Long-term depression of inhibitory synaptic transmission induced by spike-timing dependent plasticity requires coactivation of endocannabinoid and muscarinic receptors.

PubMed

Ahumada, Juan; Fernández de Sevilla, David; Couve, Alejandro; Buño, Washington; Fuenzalida, Marco

2013-12-01

The precise timing of pre-postsynaptic activity is vital for the induction of long-term potentiation (LTP) or depression (LTD) at many central synapses. We show in synapses of rat CA1 pyramidal neurons in vitro that spike timing dependent plasticity (STDP) protocols that induce LTP at glutamatergic synapses can evoke LTD of inhibitory postsynaptic currents or STDP-iLTD. The STDP-iLTD requires a postsynaptic Ca(2+) increase, a release of endocannabinoids (eCBs), the activation of type-1 endocananabinoid receptors and presynaptic muscarinic receptors that mediate a decreased probability of GABA release. In contrast, the STDP-iLTD is independent of the activation of nicotinic receptors, GABAB Rs and G protein-coupled postsynaptic receptors at pyramidal neurons. We determine that the downregulation of presynaptic Cyclic adenosine monophosphate/protein Kinase A pathways is essential for the induction of STDP-iLTD. These results suggest a novel mechanism by which the activation of cholinergic neurons and retrograde signaling by eCBs can modulate the efficacy of GABAergic synaptic transmission in ways that may contribute to information processing and storage in the hippocampus. Copyright © 2013 Wiley Periodicals, Inc.

Spike-timing-dependent plasticity in the human dorso-lateral prefrontal cortex.

PubMed

Casula, Elias Paolo; Pellicciari, Maria Concetta; Picazio, Silvia; Caltagirone, Carlo; Koch, Giacomo

2016-12-01

Changes in the synaptic strength of neural connections are induced by repeated coupling of activity of interconnected neurons with precise timing, a phenomenon known as spike-timing-dependent plasticity (STDP). It is debated if this mechanism exists in large-scale cortical networks in humans. We combined transcranial magnetic stimulation (TMS) with concurrent electroencephalography (EEG) to directly investigate the effects of two paired associative stimulation (PAS) protocols (fronto-parietal and parieto-frontal) of pre and post-synaptic inputs within the human fronto-parietal network. We found evidence that the dorsolateral prefrontal cortex (DLPFC) has the potential to form robust STDP. Long-term potentiation/depression of TMS-evoked cortical activity is prompted after that DLPFC stimulation is followed/preceded by posterior parietal stimulation. Such bidirectional changes are paralleled by sustained increase/decrease of high-frequency oscillatory activity, likely reflecting STDP responsivity. The current findings could be important to drive plasticity of damaged cortical circuits in patients with cognitive or psychiatric disorders. Copyright © 2016 Elsevier Inc. All rights reserved.

Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level.

PubMed

Bono, Jacopo; Clopath, Claudia

2017-09-26

Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the local non-linear processing of synaptic inputs allowed for by dendrites. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. We also explore how the connectivity between two cells is affected by these plasticity rules and by different synaptic distributions. Finally, we show that how memory retention during associative learning can be prolonged in networks of neurons by including dendrites.Synaptic plasticity is the neuronal mechanism underlying learning. Here the authors construct biophysical models of pyramidal neurons that reproduce observed plasticity gradients along the dendrite and show that dendritic spike dependent LTP which is predominant in distal sections can prolong memory retention.

Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony?

PubMed Central

Knoblauch, Andreas; Hauser, Florian; Gewaltig, Marc-Oliver; Körner, Edgar; Palm, Günther

2012-01-01

Spike synchronization is thought to have a constructive role for feature integration, attention, associative learning, and the formation of bidirectionally connected Hebbian cell assemblies. By contrast, theoretical studies on spike-timing-dependent plasticity (STDP) report an inherently decoupling influence of spike synchronization on synaptic connections of coactivated neurons. For example, bidirectional synaptic connections as found in cortical areas could be reproduced only by assuming realistic models of STDP and rate coding. We resolve this conflict by theoretical analysis and simulation of various simple and realistic STDP models that provide a more complete characterization of conditions when STDP leads to either coupling or decoupling of neurons firing in synchrony. In particular, we show that STDP consistently couples synchronized neurons if key model parameters are matched to physiological data: First, synaptic potentiation must be significantly stronger than synaptic depression for small (positive or negative) time lags between presynaptic and postsynaptic spikes. Second, spike synchronization must be sufficiently imprecise, for example, within a time window of 5–10 ms instead of 1 ms. Third, axonal propagation delays should not be much larger than dendritic delays. Under these assumptions synchronized neurons will be strongly coupled leading to a dominance of bidirectional synaptic connections even for simple STDP models and low mean firing rates at the level of spontaneous activity. PMID:22936909

Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

PubMed

Gong, Yubing; Wang, Baoying; Xie, Huijuan

2016-12-01

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate A p of STDP and become strongest at a certain A p value, and the A p value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when A p increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder.

PubMed

Antunes, Gabriela; Faria da Silva, Samuel F; Simoes de Souza, Fabio M

2018-06-01

Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.

Artificial neuron operations and spike-timing-dependent plasticity using memristive devices for brain-inspired computing

NASA Astrophysics Data System (ADS)

Marukame, Takao; Nishi, Yoshifumi; Yasuda, Shin-ichi; Tanamoto, Tetsufumi

2018-04-01

The use of memristive devices for creating artificial neurons is promising for brain-inspired computing from the viewpoints of computation architecture and learning protocol. We present an energy-efficient multiplier accumulator based on a memristive array architecture incorporating both analog and digital circuitries. The analog circuitry is used to full advantage for neural networks, as demonstrated by the spike-timing-dependent plasticity (STDP) in fabricated AlO x /TiO x -based metal-oxide memristive devices. STDP protocols for controlling periodic analog resistance with long-range stability were experimentally verified using a variety of voltage amplitudes and spike timings.

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

Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity

PubMed Central

Kleberg, Florence I.; Fukai, Tomoki; Gilson, Matthieu

2014-01-01

Spike-timing-dependent plasticity (STDP) has been well established between excitatory neurons and several computational functions have been proposed in various neural systems. Despite some recent efforts, however, there is a significant lack of functional understanding of inhibitory STDP (iSTDP) and its interplay with excitatory STDP (eSTDP). Here, we demonstrate by analytical and numerical methods that iSTDP contributes crucially to the balance of excitatory and inhibitory weights for the selection of a specific signaling pathway among other pathways in a feedforward circuit. This pathway selection is based on the high sensitivity of STDP to correlations in spike times, which complements a recent proposal for the role of iSTDP in firing-rate based selection. Our model predicts that asymmetric anti-Hebbian iSTDP exceeds asymmetric Hebbian iSTDP for supporting pathway-specific balance, which we show is useful for propagating transient neuronal responses. Furthermore, we demonstrate how STDPs at excitatory–excitatory, excitatory–inhibitory, and inhibitory–excitatory synapses cooperate to improve the pathway selection. We propose that iSTDP is crucial for shaping the network structure that achieves efficient processing of synchronous spikes. PMID:24847242

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

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

Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing

NASA Astrophysics Data System (ADS)

Lu, Ke; Li, Yi; He, Wei-Fan; Chen, Jia; Zhou, Ya-Xiong; Duan, Nian; Jin, Miao-Miao; Gu, Wei; Xue, Kan-Hao; Sun, Hua-Jun; Miao, Xiang-Shui

2018-06-01

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfO x /Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

NASA Astrophysics Data System (ADS)

Xie, Huijuan; Gong, Yubing; Wang, Baoying

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin-Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

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.

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

PubMed Central

Maass, Wolfgang

2008-01-01

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics. PMID:18846203

Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity.

PubMed

Hunzinger, Jason F; Chan, Victor H; Froemke, Robert C

2012-07-01

Studies of spike timing-dependent plasticity (STDP) have revealed that long-term changes in the strength of a synapse may be modulated substantially by temporal relationships between multiple presynaptic and postsynaptic spikes. Whereas long-term potentiation (LTP) and long-term depression (LTD) of synaptic strength have been modeled as distinct or separate functional mechanisms, here, we propose a new shared resource model. A functional consequence of our model is fast, stable, and diverse unsupervised learning of temporal multispike patterns with a biologically consistent spiking neural network. Due to interdependencies between LTP and LTD, dendritic delays, and proactive homeostatic aspects of the model, neurons are equipped to learn to decode temporally coded information within spike bursts. Moreover, neurons learn spike timing with few exposures in substantial noise and jitter. Surprisingly, despite having only one parameter, the model also accurately predicts in vitro observations of STDP in more complex multispike trains, as well as rate-dependent effects. We discuss candidate commonalities in natural long-term plasticity mechanisms.

Role of AMPA and NMDA receptors and back-propagating action potentials in spike timing-dependent plasticity.

PubMed

Fuenzalida, Marco; Fernández de Sevilla, David; Couve, Alejandro; Buño, Washington

2010-01-01

The cellular mechanisms that mediate spike timing-dependent plasticity (STDP) are largely unknown. We studied in vitro in CA1 pyramidal neurons the contribution of AMPA and N-methyl-d-aspartate (NMDA) components of Schaffer collateral (SC) excitatory postsynaptic potentials (EPSPs; EPSP(AMPA) and EPSP(NMDA)) and of the back-propagating action potential (BAP) to the long-term potentiation (LTP) induced by a STDP protocol that consisted in pairing an EPSP and a BAP. Transient blockade of EPSP(AMPA) with 7-nitro-2,3-dioxo-1,4-dihydroquinoxaline-6-carbonitrile (CNQX) during the STDP protocol prevented LTP. Contrastingly LTP was induced under transient inhibition of EPSP(AMPA) by combining SC stimulation, an imposed EPSP(AMPA)-like depolarization, and BAP or by coupling the EPSP(NMDA) evoked under sustained depolarization (approximately -40 mV) and BAP. In Mg(2+)-free solution EPSP(NMDA) and BAP also produced LTP. Suppression of EPSP(NMDA) or BAP always prevented LTP. Thus activation of NMDA receptors and BAPs are needed but not sufficient because AMPA receptor activation is also obligatory for STDP. However, a transient depolarization of another origin that unblocks NMDA receptors and a BAP may also trigger LTP.

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.

Channel noise-induced temporal coherence transitions and synchronization transitions in adaptive neuronal networks with time delay

NASA Astrophysics Data System (ADS)

Gong, Yubing; Xie, Huijuan

2017-09-01

Using spike-timing-dependent plasticity (STDP), we study the effect of channel noise on temporal coherence and synchronization of adaptive scale-free Hodgkin-Huxley neuronal networks with time delay. It is found that the spiking regularity and spatial synchronization of the neurons intermittently increase and decrease as channel noise intensity is varied, exhibiting transitions of temporal coherence and synchronization. Moreover, this phenomenon depends on time delay, STDP, and network average degree. As time delay increases, the phenomenon is weakened, however, there are optimal STDP and network average degree by which the phenomenon becomes strongest. These results show that channel noise can intermittently enhance the temporal coherence and synchronization of the delayed adaptive neuronal networks. These findings provide a new insight into channel noise for the information processing and transmission in neural systems.

Acetylcholine-modulated plasticity in reward-driven navigation: a computational study.

PubMed

Zannone, Sara; Brzosko, Zuzanna; Paulsen, Ole; Clopath, Claudia

2018-06-21

Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike-Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules.

A Re-Examination of Hebbian-Covariance Rules and Spike Timing-Dependent Plasticity in Cat Visual Cortex in vivo

PubMed Central

Frégnac, Yves; Pananceau, Marc; René, Alice; Huguet, Nazyed; Marre, Olivier; Levy, Manuel; Shulz, Daniel E.

2010-01-01

Spike timing-dependent plasticity (STDP) is considered as an ubiquitous rule for associative plasticity in cortical networks in vitro. However, limited supporting evidence for its functional role has been provided in vivo. In particular, there are very few studies demonstrating the co-occurrence of synaptic efficiency changes and alteration of sensory responses in adult cortex during Hebbian or STDP protocols. We addressed this issue by reviewing and comparing the functional effects of two types of cellular conditioning in cat visual cortex. The first one, referred to as the “covariance” protocol, obeys a generalized Hebbian framework, by imposing, for different stimuli, supervised positive and negative changes in covariance between postsynaptic and presynaptic activity rates. The second protocol, based on intracellular recordings, replicated in vivo variants of the theta-burst paradigm (TBS), proven successful in inducing long-term potentiation in vitro. Since it was shown to impose a precise correlation delay between the electrically activated thalamic input and the TBS-induced postsynaptic spike, this protocol can be seen as a probe of causal (“pre-before-post”) STDP. By choosing a thalamic region where the visual field representation was in retinotopic overlap with the intracellularly recorded cortical receptive field as the afferent site for supervised electrical stimulation, this protocol allowed to look for possible correlates between STDP and functional reorganization of the conditioned cortical receptive field. The rate-based “covariance protocol” induced significant and large amplitude changes in receptive field properties, in both kitten and adult V1 cortex. The TBS STDP-like protocol produced in the adult significant changes in the synaptic gain of the electrically activated thalamic pathway, but the statistical significance of the functional correlates was detectable mostly at the population level. Comparison of our observations with the literature leads us to re-examine the experimental status of spike timing-dependent potentiation in adult cortex. We propose the existence of a correlation-based threshold in vivo, limiting the expression of STDP-induced changes outside the critical period, and which accounts for the stability of synaptic weights during sensory cortical processing in the absence of attention or reward-gated supervision. PMID:21423533

Optimal Design for Hetero-Associative Memory: Hippocampal CA1 Phase Response Curve and Spike-Timing-Dependent Plasticity

PubMed Central

Miyata, Ryota; Ota, Keisuke; Aonishi, Toru

2013-01-01

Recently reported experimental findings suggest that the hippocampal CA1 network stores spatio-temporal spike patterns and retrieves temporally reversed and spread-out patterns. In this paper, we explore the idea that the properties of the neural interactions and the synaptic plasticity rule in the CA1 network enable it to function as a hetero-associative memory recalling such reversed and spread-out spike patterns. In line with Lengyel’s speculation (Lengyel et al., 2005), we firstly derive optimally designed spike-timing-dependent plasticity (STDP) rules that are matched to neural interactions formalized in terms of phase response curves (PRCs) for performing the hetero-associative memory function. By maximizing object functions formulated in terms of mutual information for evaluating memory retrieval performance, we search for STDP window functions that are optimal for retrieval of normal and doubly spread-out patterns under the constraint that the PRCs are those of CA1 pyramidal neurons. The system, which can retrieve normal and doubly spread-out patterns, can also retrieve reversed patterns with the same quality. Finally, we demonstrate that purposely designed STDP window functions qualitatively conform to typical ones found in CA1 pyramidal neurons. PMID:24204822

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.

