier of the noise. This entails an explicit symmetry breaking of isotropy and homogeneity that stems from the amplification of the quenched fluctuations of the network by the activity avalanches, concomitant with the excitable dynamics. We discuss the microscopic interpretation of V (r ) and propose an explicit construction of it. The coarse-grained model shows excellent agreement with simulations at the network level. The generic nature of the observed phenomena is discussed.

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.

Face classification using electronic synapses.

PubMed

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.

The histone acetyltransferase MOF activates hypothalamic polysialylation to prevent diet-induced obesity in mice

PubMed Central

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

Adaptive WTA with an analog VLSI neuromorphic learning chip.

PubMed

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.

The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker.

PubMed

Leibfried, Felix; Braun, Daniel A

2015-08-01

Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

Dimensionality and entropy of spontaneous and evoked rate activity

NASA Astrophysics Data System (ADS)

Engelken, Rainer; Wolf, Fred

Cortical circuits exhibit complex activity patterns both spontaneously and evoked by external stimuli. Finding low-dimensional structure in population activity is a challenge. What is the diversity of the collective neural activity and how is it affected by an external stimulus? Using concepts from ergodic theory, we calculate the attractor dimensionality and dynamical entropy production of these networks. We obtain these two canonical measures of the collective network dynamics from the full set of Lyapunov exponents. We consider a randomly-wired firing-rate network that exhibits chaotic rate fluctuations for sufficiently strong synaptic weights. We show that dynamical entropy scales logarithmically with synaptic coupling strength, while the attractor dimensionality saturates. Thus, despite the increasing uncertainty, the diversity of collective activity saturates for strong coupling. We find that a time-varying external stimulus drastically reduces both entropy and dimensionality. Finally, we analytically approximate the full Lyapunov spectrum in several limiting cases by random matrix theory. Our study opens a novel avenue to characterize the complex dynamics of rate networks and the geometric structure of the corresponding high-dimensional chaotic attractor. received funding from Evangelisches Studienwerk Villigst, DFG through CRC 889 and Volkswagen Foundation.

Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation

PubMed Central

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

How should spin-weighted spherical functions be defined?

NASA Astrophysics Data System (ADS)

Boyle, Michael

2016-09-01

Spin-weighted spherical functions provide a useful tool for analyzing tensor-valued functions on the sphere. A tensor field can be decomposed into complex-valued functions by taking contractions with tangent vectors on the sphere and the normal to the sphere. These component functions are usually presented as functions on the sphere itself, but this requires an implicit choice of distinguished tangent vectors with which to contract. Thus, we may more accurately say that spin-weighted spherical functions are functions of both a point on the sphere and a choice of frame in the tangent space at that point. The distinction becomes extremely important when transforming the coordinates in which these functions are expressed, because the implicit choice of frame will also transform. Here, it is proposed that spin-weighted spherical functions should be treated as functions on the spin or rotation groups, which simultaneously tracks the point on the sphere and the choice of tangent frame by rotating elements of an orthonormal basis. In practice, the functions simply take a quaternion argument and produce a complex value. This approach more cleanly reflects the geometry involved, and allows for a more elegant description of the behavior of spin-weighted functions. In this form, the spin-weighted spherical harmonics have simple expressions as elements of the Wigner 𝔇 representations, and transformations under rotation are simple. Two variants of the angular-momentum operator are defined directly in terms of the spin group; one is the standard angular-momentum operator L, while the other is shown to be related to the spin-raising operator ð.

Soft computing techniques toward modeling the water supplies of Cyprus.

PubMed

Iliadis, L; Maris, F; Tachos, S

2011-10-01

This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. Initially, ε-Regression Support Vector Machines (ε-RSVM) and fuzzy weighted ε-RSVMR models have been developed that accept five input parameters. At the same time, reliable artificial neural networks have been developed to perform the same job. The 5-fold cross validation approach has been employed in order to eliminate bad local behaviors and to produce a more representative training data set. Thus, the fuzzy weighted Support Vector Regression (SVR) combined with the fuzzy partition has been employed in an effort to enhance the quality of the results. Several rational and reliable models have been produced that can enhance the efficiency of water policy designers. Copyright © 2011 Elsevier Ltd. All rights reserved.

Design of magnetic polyplexes taken up efficiently by dendritic cell for enhanced DNA vaccine delivery.

PubMed

Nawwab Al-Deen, F M; Selomulya, C; Kong, Y Y; Xiang, S D; Ma, C; Coppel, R L; Plebanski, M

2014-02-01

Dendritic cells (DC) targeting vaccines require high efficiency for uptake, followed by DC activation and maturation. We used magnetic vectors comprising polyethylenimine (PEI)-coated superparamagnetic iron oxide nanoparticles, with hyaluronic acid (HA) of different molecular weights (<10 and 900 kDa) to reduce cytotoxicity and to facilitate endocytosis of particles into DCs via specific surface receptors. DNA encoding Plasmodium yoelii merozoite surface protein 1-19 and a plasmid encoding yellow fluorescent gene were added to the magnetic complexes with various % charge ratios of HA: PEI. The presence of magnetic fields significantly enhanced DC transfection and maturation. Vectors containing a high-molecular-weight HA with 100% charge ratio of HA: PEI yielded a better transfection efficiency than others. This phenomenon was attributed to their longer molecular chains and higher mucoadhesive properties aiding DNA condensation and stability. Insights gained should improve the design of more effective DNA vaccine delivery systems.

Mitochondrial ROS cause motor deficits induced by synaptic inactivity: Implications for synapse pruning.

PubMed

Sidlauskaite, Eva; Gibson, Jack W; Megson, Ian L; Whitfield, Philip D; Tovmasyan, Artak; Batinic-Haberle, Ines; Murphy, Michael P; Moult, Peter R; Cobley, James N

2018-06-01

Developmental synapse pruning refines burgeoning connectomes. The basic mechanisms of mitochondrial reactive oxygen species (ROS) production suggest they select inactive synapses for pruning: whether they do so is unknown. To begin to unravel whether mitochondrial ROS regulate pruning, we made the local consequences of neuromuscular junction (NMJ) pruning detectable as motor deficits by using disparate exogenous and endogenous models to induce synaptic inactivity en masse in developing Xenopus laevis tadpoles. We resolved whether: (1) synaptic inactivity increases mitochondrial ROS; and (2) chemically heterogeneous antioxidants rescue synaptic inactivity induced motor deficits. Regardless of whether it was achieved with muscle (α-bungarotoxin), nerve (α-latrotoxin) targeted neurotoxins or an endogenous pruning cue (SPARC), synaptic inactivity increased mitochondrial ROS in vivo. The manganese porphyrins MnTE-2-PyP 5+ and/or MnTnBuOE-2-PyP 5+ blocked mitochondrial ROS to significantly reduce neurotoxin and endogenous pruning cue induced motor deficits. Selectively inducing mitochondrial ROS-using mitochondria-targeted Paraquat (MitoPQ)-recapitulated synaptic inactivity induced motor deficits; which were significantly reduced by blocking mitochondrial ROS with MnTnBuOE-2-PyP 5+ . We unveil mitochondrial ROS as synaptic activity sentinels that regulate the phenotypical consequences of forced synaptic inactivity at the NMJ. Our novel results are relevant to pruning because synaptic inactivity is one of its defining features. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Mover Is a Homomeric Phospho-Protein Present on Synaptic Vesicles

PubMed Central

Kremer, Thomas; Hoeber, Jan; Kiran Akula, Asha; Urlaub, Henning; Islinger, Markus; Kirsch, Joachim; Dean, Camin; Dresbach, Thomas

2013-01-01

With remarkably few exceptions, the molecules mediating synaptic vesicle exocytosis at active zones are structurally and functionally conserved between vertebrates and invertebrates. Mover was found in a yeast-2-hybrid assay using the vertebrate-specific active zone scaffolding protein bassoon as a bait. Peptides of Mover have been reported in proteomics screens for self-interacting proteins, phosphorylated proteins, and synaptic vesicle proteins, respectively. Here, we tested the predictions arising from these screens. Using flotation assays, carbonate stripping of peripheral membrane proteins, mass spectrometry, immunogold labelling of purified synaptic vesicles, and immuno-organelle isolation, we found that Mover is indeed a peripheral synaptic vesicle membrane protein. In addition, by generating an antibody against phosphorylated Mover and Western blot analysis of fractionated rat brain, we found that Mover is a bona fide phospho-protein. The localization of Mover to synaptic vesicles is phosphorylation dependent; treatment with a phosphatase caused Mover to dissociate from synaptic vesicles. A yeast-2-hybrid screen, co-immunoprecipitation and cell-based optical assays of homomerization revealed that Mover undergoes homophilic interaction, and regions within both the N- and C- terminus of the protein are required for this interaction. Deleting a region required for homomeric interaction abolished presynaptic targeting of recombinant Mover in cultured neurons. Together, these data prove that Mover is associated with synaptic vesicles, and implicate phosphorylation and multimerization in targeting of Mover to synaptic vesicles and presynaptic sites. PMID:23723986

Feature weighting using particle swarm optimization for learning vector quantization classifier

NASA Astrophysics Data System (ADS)

Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

Electron microscopic immunocytochemical study of somatostatin neurons in the periventricular nucleus of the rat hypothalamus with special reference to their relationships with homologous neuronal processes.

PubMed

Alonso, G; Tapia-Arancibia, L; Assenmacher, I

1985-10-01

The neurons containing somatostatin in the rat periventricular nucleus were studied by using a modified electron microscopic immunocytochemical technique that improves both the penetration of immunoreagents into unembedded immunostained tissues and the preservation of ultrastructural morphology. Inside perikarya and dendrites, immunostaining was not only associated with neurosecretory granules but also with ribosomes and saccules of the cis face of the Golgi apparatus. In the axonal profiles found in this region the labeling was observed both on neurosecretory granule cores and on the limiting membrane of small synaptic-like vesicles. Throughout the periventricular nucleus, both non-synaptic and synaptic relationships were shown between labeled neurons. Non-synaptic relationships mainly consisted of direct apposition of the membranes of neighboring neurons by dendrosomatic, somasomatic or dendrodendritic contacts. These labeled perikarya and dendrites were also synaptically contacted by labeled axonal endings containing numerous aggregated synaptic-like vesicles. The physiological significance of the synaptic and non-synaptic relationships between somatostatinergic neurons is discussed in terms of possible synchronization between homologous neurons of the somatostatin neuroendocrine system and control of these neurons by a central ultra-short loop feedback mechanism.

Dynamic DNA Methylation Controls Glutamate Receptor Trafficking and Synaptic Scaling

PubMed Central

Sweatt, J. David

2016-01-01

Hebbian plasticity, including LTP and LTD, has long been regarded as important for local circuit refinement in the context of memory formation and stabilization. However, circuit development and stabilization additionally relies on non-Hebbian, homoeostatic, forms of plasticity such as synaptic scaling. Synaptic scaling is induced by chronic increases or decreases in neuronal activity. Synaptic scaling is associated with cell-wide adjustments in postsynaptic receptor density, and can occur in a multiplicative manner resulting in preservation of relative synaptic strengths across the entire neuron's population of synapses. Both active DNA methylation and de-methylation have been validated as crucial regulators of gene transcription during learning, and synaptic scaling is known to be transcriptionally dependent. However, it has been unclear whether homeostatic forms of plasticity such as synaptic scaling are regulated via epigenetic mechanisms. This review describes exciting recent work that has demonstrated a role for active changes in neuronal DNA methylation and demethylation as a controller of synaptic scaling and glutamate receptor trafficking. These findings bring together three major categories of memory-associated mechanisms that were previously largely considered separately: DNA methylation, homeostatic plasticity, and glutamate receptor trafficking. PMID:26849493

LRRK2 kinase activity regulates synaptic vesicle trafficking and neurotransmitter release through modulation of LRRK2 macro-molecular complex

PubMed Central

Cirnaru, Maria D.; Marte, Antonella; Belluzzi, Elisa; Russo, Isabella; Gabrielli, Martina; Longo, Francesco; Arcuri, Ludovico; Murru, Luca; Bubacco, Luigi; Matteoli, Michela; Fedele, Ernesto; Sala, Carlo; Passafaro, Maria; Morari, Michele; Greggio, Elisa; Onofri, Franco; Piccoli, Giovanni

2014-01-01

Mutations in Leucine-rich repeat kinase 2 gene (LRRK2) are associated with familial and sporadic Parkinson's disease (PD). LRRK2 is a complex protein that consists of multiple domains executing several functions, including GTP hydrolysis, kinase activity, and protein binding. Robust evidence suggests that LRRK2 acts at the synaptic site as a molecular hub connecting synaptic vesicles to cytoskeletal elements via a complex panel of protein-protein interactions. Here we investigated the impact of pharmacological inhibition of LRRK2 kinase activity on synaptic function. Acute treatment with LRRK2 inhibitors reduced the frequency of spontaneous currents, the rate of synaptic vesicle trafficking and the release of neurotransmitter from isolated synaptosomes. The investigation of complementary models lacking LRRK2 expression allowed us to exclude potential off-side effects of kinase inhibitors on synaptic functions. Next we studied whether kinase inhibition affects LRRK2 heterologous interactions. We found that the binding among LRRK2, presynaptic proteins and synaptic vesicles is affected by kinase inhibition. Our results suggest that LRRK2 kinase activity influences synaptic vesicle release via modulation of LRRK2 macro-molecular complex. PMID:24904275

Reduced synaptic vesicle protein degradation at lysosomes curbs TBC1D24/sky-induced neurodegeneration.

PubMed

Fernandes, Ana Clara; Uytterhoeven, Valerie; Kuenen, Sabine; Wang, Yu-Chun; Slabbaert, Jan R; Swerts, Jef; Kasprowicz, Jaroslaw; Aerts, Stein; Verstreken, Patrik

2014-11-24

Synaptic demise and accumulation of dysfunctional proteins are thought of as common features in neurodegeneration. However, the mechanisms by which synaptic proteins turn over remain elusive. In this paper, we study Drosophila melanogaster lacking active TBC1D24/Skywalker (Sky), a protein that in humans causes severe neurodegeneration, epilepsy, and DOOR (deafness, onychdystrophy, osteodystrophy, and mental retardation) syndrome, and identify endosome-to-lysosome trafficking as a mechanism for degradation of synaptic vesicle-associated proteins. In fly sky mutants, synaptic vesicles traveled excessively to endosomes. Using chimeric fluorescent timers, we show that synaptic vesicle-associated proteins were younger on average, suggesting that older proteins are more efficiently degraded. Using a genetic screen, we find that reducing endosomal-to-lysosomal trafficking, controlled by the homotypic fusion and vacuole protein sorting (HOPS) complex, rescued the neurotransmission and neurodegeneration defects in sky mutants. Consistently, synaptic vesicle proteins were older in HOPS complex mutants, and these mutants also showed reduced neurotransmission. Our findings define a mechanism in which synaptic transmission is facilitated by efficient protein turnover at lysosomes and identify a potential strategy to suppress defects arising from TBC1D24 mutations in humans. © 2014 Fernandes et al.

Measuring Synaptic Vesicle Endocytosis in Cultured Hippocampal Neurons.

PubMed

Villarreal, Seth; Lee, Sung Hoon; Wu, Ling-Gang

2017-09-04

During endocytosis, fused synaptic vesicles are retrieved at nerve terminals, allowing for vesicle recycling and thus the maintenance of synaptic transmission during repetitive nerve firing. Impaired endocytosis in pathological conditions leads to decreases in synaptic strength and brain functions. Here, we describe methods used to measure synaptic vesicle endocytosis at the mammalian hippocampal synapse in neuronal culture. We monitored synaptic vesicle protein endocytosis by fusing a synaptic vesicular membrane protein, including synaptophysin and VAMP2/synaptobrevin, at the vesicular lumenal side, with pHluorin, a pH-sensitive green fluorescent protein that increases its fluorescence intensity as the pH increases. During exocytosis, vesicular lumen pH increases, whereas during endocytosis vesicular lumen pH is re-acidified. Thus, an increase of pHluorin fluorescence intensity indicates fusion, whereas a decrease indicates endocytosis of the labelled synaptic vesicle protein. In addition to using the pHluorin imaging method to record endocytosis, we monitored vesicular membrane endocytosis by electron microscopy (EM) measurements of Horseradish peroxidase (HRP) uptake by vesicles. Finally, we monitored the formation of nerve terminal membrane pits at various times after high potassium-induced depolarization. The time course of HRP uptake and membrane pit formation indicates the time course of endocytosis.

Synaptic vesicle recycling: steps and principles

PubMed Central

Rizzoli, Silvio O

2014-01-01

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. PMID:24596248

Synaptic Contacts Enhance Cell-to-Cell Tau Pathology Propagation.

PubMed

Calafate, Sara; Buist, Arjan; Miskiewicz, Katarzyna; Vijayan, Vinoy; Daneels, Guy; de Strooper, Bart; de Wit, Joris; Verstreken, Patrik; Moechars, Diederik

2015-05-26

Accumulation of insoluble Tau protein aggregates and stereotypical propagation of Tau pathology through the brain are common hallmarks of tauopathies, including Alzheimer's disease (AD). Propagation of Tau pathology appears to occur along connected neurons, but whether synaptic contacts between neurons are facilitating propagation has not been demonstrated. Using quantitative in vitro models, we demonstrate that, in parallel to non-synaptic mechanisms, synapses, but not merely the close distance between the cells, enhance the propagation of Tau pathology between acceptor hippocampal neurons and Tau donor cells. Similarly, in an artificial neuronal network using microfluidic devices, synapses and synaptic activity are promoting neuronal Tau pathology propagation in parallel to the non-synaptic mechanisms. Our work indicates that the physical presence of synaptic contacts between neurons facilitate Tau pathology propagation. These findings can have implications for synaptic repair therapies, which may turn out to have adverse effects by promoting propagation of Tau pathology. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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.

Differentiated effect of ageing on the enzymes of Krebs' cycle, electron transfer complexes and glutamate metabolism of non-synaptic and intra-synaptic mitochondria from cerebral cortex.

PubMed

Villa, R F; Gorini, A; Hoyer, S

2006-11-01

The effect of ageing on the activity of enzymes linked to Krebs' cycle, electron transfer chain and glutamate metabolism was studied in three different types of mitochondria of cerebral cortex of 1-year old and 2-year old male Wistar rats. We assessed the maximum rate (V(max)) of the mitochondrial enzyme activities in non-synaptic perikaryal mitochondria, and in two populations of intra-synaptic mitochondria. The results indicated that: (i) in normal, steady-state cerebral cortex the values of the catalytic activities of the enzymes markedly differed in the various populations of mitochondria; (ii) in intra-synaptic mitochondria, ageing affected the catalytic properties of the enzymes linked to Krebs' cycle, electron transfer chain and glutamate metabolism; (iii) these changes were more evident in intra-synaptic "heavy" than "light" mitochondria. These results indicate a different age-related vulnerability of subpopulations of mitochondria in vivo located into synapses than non-synaptic ones.

SYNAPTIC DEPRESSION IN DEEP NEURAL NETWORKS FOR SPEECH PROCESSING.

PubMed

Zhang, Wenhao; Li, Hanyu; Yang, Minda; Mesgarani, Nima

2016-03-01

A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to improve their generalization ability. We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in the training phase. Our results suggest that more complete neuron models may further reduce the gap between the biological performance and artificial computing, resulting in networks that better generalize to novel signal conditions.

The interplay between inflammatory cytokines and the endocannabinoid system in the regulation of synaptic transmission.

PubMed

Rossi, Silvia; Motta, Caterina; Musella, Alessandra; Centonze, Diego

2015-09-01

Excessive glutamate-mediated synaptic transmission and secondary excitotoxicity have been proposed as key determinants of neurodegeneration in many neurological diseases. Soluble mediators of inflammation have recently gained attention owing to their ability to enhance glutamate transmission and affect synaptic sensitivity to neurotransmitters. In the complex crosstalk between soluble immunoactive molecules and synapses, the endocannabinoid system (ECS) plays a central role, exerting an indirect neuroprotective action by inhibiting cytokine-dependent synaptic alterations, and a direct neuroprotective effect by limiting glutamate transmission and excitotoxic damage. On the other hand, the endocannabinoid (eCB)-mediated control of synaptic transmission is altered by proinflammatory cytokines with consequent effects in central nervous system (CNS) disorders. In this review, we summarize the interactions, at the pre- and postsynaptic level, between major inflammatory cytokines and the ECS. In addition, the behavioral and clinical consequences of the modulation of synaptic transmission during neuroinflammation are discussed. This article is part of a Special Issue entitled 'Neuroimmunology and Synaptic Function'. Copyright © 2014 Elsevier Ltd. All rights reserved.