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

Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum.

PubMed

Olde Scheper, Tjeerd V; Meredith, Rhiannon M; Mansvelder, Huibert D; van Pelt, Jaap; van Ooyen, Arjen

2017-01-01

Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure.

Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

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

Yu, Haitao; Guo, Xinmeng; Wang, Jiang, E-mail: jiangwang@tju.edu.cn

2014-09-01

The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient formore » the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.« less

Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices

PubMed Central

Zarudnyi, Konstantin; Mehonic, Adnan; Montesi, Luca; Buckwell, Mark; Hudziak, Stephen; Kenyon, Anthony J.

2018-01-01

Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiOx) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks. PMID:29472837

RM-SORN: a reward-modulated self-organizing recurrent neural network.

PubMed

Aswolinskiy, Witali; Pipa, Gordon

2015-01-01

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

Delay selection by spike-timing-dependent plasticity in recurrent networks of spiking neurons receiving oscillatory inputs.

PubMed

Kerr, Robert R; Burkitt, Anthony N; Thomas, Doreen A; Gilson, Matthieu; Grayden, David B

2013-01-01

Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem.

Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs

PubMed Central

Kerr, Robert R.; Burkitt, Anthony N.; Thomas, Doreen A.; Gilson, Matthieu; Grayden, David B.

2013-01-01

Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem. PMID:23408878

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.

STDP allows fast rate-modulated coding with Poisson-like spike trains.

PubMed

Gilson, Matthieu; Masquelier, Timothée; Hugues, Etienne

2011-10-01

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (~10-20 ms) for sufficiently many inputs (~100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks.

STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains

PubMed Central

Hugues, Etienne

2011-01-01

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (∼10–20 ms) for sufficiently many inputs (∼100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks. PMID:22046113

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

NASA Astrophysics Data System (ADS)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System

PubMed Central

Mikaitis, Mantas; Pineda García, Garibaldi; Knight, James C.; Furber, Steve B.

2018-01-01

SpiNNaker is a digital neuromorphic architecture, designed specifically for the low power simulation of large-scale spiking neural networks at speeds close to biological real-time. Unlike other neuromorphic systems, SpiNNaker allows users to develop their own neuron and synapse models as well as specify arbitrary connectivity. As a result SpiNNaker has proved to be a powerful tool for studying different neuron models as well as synaptic plasticity—believed to be one of the main mechanisms behind learning and memory in the brain. A number of Spike-Timing-Dependent-Plasticity(STDP) rules have already been implemented on SpiNNaker and have been shown to be capable of solving various learning tasks in real-time. However, while STDP is an important biological theory of learning, it is a form of Hebbian or unsupervised learning and therefore does not explain behaviors that depend on feedback from the environment. Instead, learning rules based on neuromodulated STDP (three-factor learning rules) have been shown to be capable of solving reinforcement learning tasks in a biologically plausible manner. In this paper we demonstrate for the first time how a model of three-factor STDP, with the third-factor representing spikes from dopaminergic neurons, can be implemented on the SpiNNaker neuromorphic system. Using this learning rule we first show how reward and punishment signals can be delivered to a single synapse before going on to demonstrate it in a larger network which solves the credit assignment problem in a Pavlovian conditioning experiment. Because of its extra complexity, we find that our three-factor learning rule requires approximately 2× as much processing time as the existing SpiNNaker STDP learning rules. However, we show that it is still possible to run our Pavlovian conditioning model with up to 1 × 104 neurons in real-time, opening up new research opportunities for modeling behavioral learning on SpiNNaker. PMID:29535600

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

PubMed

Lajoie, Guillaume; Krouchev, Nedialko I; Kalaska, John F; Fairhall, Adrienne L; Fetz, Eberhard E

2017-02-01

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface

PubMed Central

Lajoie, Guillaume; Kalaska, John F.; Fairhall, Adrienne L.; Fetz, Eberhard E.

2017-01-01

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity. PMID:28151957

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

NASA Astrophysics Data System (ADS)

Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

2016-06-01

Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

Interplay between Short- and Long-Term Plasticity in Cell-Assembly Formation

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2014-01-01

Various hippocampal and neocortical synapses of mammalian brain show both short-term plasticity and long-term plasticity, which are considered to underlie learning and memory by the brain. According to Hebb’s postulate, synaptic plasticity encodes memory traces of past experiences into cell assemblies in cortical circuits. However, it remains unclear how the various forms of long-term and short-term synaptic plasticity cooperatively create and reorganize such cell assemblies. Here, we investigate the mechanism in which the three forms of synaptic plasticity known in cortical circuits, i.e., spike-timing-dependent plasticity (STDP), short-term depression (STD) and homeostatic plasticity, cooperatively generate, retain and reorganize cell assemblies in a recurrent neuronal network model. We show that multiple cell assemblies generated by external stimuli can survive noisy spontaneous network activity for an adequate range of the strength of STD. Furthermore, our model predicts that a symmetric temporal window of STDP, such as observed in dopaminergic modulations on hippocampal neurons, is crucial for the retention and integration of multiple cell assemblies. These results may have implications for the understanding of cortical memory processes. PMID:25007209

Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity

PubMed Central

Waddington, Amelia; Appleby, Peter A.; De Kamps, Marc; Cohen, Netta

2012-01-01

Synfire chains have long been proposed to generate precisely timed sequences of neural activity. Such activity has been linked to numerous neural functions including sensory encoding, cognitive and motor responses. In particular, it has been argued that synfire chains underlie the precise spatiotemporal firing patterns that control song production in a variety of songbirds. Previous studies have suggested that the development of synfire chains requires either initial sparse connectivity or strong topological constraints, in addition to any synaptic learning rules. Here, we show that this necessity can be removed by using a previously reported but hitherto unconsidered spike-timing-dependent plasticity (STDP) rule and activity-dependent excitability. Under this rule the network develops stable synfire chains that possess a non-trivial, scalable multi-layer structure, in which relative layer sizes appear to follow a universal function. Using computational modeling and a coarse grained random walk model, we demonstrate the role of the STDP rule in growing, molding and stabilizing the chain, and link model parameters to the resulting structure. PMID:23162457

Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model

PubMed Central

Luque, Niceto R.; Garrido, Jesús A.; Naveros, Francisco; Carrillo, Richard R.; D'Angelo, Egidio; Ros, Eduardo

2016-01-01

Deep cerebellar nuclei neurons receive both inhibitory (GABAergic) synaptic currents from Purkinje cells (within the cerebellar cortex) and excitatory (glutamatergic) synaptic currents from mossy fibers. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP) located at different cerebellar sites (parallel fibers to Purkinje cells, mossy fibers to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells) in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibers to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP) and inhibitory (i-STDP) mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibers to Purkinje cells synapses and then transferred to mossy fibers to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation toward optimizing its working range). PMID:26973504

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.

A framework for plasticity implementation on the SpiNNaker neural architecture.

PubMed

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

2014-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

A framework for plasticity implementation on the SpiNNaker neural architecture

PubMed Central

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A.; Furber, Steve B.; Benosman, Ryad B.

2015-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system. PMID:25653580

Learning through ferroelectric domain dynamics in solid-state synapses

NASA Astrophysics Data System (ADS)

Boyn, Sören; Grollier, Julie; Lecerf, Gwendal; Xu, Bin; Locatelli, Nicolas; Fusil, Stéphane; Girod, Stéphanie; Carrétéro, Cécile; Garcia, Karin; Xavier, Stéphane; Tomas, Jean; Bellaiche, Laurent; Bibes, Manuel; Barthélémy, Agnès; Saïghi, Sylvain; Garcia, Vincent

2017-04-01

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2015-01-01

The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP) is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory. PMID:25910189

A forecast-based STDP rule suitable for neuromorphic implementation.

PubMed

Davies, S; Galluppi, F; Rast, A D; Furber, S B

2012-08-01

Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning rules. The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes. Approaches that relate learning features to the membrane potential of the post-synaptic neuron have emerged as possible alternatives to the more common STDP rule, with various implementations and approximations. Here we use a new type of neuromorphic hardware, SpiNNaker, which represents the flexible "neuromimetic" architecture, to demonstrate a new approach to this problem. Based on the standard STDP algorithm with modifications and approximations, a new rule, called STDP TTS (Time-To-Spike) relates the membrane potential with the Long Term Potentiation (LTP) part of the basic STDP rule. Meanwhile, we use the standard STDP rule for the Long Term Depression (LTD) part of the algorithm. We show that on the basis of the membrane potential it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike. In our system these approximations allow efficient memory access, reducing the overall computational time and the memory bandwidth required. The improvements here presented are significant for real-time applications such as the ones for which the SpiNNaker system has been designed. We present simulation results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. On-chip results show that the STDP TTS algorithm allows the neural network to adapt and detect the incoming pattern with improvements both in the reliability of, and the time required for, consistent output. Through the approximations we suggest in this paper, we introduce a learning rule that is easy to implement both in event-driven simulators and in dedicated hardware, reducing computational complexity relative to the standard STDP rule. Such a rule offers a promising solution, complementary to standard STDP evaluation algorithms, for real-time learning using spiking neural networks in time-critical applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks

PubMed Central

Kato, Hideyuki; Ikeguchi, Tohru

2016-01-01

Specific memory might be stored in a subnetwork consisting of a small population of neurons. To select neurons involved in memory formation, neural competition might be essential. In this paper, we show that excitable neurons are competitive and organize into two assemblies in a recurrent network with spike timing-dependent synaptic plasticity (STDP) and axonal conduction delays. Neural competition is established by the cooperation of spontaneously induced neural oscillation, axonal conduction delays, and STDP. We also suggest that the competition mechanism in this paper is one of the basic functions required to organize memory-storing subnetworks into fine-scale cortical networks. PMID:26840529

Learning through ferroelectric domain dynamics in solid-state synapses

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

Boyn, Soren; Grollier, Julie; Lecerf, Gwendal

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport andmore » atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Finally, based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.« less

Learning through ferroelectric domain dynamics in solid-state synapses

DOE PAGES

Boyn, Soren; Grollier, Julie; Lecerf, Gwendal; ...

2017-04-03

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport andmore » atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Finally, based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.« less

Long-Term Memory Stabilized by Noise-Induced Rehearsal

PubMed Central

Wei, Yi

2014-01-01

Cortical networks can maintain memories for decades despite the short lifetime of synaptic strengths. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of ongoing spike-timing-dependent plasticity (STDP) on the stability of memory patterns stored in synapses of an attractor neural network. We show that certain classes of STDP rules can stabilize all stored memory patterns despite a short lifetime of synapses. In our model, unstructured neural noise, after passing through the recurrent network connections, carries the imprint of all memory patterns in temporal correlations. STDP, combined with these correlations, leads to reinforcement of all stored patterns, even those that are never explicitly visited. Our findings may provide the functional reason for irregular spiking displayed by cortical neurons and justify models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections. PMID:25411507

Different propagation speeds of recalled sequences in plastic spiking neural networks

NASA Astrophysics Data System (ADS)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.

Bimodal stimulus timing-dependent plasticity in primary auditory cortex is altered after noise exposure with and without tinnitus

PubMed Central

Koehler, Seth D.; Shore, Susan E.

2015-01-01

Central auditory circuits are influenced by the somatosensory system, a relationship that may underlie tinnitus generation. In the guinea pig dorsal cochlear nucleus (DCN), pairing spinal trigeminal nucleus (Sp5) stimulation with tones at specific intervals and orders facilitated or suppressed subsequent tone-evoked neural responses, reflecting spike timing-dependent plasticity (STDP). Furthermore, after noise-induced tinnitus, bimodal responses in DCN were shifted from Hebbian to anti-Hebbian timing rules with less discrete temporal windows, suggesting a role for bimodal plasticity in tinnitus. Here, we aimed to determine if multisensory STDP principles like those in DCN also exist in primary auditory cortex (A1), and whether they change following noise-induced tinnitus. Tone-evoked and spontaneous neural responses were recorded before and 15 min after bimodal stimulation in which the intervals and orders of auditory-somatosensory stimuli were randomized. Tone-evoked and spontaneous firing rates were influenced by the interval and order of the bimodal stimuli, and in sham-controls Hebbian-like timing rules predominated as was seen in DCN. In noise-exposed animals with and without tinnitus, timing rules shifted away from those found in sham-controls to more anti-Hebbian rules. Only those animals with evidence of tinnitus showed increased spontaneous firing rates, a purported neurophysiological correlate of tinnitus in A1. Together, these findings suggest that bimodal plasticity is also evident in A1 following noise damage and may have implications for tinnitus generation and therapeutic intervention across the central auditory circuit. PMID:26289461

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

Enabling an Integrated Rate-temporal Learning Scheme on Memristor

NASA Astrophysics Data System (ADS)

He, Wei; Huang, Kejie; Ning, Ning; Ramanathan, Kiruthika; Li, Guoqi; Jiang, Yu; Sze, Jiayin; Shi, Luping; Zhao, Rong; Pei, Jing

2014-04-01

Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems.

On the applicability of STDP-based learning mechanisms to spiking neuron network models

NASA Astrophysics Data System (ADS)

Sboev, A.; Vlasov, D.; Serenko, A.; Rybka, R.; Moloshnikov, I.

2016-11-01

The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

Long-term memory stabilized by noise-induced rehearsal.

PubMed

Wei, Yi; Koulakov, Alexei A

2014-11-19

Cortical networks can maintain memories for decades despite the short lifetime of synaptic strengths. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of ongoing spike-timing-dependent plasticity (STDP) on the stability of memory patterns stored in synapses of an attractor neural network. We show that certain classes of STDP rules can stabilize all stored memory patterns despite a short lifetime of synapses. In our model, unstructured neural noise, after passing through the recurrent network connections, carries the imprint of all memory patterns in temporal correlations. STDP, combined with these correlations, leads to reinforcement of all stored patterns, even those that are never explicitly visited. Our findings may provide the functional reason for irregular spiking displayed by cortical neurons and justify models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections. Copyright © 2014 the authors 0270-6474/14/3415804-12$15.00/0.

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

PubMed

Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

2017-08-01

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Precise Synaptic Efficacy Alignment Suggests Potentiation Dominated Learning.

PubMed

Hartmann, Christoph; Miner, Daniel C; Triesch, Jochen

2015-01-01

Recent evidence suggests that parallel synapses from the same axonal branch onto the same dendritic branch have almost identical strength. It has been proposed that this alignment is only possible through learning rules that integrate activity over long time spans. However, learning mechanisms such as spike-timing-dependent plasticity (STDP) are commonly assumed to be temporally local. Here, we propose that the combination of temporally local STDP and a multiplicative synaptic normalization mechanism is sufficient to explain the alignment of parallel synapses. To address this issue, we introduce three increasingly complex models: First, we model the idealized interaction of STDP and synaptic normalization in a single neuron as a simple stochastic process and derive analytically that the alignment effect can be described by a so-called Kesten process. From this we can derive that synaptic efficacy alignment requires potentiation-dominated learning regimes. We verify these conditions in a single-neuron model with independent spiking activities but more realistic synapses. As expected, we only observe synaptic efficacy alignment for long-term potentiation-biased STDP. Finally, we explore how well the findings transfer to recurrent neural networks where the learning mechanisms interact with the correlated activity of the network. We find that due to the self-reinforcing correlations in recurrent circuits under STDP, alignment occurs for both long-term potentiation- and depression-biased STDP, because the learning will be potentiation dominated in both cases due to the potentiating events induced by correlated activity. This is in line with recent results demonstrating a dominance of potentiation over depression during waking and normalization during sleep. This leads us to predict that individual spine pairs will be more similar after sleep compared to after sleep deprivation. In conclusion, we show that synaptic normalization in conjunction with coordinated potentiation--in this case, from STDP in the presence of correlated pre- and post-synaptic activity--naturally leads to an alignment of parallel synapses.

Limits to high-speed simulations of spiking neural networks using general-purpose computers.

PubMed

Zenke, Friedemann; Gerstner, Wulfram

2014-01-01

To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

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

A real-time spiking cerebellum model for learning robot control.

PubMed

Carrillo, Richard R; Ros, Eduardo; Boucheny, Christian; Coenen, Olivier J-M D

2008-01-01

We describe a neural network model of the cerebellum based on integrate-and-fire spiking neurons with conductance-based synapses. The neuron characteristics are derived from our earlier detailed models of the different cerebellar neurons. We tested the cerebellum model in a real-time control application with a robotic platform. Delays were introduced in the different sensorimotor pathways according to the biological system. The main plasticity in the cerebellar model is a spike-timing dependent plasticity (STDP) at the parallel fiber to Purkinje cell connections. This STDP is driven by the inferior olive (IO) activity, which encodes an error signal using a novel probabilistic low frequency model. We demonstrate the cerebellar model in a robot control system using a target-reaching task. We test whether the system learns to reach different target positions in a non-destructive way, therefore abstracting a general dynamics model. To test the system's ability to self-adapt to different dynamical situations, we present results obtained after changing the dynamics of the robotic platform significantly (its friction and load). The experimental results show that the cerebellar-based system is able to adapt dynamically to different contexts.

Emergent gamma synchrony in all-to-all interneuronal networks.

PubMed

Ratnadurai-Giridharan, Shivakeshavan; Khargonekar, Pramod P; Talathi, Sachin S

2015-01-01

We investigate the emergence of in-phase synchronization in a heterogeneous network of coupled inhibitory interneurons in the presence of spike timing dependent plasticity (STDP). Using a simple network of two mutually coupled interneurons (2-MCI), we first study the effects of STDP on in-phase synchronization. We demonstrate that, with STDP, the 2-MCI network can evolve to either a state of stable 1:1 in-phase synchronization or exhibit multiple regimes of higher order synchronization states. We show that the emergence of synchronization induces a structural asymmetry in the 2-MCI network such that the synapses onto the high frequency firing neurons are potentiated, while those onto the low frequency firing neurons are de-potentiated, resulting in the directed flow of information from low frequency firing neurons to high frequency firing neurons. Finally, we demonstrate that the principal findings from our analysis of the 2-MCI network contribute to the emergence of robust synchronization in the Wang-Buzsaki network (Wang and Buzsáki, 1996) of all-to-all coupled inhibitory interneurons (100-MCI) for a significantly larger range of heterogeneity in the intrinsic firing rate of the neurons in the network. We conclude that STDP of inhibitory synapses provide a viable mechanism for robust neural synchronization.

Emergent gamma synchrony in all-to-all interneuronal networks

PubMed Central

Ratnadurai-Giridharan, Shivakeshavan; Khargonekar, Pramod P.; Talathi, Sachin S.

2015-01-01

We investigate the emergence of in-phase synchronization in a heterogeneous network of coupled inhibitory interneurons in the presence of spike timing dependent plasticity (STDP). Using a simple network of two mutually coupled interneurons (2-MCI), we first study the effects of STDP on in-phase synchronization. We demonstrate that, with STDP, the 2-MCI network can evolve to either a state of stable 1:1 in-phase synchronization or exhibit multiple regimes of higher order synchronization states. We show that the emergence of synchronization induces a structural asymmetry in the 2-MCI network such that the synapses onto the high frequency firing neurons are potentiated, while those onto the low frequency firing neurons are de-potentiated, resulting in the directed flow of information from low frequency firing neurons to high frequency firing neurons. Finally, we demonstrate that the principal findings from our analysis of the 2-MCI network contribute to the emergence of robust synchronization in the Wang-Buzsaki network (Wang and Buzsáki, 1996) of all-to-all coupled inhibitory interneurons (100-MCI) for a significantly larger range of heterogeneity in the intrinsic firing rate of the neurons in the network. We conclude that STDP of inhibitory synapses provide a viable mechanism for robust neural synchronization. PMID:26528174

Synaptic dynamics regulation in response to high frequency stimulation in neuronal networks

NASA Astrophysics Data System (ADS)

Su, Fei; Wang, Jiang; Li, Huiyan; Wei, Xile; Yu, Haitao; Deng, Bin

2018-02-01

High frequency stimulation (HFS) has confirmed its ability in modulating the pathological neural activities. However its detailed mechanism is unclear. This study aims to explore the effects of HFS on neuronal networks dynamics. First, the two-neuron FitzHugh-Nagumo (FHN) networks with static coupling strength and the small-world FHN networks with spike-time-dependent plasticity (STDP) modulated synaptic coupling strength are constructed. Then, the multi-scale method is used to transform the network models into equivalent averaged models, where the HFS intensity is modeled as the ratio between stimulation amplitude and frequency. Results show that in static two-neuron networks, there is still synaptic current projected to the postsynaptic neuron even if the presynaptic neuron is blocked by the HFS. In the small-world networks, the effects of the STDP adjusting rate parameter on the inactivation ratio and synchrony degree increase with the increase of HFS intensity. However, only when the HFS intensity becomes very large can the STDP time window parameter affect the inactivation ratio and synchrony index. Both simulation and numerical analysis demonstrate that the effects of HFS on neuronal network dynamics are realized through the adjustment of synaptic variable and conductance.

Unsupervised Feature Learning With Winner-Takes-All Based STDP

PubMed Central

Ferré, Paul; Mamalet, Franck; Thorpe, Simon J.

2018-01-01

We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule. We show equivalence between rank order coding Leaky-Integrate-and-Fire neurons and ReLU artificial neurons when applied to non-temporal data. We apply this to images using rank-order coding, which allows us to perform a full network simulation with a single feed-forward pass using GPU hardware. Next we introduce a binary STDP learning rule compatible with training on batches of images. Two mechanisms to stabilize the training are also presented : a Winner-Takes-All (WTA) framework which selects the most relevant patches to learn from along the spatial dimensions, and a simple feature-wise normalization as homeostatic process. This learning process allows us to train multi-layer architectures of convolutional sparse features. We apply our method to extract features from the MNIST, ETH80, CIFAR-10, and STL-10 datasets and show that these features are relevant for classification. We finally compare these results with several other state of the art unsupervised learning methods. PMID:29674961

Synthetic Modeling of Autonomous Learning with a Chaotic Neural Network

NASA Astrophysics Data System (ADS)

Funabashi, Masatoshi

We investigate the possible role of intermittent chaotic dynamics called chaotic itinerancy, in interaction with nonsupervised learnings that reinforce and weaken the neural connection depending on the dynamics itself. We first performed hierarchical stability analysis of the Chaotic Neural Network model (CNN) according to the structure of invariant subspaces. Irregular transition between two attractor ruins with positive maximum Lyapunov exponent was triggered by the blowout bifurcation of the attractor spaces, and was associated with riddled basins structure. We secondly modeled two autonomous learnings, Hebbian learning and spike-timing-dependent plasticity (STDP) rule, and simulated the effect on the chaotic itinerancy state of CNN. Hebbian learning increased the residence time on attractor ruins, and produced novel attractors in the minimum higher-dimensional subspace. It also augmented the neuronal synchrony and established the uniform modularity in chaotic itinerancy. STDP rule reduced the residence time on attractor ruins, and brought a wide range of periodicity in emerged attractors, possibly including strange attractors. Both learning rules selectively destroyed and preserved the specific invariant subspaces, depending on the neuron synchrony of the subspace where the orbits are situated. Computational rationale of the autonomous learning is discussed in connectionist perspective.

A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP

PubMed Central

Balduzzi, David; Tononi, Giulio

2012-01-01

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips. PMID:22615855

Using the virtual brain to reveal the role of oscillations and plasticity in shaping brain's dynamical landscape.

PubMed

Roy, Dipanjan; Sigala, Rodrigo; Breakspear, Michael; McIntosh, Anthony Randal; Jirsa, Viktor K; Deco, Gustavo; Ritter, Petra

2014-12-01

Spontaneous brain activity, that is, activity in the absence of controlled stimulus input or an explicit active task, is topologically organized in multiple functional networks (FNs) maintaining a high degree of coherence. These "resting state networks" are constrained by the underlying anatomical connectivity between brain areas. They are also influenced by the history of task-related activation. The precise rules that link plastic changes and ongoing dynamics of resting-state functional connectivity (rs-FC) remain unclear. Using the framework of the open source neuroinformatics platform "The Virtual Brain," we identify potential computational mechanisms that alter the dynamical landscape, leading to reconfigurations of FNs. Using a spiking neuron model, we first demonstrate that network activity in the absence of plasticity is characterized by irregular oscillations between low-amplitude asynchronous states and high-amplitude synchronous states. We then demonstrate the capability of spike-timing-dependent plasticity (STDP) combined with intrinsic alpha (8-12 Hz) oscillations to efficiently influence learning. Further, we show how alpha-state-dependent STDP alters the local area dynamics from an irregular to a highly periodic alpha-like state. This is an important finding, as the cortical input from the thalamus is at the rate of alpha. We demonstrate how resulting rhythmic cortical output in this frequency range acts as a neuronal tuner and, hence, leads to synchronization or de-synchronization between brain areas. Finally, we demonstrate that locally restricted structural connectivity changes influence local as well as global dynamics and lead to altered rs-FC.

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.

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

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

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.

STDP-based spiking deep convolutional neural networks for object recognition.

PubMed

Kheradpisheh, Saeed Reza; Ganjtabesh, Mohammad; Thorpe, Simon J; Masquelier, Timothée

2018-03-01

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware solutions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Synchronization in a noise-driven developing neural network

NASA Astrophysics Data System (ADS)

Lin, I.-H.; Wu, R.-K.; Chen, C.-M.

2011-11-01

We use computer simulations to investigate the structural and dynamical properties of a developing neural network whose activity is driven by noise. Structurally, the constructed neural networks in our simulations exhibit the small-world properties that have been observed in several neural networks. The dynamical change of neuronal membrane potential is described by the Hodgkin-Huxley model, and two types of learning rules, including spike-timing-dependent plasticity (STDP) and inverse STDP, are considered to restructure the synaptic strength between neurons. Clustered synchronized firing (SF) of the network is observed when the network connectivity (number of connections/maximal connections) is about 0.75, in which the firing rate of neurons is only half of the network frequency. At the connectivity of 0.86, all neurons fire synchronously at the network frequency. The network SF frequency increases logarithmically with the culturing time of a growing network and decreases exponentially with the delay time in signal transmission. These conclusions are consistent with experimental observations. The phase diagrams of SF in a developing network are investigated for both learning rules.

On building a memory evolutive system for application to learning and cognition modeling.

PubMed

de Lima do Rego Monteiro, Julio; Kogler, Joao Eduardo; Ribeiro, Joao Henrique Ranhel; Netto, Marcio Lobo

2010-01-01

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich's formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.

PubMed

Higgins, Irina; Stringer, Simon; Schnupp, Jan

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

PubMed Central

Stringer, Simon

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable. PMID:28797034

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.