Radixin regulates synaptic GABAA receptor density and is essential for reversal learning and short-term memory

PubMed Central

Hausrat, Torben J.; Muhia, Mary; Gerrow, Kimberly; Thomas, Philip; Hirdes, Wiebke; Tsukita, Sachiko; Heisler, Frank F.; Herich, Lena; Dubroqua, Sylvain; Breiden, Petra; Feldon, Joram; Schwarz, Jürgen R; Yee, Benjamin K.; Smart, Trevor G.; Triller, Antoine; Kneussel, Matthias

2015-01-01

Neurotransmitter receptor density is a major variable in regulating synaptic strength. Receptors rapidly exchange between synapses and intracellular storage pools through endocytic recycling. In addition, lateral diffusion and confinement exchanges surface membrane receptors between synaptic and extrasynaptic sites. However, the signals that regulate this transition are currently unknown. GABAA receptors containing α5-subunits (GABAAR-α5) concentrate extrasynaptically through radixin (Rdx)-mediated anchorage at the actin cytoskeleton. Here we report a novel mechanism that regulates adjustable plasma membrane receptor pools in the control of synaptic receptor density. RhoA/ROCK signalling regulates an activity-dependent Rdx phosphorylation switch that uncouples GABAAR-α5 from its extrasynaptic anchor, thereby enriching synaptic receptor numbers. Thus, the unphosphorylated form of Rdx alters mIPSCs. Rdx gene knockout impairs reversal learning and short-term memory, and Rdx phosphorylation in wild-type mice exhibits experience-dependent changes when exposed to novel environments. Our data suggest an additional mode of synaptic plasticity, in which extrasynaptic receptor reservoirs supply synaptic GABAARs. PMID:25891999

Diverse modes of synaptic signaling, regulation, and plasticity distinguish two classes of C. elegans glutamatergic neurons.

PubMed

Ventimiglia, Donovan; Bargmann, Cornelia I

2017-11-21

Synaptic vesicle release properties vary between neuronal cell types, but in most cases the molecular basis of this heterogeneity is unknown. Here, we compare in vivo synaptic properties of two neuronal classes in the C. elegans central nervous system, using VGLUT-pHluorin to monitor synaptic vesicle exocytosis and retrieval in intact animals. We show that the glutamatergic sensory neurons AWC ON and ASH have distinct synaptic dynamics associated with tonic and phasic synaptic properties, respectively. Exocytosis in ASH and AWC ON is differentially affected by SNARE-complex regulators that are present in both neurons: phasic ASH release is strongly dependent on UNC-13, whereas tonic AWC ON release relies upon UNC-18 and on the protein kinase C homolog PKC-1. Strong stimuli that elicit high calcium levels increase exocytosis and retrieval rates in AWC ON , generating distinct tonic and evoked synaptic modes. These results highlight the differential deployment of shared presynaptic proteins in neuronal cell type-specific functions.

Diverse modes of synaptic signaling, regulation, and plasticity distinguish two classes of C. elegans glutamatergic neurons

PubMed Central

Ventimiglia, Donovan

2017-01-01

Synaptic vesicle release properties vary between neuronal cell types, but in most cases the molecular basis of this heterogeneity is unknown. Here, we compare in vivo synaptic properties of two neuronal classes in the C. elegans central nervous system, using VGLUT-pHluorin to monitor synaptic vesicle exocytosis and retrieval in intact animals. We show that the glutamatergic sensory neurons AWCON and ASH have distinct synaptic dynamics associated with tonic and phasic synaptic properties, respectively. Exocytosis in ASH and AWCON is differentially affected by SNARE-complex regulators that are present in both neurons: phasic ASH release is strongly dependent on UNC-13, whereas tonic AWCON release relies upon UNC-18 and on the protein kinase C homolog PKC-1. Strong stimuli that elicit high calcium levels increase exocytosis and retrieval rates in AWCON, generating distinct tonic and evoked synaptic modes. These results highlight the differential deployment of shared presynaptic proteins in neuronal cell type-specific functions. PMID:29160768

Synaptic transmission block by presynaptic injection of oligomeric amyloid beta

PubMed Central

Moreno, Herman; Yu, Eunah; Pigino, Gustavo; Hernandez, Alejandro I.; Kim, Natalia; Moreira, Jorge E.; Sugimori, Mutsuyuki; Llinás, Rodolfo R.

2009-01-01

Early Alzheimer's disease (AD) pathophysiology is characterized by synaptic changes induced by degradation products of amyloid precursor protein (APP). The exact mechanisms of such modulation are unknown. Here, we report that nanomolar concentrations of intraaxonal oligomeric (o)Aβ42, but not oAβ40 or extracellular oAβ42, acutely inhibited synaptic transmission at the squid giant synapse. Further characterization of this phenotype demonstrated that presynaptic calcium currents were unaffected. However, electron microscopy experiments revealed diminished docked synaptic vesicles in oAβ42-microinjected terminals, without affecting clathrin-coated vesicles. The molecular events of this modulation involved casein kinase 2 and the synaptic vesicle rapid endocytosis pathway. These findings open the possibility of a new therapeutic target aimed at ameliorating synaptic dysfunction in AD. PMID:19304802

Short-Term Synaptic Plasticity Regulation in Solution-Gated Indium-Gallium-Zinc-Oxide Electric-Double-Layer Transistors.

PubMed

Wan, Chang Jin; Liu, Yang Hui; Zhu, Li Qiang; Feng, Ping; Shi, Yi; Wan, Qing

2016-04-20

In the biological nervous system, synaptic plasticity regulation is based on the modulation of ionic fluxes, and such regulation was regarded as the fundamental mechanism underlying memory and learning. Inspired by such biological strategies, indium-gallium-zinc-oxide (IGZO) electric-double-layer (EDL) transistors gated by aqueous solutions were proposed for synaptic behavior emulations. Short-term synaptic plasticity, such as paired-pulse facilitation, high-pass filtering, and orientation tuning, was experimentally emulated in these EDL transistors. Most importantly, we found that such short-term synaptic plasticity can be effectively regulated by alcohol (ethyl alcohol) and salt (potassium chloride) additives. Our results suggest that solution gated oxide-based EDL transistors could act as the platforms for short-term synaptic plasticity emulation.

Roles of somatic A-type K(+) channels in the synaptic plasticity of hippocampal neurons.

PubMed

Yang, Yoon-Sil; Kim, Kyeong-Deok; Eun, Su-Yong; Jung, Sung-Cherl

2014-06-01

In the mammalian brain, information encoding and storage have been explained by revealing the cellular and molecular mechanisms of synaptic plasticity at various levels in the central nervous system, including the hippocampus and the cerebral cortices. The modulatory mechanisms of synaptic excitability that are correlated with neuronal tasks are fundamental factors for synaptic plasticity, and they are dependent on intracellular Ca(2+)-mediated signaling. In the present review, the A-type K(+) (IA) channel, one of the voltage-dependent cation channels, is considered as a key player in the modulation of Ca(2+) influx through synaptic NMDA receptors and their correlated signaling pathways. The cellular functions of IA channels indicate that they possibly play as integral parts of synaptic and somatic complexes, completing the initiation and stabilization of memory.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

Lefschetz thimbles in fermionic effective models with repulsive vector-field

NASA Astrophysics Data System (ADS)

Mori, Yuto; Kashiwa, Kouji; Ohnishi, Akira

2018-06-01

We discuss two problems in complexified auxiliary fields in fermionic effective models, the auxiliary sign problem associated with the repulsive vector-field and the choice of the cut for the scalar field appearing from the logarithmic function. In the fermionic effective models with attractive scalar and repulsive vector-type interaction, the auxiliary scalar and vector fields appear in the path integral after the bosonization of fermion bilinears. When we make the path integral well-defined by the Wick rotation of the vector field, the oscillating Boltzmann weight appears in the partition function. This "auxiliary" sign problem can be solved by using the Lefschetz-thimble path-integral method, where the integration path is constructed in the complex plane. Another serious obstacle in the numerical construction of Lefschetz thimbles is caused by singular points and cuts induced by multivalued functions of the complexified scalar field in the momentum integration. We propose a new prescription which fixes gradient flow trajectories on the same Riemann sheet in the flow evolution by performing the momentum integration in the complex domain.

Experimental implementation of a biometric laser synaptic sensor.

PubMed

Pisarchik, Alexander N; Sevilla-Escoboza, Ricardo; Jaimes-Reátegui, Rider; Huerta-Cuellar, Guillermo; García-Lopez, J Hugo; Kazantsev, Victor B

2013-12-16

We fabricate a biometric laser fiber synaptic sensor to transmit information from one neuron cell to the other by an optical way. The optical synapse is constructed on the base of an erbium-doped fiber laser, whose pumped diode current is driven by a pre-synaptic FitzHugh-Nagumo electronic neuron, and the laser output controls a post-synaptic FitzHugh-Nagumo electronic neuron. The implemented laser synapse displays very rich dynamics, including fixed points, periodic orbits with different frequency-locking ratios and chaos. These regimes can be beneficial for efficient biorobotics, where behavioral flexibility subserved by synaptic connectivity is a challenge.

Time-dependent decreases in nucleus accumbens AMPA/NMDA ratio and incubation of sucrose craving in adolescent and adult rats.