Implementation of a spike-based perceptron learning rule using TiO2-x memristors.

PubMed

Mostafa, Hesham; Khiat, Ali; Serb, Alexander; Mayr, Christian G; Indiveri, Giacomo; Prodromakis, Themis

2015-01-01

Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic "cognitive" capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode.

The effect of synaptic plasticity on orientation selectivity in a balanced model of primary visual cortex

PubMed Central

Gonzalo Cogno, Soledad; Mato, Germán

2015-01-01

Orientation selectivity is ubiquitous in the primary visual cortex (V1) of mammals. In cats and monkeys, V1 displays spatially ordered maps of orientation preference. Instead, in mice, squirrels, and rats, orientation selective neurons in V1 are not spatially organized, giving rise to a seemingly random pattern usually referred to as a salt-and-pepper layout. The fact that such different organizations can sharpen orientation tuning leads to question the structural role of the intracortical connections; specifically the influence of plasticity and the generation of functional connectivity. In this work, we analyze the effect of plasticity processes on orientation selectivity for both scenarios. We study a computational model of layer 2/3 and a reduced one-dimensional model of orientation selective neurons, both in the balanced state. We analyze two plasticity mechanisms. The first one involves spike-timing dependent plasticity (STDP), while the second one considers the reconnection of the interactions according to the preferred orientations of the neurons. We find that under certain conditions STDP can indeed improve selectivity but it works in a somehow unexpected way, that is, effectively decreasing the modulated part of the intracortical connectivity as compared to the non-modulated part of it. For the reconnection mechanism we find that increasing functional connectivity leads, in fact, to a decrease in orientation selectivity if the network is in a stable balanced state. Both counterintuitive results are a consequence of the dynamics of the balanced state. We also find that selectivity can increase due to a reconnection process if the resulting connections give rise to an unstable balanced state. We compare these findings with recent experimental results. PMID:26347615

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

The BDNF Val66Met polymorphism impairs synaptic transmission and plasticity in the infralimbic medial prefrontal cortex

PubMed Central

Pattwell, Siobhan S.; Bath, Kevin G.; Perez-Castro, Rosalia; Lee, Francis S.; Chao, Moses V.; Ninan, Ipe

2012-01-01

The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism is a common human single nucleotide polymorphism (SNP) that affects the regulated release of BDNF, and has been implicated in affective disorders and cognitive dysfunction. A decreased activation of the infralimbic medial prefrontal cortex (IL-mPFC), a brain region critical for the regulation of affective behaviors, has been described in BDNFMet carriers. However, it is unclear whether and how the Val66Met polymorphism affects the IL-mPFC synapses. Here we report that spike timing-dependent plasticity (STDP) was absent in the IL-mPFC pyramidal neurons from BDNFMet/Met mice, a mouse that recapitulates the specific phenotypic properties of the human BDNF Val66Met polymorphism. Also, we observed a decrease in N-methyl-D-aspartic acid (NMDA) and γ-aminobutyric acid (GABA) receptor-mediated synaptic transmission in the pyramidal neurons of BDNFMet/Met mice. While BDNF enhanced non-NMDA receptor transmission and depressed GABA receptor transmission in the wild-type mice, both effects were absent in BDNFMet/Met mice after BDNF treatment. Indeed, exogenous BDNF reversed the deficits in STDP and NMDA receptor transmission in BDNFMet/Met neurons. BDNF-mediated selective reversal of the deficit in plasticity and NMDA receptor transmission, but its lack of effect on GABA and non-NMDA receptor transmission in BDNFMet/Met mice, suggests separate mechanisms of Val66Met polymorphism upon synaptic transmission. The effect of the Val66Met polymorphism on synaptic transmission and plasticity in the IL-mPFC represents a mechanism to account for this SNP's impact on affective disorders and cognitive dysfunction. PMID:22396415

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks

PubMed Central

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections. PMID:28197088

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

PubMed

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

Energy-efficient STDP-based learning circuits with memristor synapses

NASA Astrophysics Data System (ADS)

Wu, Xinyu; Saxena, Vishal; Campbell, Kristy A.

2014-05-01

It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of a dense network of neurons and the synapses formed between the axons and dendrites. Further, unsupervised or supervised competitive learning schemes are being investigated for global training of the network. In contrast to a software implementation, hardware realization of these networks requires massive circuit overhead for addressing and individually updating network weights. Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STDP) to efficiently update the network weights locally. To realize SNNs on a chip, we propose to use densely integrating mixed-signal integrate-andfire neurons (IFNs) and cross-point arrays of memristors in back-end-of-the-line (BEOL) of CMOS chips. Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<1 pJ/spike/synapse), even at spike rate greater than 10 MHz. We present circuit design details and simulation results of the IFN with memristor synapses, its response to incoming spike trains and STDP learning characterization.

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

The Convallis Rule for Unsupervised Learning in Cortical Networks

PubMed Central

Yger, Pierre; Harris, Kenneth D.

2013-01-01

The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the “Convallis rule”, mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex. PMID:24204224

Synaptic plasticity modulates autonomous transitions between waking and sleep states: Insights from a Morris-Lecar model

NASA Astrophysics Data System (ADS)

Ciszak, Marzena; Bellesi, Michele

2011-12-01

The transitions between waking and sleep states are characterized by considerable changes in neuronal firing. During waking, neurons fire tonically at irregular intervals and a desynchronized activity is observed at the electroencephalogram. This activity becomes synchronized with slow wave sleep onset when neurons start to oscillate between periods of firing (up-states) and periods of silence (down-states). Recently, it has been proposed that the connections between neurons undergo potentiation during waking, whereas they weaken during slow wave sleep. Here, we propose a dynamical model to describe basic features of the autonomous transitions between such states. We consider a network of coupled neurons in which the strength of the interactions is modulated by synaptic long term potentiation and depression, according to the spike time-dependent plasticity rule (STDP). The model shows that the enhancement of synaptic strength between neurons occurring in waking increases the propensity of the network to synchronize and, conversely, desynchronization appears when the strength of the connections become weaker. Both transitions appear spontaneously, but the transition from sleep to waking required a slight modification of the STDP rule with the introduction of a mechanism which becomes active during sleep and changes the proportion between potentiation and depression in accordance with biological data. At the neuron level, transitions from desynchronization to synchronization and vice versa can be described as a bifurcation between two different states, whose dynamical regime is modulated by synaptic strengths, thus suggesting that transition from a state to an another can be determined by quantitative differences between potentiation and depression.

A scalable neural chip with synaptic electronics using CMOS integrated memristors.

PubMed

Cruz-Albrecht, Jose M; Derosier, Timothy; Srinivasa, Narayan

2013-09-27

The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal-oxide-semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73 728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior.

STDP in lateral connections creates category-based perceptual cycles for invariance learning with multiple stimuli.

PubMed

Evans, Benjamin D; Stringer, Simon M

2015-04-01

Learning to recognise objects and faces is an important and challenging problem tackled by the primate ventral visual system. One major difficulty lies in recognising an object despite profound differences in the retinal images it projects, due to changes in view, scale, position and other identity-preserving transformations. Several models of the ventral visual system have been successful in coping with these issues, but have typically been privileged by exposure to only one object at a time. In natural scenes, however, the challenges of object recognition are typically further compounded by the presence of several objects which should be perceived as distinct entities. In the present work, we explore one possible mechanism by which the visual system may overcome these two difficulties simultaneously, through segmenting unseen (artificial) stimuli using information about their category encoded in plastic lateral connections. We demonstrate that these experience-guided lateral interactions robustly organise input representations into perceptual cycles, allowing feed-forward connections trained with spike-timing-dependent plasticity to form independent, translation-invariant output representations. We present these simulations as a functional explanation for the role of plasticity in the lateral connectivity of visual cortex.

When Long-Range Zero-Lag Synchronization is Feasible in Cortical Networks

PubMed Central

Viriyopase, Atthaphon; Bojak, Ingo; Zeitler, Magteld; Gielen, Stan

2012-01-01

Many studies have reported long-range synchronization of neuronal activity between brain areas, in particular in the beta and gamma bands with frequencies in the range of 14–30 and 40–80 Hz, respectively. Several studies have reported synchrony with zero phase lag, which is remarkable considering the synaptic and conduction delays inherent in the connections between distant brain areas. This result has led to many speculations about the possible functional role of zero-lag synchrony, such as for neuronal communication, attention, memory, and feature binding. However, recent studies using recordings of single-unit activity and local field potentials report that neuronal synchronization may occur with non-zero phase lags. This raises the questions whether zero-lag synchrony can occur in the brain and, if so, under which conditions. We used analytical methods and computer simulations to investigate which connectivity between neuronal populations allows or prohibits zero-lag synchrony. We did so for a model where two oscillators interact via a relay oscillator. Analytical results and computer simulations were obtained for both type I Mirollo–Strogatz neurons and type II Hodgkin–Huxley neurons. We have investigated the dynamics of the model for various types of synaptic coupling and importantly considered the potential impact of Spike-Timing Dependent Plasticity (STDP) and its learning window. We confirm previous results that zero-lag synchrony can be achieved in this configuration. This is much easier to achieve with Hodgkin–Huxley neurons, which have a biphasic phase response curve, than for type I neurons. STDP facilitates zero-lag synchrony as it adjusts the synaptic strengths such that zero-lag synchrony is feasible for a much larger range of parameters than without STDP. PMID:22866034

Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems.

PubMed

Jaafar, Ayoub H; Gray, Robert J; Verrelli, Emanuele; O'Neill, Mary; Kelly, Stephen M; Kemp, Neil T

2017-11-09

Optical control of memristors opens the route to new applications in optoelectronic switching and neuromorphic computing. Motivated by the need for reversible and latched optical switching we report on the development of a memristor with electronic properties tunable and switchable by wavelength and polarization specific light. The device consists of an optically active azobenzene polymer, poly(disperse red 1 acrylate), overlaying a forest of vertically aligned ZnO nanorods. Illumination induces trans-cis isomerization of the azobenzene molecules, which expands or contracts the polymer layer and alters the resistance of the off/on states, their ratio and retention time. The reversible optical effect enables dynamic control of a memristor's learning properties including control of synaptic potentiation and depression, optical switching between short-term and long-term memory and optical modulation of the synaptic efficacy via spike timing dependent plasticity. The work opens the route to the dynamic patterning of memristor networks both spatially and temporally by light, thus allowing the development of new optically reconfigurable neural networks and adaptive electronic circuits.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

PubMed

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas; Chicca, Elisabetta

2012-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

PubMed Central

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L.; Wennekers, Thomas; Chicca, Elisabetta

2011-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems. PMID:22347163

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

PubMed Central

Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

2015-01-01

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. PMID:26714277

Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule.

PubMed

Beyeler, Michael; Dutt, Nikil D; Krichmar, Jeffrey L

2013-12-01

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex

PubMed Central

Chou, Ting-Shuo; Bucci, Liam D.; Krichmar, Jeffrey L.

2015-01-01

Neurorobots enable researchers to study how behaviors are produced by neural mechanisms in an uncertain, noisy, real-world environment. To investigate how the somatosensory system processes noisy, real-world touch inputs, we introduce a neurorobot called CARL-SJR, which has a full-body tactile sensory area. The design of CARL-SJR is such that it encourages people to communicate with it through gentle touch. CARL-SJR provides feedback to users by displaying bright colors on its surface. In the present study, we show that CARL-SJR is capable of learning associations between conditioned stimuli (CS; a color pattern on its surface) and unconditioned stimuli (US; a preferred touch pattern) by applying a spiking neural network (SNN) with neurobiologically inspired plasticity. Specifically, we modeled the primary somatosensory cortex, prefrontal cortex, striatum, and the insular cortex, which is important for hedonic touch, to process noisy data generated directly from CARL-SJR's tactile sensory area. To facilitate learning, we applied dopamine-modulated Spike Timing Dependent Plasticity (STDP) to our simulated prefrontal cortex, striatum, and insular cortex. To cope with noisy, varying inputs, the SNN was tuned to produce traveling waves of activity that carried spatiotemporal information. Despite the noisy tactile sensors, spike trains, and variations in subject hand swipes, the learning was quite robust. Further, insular cortex activities in the incremental pathway of dopaminergic reward system allowed us to control CARL-SJR's preference for touch direction without heavily pre-processed inputs. The emerged behaviors we found in this model match animal's behaviors wherein they prefer touch in particular areas and directions. Thus, the results in this paper could serve as an explanation on the underlying neural mechanisms for developing tactile preferences and hedonic touch. PMID:26257639

Timing-dependent LTP and LTD in mouse primary visual cortex following different visual deprivation models

PubMed Central

Chen, Xia; Fu, Junhong; Cheng, Wenbo; Song, Desheng; Qu, Xiaolei; Yang, Zhuo; Zhao, Kanxing

2017-01-01

Visual deprivation during the critical period induces long-lasting changes in cortical circuitry by adaptively modifying neuro-transmission and synaptic connectivity at synapses. Spike timing-dependent plasticity (STDP) is considered a strong candidate for experience-dependent changes. However, the visual deprivation forms that affect timing-dependent long-term potentiation(LTP) and long-term depression(LTD) remain unclear. Here, we demonstrated the temporal window changes of tLTP and tLTD, elicited by coincidental pre- and post-synaptic firing, following different modes of 6-day visual deprivation. Markedly broader temporal windows were found in robust tLTP and tLTD in the V1M of the deprived visual cortex in mice after 6-day MD and DE. The underlying mechanism for the changes seen with visual deprivation in juvenile mice using 6 days of dark exposure or monocular lid suture involves an increased fraction of NR2b-containing NMDAR and the consequent prolongation of NMDAR-mediated response duration. Moreover, a decrease in NR2A protein expression at the synapse is attributable to the reduction of the NR2A/2B ratio in the deprived cortex. PMID:28520739

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Integrated neuron circuit for implementing neuromorphic system with synaptic device

NASA Astrophysics Data System (ADS)

Lee, Jeong-Jun; Park, Jungjin; Kwon, Min-Woo; Hwang, Sungmin; Kim, Hyungjin; Park, Byung-Gook

2018-02-01

In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).