PubMed

Counotte, Danielle S; Schiefer, Christopher; Shaham, Yavin; O'Donnell, Patricio

2014-04-01

There is evidence that cue-induced sucrose seeking progressively increases after cessation of oral sucrose self-administration (incubation of sucrose craving) in both adolescent and adult rats. The synaptic plasticity changes associated with this incubation at different age groups are unknown. We assessed whether incubation of sucrose craving in rats trained to self-administer sucrose as young adolescents, adolescents, or adults is associated with changes in 2-amino-3-(3-hydroxy-5-methyl-isoxazol-4-yl)propanoic acid (AMPA)/N-methyl-D-aspartate (NMDA) ratio (a measure of postsynaptic changes in synaptic strength) in nucleus accumbens. Three age groups initiated oral sucrose self-administration training (10 days) on postnatal day (P) 35 (young adolescents), P42 (adolescents), or P70 (adults). They were then tested for cue-induced sucrose seeking (assessed in an extinction test) on abstinence days 1 and 21. Separate groups of rats were trained to self-administer sucrose or water (a control condition), and assessed for AMPA/NMDA ratio in nucleus accumbens on abstinence days 1-3 and 21. Adult rats earned more sucrose rewards, but sucrose intake per body weight was higher in young adolescent rats. Time-dependent increases in cue-induced sucrose seeking (incubation of sucrose craving) were more pronounced in adult rats, less pronounced in adolescents, and not detected in young adolescents. On abstinence day 21, but not days 1-3, AMPA/NMDA ratio in nucleus accumbens were decreased in rats that self-administered sucrose as adults and adolescents, but not young adolescents. Our data demonstrate age-dependent changes in magnitude of incubation of sucrose craving and nucleus accumbens synaptic plasticity after cessation of sucrose self-administration.

Network evolution induced by asynchronous stimuli through spike-timing-dependent plasticity.

PubMed

Yuan, Wu-Jie; Zhou, Jian-Fang; Zhou, Changsong

2013-01-01

In sensory neural system, external asynchronous stimuli play an important role in perceptual learning, associative memory and map development. However, the organization of structure and dynamics of neural networks induced by external asynchronous stimuli are not well understood. Spike-timing-dependent plasticity (STDP) is a typical synaptic plasticity that has been extensively found in the sensory systems and that has received much theoretical attention. This synaptic plasticity is highly sensitive to correlations between pre- and postsynaptic firings. Thus, STDP is expected to play an important role in response to external asynchronous stimuli, which can induce segregative pre- and postsynaptic firings. In this paper, we study the impact of external asynchronous stimuli on the organization of structure and dynamics of neural networks through STDP. We construct a two-dimensional spatial neural network model with local connectivity and sparseness, and use external currents to stimulate alternately on different spatial layers. The adopted external currents imposed alternately on spatial layers can be here regarded as external asynchronous stimuli. Through extensive numerical simulations, we focus on the effects of stimulus number and inter-stimulus timing on synaptic connecting weights and the property of propagation dynamics in the resulting network structure. Interestingly, the resulting feedforward structure induced by stimulus-dependent asynchronous firings and its propagation dynamics reflect both the underlying property of STDP. The results imply a possible important role of STDP in generating feedforward structure and collective propagation activity required for experience-dependent map plasticity in developing in vivo sensory pathways and cortices. The relevance of the results to cue-triggered recall of learned temporal sequences, an important cognitive function, is briefly discussed as well. Furthermore, this finding suggests a potential application for examining STDP by measuring neural population activity in a cultured neural network.

The effect of soybean isoflavone on the dysregulation of NMDA receptor signaling pathway induced by β-amyloid peptides 1-42 in rats.

PubMed

Xi, Yuan-Di; Ding, Juan; Han, Jing; Zhang, Dan-Di; Liu, Jin-Meng; Feng, Ling-Li; Xiao, Rong

2015-05-01

Synaptic damage is the key factor of cognitive impairment. The purpose of this study was to understand the effect of soybean isoflavone (SIF) on synaptic damage induced by β-amyloid peptide 1-42 (Aβ1-42) in rats. Adult male Wistar rats were randomly divided into control, Aβ1-42, SIF, and SIF + Aβ1-42 (SIF pretreatment) groups according to body weight. SIF was treated orally by gavage in SIF and SIF + Aβ1-42 groups. After 14 days pretreatment with SIF or vehicle, Aβ1-42 was injected into the lateral cerebral ventricle of rats in Aβ1-42 and SIF + Aβ1-42 groups using miniosmotic pump. The level of Aβ1-42 and the expression of N-methyl-D-aspartic-acid receptor (NMDAR) were observed by immunohistochemistry. Reverse transcriptase polymerase chain reaction was used to detect the mRNA levels of NMDAR, calmodulin (CaM), calcium/CaM-dependent protein kinase II (CaMKII), cAMP-response element binding protein (CREB), and brain-derived neurotrophic factor (BDNF). The results showed that Aβ1-42 down-regulated mRNA and protein expression of the NR1 and NR2B subunits of NMDAR, SIF pretreatment could reverse these changes. The mRNA expression of CaM, CaMKII, CREB, and BDNF were down-regulated by Aβ1-42, but they were all regulated by SIF pretreatment. These results suggest that SIF pretreatment could antagonize the neuron damage in rats induced by Aβ1-42, and its mechanism might be associated with the NMDA receptor and CaM/CaMKII/CREB/BDNF signaling pathway, which are the synaptic plasticity-related molecules.

Rescuing effects of RXR agonist bexarotene on aging-related synapse loss depend on neuronal LRP1.

PubMed

Tachibana, Masaya; Shinohara, Mitsuru; Yamazaki, Yu; Liu, Chia-Chen; Rogers, Justin; Bu, Guojun; Kanekiyo, Takahisa

2016-03-01

Apolipoprotein E (apoE) plays a critical role in maintaining synaptic integrity by transporting cholesterol to neurons through the low-density lipoprotein receptor related protein-1 (LRP1). Bexarotene, a retinoid X receptor (RXR) agonist, has been reported to have potential beneficial effects on cognition by increasing brain apoE levels and lipidation. To investigate the effects of bexarotene on aging-related synapse loss and the contribution of neuronal LRP1 to the pathway, forebrain neuron-specific LRP1 knockout (nLrp1(-/-)) and littermate control mice were administered with bexarotene-formulated diet (100mg/kg/day) or control diet at the age of 20-24 months for 8 weeks. Upon bexarotene treatment, levels of brain apoE and ATP-binding cassette sub-family A member 1 (ABCA1) were significantly increased in both mice. While levels of PSD95, glutamate receptor 1 (GluR1), and N-methyl-d-aspartate receptor NR1 subunit (NR1), which are key postsynaptic proteins that regulate synaptic plasticity, were decreased with aging, they were restored by bexarotene treatment in the brains of control but not nLrp1(-/-) mice. These results indicate that the beneficial effects of bexarotene on synaptic integrity depend on the presence of neuronal LRP1. However, we also found that bexarotene treatment led to the activation of glial cells, weight loss and hepatomegaly, which are likely due to hepatic failure. Taken together, our results demonstrate that apoE-targeted treatment through the RXR pathway has a potential beneficial effect on synapses during aging; however, the therapeutic application of bexarotene requires extreme caution due to its toxic side effects. Copyright © 2015 Elsevier Inc. All rights reserved.

Synaptic and extrasynaptic traces of long-term memory: the ID molecule theory.

PubMed

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.

A compound memristive synapse model for statistical learning through STDP in spiking neural networks

PubMed Central

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

Purification of a post-synaptic neurotoxic phospholipase A2 from Naja naja venom and its inhibition by a glycoprotein from Withania somnifera.

PubMed

Machiah, Deepa K; Gowda, T Veerabasappa

2006-06-01

A post-synaptic neurotoxic phospholipase A(2) (PLA(2)) has been purified from Indian cobra Naja naja venom. It was associated with a peptide in the venom. The association was disrupted using 8 M urea. It is denoted to be a basic protein by its behavior on both ion exchange chromatography and electrophoresis. It is toxic to mice, LD(50) 1.9 mg/kg body weight (ip). It is proved to be post-synaptic PLA(2) by chymographic experiment using frog nerve-muscle preparation. A glycoprotein, (WSG) was isolated from a folk medicinal plant Withania somnifera. The WSG inhibited the phospholipase A(2) activity of NN-XIa-PLA(2,) isolated from the cobra venom, completely at a mole-to-mole ratio of 1:2 (NN-XIa-PLA(2): WSG) but failed to neutralize the toxicity of the molecule. However, it reduced the toxicity as well as prolonged the death time of the experimental mice approximately 10 times when compared to venom alone. The WSG also inhibited several other PLA(2) isoforms from the venom to varying extent. The interaction of the WSG with the PLA(2) is confirmed by fluorescence quenching and gel-permeation chromatography. Chemical modification of the active histidine residue of PLA(2) using p-brophenacyl bromide resulted in the loss of both catalytic activity as well as neurotoxicity of the molecule. These findings suggest that the venom PLA(2) has multiple sites on it; perhaps some of them are overlapping. Application of the plant extract on snakebite wound confirms the medicinal value associated with the plant.

Infinite flag varieties and conjugacy theorems

PubMed Central

Peterson, Dale H.; Kac, Victor G.

1983-01-01

We study the orbit of a highest-weight vector in an integrable highest-weight module of the group G associated to a Kac-Moody algebra [unk](A). We obtain applications to the geometric structure of the associated flag varieties and to the algebraic structure of [unk](A). In particular, we prove conjugacy theorems for Cartan and Borel subalgebras of [unk](A), so that the Cartan matrix A is an invariant of [unk](A). PMID:16593298

An Optimization Principle for Deriving Nonequilibrium Statistical Models of Hamiltonian Dynamics

NASA Astrophysics Data System (ADS)

Turkington, Bruce

2013-08-01

A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. Given a vector of resolved variables, selected to describe the macroscopic state of the system, a family of quasi-equilibrium probability densities on phase space corresponding to the resolved variables is employed as a statistical model, and the evolution of the mean resolved vector is estimated by optimizing over paths of these densities. Specifically, a cost function is constructed to quantify the lack-of-fit to the microscopic dynamics of any feasible path of densities from the statistical model; it is an ensemble-averaged, weighted, squared-norm of the residual that results from submitting the path of densities to the Liouville equation. The path that minimizes the time integral of the cost function determines the best-fit evolution of the mean resolved vector. The closed reduced equations satisfied by the optimal path are derived by Hamilton-Jacobi theory. When expressed in terms of the macroscopic variables, these equations have the generic structure of governing equations for nonequilibrium thermodynamics. In particular, the value function for the optimization principle coincides with the dissipation potential that defines the relation between thermodynamic forces and fluxes. The adjustable closure parameters in the best-fit reduced equations depend explicitly on the arbitrary weights that enter into the lack-of-fit cost function. Two particular model reductions are outlined to illustrate the general method. In each example the set of weights in the optimization principle contracts into a single effective closure parameter.

Zinc Transporter 3 Is Involved in Learned Fear and Extinction, but Not in Innate Fear

ERIC Educational Resources Information Center

Martel, Guillaume; Hevi, Charles; Friebely, Olivia; Baybutt, Trevor; Shumyatsky, Gleb P.

2010-01-01

Synaptically released Zn[superscript 2+] is a potential modulator of neurotransmission and synaptic plasticity in fear-conditioning pathways. Zinc transporter 3 (ZnT3) knock-out (KO) mice are well suited to test the role of zinc in learned fear, because ZnT3 is colocalized with synaptic zinc, responsible for its transport to synaptic vesicles,…

Spontaneous Release Regulates Synaptic Scaling in the Embryonic Spinal Network In Vivo

PubMed Central

Garcia-Bereguiain, Miguel Angel; Gonzalez-Islas, Carlos; Lindsly, Casie

2016-01-01

Homeostatic plasticity mechanisms maintain cellular or network spiking activity within a physiologically functional range through compensatory changes in synaptic strength or intrinsic cellular excitability. Synaptic scaling is one form of homeostatic plasticity that is triggered after blockade of spiking or neurotransmission in which the strengths of all synaptic inputs to a cell are multiplicatively scaled upward or downward in a compensatory fashion. We have shown previously that synaptic upscaling could be triggered in chick embryo spinal motoneurons by complete blockade of spiking or GABAA receptor (GABAAR) activation for 2 d in vivo. Here, we alter GABAAR activation in a more physiologically relevant manner by chronically adjusting presynaptic GABA release in vivo using nicotinic modulators or an mGluR2 agonist. Manipulating GABAAR activation in this way triggered scaling in a mechanistically similar manner to scaling induced by complete blockade of GABAARs. Remarkably, we find that altering action-potential (AP)-independent spontaneous release was able to fully account for the observed bidirectional scaling, whereas dramatic changes in spiking activity associated with spontaneous network activity had little effect on quantal amplitude. The reliance of scaling on an AP-independent process challenges the plasticity's relatedness to spiking in the living embryonic spinal network. Our findings have implications for the trigger and function of synaptic scaling and suggest that spontaneous release functions to regulate synaptic strength homeostatically in vivo. SIGNIFICANCE STATEMENT Homeostatic synaptic scaling is thought to prevent inappropriate levels of spiking activity through compensatory adjustments in the strength of synaptic inputs. Therefore, it is thought that perturbations in spike rate trigger scaling. Here, we find that dramatic changes in spiking activity in the embryonic spinal cord have little effect on synaptic scaling; conversely, alterations in GABAA receptor activation due to action-potential-independent GABA vesicle release can trigger scaling. The findings suggest that scaling in the living embryonic spinal cord functions to maintain synaptic strength and challenge the view that scaling acts to regulate spiking activity homeostatically. Finally, the results indicate that fetal exposure to drugs that influence GABA spontaneous release, such as nicotine, could profoundly affect synaptic maturation. PMID:27383600

Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin

PubMed Central

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

A path model for Whittaker vectors

NASA Astrophysics Data System (ADS)

Di Francesco, Philippe; Kedem, Rinat; Turmunkh, Bolor

2017-06-01

In this paper we construct weighted path models to compute Whittaker vectors in the completion of Verma modules, as well as Whittaker functions of fundamental type, for all finite-dimensional simple Lie algebras, affine Lie algebras, and the quantum algebra U_q(slr+1) . This leads to series expressions for the Whittaker functions. We show how this construction leads directly to the quantum Toda equations satisfied by these functions, and to the q-difference equations in the quantum case. We investigate the critical limit of affine Whittaker functions computed in this way.

Shank3 Is Part of a Zinc-Sensitive Signaling System That Regulates Excitatory Synaptic Strength.

PubMed

Arons, Magali H; Lee, Kevin; Thynne, Charlotte J; Kim, Sally A; Schob, Claudia; Kindler, Stefan; Montgomery, Johanna M; Garner, Craig C

2016-08-31

Shank3 is a multidomain scaffold protein localized to the postsynaptic density of excitatory synapses. Functional studies in vivo and in vitro support the concept that Shank3 is critical for synaptic plasticity and the trans-synaptic coupling between the reliability of presynaptic neurotransmitter release and postsynaptic responsiveness. However, how Shank3 regulates synaptic strength remains unclear. The C terminus of Shank3 contains a sterile alpha motif (SAM) domain that is essential for its postsynaptic localization and also binds zinc, thus raising the possibility that changing zinc levels modulate Shank3 function in dendritic spines. In support of this hypothesis, we find that zinc is a potent regulator of Shank3 activation and dynamics in rat hippocampal neurons. Moreover, we show that zinc modulation of synaptic transmission is Shank3 dependent. Interestingly, an autism spectrum disorder (ASD)-associated variant of Shank3 (Shank3(R87C)) retains its zinc sensitivity and supports zinc-dependent activation of AMPAR-mediated synaptic transmission. However, elevated zinc was unable to rescue defects in trans-synaptic signaling caused by the R87C mutation, implying that trans-synaptic increases in neurotransmitter release are not necessary for the postsynaptic effects of zinc. Together, these data suggest that Shank3 is a key component of a zinc-sensitive signaling system, regulating synaptic strength that may be impaired in ASD. Shank3 is a postsynaptic protein associated with neurodevelopmental disorders such as autism and schizophrenia. In this study, we show that Shank3 is a key component of a zinc-sensitive signaling system that regulates excitatory synaptic transmission. Intriguingly, an autism-associated mutation in Shank3 partially impairs this signaling system. Therefore, perturbation of zinc homeostasis may impair, not only synaptic functionality and plasticity, but also may lead to cognitive and behavioral abnormalities seen in patients with psychiatric disorders. Copyright © 2016 the authors 0270-6474/16/369124-11$15.00/0.

Aβ-Induced Synaptic Alterations Require the E3 Ubiquitin Ligase Nedd4-1.

PubMed

Rodrigues, Elizabeth M; Scudder, Samantha L; Goo, Marisa S; Patrick, Gentry N

2016-02-03

Alzheimer's disease (AD) is a neurodegenerative disease in which patients experience progressive cognitive decline. A wealth of evidence suggests that this cognitive impairment results from synaptic dysfunction in affected brain regions caused by cleavage of amyloid precursor protein into the pathogenic peptide amyloid-β (Aβ). Specifically, it has been shown that Aβ decreases surface AMPARs, dendritic spine density, and synaptic strength, and also alters synaptic plasticity. The precise molecular mechanisms by which this occurs remain unclear. Here we demonstrate a role for ubiquitination in Aβ-induced synaptic dysfunction in cultured rat neurons. We find that Aβ promotes the ubiquitination of AMPARs, as well as the redistribution and recruitment of Nedd4-1, a HECT E3 ubiquitin ligase we previously demonstrated to target AMPARs for ubiquitination and degradation. Strikingly, we show that Nedd4-1 is required for Aβ-induced reductions in surface AMPARs, synaptic strength, and dendritic spine density. Our findings, therefore, indicate an important role for Nedd4-1 and ubiquitin in the synaptic alterations induced by Aβ. Synaptic changes in Alzheimer's disease (AD) include surface AMPAR loss, which can weaken synapses. In a cell culture model of AD, we found that AMPAR loss correlates with increased AMPAR ubiquitination. In addition, the ubiquitin ligase Nedd4-1, known to ubiquitinate AMPARs, is recruited to synapses in response to Aβ. Strikingly, reducing Nedd4-1 levels in this model prevented surface AMPAR loss and synaptic weakening. These findings suggest that, in AD, Nedd4-1 may ubiquitinate AMPARs to promote their internalization and weaken synaptic strength, similar to what occurs in Nedd4-1's established role in homeostatic synaptic scaling. This is the first demonstration of Aβ-mediated control of a ubiquitin ligase to regulate surface AMPAR expression. Copyright © 2016 the authors 0270-6474/16/361590-06$15.00/0.

SAD-B kinase regulates pre-synaptic vesicular dynamics at hippocampal Schaffer collateral synapses and affects contextual fear memory.

PubMed

Watabe, Ayako M; Nagase, Masashi; Hagiwara, Akari; Hida, Yamato; Tsuji, Megumi; Ochiai, Toshitaka; Kato, Fusao; Ohtsuka, Toshihisa

2016-01-01

Synapses of amphids defective (SAD)-A/B kinases control various steps in neuronal development and differentiation, such as axon specifications and maturation in central and peripheral nervous systems. At mature pre-synaptic terminals, SAD-B is associated with synaptic vesicles and the active zone cytomatrix; however, how SAD-B regulates neurotransmission and synaptic plasticity in vivo remains unclear. Thus, we used SAD-B knockout (KO) mice to study the function of this pre-synaptic kinase in the brain. We found that the paired-pulse ratio was significantly enhanced at Shaffer collateral synapses in the hippocampal CA1 region in SAD-B KO mice compared with wild-type littermates. We also found that the frequency of the miniature excitatory post-synaptic current was decreased in SAD-B KO mice. Moreover, synaptic depression following prolonged low-frequency synaptic stimulation was significantly enhanced in SAD-B KO mice. These results suggest that SAD-B kinase regulates vesicular release probability at pre-synaptic terminals and is involved in vesicular trafficking and/or regulation of the readily releasable pool size. Finally, we found that hippocampus-dependent contextual fear learning was significantly impaired in SAD-B KO mice. These observations suggest that SAD-B kinase plays pivotal roles in controlling vesicular release properties and regulating hippocampal function in the mature brain. Synapses of amphids defective (SAD)-A/B kinases control various steps in neuronal development and differentiation, but their roles in mature brains were only partially known. Here, we demonstrated, at mature pre-synaptic terminals, that SAD-B regulates vesicular release probability and synaptic plasticity. Moreover, hippocampus-dependent contextual fear learning was significantly impaired in SAD-B KO mice, suggesting that SAD-B kinase plays pivotal roles in controlling vesicular release properties and regulating hippocampal function in the mature brain. © 2015 International Society for Neurochemistry.