Reward-based learning under hardware constraints-using a RISC processor embedded in a neuromorphic substrate.

PubMed

Friedmann, Simon; Frémaux, Nicolas; Schemmel, Johannes; Gerstner, Wulfram; Meier, Karlheinz

2013-01-01

In this study, we propose and analyze in simulations a new, highly flexible method of implementing synaptic plasticity in a wafer-scale, accelerated neuromorphic hardware system. The study focuses on globally modulated STDP, as a special use-case of this method. Flexibility is achieved by embedding a general-purpose processor dedicated to plasticity into the wafer. To evaluate the suitability of the proposed system, we use a reward modulated STDP rule in a spike train learning task. A single layer of neurons is trained to fire at specific points in time with only the reward as feedback. This model is simulated to measure its performance, i.e., the increase in received reward after learning. Using this performance as baseline, we then simulate the model with various constraints imposed by the proposed implementation and compare the performance. The simulated constraints include discretized synaptic weights, a restricted interface between analog synapses and embedded processor, and mismatch of analog circuits. We find that probabilistic updates can increase the performance of low-resolution weights, a simple interface between analog synapses and processor is sufficient for learning, and performance is insensitive to mismatch. Further, we consider communication latency between wafer and the conventional control computer system that is simulating the environment. This latency increases the delay, with which the reward is sent to the embedded processor. Because of the time continuous operation of the analog synapses, delay can cause a deviation of the updates as compared to the not delayed situation. We find that for highly accelerated systems latency has to be kept to a minimum. This study demonstrates the suitability of the proposed implementation to emulate the selected reward modulated STDP learning rule. It is therefore an ideal candidate for implementation in an upgraded version of the wafer-scale system developed within the BrainScaleS project.

Reward-based learning under hardware constraints—using a RISC processor embedded in a neuromorphic substrate

PubMed Central

Friedmann, Simon; Frémaux, Nicolas; Schemmel, Johannes; Gerstner, Wulfram; Meier, Karlheinz

2013-01-01

In this study, we propose and analyze in simulations a new, highly flexible method of implementing synaptic plasticity in a wafer-scale, accelerated neuromorphic hardware system. The study focuses on globally modulated STDP, as a special use-case of this method. Flexibility is achieved by embedding a general-purpose processor dedicated to plasticity into the wafer. To evaluate the suitability of the proposed system, we use a reward modulated STDP rule in a spike train learning task. A single layer of neurons is trained to fire at specific points in time with only the reward as feedback. This model is simulated to measure its performance, i.e., the increase in received reward after learning. Using this performance as baseline, we then simulate the model with various constraints imposed by the proposed implementation and compare the performance. The simulated constraints include discretized synaptic weights, a restricted interface between analog synapses and embedded processor, and mismatch of analog circuits. We find that probabilistic updates can increase the performance of low-resolution weights, a simple interface between analog synapses and processor is sufficient for learning, and performance is insensitive to mismatch. Further, we consider communication latency between wafer and the conventional control computer system that is simulating the environment. This latency increases the delay, with which the reward is sent to the embedded processor. Because of the time continuous operation of the analog synapses, delay can cause a deviation of the updates as compared to the not delayed situation. We find that for highly accelerated systems latency has to be kept to a minimum. This study demonstrates the suitability of the proposed implementation to emulate the selected reward modulated STDP learning rule. It is therefore an ideal candidate for implementation in an upgraded version of the wafer-scale system developed within the BrainScaleS project. PMID:24065877

Unsupervised learning in neural networks with short range synapses

NASA Astrophysics Data System (ADS)

Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

2013-01-01

Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

A geographic information system analysis of the impact of a statewide acute stroke emergency medical services routing protocol on community hospital bypass.

PubMed

Asimos, Andrew W; Ward, Shana; Brice, Jane H; Enright, Dianne; Rosamond, Wayne D; Goldstein, Larry B; Studnek, Jonathan

2014-01-01

Our goal was to determine if a statewide Emergency Medical Services (EMSs) Stroke Triage and Destination Plan (STDP), specifying bypass of hospitals unable to routinely treat stroke patients with thrombolytics (community hospitals), changed bypass frequency of those hospitals. Using a statewide EMS database, we identified stroke patients eligible for community hospital bypass and compared bypass frequency 1-year before and after STDP implementation. Symptom onset time was missing for 48% of pre-STDP (n = 2385) and 29% of post-STDP (n = 1612) cases. Of the remaining cases with geocodable scene addresses, 58% (1301) in the pre-STDP group and 61% (2,078) in the post-STDP group were ineligible for bypass, because a community hospital was not the closest hospital to the stroke event location. Because of missing data records for some EMS agencies in 1 or both study periods, we included EMS agencies from only 49 of 100 North Carolina counties in our analysis. Additionally, we found conflicting hospital classifications by different EMS agencies for 35% of all hospitals (n = 38 of 108). Given these limitations, we found similar community hospital bypass rates before and after STDP implementation (64%, n = 332 of 520 vs. 63%, n = 345 of 552; P = .65). Missing symptom duration time and data records in our state's EMS data system, along with conflicting hospital classifications between EMS agencies limit the ability to study statewide stroke routing protocols. Bypass policies may apply to a minority of patients because a community hospital is not the closest hospital to most stroke events. Given these limitations, we found no difference in community hospital bypass rates after implementation of the STDP. Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Spiking neural network model for memorizing sequences with forward and backward recall.

PubMed

Borisyuk, Roman; Chik, David; Kazanovich, Yakov; da Silva Gomes, João

2013-06-01

We present an oscillatory network of conductance based spiking neurons of Hodgkin-Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Demonstration of Synaptic Behaviors and Resistive Switching Characterizations by Proton Exchange Reactions in Silicon Oxide

PubMed Central

Chang, Yao-Feng; Fowler, Burt; Chen, Ying-Chen; Zhou, Fei; Pan, Chih-Hung; Chang, Ting-Chang; Lee, Jack C.

2016-01-01

We realize a device with biological synaptic behaviors by integrating silicon oxide (SiOx) resistive switching memory with Si diodes. Minimal synaptic power consumption due to sneak-path current is achieved and the capability for spike-induced synaptic behaviors is demonstrated, representing critical milestones for the use of SiO2–based materials in future neuromorphic computing applications. Biological synaptic behaviors such as long-term potentiation (LTP), long-term depression (LTD) and spike-timing dependent plasticity (STDP) are demonstrated systematically using a comprehensive analysis of spike-induced waveforms, and represent interesting potential applications for SiOx-based resistive switching materials. The resistive switching SET transition is modeled as hydrogen (proton) release from (SiH)2 to generate the hydrogen bridge defect, and the RESET transition is modeled as an electrochemical reaction (proton capture) that re-forms (SiH)2. The experimental results suggest a simple, robust approach to realize programmable neuromorphic chips compatible with large-scale CMOS manufacturing technology. PMID:26880381

Demonstration of Synaptic Behaviors and Resistive Switching Characterizations by Proton Exchange Reactions in Silicon Oxide

NASA Astrophysics Data System (ADS)

Chang, Yao-Feng; Fowler, Burt; Chen, Ying-Chen; Zhou, Fei; Pan, Chih-Hung; Chang, Ting-Chang; Lee, Jack C.

2016-02-01

We realize a device with biological synaptic behaviors by integrating silicon oxide (SiOx) resistive switching memory with Si diodes. Minimal synaptic power consumption due to sneak-path current is achieved and the capability for spike-induced synaptic behaviors is demonstrated, representing critical milestones for the use of SiO2-based materials in future neuromorphic computing applications. Biological synaptic behaviors such as long-term potentiation (LTP), long-term depression (LTD) and spike-timing dependent plasticity (STDP) are demonstrated systematically using a comprehensive analysis of spike-induced waveforms, and represent interesting potential applications for SiOx-based resistive switching materials. The resistive switching SET transition is modeled as hydrogen (proton) release from (SiH)2 to generate the hydrogen bridge defect, and the RESET transition is modeled as an electrochemical reaction (proton capture) that re-forms (SiH)2. The experimental results suggest a simple, robust approach to realize programmable neuromorphic chips compatible with large-scale CMOS manufacturing technology.

Enhanced polychronization in a spiking network with metaplasticity.

PubMed

Guise, Mira; Knott, Alistair; Benuskova, Lubica

2015-01-01

Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002). In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004). Polychronous groups (PNGs) develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP), but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

PubMed

Cheung, Kit; Schultz, Simon R; Luk, Wayne

2015-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

PubMed Central

Cheung, Kit; Schultz, Simon R.; Luk, Wayne

2016-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542

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.

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.

A neuromorphic model of motor overflow in focal hand dystonia due to correlated sensory input

NASA Astrophysics Data System (ADS)

Sohn, Won Joon; Niu, Chuanxin M.; Sanger, Terence D.

2016-10-01

Objective. Motor overflow is a common and frustrating symptom of dystonia, manifested as unintentional muscle contraction that occurs during an intended voluntary movement. Although it is suspected that motor overflow is due to cortical disorganization in some types of dystonia (e.g. focal hand dystonia), it remains elusive which mechanisms could initiate and, more importantly, perpetuate motor overflow. We hypothesize that distinct motor elements have low risk of motor overflow if their sensory inputs remain statistically independent. But when provided with correlated sensory inputs, pre-existing crosstalk among sensory projections will grow under spike-timing-dependent-plasticity (STDP) and eventually produce irreversible motor overflow. Approach. We emulated a simplified neuromuscular system comprising two anatomically distinct digital muscles innervated by two layers of spiking neurons with STDP. The synaptic connections between layers included crosstalk connections. The input neurons received either independent or correlated sensory drive during 4 days of continuous excitation. The emulation is critically enabled and accelerated by our neuromorphic hardware created in previous work. Main results. When driven by correlated sensory inputs, the crosstalk synapses gained weight and produced prominent motor overflow; the growth of crosstalk synapses resulted in enlarged sensory representation reflecting cortical reorganization. The overflow failed to recede when the inputs resumed their original uncorrelated statistics. In the control group, no motor overflow was observed. Significance. Although our model is a highly simplified and limited representation of the human sensorimotor system, it allows us to explain how correlated sensory input to anatomically distinct muscles is by itself sufficient to cause persistent and irreversible motor overflow. Further studies are needed to locate the source of correlation in sensory input.

A Hybrid CMOS-Memristor Neuromorphic Synapse.

PubMed

Azghadi, Mostafa Rahimi; Linares-Barranco, Bernabe; Abbott, Derek; Leong, Philip H W

2017-04-01

Although data processing technology continues to advance at an astonishing rate, computers with brain-like processing capabilities still elude us. It is envisioned that such computers may be achieved by the fusion of neuroscience and nano-electronics to realize a brain-inspired platform. This paper proposes a high-performance nano-scale Complementary Metal Oxide Semiconductor (CMOS)-memristive circuit, which mimics a number of essential learning properties of biological synapses. The proposed synaptic circuit that is composed of memristors and CMOS transistors, alters its memristance in response to timing differences among its pre- and post-synaptic action potentials, giving rise to a family of Spike Timing Dependent Plasticity (STDP). The presented design advances preceding memristive synapse designs with regards to the ability to replicate essential behaviours characterised in a number of electrophysiological experiments performed in the animal brain, which involve higher order spike interactions. Furthermore, the proposed hybrid device CMOS area is estimated as [Formula: see text] in a [Formula: see text] process-this represents a factor of ten reduction in area with respect to prior CMOS art. The new design is integrated with silicon neurons in a crossbar array structure amenable to large-scale neuromorphic architectures and may pave the way for future neuromorphic systems with spike timing-dependent learning features. These systems are emerging for deployment in various applications ranging from basic neuroscience research, to pattern recognition, to Brain-Machine-Interfaces.

Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity

PubMed Central

Eguchi, Akihiro; Neymotin, Samuel A.; Stringer, Simon M.

2014-01-01

Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red–green and blue–yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell). PMID:24659956

Learning tinnitus

NASA Astrophysics Data System (ADS)

van Hemmen, J. Leo

Tinnitus, implying the perception of sound without the presence of any acoustical stimulus, is a chronic and serious problem for about 2% of the human population. In many cases, tinnitus is a pitch-like sensation associated with a hearing loss that confines the tinnitus frequency to an interval of the tonotopic axis. Even in patients with a normal audiogram the presence of tinnitus may be associated with damage of hair-cell function in this interval. It has been suggested that homeostatic regulation and, hence, increase of activity leads to the emergence of tinnitus. For patients with hearing loss, we present spike-timing-dependent Hebbian plasticity (STDP) in conjunction with homeostasis as a mechanism for ``learning'' tinnitus in a realistic neuronal network with tonotopically arranged synaptic excitation and inhibition. In so doing we use both dynamical scaling of the synaptic strengths and altering the resting potential of the cells. The corresponding simulations are robust to parameter changes. Understanding the mechanisms of tinnitus induction, such as here, may help improving therapy. Work done in collaboration with Julie Goulet and Michael Schneider. JLvH has been supported partially by BCCN - Munich.

Self-organization of synchronous activity propagation in neuronal networks driven by local excitation

PubMed Central

Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen

2015-01-01

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence. PMID:26089794

Network models of frequency modulated sweep detection.