Shank3 Is Part of a Zinc-Sensitive Signaling System That Regulates Excitatory Synaptic Strength

PubMed Central

Arons, Magali H.; Lee, Kevin; Thynne, Charlotte J.; Kim, Sally A.; Schob, Claudia; Kindler, Stefan

2016-01-01

Shank3 is a multidomain scaffold protein localized to the postsynaptic density of excitatory synapses. Functional studies in vivo and in vitro support the concept that Shank3 is critical for synaptic plasticity and the trans-synaptic coupling between the reliability of presynaptic neurotransmitter release and postsynaptic responsiveness. However, how Shank3 regulates synaptic strength remains unclear. The C terminus of Shank3 contains a sterile alpha motif (SAM) domain that is essential for its postsynaptic localization and also binds zinc, thus raising the possibility that changing zinc levels modulate Shank3 function in dendritic spines. In support of this hypothesis, we find that zinc is a potent regulator of Shank3 activation and dynamics in rat hippocampal neurons. Moreover, we show that zinc modulation of synaptic transmission is Shank3 dependent. Interestingly, an autism spectrum disorder (ASD)-associated variant of Shank3 (Shank3R87C) retains its zinc sensitivity and supports zinc-dependent activation of AMPAR-mediated synaptic transmission. However, elevated zinc was unable to rescue defects in trans-synaptic signaling caused by the R87C mutation, implying that trans-synaptic increases in neurotransmitter release are not necessary for the postsynaptic effects of zinc. Together, these data suggest that Shank3 is a key component of a zinc-sensitive signaling system, regulating synaptic strength that may be impaired in ASD. SIGNIFICANCE STATEMENT Shank3 is a postsynaptic protein associated with neurodevelopmental disorders such as autism and schizophrenia. In this study, we show that Shank3 is a key component of a zinc-sensitive signaling system that regulates excitatory synaptic transmission. Intriguingly, an autism-associated mutation in Shank3 partially impairs this signaling system. Therefore, perturbation of zinc homeostasis may impair, not only synaptic functionality and plasticity, but also may lead to cognitive and behavioral abnormalities seen in patients with psychiatric disorders. PMID:27581454

Longitudinal evidence for anterograde trans-synaptic degeneration after optic neuritis

PubMed Central

Goodkin, Olivia; Altmann, Daniel R.; Jenkins, Thomas M.; Miszkiel, Katherine; Mirigliani, Alessia; Fini, Camilla; Gandini Wheeler-Kingshott, Claudia A. M.; Thompson, Alan J.; Ciccarelli, Olga; Toosy, Ahmed T.

2016-01-01

Abstract In multiple sclerosis, microstructural damage of normal-appearing brain tissue is an important feature of its pathology. Understanding these mechanisms is vital to help develop neuroprotective strategies. The visual pathway is a key model to study mechanisms of damage and recovery in demyelination. Anterograde trans-synaptic degeneration across the lateral geniculate nuclei has been suggested as a mechanism of tissue damage to explain optic radiation abnormalities seen in association with demyelinating disease and optic neuritis, although evidence for this has relied solely on cross-sectional studies. We therefore aimed to assess: (i) longitudinal changes in the diffusion properties of optic radiations after optic neuritis suggesting trans-synaptic degeneration; (ii) the predictive value of early optic nerve magnetic resonance imaging measures for late optic radiations changes; and (iii) the impact on visual outcome of both optic nerve and brain post-optic neuritis changes. Twenty-eight consecutive patients with acute optic neuritis and eight healthy controls were assessed visually (logMAR, colour vision, and Sloan 1.25%, 5%, 25%) and by magnetic resonance imaging, at baseline, 3, 6, and 12 months. Magnetic resonance imaging sequences performed (and metrics obtained) were: (i) optic nerve fluid-attenuated inversion-recovery (optic nerve cross-sectional area); (ii) optic nerve proton density fast spin-echo (optic nerve proton density-lesion length); (iii) optic nerve post-gadolinium T 1 -weighted (Gd-enhanced lesion length); and (iv) brain diffusion-weighted imaging (to derive optic radiation fractional anisotropy, radial diffusivity, and axial diffusivity). Mixed-effects and multivariate regression models were performed, adjusting for age, gender, and optic radiation lesion load. These identified changes over time and associations between early optic nerve measures and 1-year global optic radiation/clinical measures. The fractional anisotropy in patients’ optic radiations decreased ( P = 0.018) and radial diffusivity increased ( P = 0.002) over 1 year following optic neuritis, whereas optic radiation measures were unchanged in controls. Also, smaller cross-sectional areas of affected optic nerves at 3 months post-optic neuritis predicted lower fractional anisotropy and higher radial diffusivity at 1 year ( P = 0.007) in the optic radiations, whereas none of the inflammatory measures of the optic nerve predicted changes in optic radiations. Finally, greater Gd-enhanced lesion length at baseline and greater optic nerve proton density-lesion length at 1 year were associated with worse visual function at 1 year ( P = 0.034 for both). Neither the cross-sectional area of the affected optic nerve after optic neuritis nor the damage in optic radiations was associated with 1-year visual outcome. Our longitudinal study shows that, after optic neuritis, there is progressive damage to the optic radiations, greater in patients with early residual optic nerve atrophy, even after adjusting for optic radiation lesions. These findings provide evidence for trans-synaptic degeneration. PMID:26912640

Contributions of Bcl-xL to acute and long term changes in bioenergetics during neuronal plasticity.

PubMed

Jonas, Elizabeth A

2014-08-01

Mitochondria manufacture and release metabolites and manage calcium during neuronal activity and synaptic transmission, but whether long term alterations in mitochondrial function contribute to the neuronal plasticity underlying changes in organism behavior patterns is still poorly understood. Although normal neuronal plasticity may determine learning, in contrast a persistent decline in synaptic strength or neuronal excitability may portend neurite retraction and eventual somatic death. Anti-death proteins such as Bcl-xL not only provide neuroprotection at the neuronal soma during cell death stimuli, but also appear to enhance neurotransmitter release and synaptic growth and development. It is proposed that Bcl-xL performs these functions through its ability to regulate mitochondrial release of bioenergetic metabolites and calcium, and through its ability to rapidly alter mitochondrial positioning and morphology. Bcl-xL also interacts with proteins that directly alter synaptic vesicle recycling. Bcl-xL translocates acutely to sub-cellular membranes during neuronal activity to achieve changes in synaptic efficacy. After stressful stimuli, pro-apoptotic cleaved delta N Bcl-xL (ΔN Bcl-xL) induces mitochondrial ion channel activity leading to synaptic depression and this is regulated by caspase activation. During physiological states of decreased synaptic stimulation, loss of mitochondrial Bcl-xL and low level caspase activation occur prior to the onset of long term decline in synaptic efficacy. The degree to which Bcl-xL changes mitochondrial membrane permeability may control the direction of change in synaptic strength. The small molecule Bcl-xL inhibitor ABT-737 has been useful in defining the role of Bcl-xL in synaptic processes. Bcl-xL is crucial to the normal health of neurons and synapses and its malfunction may contribute to neurodegenerative disease. Copyright © 2013. Published by Elsevier B.V.

Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate

PubMed Central

Wallisch, Pascal; Ostojic, Srdjan

2016-01-01

Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity. SIGNIFICANCE STATEMENT Synaptic plasticity, the change in efficacy of connections between neurons, is thought to underlie learning and memory. The dominant paradigm posits that the precise timing of neural action potentials (APs) is central for plasticity induction. This concept is based on experiments using highly regular and stereotyped patterns of APs, in stark contrast with natural neuronal activity. Using synaptic plasticity models, we investigated how irregular, in vivo-like activity shapes synaptic plasticity. We found that synaptic changes induced by precise timing of APs are much weaker than suggested by regular stimulation protocols, and can be equivalently induced by modest variations of the AP rate alone. Our results call into question the dominant role of precise AP timing for plasticity in natural conditions. PMID:27807166

Free radical production and antioxidant status in brain cortex non-synaptic mitochondria and synaptosomes at alcohol hangover onset.

PubMed

Karadayian, Analía G; Malanga, Gabriela; Czerniczyniec, Analía; Lombardi, Paulina; Bustamante, Juanita; Lores-Arnaiz, Silvia

2017-07-01

Alcohol hangover (AH) is the pathophysiological state after a binge-like drinking. We have previously demonstrated that AH induced bioenergetics impairments in a total fresh mitochondrial fraction in brain cortex and cerebellum. The aim of this work was to determine free radical production and antioxidant systems in non-synaptic mitochondria and synaptosomes in control and hangover animals. Superoxide production was not modified in non-synaptic mitochondria while a 17.5% increase was observed in synaptosomes. A similar response was observed for cardiolipin content as no changes were evidenced in non-synaptic mitochondria while a 55% decrease in cardiolipin content was found in synaptosomes. Hydrogen peroxide production was 3-fold increased in non-synaptic mitochondria and 4-fold increased in synaptosomes. In the presence of deprenyl, synaptosomal H 2 O 2 production was 67% decreased in the AH condition. Hydrogen peroxide generation was not affected by deprenyl addition in non-synaptic mitochondria from AH mice. MAO activity was 57% increased in non-synaptic mitochondria and 3-fold increased in synaptosomes. Catalase activity was 40% and 50% decreased in non-synaptic mitochondria and synaptosomes, respectively. Superoxide dismutase was 60% decreased in non-synaptic mitochondria and 80% increased in synaptosomal fractions. On the other hand, GSH (glutathione) content was 43% and 17% decreased in synaptosomes and cytosol. GSH-related enzymes were mostly affected in synaptosomes fractions by AH condition. Acetylcholinesterase activity in synaptosomes was 11% increased due to AH. The present work reveals that AH provokes an imbalance in the cellular redox homeostasis mainly affecting mitochondria present in synaptic terminals. Copyright © 2017 Elsevier Inc. All rights reserved.

Programmable synaptic chip for electronic neural networks

NASA Technical Reports Server (NTRS)

Moopenn, A.; Langenbacher, H.; Thakoor, A. P.; Khanna, S. K.