PubMed

Skorheim, Steven; Razak, Khaleel; Bazhenov, Maxim

2014-01-01

Frequency modulated (FM) sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data: (1) The 'facilitation' model contains frequency selective cells operating as coincidence detectors, summing up multiple excitatory inputs with different time delays. (2) The 'duration tuned' model depends on interactions between delayed excitation and early inhibition. The strength of delayed excitation determines the preferred duration. Inhibitory rebound can reinforce the delayed excitation. (3) The 'inhibitory sideband' model uses frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections results in neurons responsive to sweeps in a single direction of sufficient sweep rate. Variations of these properties, can explain the diversity of rate-dependent direction selectivity seen across species. We show that the inhibitory sideband model can be trained using spike timing dependent plasticity (STDP) to develop direction selectivity from a non-selective network. These models provide a means to compare the proposed synaptic and spectrotemporal mechanisms of FM sweep processing and can be utilized to explore cellular mechanisms underlying experience- or training-dependent changes in spectrotemporal processing across animal models. Given the analogy between FM sweeps and visual motion, these models can serve a broader function in studying stimulus movement across sensory epithelia.

A plastic corticostriatal circuit model of adaptation in perceptual decision making

PubMed Central

Hsiao, Pao-Yueh; Lo, Chung-Chuan

2013-01-01

The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. PMID:24339814

Outcome Research on Short-Term Psychodynamic Psychotherapy: A Preliminary Review.

ERIC Educational Resources Information Center

White, Scott Allyn

This paper reviews the empirical research on short-term psychodynamic psychotherapy (STDP). It begins with a brief history of STDP, identifying current developers and researchers of STDP and listing commonalities among various short-term dynamic psychotherapies. In this review, research is grouped broadly into two categories: controlled…

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

PubMed

Bush, Daniel; Philippides, Andrew; Husbands, Phil; O'Shea, Michael

2010-07-01

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

Control of Abnormal Synchronization in Neurological Disorders

PubMed Central

Popovych, Oleksandr V.; Tass, Peter A.

2014-01-01

In the nervous system, synchronization processes play an important role, e.g., in the context of information processing and motor control. However, pathological, excessive synchronization may strongly impair brain function and is a hallmark of several neurological disorders. This focused review addresses the question of how an abnormal neuronal synchronization can specifically be counteracted by invasive and non-invasive brain stimulation as, for instance, by deep brain stimulation for the treatment of Parkinson’s disease, or by acoustic stimulation for the treatment of tinnitus. On the example of coordinated reset (CR) neuromodulation, we illustrate how insights into the dynamics of complex systems contribute to successful model-based approaches, which use methods from synergetics, non-linear dynamics, and statistical physics, for the development of novel therapies for normalization of brain function and synaptic connectivity. Based on the intrinsic multistability of the neuronal populations induced by spike timing-dependent plasticity (STDP), CR neuromodulation utilizes the mutual interdependence between synaptic connectivity and dynamics of the neuronal networks in order to restore more physiological patterns of connectivity via desynchronization of neuronal activity. The very goal is to shift the neuronal population by stimulation from an abnormally coupled and synchronized state to a desynchronized regime with normalized synaptic connectivity, which significantly outlasts the stimulation cessation, so that long-lasting therapeutic effects can be achieved. PMID:25566174

Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA.

PubMed

Stratmann, Philipp; Lakatos, Dominic; Albu-Schäffer, Alin

2016-01-01

There are multiple indications that the nervous system of animals tunes muscle output to exploit natural dynamics of the elastic locomotor system and the environment. This is an advantageous strategy especially in fast periodic movements, since the elastic elements store energy and increase energy efficiency and movement speed. Experimental evidence suggests that coordination among joints involves proprioceptive input and neuromodulatory influence originating in the brain stem. However, the neural strategies underlying the coordination of fast periodic movements remain poorly understood. Based on robotics control theory, we suggest that the nervous system implements a mechanism to accomplish coordination between joints by a linear coordinate transformation from the multi-dimensional space representing proprioceptive input at the joint level into a one-dimensional controller space. In this one-dimensional subspace, the movements of a whole limb can be driven by a single oscillating unit as simple as a reflex interneuron. The output of the oscillating unit is transformed back to joint space via the same transformation. The transformation weights correspond to the dominant principal component of the movement. In this study, we propose a biologically plausible neural network to exemplify that the central nervous system (CNS) may encode our controller design. Using theoretical considerations and computer simulations, we demonstrate that spike-timing-dependent plasticity (STDP) for the input mapping and serotonergic neuromodulation for the output mapping can extract the dominant principal component of sensory signals. Our simulations show that our network can reliably control mechanical systems of different complexity and increase the energy efficiency of ongoing cyclic movements. The proposed network is simple and consistent with previous biologic experiments. Thus, our controller could serve as a candidate to describe the neural control of fast, energy-efficient, periodic movements involving multiple coupled joints.

Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA

PubMed Central

Stratmann, Philipp; Lakatos, Dominic; Albu-Schäffer, Alin

2016-01-01

There are multiple indications that the nervous system of animals tunes muscle output to exploit natural dynamics of the elastic locomotor system and the environment. This is an advantageous strategy especially in fast periodic movements, since the elastic elements store energy and increase energy efficiency and movement speed. Experimental evidence suggests that coordination among joints involves proprioceptive input and neuromodulatory influence originating in the brain stem. However, the neural strategies underlying the coordination of fast periodic movements remain poorly understood. Based on robotics control theory, we suggest that the nervous system implements a mechanism to accomplish coordination between joints by a linear coordinate transformation from the multi-dimensional space representing proprioceptive input at the joint level into a one-dimensional controller space. In this one-dimensional subspace, the movements of a whole limb can be driven by a single oscillating unit as simple as a reflex interneuron. The output of the oscillating unit is transformed back to joint space via the same transformation. The transformation weights correspond to the dominant principal component of the movement. In this study, we propose a biologically plausible neural network to exemplify that the central nervous system (CNS) may encode our controller design. Using theoretical considerations and computer simulations, we demonstrate that spike-timing-dependent plasticity (STDP) for the input mapping and serotonergic neuromodulation for the output mapping can extract the dominant principal component of sensory signals. Our simulations show that our network can reliably control mechanical systems of different complexity and increase the energy efficiency of ongoing cyclic movements. The proposed network is simple and consistent with previous biologic experiments. Thus, our controller could serve as a candidate to describe the neural control of fast, energy-efficient, periodic movements involving multiple coupled joints. PMID:27014051

A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus.

PubMed

Diederich, Nick; Bartsch, Thorsten; Kohlstedt, Hermann; Ziegler, Martin

2018-06-19

Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout.

PubMed

Das, Anup; Pradhapan, Paruthi; Groenendaal, Willemijn; Adiraju, Prathyusha; Rajan, Raj Thilak; Catthoor, Francky; Schaafsma, Siebren; Krichmar, Jeffrey L; Dutt, Nikil; Van Hoof, Chris

2018-03-01

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices. Copyright © 2018 Elsevier Ltd. All rights reserved.

The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

PubMed

Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

2011-06-01

The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

[Spectrofluorometric detection of protein with a novel hydrophilic cyanine dye].

PubMed

Lin, Xu-Cong; Guo, Liang-Qia; Lin, Yan-Xia; Xie, Zeng-Hong

2007-09-01

A sensitive fluorescence quantitative determination for bovine serum albumin (BSA) or human serum albumin (HSA) has been developed by using a new hydrophilic cyanine dye 1, 1'-sulfonopropyl-3,3,3', 3'-tetramethylindolium-5,5'-disulfonic potassium (STDP) as a fluorescence probe. Using BSA as a representative protein, characteristics of the fluorescence reaction of STDP with protein were investigated. Effects of the concentration of the hydrophilic cyanine dye, pH value of the buffer solution, and ion-intensity of NaCl were also studied as well as the ratio of ethanol. In the citrate-HCl buffer solution, the fluorescence emission wavelength of BSA-STDP system was 562 nm with the maximum excitation wavelength of 548 nm, and the Stokes displacement was 14 nm. With the pH ranging from 1.0 to 2.0, the fluorescence was increasing and up to the maximum at pH 2.0. However, in the pH range of 3.0-5.0, the interaction of BSA and STDP was weakened due to the decrease in positive charge on the BSA chain, which resulted in an observable decrease of the enhancement of the fluorescence intensity. At the optimum pH of 2.0, electrostatic interactions of positive charges of the BSA chain and negative charges on the sulfonic groups of STDP were carried out. The interactions of the indole group of STDP and some active groups of BSA (viz. amido, carboxyl or sulfhydryl) were also achieved, and resulted in the combination of indole group of cyanine dye into the chain of BSA. So the hydrophobic effect and the protection provided by the skeleton chain of BSA were both improved to prevent the fluorescent energy of STDP from losing in the solution, which caused a notable fluorescence increase with an observable shift to the longer emission wavelength. Furthermore, with the augmentation of BSA, the alpha-helix structure of BSA molecular turned from the unwrapped state to the enfolded state, in favor of restraining free-oscillation of fluorescence probe in the solution and maintaining a high energy transfer efficiency. Such a fact fueled a highly enhancement of the fluorescence too. Besides, effects of the concentration of cyanine dye on the determination of BSA were also investigated. The fluorescence intensity (DeltaF) was enhanced with the increase in the quantity of STDP and gained the peak at 1.00 micromol x L(-1). However, when STDP ranged from 1.50 to 5.00 micromol x L(-1), some negative congregate effects on the nature of cyanine dye might happen and resulted in a too high fluorescence background. A rapid decrease of the fluorescence intensity was observed. The effects of ion-intensity of NaCl and ethanol on the fluorescence of BSA-STDP system were obvious. Though the fluorescence still remained high at the level of NaCl of 0.025 mol x L(-1), a rapid decrease happen at the level of NaCl from 0.05 to 0.15 mol x L(-1). With the addition of ethanol, the dissolvation capacity of both STDP and BSA was improved and their interactions were accelerated. An increasing fluorescence with the augment of ethanol was obtained and the maximum was achieved with the ratio of ethanol at 10%. Influences of coexistent substances such as amino acid, metal ions such as Cu2+, Na+, Ca2+, Mg2+, Al3+ and Fe3+ were also investigated. Most substances had no notable influences on the determination of BSA except Fe3+ and Cu2+ ions. Under the optimum conditions, the fluorescence of STDP was enhanced markedly with the addition of the BSA or HSA protein. Good calibration curves of the proteins were obtained in the range of 0.20-15.00 microg x mL(-1) for BSA and 0.20-12.00 microg x mL(-1) for HSA with detection limits (3sigma/K) of 0.01 microg x mL(-1). Applied to simulant BSA samples, this method was adaptable. And the results were satisfied with good recoveries ranging from 94.5% to 103.3% at the revels of 4.00, 6.00 and 8.00 microg x mL(-1) respectively.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

PubMed

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

PubMed Central

Staras, Kevin

2016-01-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

The Achievement of Therapeutic Objectives Scale: Interrater Reliability and Sensitivity to Change in Short-Term Dynamic Psychotherapy and Cognitive Therapy

ERIC Educational Resources Information Center

Valen, Jakob; Ryum, Truls; Svartberg, Martin; Stiles, Tore C.; McCullough, Leigh

2011-01-01

This study examined interrater reliability and sensitivity to change of the Achievement of Therapeutic Objectives Scale (ATOS; McCullough, Larsen, et al., 2003) in short-term dynamic psychotherapy (STDP) and cognitive therapy (CT). The ATOS is a process scale originally developed to assess patients' achievements of treatment objectives in STDP,…

VLSI circuits implementing computational models of neocortical circuits.

PubMed

Wijekoon, Jayawan H B; Dudek, Piotr

2012-09-15

This paper overviews the design and implementation of three neuromorphic integrated circuits developed for the COLAMN ("Novel Computing Architecture for Cognitive Systems based on the Laminar Microcircuitry of the Neocortex") project. The circuits are implemented in a standard 0.35 μm CMOS technology and include spiking and bursting neuron models, and synapses with short-term (facilitating/depressing) and long-term (STDP and dopamine-modulated STDP) dynamics. They enable execution of complex nonlinear models in accelerated-time, as compared with biology, and with low power consumption. The neural dynamics are implemented using analogue circuit techniques, with digital asynchronous event-based input and output. The circuits provide configurable hardware blocks that can be used to simulate a variety of neural networks. The paper presents experimental results obtained from the fabricated devices, and discusses the advantages and disadvantages of the analogue circuit approach to computational neural modelling. Copyright © 2012 Elsevier B.V. All rights reserved.

Two New Real-Time PCR-based Surveillance Systems for “Candidatus Liberibacter” Species Detection

USDA-ARS?s Scientific Manuscript database

We developed two novel surveillance systems for “Candidatus Liberibacter” (CL) species detection and identification. The first system is called “single tube dual primer Taq-Man PCR” (STDP). The procedure involves two sequential rounds of PCR using the CL asiaticus species-specific outer and inner pr...

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning

PubMed Central

Kappel, David; Nessler, Bernhard; Maass, Wolfgang

2014-01-01

In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but all-important information processing capability. This capability emerges in the presence of noise automatically through effects of STDP on connections between pyramidal cells in Winner-Take-All circuits with lateral excitation. In fact, one can show that these motifs endow cortical microcircuits with functional properties of a hidden Markov model, a generic model for solving such tasks through probabilistic inference. Whereas in engineering applications this model is adapted to specific tasks through offline learning, we show here that a major portion of the functionality of hidden Markov models arises already from online applications of STDP, without any supervision or rewards. We demonstrate the emergent computing capabilities of the model through several computer simulations. The full power of hidden Markov model learning can be attained through reward-gated STDP. This is due to the fact that these mechanisms enable a rejection sampling approximation to theoretically optimal learning. We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task. PMID:24675787

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

PubMed

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

PubMed Central

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns. PMID:26900845

Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture

PubMed Central

Knight, James C.; Furber, Steve B.

2016-01-01

While the adult human brain has approximately 8.8 × 1010 neurons, this number is dwarfed by its 1 × 1015 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows 4× more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously. PMID:27683540

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.

[Experimental determination of the time-dependent extent of after-burning with reference to possibilities of the plastic surgery reconstruction of 3d degree burns].

PubMed

Bäumer, F; Henrich, H A; Ussmüller, J

1986-02-01

The present experiments try to answer the question as to the time-dependent extent of the after-burning process after full-thickness burn (third degree). For an early plastic surgical treatment it was of interest to determine the most early time of escharotomy. The time-dependent spreading of the after-burning area reached its maximum five days after the burn injury. The after-burning area was marked by intravenous injections of Patentblau which caused distinct intravital colouring. Subsequently no further progress could be observed. In the present experiments we suggest this time as the earliest time for plastic covering in case it would be dependent upon the end of the after-burning process.

Studies on the effect of storage time and plasticizers on the structural variations in thermoplastic starch.

PubMed

Schmitt, H; Guidez, A; Prashantha, K; Soulestin, J; Lacrampe, M F; Krawczak, P

2015-01-22

Starch was combined with plasticizers such as glycerol, sorbitol, glycerol/sorbitol and urea/ethanolamine blends by means of high shear extrusion process to prepare thermoplastic starch (TPS). Effect of storage time and plasticizers on the structural stability of melt processed TPS was investigated. Morphological observation, X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy reveal that melt extrusion process is efficient in transforming granular starch into a plasticized starch for all plasticizer compositions. XRD analysis highlights major changes in the microstructure of plasticized starch, and dependence of crystalline type and degree of crystallinity mainly on the plasticizer composition and storage time. Dynamical mechanical analysis (DMA) yields a decrease of the peak intensity of loss factor with aging time. The effect of ageing on tensile strength also appears to be highly dependent on the plasticizer composition. Thus, through different plasticizer combinations and ageing, starch-based materials with significant differences in tensile properties can be obtained, which may be tuned to meet the requirements of a wide range of applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

Relative age effects in Swiss talent development - a nationwide analysis of all sports.

PubMed

Romann, Michael; Rössler, Roland; Javet, Marie; Faude, Oliver

2018-09-01

Relative age effects (RAE) generate consistent participation inequalities and selection biases in sports. The study aimed to investigate RAE across all sports of the national Swiss talent development programme (STDP). In this study, 18 859 youth athletes (female N = 5353; mean age: 14.8 ± 2.5 y and male N = 13 506; mean age: 14.4 ± 2.4 y) in 70 sports who participated in the 2014 competitive season were evaluated. The sample was subdivided by sex and the national level selection (NLS, N = 2464). Odds ratios (ORs) of relative age quarters (Q1-Q4) and 95% confidence intervals (CI) were calculated. In STDP, small RAE were evident for females (OR 1.35 (95%-CI 1.24, 1.47)) and males (OR 1.84 (95%-CI 1.74, 1.95)). RAE were similar in female NLS athletes (OR 1.30 (95%-CI 1.08, 1.57)) and larger in male NLS athletes (OR 2.40 (95%-CI 1.42, 1.97)) compared to athletes in the lower selection level. In STDP, RAE are evident for both sexes in several sports with popular sports showing higher RAE. RAE were larger in males than females. A higher selection level showed higher RAE only for males. In Switzerland, talent identification and development should be considered as a long-term process.

Patient affect experiencing following therapist interventions in short-term dynamic psychotherapy.

PubMed

Town, Joel M; Hardy, Gillian E; McCullough, Leigh; Stride, Chris

2012-01-01

The aim of this research was to examine the relationship between therapist interventions and patient affect responses in Short-Term Dynamic Psychotherapy (STDP). The Affect Experiencing subscale from the Achievement of Therapeutic Objectives Scale (ATOS) was adapted to measure individual immediate affect experiencing (I-AES) responses in relation to therapist interventions coded within the preceding speaking turn, using the Psychotherapy Interaction Coding (PIC) system. A hierarchical linear modelling procedure was used to assess the change in affect experiencing and the relationship between affect experiencing and therapist interventions within and across segments of therapy. Process data was taken from six STDP cases; in total 24 hours of video-taped sessions were examined. Therapist interventions were found to account for a statistically significant amount of variance in immediate affect experiencing. Higher levels of immediate affect experiencing followed the therapist's use of Confrontation, Clarification and Support compared to Questions, Self-disclosure and Information interventions. Therapist Confrontation interventions that attempted to direct pressure towards either the visceral experience of affect or a patient's defences against feelings led to the highest levels of immediate affect experiencing. The type of therapist intervention accounts for a small but significant amount of the variation observed in a patient's immediate emotional arousal. Empirical findings support clinical theory in STDP that suggests strategic verbal responses promote the achievement of this specific therapeutic objective.

Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences

PubMed Central

Bouchard, Kristofer E.; Ganguli, Surya; Brainard, Michael S.

2015-01-01

The majority of distinct sensory and motor events occur as temporally ordered sequences with rich probabilistic structure. Sequences can be characterized by the probability of transitioning from the current state to upcoming states (forward probability), as well as the probability of having transitioned to the current state from previous states (backward probability). Despite the prevalence of probabilistic sequencing of both sensory and motor events, the Hebbian mechanisms that mold synapses to reflect the statistics of experienced probabilistic sequences are not well understood. Here, we show through analytic calculations and numerical simulations that Hebbian plasticity (correlation, covariance, and STDP) with pre-synaptic competition can develop synaptic weights equal to the conditional forward transition probabilities present in the input sequence. In contrast, post-synaptic competition can develop synaptic weights proportional to the conditional backward probabilities of the same input sequence. We demonstrate that to stably reflect the conditional probability of a neuron's inputs and outputs, local Hebbian plasticity requires balance between competitive learning forces that promote synaptic differentiation and homogenizing learning forces that promote synaptic stabilization. The balance between these forces dictates a prior over the distribution of learned synaptic weights, strongly influencing both the rate at which structure emerges and the entropy of the final distribution of synaptic weights. Together, these results demonstrate a simple correspondence between the biophysical organization of neurons, the site of synaptic competition, and the temporal flow of information encoded in synaptic weights by Hebbian plasticity while highlighting the utility of balancing learning forces to accurately encode probability distributions, and prior expectations over such probability distributions. PMID:26257637

Estimating Tunnel Strain in the Weak and Schistose Rock Mass Influenced by Stress Anisotropy: An Evaluation Based on Three Tunnel Cases from Nepal

NASA Astrophysics Data System (ADS)

Panthi, Krishna Kanta; Shrestha, Pawan Kumar

2018-06-01

Total plastic deformation in tunnels passing through weak and schistose rock mass consists of both time-independent and time-dependent deformations. The extent of this total deformation is heavily influenced by the rock mass deformability properties and in situ stress condition prevailing in the area. If in situ stress is not isotropic, the deformation magnitude is not only different along the longitudinal alignment but also along the periphery of the tunnel wall. This manuscript first evaluates the long-term plastic deformation records of three tunnel projects from the Nepal Himalaya and identifies interlink between the time-independent and time-dependent deformations using the convergence law proposed by Sulem et al. (Int J Rock Mech Min Sci Geomech 24(3):145-154, 1987a, Int J Rock Mech Min Sci Geomech 24(3):155-164, 1987b). Secondly, the manuscript attempts to establish a correlation between plastic deformations (tunnel strain) and rock mass deformable properties, support pressure and in situ stress conditions. Finally, patterns of time-independent and time-dependent plastic deformations are also evaluated and discussed. The long-term plastic deformation records of 24 tunnel sections representing four different rock types of three different headrace tunnel cases from Nepal Himalaya are extensively used in this endeavor. The authors believe that the proposed findings will be a step further in analysis of plastic deformations in tunnels passing through weak and schistose rock mass and along the anisotropic stress conditions.

Multilevel Resistance Programming in Conductive Bridge Resistive Memory

NASA Astrophysics Data System (ADS)

Mahalanabis, Debayan

This work focuses on the existence of multiple resistance states in a type of emerging non-volatile resistive memory device known commonly as Programmable Metallization Cell (PMC) or Conductive Bridge Random Access Memory (CBRAM), which can be important for applications such as multi-bit memory as well as non-volatile logic and neuromorphic computing. First, experimental data from small signal, quasi-static and pulsed mode electrical characterization of such devices are presented which clearly demonstrate the inherent multi-level resistance programmability property in CBRAM devices. A physics based analytical CBRAM compact model is then presented which simulates the ion-transport dynamics and filamentary growth mechanism that causes resistance change in such devices. Simulation results from the model are fitted to experimental dynamic resistance switching characteristics. The model designed using Verilog-a language is computation-efficient and can be integrated with industry standard circuit simulation tools for design and analysis of hybrid circuits involving both CMOS and CBRAM devices. Three main circuit applications for CBRAM devices are explored in this work. Firstly, the susceptibility of CBRAM memory arrays to single event induced upsets is analyzed via compact model simulation and experimental heavy ion testing data that show possibility of both high resistance to low resistance and low resistance to high resistance transitions due to ion strikes. Next, a non-volatile sense amplifier based flip-flop architecture is proposed which can help make leakage power consumption negligible by allowing complete shutdown of power supply while retaining its output data in CBRAM devices. Reliability and energy consumption of the flip-flop circuit for different CBRAM low resistance levels and supply voltage values are analyzed and compared to CMOS designs. Possible extension of this architecture for threshold logic function computation using the CBRAM devices as re-configurable resistive weights is also discussed. Lastly, Spike timing dependent plasticity (STDP) based gradual resistance change behavior in CBRAM device fabricated in back-end-of-line on a CMOS die containing integrate and fire CMOS neuron circuits is demonstrated for the first time which indicates the feasibility of using CBRAM devices as electronic synapses in spiking neural network hardware implementations for non-Boolean neuromorphic computing.

Mechanical and time-dependent behavior of wood-plastic composites subjected to tension and compression

Treesearch

Scott E. Hamel; John C. Hermanson; Steven M. Cramer

2012-01-01

The thermoplastics within wood—plastic composites (WPCs) are known to experience significant time-dependent deformation or creep. In some formulations, creep deformation can be twice as much as the initial quasi-static strain in as little as 4 days. While extensive work has been done on the creep behavior of pure polymers, little information is available on the...

Avalanches and plasticity for colloids in a time dependent optical trap

DOE PAGES

Olson Reichhardt, Cynthia Jane; McDermott, Danielle Marie; Reichhardt, Charles

2015-08-25

Here, with the use of optical traps it is possible to confine assemblies of colloidal particles in two-dimensional and quasi-one-dimensional arrays. Here we examine how colloidal particles rearrange in a quasi-one-dimensional trap with a time dependent confining potential. The particle motion occurs both through slow elastic uniaxial distortions as well as through abrupt large-scale two-dimensional avalanches associated with plastic rearrangements. During the avalanches the particle velocity distributions extend over a broad range and can be fit to a power law consistent with other studies of plastic events mediated by dislocations.

Dynamic modulation of spike timing-dependent calcium influx during corticostriatal upstates

PubMed Central

Evans, R. C.; Maniar, Y. M.

2013-01-01

The striatum of the basal ganglia demonstrates distinctive upstate and downstate membrane potential oscillations during slow-wave sleep and under anesthetic. The upstates generate calcium transients in the dendrites, and the amplitude of these calcium transients depends strongly on the timing of the action potential (AP) within the upstate. Calcium is essential for synaptic plasticity in the striatum, and these large calcium transients during the upstates may control which synapses undergo plastic changes. To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). We have implemented sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion, which accurately replicate published data. Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. These findings have implications for synaptic plasticity in vivo during sleep when the upstate-downstate pattern is prominent in the striatum. PMID:23843436

Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning.

PubMed

Costa, Rui Ponte; Froemke, Robert C; Sjöström, P Jesper; van Rossum, Mark Cw

2015-08-26

Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.

Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules.

PubMed

Frémaux, Nicolas; Gerstner, Wulfram

2015-01-01

Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide "when" to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks.

PubMed

Scarpetta, Silvia; Giacco, Ferdinando

2013-04-01

We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at different time scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stable precise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units.

Time course of the induction of homeostatic plasticity generated by repeated transcranial direct current stimulation of the human motor cortex.

PubMed

Fricke, K; Seeber, A A; Thirugnanasambandam, N; Paulus, W; Nitsche, M A; Rothwell, J C

2011-03-01

Several mechanisms have been proposed that control the amount of plasticity in neuronal circuits and guarantee dynamic stability of neuronal networks. Homeostatic plasticity suggests that the ease with which a synaptic connection is facilitated/suppressed depends on the previous amount of network activity. We describe how such homeostatic-like interactions depend on the time interval between two conditioning protocols and on the duration of the preconditioning protocol. We used transcranial direct current stimulation (tDCS) to produce short-lasting plasticity in the motor cortex of healthy humans. In the main experiment, we compared the aftereffect of a single 5-min session of anodal or cathodal tDCS with the effect of a 5-min tDCS session preceded by an identical 5-min conditioning session administered 30, 3, or 0 min beforehand. Five-minute anodal tDCS increases excitability for about 5 min. The same duration of cathodal tDCS reduces excitability. Increasing the duration of tDCS to 10 min prolongs the duration of the effects. If two 5-min periods of tDCS are applied with a 30-min break between them, the effect of the second period of tDCS is identical to that of 5-min stimulation alone. If the break is only 3 min, then the second session has the opposite effect to 5-min tDCS given alone. Control experiments show that these shifts in the direction of plasticity evolve during the 10 min after the first tDCS session and depend on the duration of the first tDCS but not on intracortical inhibition and facilitation. The results are compatible with a time-dependent "homeostatic-like" rule governing the response of the human motor cortex to plasticity probing protocols.

Experimental evaluation criteria for constitutive models of time dependent cyclic plasticity

NASA Technical Reports Server (NTRS)

Martin, J. F.

1986-01-01

Notched members were tested at temperatures far above those recorded till now. Simulation of the notch root stress response was accomplished to establish notch stress-strain behavior. Cyclic stress-strain profiles across the net-section were recorded and on-line direct notch strain control was accomplished. Data are compared to three analysis techniques with good results. The objective of the study is to generate experimental data that can be used to evaluate the accuracy of constitutive models of time dependent cyclic plasticity.

Development of mathematical models for automation of strength calculation during plastic deformation processing

NASA Astrophysics Data System (ADS)

Steposhina, S. V.; Fedonin, O. N.