1988-01-01

A binary synaptic matrix chip has been developed for electronic neural networks. The matrix chip contains a programmable 32X32 array of 'long channel' NMOSFET binary connection elements implemented in a 3-micron bulk CMOS process. Since the neurons are kept off-chip, the synaptic chip serves as a 'cascadable' building block for a multi-chip synaptic network as large as 512X512 in size. As an alternative to the programmable NMOSFET (long channel) connection elements, tailored thin film resistors are deposited, in series with FET switches, on some CMOS test chips, to obtain the weak synaptic connections. Although deposition and patterning of the resistors require additional processing steps, they promise substantial savings in silicon area. The performance of synaptic chip in a 32-neuron breadboard system in an associative memory test application is discussed.

Phosphodiesterase Inhibition to Target the Synaptic Dysfunction in Alzheimer's Disease

NASA Astrophysics Data System (ADS)

Bales, Kelly R.; Plath, Niels; Svenstrup, Niels; Menniti, Frank S.

Alzheimer's Disease (AD) is a disease of synaptic dysfunction that ultimately proceeds to neuronal death. There is a wealth of evidence that indicates the final common mediator of this neurotoxic process is the formation and actions on synaptotoxic b-amyloid (Aβ). The premise in this review is that synaptic dysfunction may also be an initiating factor in for AD and promote synaptotoxic Aβ formation. This latter hypothesis is consistent with the fact that the most common risk factors for AD, apolipoprotein E (ApoE) allele status, age, education, and fitness, encompass suboptimal synaptic function. Thus, the synaptic dysfunction in AD may be both cause and effect, and remediating synaptic dysfunction in AD may have acute effects on the symptoms present at the initiation of therapy and also slow disease progression. The cyclic nucleotide (cAMP and cGMP) signaling systems are intimately involved in the regulation of synaptic homeostasis. The phosphodiesterases (PDEs) are a superfamily of enzymes that critically regulate spatial and temporal aspects of cyclic nucleotide signaling through metabolic inactivation of cAMP and cGMP. Thus, targeting the PDEs to promote improved synaptic function, or 'synaptic resilience', may be an effective and facile approach to new symptomatic and disease modifying therapies for AD. There continues to be a significant drug discovery effort aimed at discovering PDE inhibitors to treat a variety of neuropsychiatric disorders. Here we review the current status of those efforts as they relate to potential new therapies for AD.

Enduring medial perforant path short-term synaptic depression at high pressure.

PubMed

Talpalar, Adolfo E; Giugliano, Michele; Grossman, Yoram

2010-01-01

The high pressure neurological syndrome develops during deep-diving (>1.1 MPa) involving impairment of cognitive functions, alteration of synaptic transmission and increased excitability in cortico-hippocampal areas. The medial perforant path (MPP), connecting entorhinal cortex with the hippocampal formation, displays synaptic frequency-dependent-depression (FDD) under normal conditions. Synaptic FDD is essential for specific functions of various neuronal networks. We used rat cortico-hippocampal slices and computer simulations for studying the effects of pressure and its interaction with extracellular Ca(2+) ([Ca(2+)](o)) on FDD at the MPP synapses. At atmospheric pressure, high [Ca(2+)](o) (4-6 mM) saturated single MPP field EPSP (fEPSP) and increased FDD in response to short trains at 50 Hz. High pressure (HP; 10.1 MPa) depressed single fEPSPs by 50%. Increasing [Ca(2+)](o) to 4 mM at HP saturated synaptic response at a subnormal level (only 20% recovery of single fEPSPs), but generated a FDD similar to atmospheric pressure. Mathematical model analysis of the fractions of synaptic resources used by each fEPSP during trains (normalized to their maximum) and the total fraction utilized within a train indicate that HP depresses synaptic activity also by reducing synaptic resources. This data suggest that MPP synapses may be modulated, in addition to depression of single events, by reduction of synaptic resources and then may have the ability to conserve their dynamic properties under different conditions.

Enduring Medial Perforant Path Short-Term Synaptic Depression at High Pressure

PubMed Central

Talpalar, Adolfo E.; Giugliano, Michele; Grossman, Yoram

2010-01-01

The high pressure neurological syndrome develops during deep-diving (>1.1 MPa) involving impairment of cognitive functions, alteration of synaptic transmission and increased excitability in cortico-hippocampal areas. The medial perforant path (MPP), connecting entorhinal cortex with the hippocampal formation, displays synaptic frequency-dependent-depression (FDD) under normal conditions. Synaptic FDD is essential for specific functions of various neuronal networks. We used rat cortico-hippocampal slices and computer simulations for studying the effects of pressure and its interaction with extracellular Ca2+ ([Ca2+]o) on FDD at the MPP synapses. At atmospheric pressure, high [Ca2+]o (4–6 mM) saturated single MPP field EPSP (fEPSP) and increased FDD in response to short trains at 50 Hz. High pressure (HP; 10.1 MPa) depressed single fEPSPs by 50%. Increasing [Ca2+]o to 4 mM at HP saturated synaptic response at a subnormal level (only 20% recovery of single fEPSPs), but generated a FDD similar to atmospheric pressure. Mathematical model analysis of the fractions of synaptic resources used by each fEPSP during trains (normalized to their maximum) and the total fraction utilized within a train indicate that HP depresses synaptic activity also by reducing synaptic resources. This data suggest that MPP synapses may be modulated, in addition to depression of single events, by reduction of synaptic resources and then may have the ability to conserve their dynamic properties under different conditions. PMID:21048901

DFsn collaborates with Highwire to down-regulate the Wallenda/DLK kinase and restrain synaptic terminal growth

PubMed Central

Wu, Chunlai; Daniels, Richard W; DiAntonio, Aaron

2007-01-01

Background The growth of new synapses shapes the initial formation and subsequent rearrangement of neural circuitry. Genetic studies have demonstrated that the ubiquitin ligase Highwire restrains synaptic terminal growth by down-regulating the MAP kinase kinase kinase Wallenda/dual leucine zipper kinase (DLK). To investigate the mechanism of Highwire action, we have identified DFsn as a binding partner of Highwire and characterized the roles of DFsn in synapse development, synaptic transmission, and the regulation of Wallenda/DLK kinase abundance. Results We identified DFsn as an F-box protein that binds to the RING-domain ubiquitin ligase Highwire and that can localize to the Drosophila neuromuscular junction. Loss-of-function mutants for DFsn have a phenotype that is very similar to highwire mutants – there is a dramatic overgrowth of synaptic termini, with a large increase in the number of synaptic boutons and branches. In addition, synaptic transmission is impaired in DFsn mutants. Genetic interactions between DFsn and highwire mutants indicate that DFsn and Highwire collaborate to restrain synaptic terminal growth. Finally, DFsn regulates the levels of the Wallenda/DLK kinase, and wallenda is necessary for DFsn-dependent synaptic terminal overgrowth. Conclusion The F-box protein DFsn binds the ubiquitin ligase Highwire and is required to down-regulate the levels of the Wallenda/DLK kinase and restrain synaptic terminal growth. We propose that DFsn and Highwire participate in an evolutionarily conserved ubiquitin ligase complex whose substrates regulate the structure and function of synapses. PMID:17697379

Generalized Analysis Tools for Multi-Spacecraft Missions

NASA Astrophysics Data System (ADS)

Chanteur, G. M.

2011-12-01

Analysis tools for multi-spacecraft missions like CLUSTER or MMS have been designed since the end of the 90's to estimate gradients of fields or to characterize discontinuities crossed by a cluster of spacecraft. Different approaches have been presented and discussed in the book "Analysis Methods for Multi-Spacecraft Data" published as Scientific Report 001 of the International Space Science Institute in Bern, Switzerland (G. Paschmann and P. Daly Eds., 1998). On one hand the approach using methods of least squares has the advantage to apply to any number of spacecraft [1] but is not convenient to perform analytical computation especially when considering the error analysis. On the other hand the barycentric approach is powerful as it provides simple analytical formulas involving the reciprocal vectors of the tetrahedron [2] but appears limited to clusters of four spacecraft. Moreover the barycentric approach allows to derive theoretical formulas for errors affecting the estimators built from the reciprocal vectors [2,3,4]. Following a first generalization of reciprocal vectors proposed by Vogt et al [4] and despite the present lack of projects with more than four spacecraft we present generalized reciprocal vectors for a cluster made of any number of spacecraft : each spacecraft is given a positive or nul weight. The non-coplanarity of at least four spacecraft with strictly positive weights is a necessary and sufficient condition for this analysis to be enabled. Weights given to spacecraft allow to minimize the influence of some spacecraft if its location or the quality of its data are not appropriate, or simply to extract subsets of spacecraft from the cluster. Estimators presented in [2] are generalized within this new frame except for the error analysis which is still under investigation. References [1] Harvey, C. C.: Spatial Gradients and the Volumetric Tensor, in: Analysis Methods for Multi-Spacecraft Data, G. Paschmann and P. Daly (eds.), pp. 307-322, ISSI SR-001, 1998. [2] Chanteur, G.: Spatial Interpolation for Four Spacecraft: Theory, in: Analysis Methods for Multi-Spacecraft Data, G. Paschmann and P. Daly (eds.), pp. 371-393, ISSI SR-001, 1998. [3] Chanteur, G.: Accuracy of field gradient estimations by Cluster: Explanation of its dependency upon elongation and planarity of the tetrahedron, pp. 265-268, ESA SP-449, 2000. [4] Vogt, J., Paschmann, G., and Chanteur, G.: Reciprocal Vectors, pp. 33-46, ISSI SR-008, 2008.

Gq-DREADD Selectively Initiates Glial Glutamate Release and Inhibits Cue-induced Cocaine Seeking

PubMed Central

Scofield Michael, D.; Boger Heather, A.; Smith Rachel, J.; Li, Hao; Haydon Philip, G.; Kalivas Peter, W.

2015-01-01

Background Glial cells of the central nervous system directly influence neuronal activity by releasing neuroactive small molecules, including glutamate. Long-lasting cocaine-induced reductions in extracellular glutamate in the nucleus accumbens core (NAcore) affect synaptic plasticity responsible for relapse vulnerability. Methods We transduced NAcore astrocytes with an AAV viral vector expressing hM3Dq (Gq) DREADD under control of the glial fibrillary acidic protein (GFAP) promoter in 62 male Sprague Dawley rats, 4 dnSNARE mice and 4 wild type littermates. Using glutamate biosensors we measured NAcore glutamate levels following intracranial or systemic administration of clozapine-N-oxide (CNO), and tested the ability of systemic CNO to inhibit reinstated cocaine or sucrose seeking following self-administration (SA) and extinction training. Results Administration of CNO in GFAP-Gq-DREADD transfected animals increased NAcore extracellular glutamate levels in vivo. The glial origin of released glutamate was validated by an absence of CNO-mediated release in mice expressing a dominant-negative SNARE variant in glia. Also, CNO-mediated release was relatively insensitive to N-type calcium channel blockade. Systemic administration of CNO inhibited cue-induced reinstatement of cocaine seeking in rats extinguished from cocaine, but not sucrose SA. The capacity to inhibit reinstated cocaine-seeking was prevented by systemic administration of the group II metabotropic glutamate receptor (mGluR2/3) antagonist LY341495. Conclusions DREADD-mediated glutamate gliotransmission inhibited cue-induced reinstatement of cocaine seeking by stimulating release-regulating mGluR2/3 autoreceptors to inhibit cue-induced synaptic glutamate spillover. PMID:25861696

Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

PubMed

Bhaduri, Aritra; Banerjee, Amitava; Roy, Subhrajit; Kar, Sougata; Basu, Arindam

2018-03-01

We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in [Formula: see text]m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a [Formula: see text] V and dissipates 19 nW of static power per neuronal cell and [Formula: see text] 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.

Alleviating neuropathic pain mechanical allodynia by increasing Cdh1 in the anterior cingulate cortex.

PubMed

Tan, Wei; Yao, Wen-Long; Hu, Rong; Lv, You-You; Wan, Li; Zhang, Chuan-Han; Zhu, Chang

2015-09-12

Plastic changes in the anterior cingulate cortex (ACC) are critical in the pathogenesis of pain hypersensitivity caused by injury to peripheral nerves. Cdh1, a co-activator subunit of anaphase-promoting complex/cyclosome (APC/C) regulates synaptic differentiation and transmission. Based on this, we hypothesised that the APC/C-Cdh1 played an important role in long-term plastic changes induced by neuropathic pain in ACC. We employed spared nerve injury (SNI) model in rat and found Cdh1 protein level in the ACC was down-regulated 3, 7 and 14 days after SNI surgery. We detected increase in c-Fos expression, numerical increase of organelles, swollen myelinated fibre and axon collapse of neuronal cells in the ACC of SNI rat. Additionally, AMPA receptor GluR1 subunit protein level was up-regulated on the membrane through a pathway that involves EphA4 mediated by APC/C-Cdh1, 3 and 7 days after SNI surgery. To confirm the effect of Cdh1 in neuropathic pain, Cdh1-expressing lentivirus was injected into the ACC of SNI rat. Intra-ACC treatment with Cdh1-expressing lentivirus vectors elevated Cdh1 levels, erased synaptic strengthening, as well as alleviating established mechanical allodynia in SNI rats. We also found Cdh1-expressing lentivirus normalised SNI-induced redistribution of AMPA receptor GluR1 subunit in ACC by regulating AMPA receptor trafficking. These results provide evidence that Cdh1 in ACC synapses may offer a novel therapeutic strategy for treating chronic neuropathic pain.

The influence of synaptic size on AMPA receptor activation: a Monte Carlo model.

PubMed

Montes, Jesus; Peña, Jose M; DeFelipe, Javier; Herreras, Oscar; Merchan-Perez, Angel

2015-01-01

Physiological and electron microscope studies have shown that synapses are functionally and morphologically heterogeneous and that variations in size of synaptic junctions are related to characteristics such as release probability and density of postsynaptic AMPA receptors. The present article focuses on how these morphological variations impact synaptic transmission. We based our study on Monte Carlo computational simulations of simplified model synapses whose morphological features have been extracted from hundreds of actual synaptic junctions reconstructed by three-dimensional electron microscopy. We have examined the effects that parameters such as synaptic size or density of AMPA receptors have on the number of receptors that open after release of a single synaptic vesicle. Our results indicate that the maximum number of receptors that will open after the release of a single synaptic vesicle may show a ten-fold variation in the whole population of synapses. When individual synapses are considered, there is also a stochastical variability that is maximal in small synapses with low numbers of receptors. The number of postsynaptic receptors and the size of the synaptic junction are the most influential parameters, while the packing density of receptors or the concentration of extrasynaptic transporters have little or no influence on the opening of AMPA receptors.

Myopic (HD-PTP, PTPN23) selectively regulates synaptic neuropeptide release.

PubMed

Bulgari, Dinara; Jha, Anupma; Deitcher, David L; Levitan, Edwin S

2018-02-13

Neurotransmission is mediated by synaptic exocytosis of neuropeptide-containing dense-core vesicles (DCVs) and small-molecule transmitter-containing small synaptic vesicles (SSVs). Exocytosis of both vesicle types depends on Ca 2+ and shared secretory proteins. Here, we show that increasing or decreasing expression of Myopic (mop, HD-PTP, PTPN23), a Bro1 domain-containing pseudophosphatase implicated in neuronal development and neuropeptide gene expression, increases synaptic neuropeptide stores at the Drosophila neuromuscular junction (NMJ). This occurs without altering DCV content or transport, but synaptic DCV number and age are increased. The effect on synaptic neuropeptide stores is accounted for by inhibition of activity-induced Ca 2+ -dependent neuropeptide release. cAMP-evoked Ca 2+ -independent synaptic neuropeptide release also requires optimal Myopic expression, showing that Myopic affects the DCV secretory machinery shared by cAMP and Ca 2+ pathways. Presynaptic Myopic is abundant at early endosomes, but interaction with the endosomal sorting complex required for transport III (ESCRT III) protein (CHMP4/Shrub) that mediates Myopic's effect on neuron pruning is not required for control of neuropeptide release. Remarkably, in contrast to the effect on DCVs, Myopic does not affect release from SSVs. Therefore, Myopic selectively regulates synaptic DCV exocytosis that mediates peptidergic transmission at the NMJ.

Estimating synaptic parameters from mean, variance, and covariance in trains of synaptic responses.

PubMed

Scheuss, V; Neher, E

2001-10-01

Fluctuation analysis of synaptic transmission using the variance-mean approach has been restricted in the past to steady-state responses. Here we extend this method to short repetitive trains of synaptic responses, during which the response amplitudes are not stationary. We consider intervals between trains, long enough so that the system is in the same average state at the beginning of each train. This allows analysis of ensemble means and variances for each response in a train separately. Thus, modifications in synaptic efficacy during short-term plasticity can be attributed to changes in synaptic parameters. In addition, we provide practical guidelines for the analysis of the covariance between successive responses in trains. Explicit algorithms to estimate synaptic parameters are derived and tested by Monte Carlo simulations on the basis of a binomial model of synaptic transmission, allowing for quantal variability, heterogeneity in the release probability, and postsynaptic receptor saturation and desensitization. We find that the combined analysis of variance and covariance is advantageous in yielding an estimate for the number of release sites, which is independent of heterogeneity in the release probability under certain conditions. Furthermore, it allows one to calculate the apparent quantal size for each response in a sequence of stimuli.

Synaptic heterogeneity and stimulus-induced modulation of depression in central synapses.

PubMed

Hunter, J D; Milton, J G

2001-08-01

Short-term plasticity is a pervasive feature of synapses. Synapses exhibit many forms of plasticity operating over a range of time scales. We develop an optimization method that allows rapid characterization of synapses with multiple time scales of facilitation and depression. Investigation of paired neurons that are postsynaptic to the same identified interneuron in the buccal ganglion of Aplysia reveals that the responses of the two neurons differ in the magnitude of synaptic depression. Also, for single neurons, prolonged stimulation of the presynaptic neuron causes stimulus-induced increases in the early phase of synaptic depression. These observations can be described by a model that incorporates two availability factors, e.g., depletable vesicle pools or desensitizing receptor populations, with different time courses of recovery, and a single facilitation component. This model accurately predicts the responses to novel stimuli. The source of synaptic heterogeneity is identified with variations in the relative sizes of the two availability factors, and the stimulus-induced decrement in the early synaptic response is explained by a slowing of the recovery rate of one of the availability factors. The synaptic heterogeneity and stimulus-induced modifications in synaptic depression observed here emphasize that synaptic efficacy depends on both the individual properties of synapses and their past history.

Differential Roles of Postsynaptic Density-93 Isoforms in Regulating Synaptic Transmission

PubMed Central

Krüger, Juliane M.; Favaro, Plinio D.; Liu, Mingna; Kitlińska, Agata; Huang, Xiaojie; Raabe, Monika; Akad, Derya S.; Liu, Yanling; Urlaub, Henning; Dong, Yan; Xu, Weifeng

2013-01-01

In the postsynaptic density of glutamatergic synapses, the discs large (DLG)-membrane-associated guanylate kinase (MAGUK) family of scaffolding proteins coordinates a multiplicity of signaling pathways to maintain and regulate synaptic transmission. Postsynaptic density-93 (PSD-93) is the most variable paralog in this family; it exists in six different N-terminal isoforms. Probably because of the structural and functional variability of these isoforms, the synaptic role of PSD-93 remains controversial. To accurately characterize the synaptic role of PSD-93, we quantified the expression of all six isoforms in the mouse hippocampus and examined them individually in hippocampal synapses. Using molecular manipulations, including overexpression, gene knockdown, PSD-93 knock-out mice combined with biochemical assays, and slice electrophysiology both in rat and mice, we demonstrate that PSD-93 is required at different developmental synaptic states to maintain the strength of excitatory synaptic transmission. This strength is differentially regulated by the six isoforms of PSD-93, including regulations of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor-active and inactive synapses, and activity-dependent modulations. Collectively, these results demonstrate that alternative combinations of N-terminal PSD-93 isoforms and DLG-MAGUK paralogs can fine-tune signaling scaffolds to adjust synaptic needs to regulate synaptic transmission. PMID:24068818