2018-03-01

Dependencies that make it possible to automate the force calculation during surface plastic deformation (SPD) processing and, thus, to shorten the time for technological preparation of production have been developed.

Functional requirements for reward-modulated spike-timing-dependent plasticity.

PubMed

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2010-10-06

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

Evaluation of beach cleanup effects using linear system analysis.

PubMed

Kataoka, Tomoya; Hinata, Hirofumi

2015-02-15

We established a method for evaluating beach cleanup effects (BCEs) based on a linear system analysis, and investigated factors determining BCEs. Here we focus on two BCEs: decreasing the total mass of toxic metals that could leach into a beach from marine plastics and preventing the fragmentation of marine plastics on the beach. Both BCEs depend strongly on the average residence time of marine plastics on the beach (τ(r)) and the period of temporal variability of the input flux of marine plastics (T). Cleanups on the beach where τ(r) is longer than T are more effective than those where τ(r) is shorter than T. In addition, both BCEs are the highest near the time when the remnants of plastics reach the local maximum (peak time). Therefore, it is crucial to understand the following three factors for effective cleanups: the average residence time, the plastic input period and the peak time. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

PubMed Central

Frémaux, Nicolas; Gerstner, Wulfram

2016-01-01

Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide “when” to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators. PMID:26834568

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

NASA Astrophysics Data System (ADS)

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-01

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning.

PubMed

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-13

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

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.

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.

PubMed

Jonke, Zeno; Legenstein, Robert; Habenschuss, Stefan; Maass, Wolfgang

2017-08-30

Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence, one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike timing-dependent plasticity shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through spike timing-dependent plasticity: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code. SIGNIFICANCE STATEMENT We analyze emergent computational properties of a ubiquitous cortical microcircuit motif: populations of pyramidal cells that are densely interconnected with inhibitory neurons. Simulations of this model predict that sparse assembly codes emerge in this microcircuit motif under spike timing-dependent plasticity. Furthermore, we show that different assemblies will represent different hidden sources of upstream firing activity. Hence, we propose that spike timing-dependent plasticity enables this microcircuit motif to perform a fundamental computational operation on neural activity patterns. Copyright © 2017 the authors 0270-6474/17/378511-13$15.00/0.

Evolution of phenotypic plasticity and environmental tolerance of a labile quantitative character in a fluctuating environment.

PubMed

Lande, R

2014-05-01

Quantitative genetic models of evolution of phenotypic plasticity are used to derive environmental tolerance curves for a population in a changing environment, providing a theoretical foundation for integrating physiological and community ecology with evolutionary genetics of plasticity and norms of reaction. Plasticity is modelled for a labile quantitative character undergoing continuous reversible development and selection in a fluctuating environment. If there is no cost of plasticity, a labile character evolves expected plasticity equalling the slope of the optimal phenotype as a function of the environment. This contrasts with previous theory for plasticity influenced by the environment at a critical stage of early development determining a constant adult phenotype on which selection acts, for which the expected plasticity is reduced by the environmental predictability over the discrete time lag between development and selection. With a cost of plasticity in a labile character, the expected plasticity depends on the cost and on the environmental variance and predictability averaged over the continuous developmental time lag. Environmental tolerance curves derived from this model confirm traditional assumptions in physiological ecology and provide new insights. Tolerance curve width increases with larger environmental variance, but can only evolve within a limited range. The strength of the trade-off between tolerance curve height and width depends on the cost of plasticity. Asymmetric tolerance curves caused by male sterility at high temperature are illustrated. A simple condition is given for a large transient increase in plasticity and tolerance curve width following a sudden change in average environment. © 2014 The Author. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Spike-timing dependent plasticity in primate corticospinal connections induced during free behavior

PubMed Central

Nishimura, Yukio; Perlmutter, Steve I.; Eaton, Ryan W.; Fetz, Eberhard E.

2014-01-01

Motor learning and functional recovery from brain damage involve changes in the strength of synaptic connections between neurons. Relevant in vivo evidence on the underlying cellular mechanisms remains limited and indirect. We found that the strength of neural connections between motor cortex and spinal cord in monkeys can be modified with an autonomous recurrent neural interface that delivers electrical stimuli in the spinal cord triggered by action potentials of corticospinal cells during free behavior. The activity-dependent stimulation modified the strength of the terminal connections of single corticomotoneuronal cells, consistent with a bidirectional spike-timing dependent plasticity rule previously derived from in vitro experiments. For some cells the changes lasted for days after the end of conditioning, but most effects eventually reverted to preconditioning levels. These results provide the first direct evidence of corticospinal synaptic plasticity in vivo at the level of single neurons induced by normal firing patterns during free behavior. PMID:24210907

Origin of the spike-timing-dependent plasticity rule

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Choi, M. Y.

2016-08-01

A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing-dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

Stress-dependent grain size evolution of nanocrystalline Ni-W and its impact on friction behavior

DOE PAGES

Argibay, N.; Furnish, T. A.; Boyce, B. L.; ...

2016-06-07

The friction behavior of ultra-nanocrystalline Ni-W coatings was investigated. A critical stress threshold was identified below which friction remained low, and above which a time-dependent evolution toward higher friction behavior occurred. Founded on established plasticity models we propose a correlation between surface grain size and applied stress that can be used to predict the critical stress separating the two friction regimes. Lastly, this interpretation of plasticity models suggests that macro-scale low and high friction regimes are respectively associated with the nano-scale mechanisms of grain boundary and dislocation-mediated plasticity.

Neuromodulation, development and synaptic plasticity.

PubMed

Foehring, R C; Lorenzon, N M

1999-03-01

We discuss parallels in the mechanisms underlying use-dependent synaptic plasticity during development and long-term potentiation (LTP) and long-term depression (LTD) in neocortical synapses. Neuromodulators, such as norepinephrine, serotonin, and acetylcholine have also been implicated in regulating both developmental plasticity and LTP/LTD. There are many potential levels of interaction between neuromodulators and plasticity. Ion channels are substrates for modulation in many cell types. We discuss examples of modulation of voltage-gated Ca2+ channels and Ca(2+)-dependent K+ channels and the consequences for neocortical pyramidal cell firing behaviour. At the time when developmental plasticity is most evident in rat cortex, the substrate for modulation is changing as the densities and relative proportions of various ion channels types are altered during ontogeny. We discuss examples of changes in K+ and Ca2+ channels and the consequence for modulation of neuronal activity.

Spike-timing dependent inhibitory plasticity to learn a selective gating of backpropagating action potentials.

PubMed

Wilmes, Katharina Anna; Schleimer, Jan-Hendrik; Schreiber, Susanne

2017-04-01

Inhibition is known to influence the forward-directed flow of information within neurons. However, also regulation of backward-directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory control of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Although such a mechanism is possible, it requires a precise timing of inhibition to annihilate bAPs without impairment of forward-directed excitatory information flow. Here, we propose a specific learning rule for inhibitory synapses to automatically generate the correct timing to gate bAPs in pyramidal cells when embedded in a local circuit of feedforward inhibition. Based on computational modeling of multi-compartmental neurons with physiological properties, we demonstrate that a learning rule with anti-Hebbian shape can establish the required temporal precision. In contrast to classical spike-timing dependent plasticity of excitatory synapses, the proposed inhibitory learning mechanism does not necessarily require the definition of an upper bound of synaptic weights because of its tendency to self-terminate once annihilation of bAPs has been reached. Our study provides a functional context in which one of the many time-dependent learning rules that have been observed experimentally - specifically, a learning rule with anti-Hebbian shape - is assigned a relevant role for inhibitory synapses. Moreover, the described mechanism is compatible with an upregulation of excitatory plasticity by disinhibition. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Ground Robotic Hand Applications for the Space Program study (GRASP)

NASA Astrophysics Data System (ADS)

Grissom, William A.; Rafla, Nader I.

1992-04-01

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time.

Ground Robotic Hand Applications for the Space Program study (GRASP)

NASA Technical Reports Server (NTRS)

Grissom, William A.; Rafla, Nader I. (Editor)

1992-01-01

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time.

On numerical integration and computer implementation of viscoplastic models

NASA Technical Reports Server (NTRS)

Chang, T. Y.; Chang, J. P.; Thompson, R. L.

1985-01-01

Due to the stringent design requirement for aerospace or nuclear structural components, considerable research interests have been generated on the development of constitutive models for representing the inelastic behavior of metals at elevated temperatures. In particular, a class of unified theories (or viscoplastic constitutive models) have been proposed to simulate material responses such as cyclic plasticity, rate sensitivity, creep deformations, strain hardening or softening, etc. This approach differs from the conventional creep and plasticity theory in that both the creep and plastic deformations are treated as unified time-dependent quantities. Although most of viscoplastic models give better material behavior representation, the associated constitutive differential equations have stiff regimes which present numerical difficulties in time-dependent analysis. In this connection, appropriate solution algorithm must be developed for viscoplastic analysis via finite element method.

CCR5 is a suppressor for cortical plasticity and hippocampal learning and memory

PubMed Central

Zhou, Miou; Greenhill, Stuart; Huang, Shan; Silva, Tawnie K; Sano, Yoshitake; Wu, Shumin; Cai, Ying; Nagaoka, Yoshiko; Sehgal, Megha; Cai, Denise J; Lee, Yong-Seok; Fox, Kevin; Silva, Alcino J

2016-01-01

Although the role of CCR5 in immunity and in HIV infection has been studied widely, its role in neuronal plasticity, learning and memory is not understood. Here, we report that decreasing the function of CCR5 increases MAPK/CREB signaling, long-term potentiation (LTP), and hippocampus-dependent memory in mice, while neuronal CCR5 overexpression caused memory deficits. Decreasing CCR5 function in mouse barrel cortex also resulted in enhanced spike timing dependent plasticity and consequently, dramatically accelerated experience-dependent plasticity. These results suggest that CCR5 is a powerful suppressor for plasticity and memory, and CCR5 over-activation by viral proteins may contribute to HIV-associated cognitive deficits. Consistent with this hypothesis, the HIV V3 peptide caused LTP, signaling and memory deficits that were prevented by Ccr5 knockout or knockdown. Overall, our results demonstrate that CCR5 plays an important role in neuroplasticity, learning and memory, and indicate that CCR5 has a role in the cognitive deficits caused by HIV. DOI: http://dx.doi.org/10.7554/eLife.20985.001 PMID:27996938

Enabling Functional Neural Circuit Simulations with Distributed Computing of Neuromodulated Plasticity

PubMed Central

Potjans, Wiebke; Morrison, Abigail; Diesmann, Markus

2010-01-01

A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e., on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator, or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity. PMID:21151370

Properties of vapor detector arrays formed through plasticization of carbon black-organic polymer composites.

PubMed

Koscho, Michael E; Grubbs, Robert H; Lewis, Nathan S

2002-03-15

Arrays of vapor detectors have been formed through addition of varying mass fractions of the plasticizer diethylene glycol dibenzoate to carbon black-polymer composites of poly(vinyl acetate) (PVAc) or of poly(N-vinylpyrrolidone). Addition of plasticizer in 5% mass fraction increments produced 20 compositionally different detectors from each polymer composite. Differences in vapor sorption and permeability that effected changes in the dc electrical resistance response of these compositionally different detectors allowed identification and classification of various test analytes using standard chemometric methods. Glass transition temperatures, Tg, were measured using differential scanning calorimetry for plasticized polymers having a mass fraction of 0, 0.10, 0.20, 0.30, 0.40, or 0.50 of plasticizer in the composite. The plasticized PVAc composites with Tg < 25 degrees C showed rapid responses at room temperature to all of the test analyte vapors studied in this work, whereas composites with Tg > 25 degrees C showed response times that were highly dependent on the polymer/analyte combination. These composites showed a discontinuity in the temperature dependence of their resistance, and this discontinuity provided a simple method for determining the Tg of the composite and for determining the temperature or plasticizer mass fraction above which rapid resistance responses could be obtained for all members of the test set of analyte vapors. The plasticization approach provides a method for achieving rapid detector response times as well as for producing a large number of chemically different vapor detectors from a limited number of initial chemical feedstocks.

Post-cyclic behavior of low plasticity silt under full and limited liquefaction using triaxial compression testing.

DOT National Transportation Integrated Search

2010-02-01

During an earthquake, liquefaction does not happen all the time. It depends on the duration and magnitude of the earthquake and the properties (with relationship to resistance of liquefaction) of the low plasticity silt. Under low duration or magnitu...

Climate change and temperature-dependent sex determination: can individual plasticity in nesting phenology prevent extreme sex ratios?

PubMed

Schwanz, Lisa E; Janzen, Fredric J

2008-01-01

Under temperature-dependent sex determination (TSD), temperatures experienced by embryos during development determine the sex of the offspring. Consequently, populations of organisms with TSD have the potential to be strongly impacted by climatic warming that could bias offspring sex ratio, a fundamental demographic parameter involved in population dynamics. Moreover, many taxa with TSD are imperiled, so research on this phenomenon, particularly long-term field study, has assumed great urgency. Recently, turtles with TSD have joined the diverse list of taxa that have demonstrated population-level changes in breeding phenology in response to recent climate change. This raises the possibility that any adverse impacts of climate change on populations may be alleviated by individual plasticity in nesting phenology. Here, we examine data from a long-term study on a population of painted turtles (Chrysemys picta) to determine whether changes in phenology are due to individual plasticity and whether individual plasticity in the timing of nesting has the capacity to offset the sex ratio effects of a rise in climatic temperature. We find that individual females show plasticity in the date of first nesting each year, and that this plasticity depends on the climate from the previous winter. First nesting date is not repeatable within individuals, suggesting that it would not respond to selection. Sex ratios of hatchlings within a nest declined nonsignificantly over the nesting season. However, small increases in summer temperature had a much stronger effect on nest sex ratios than did laying nests earlier in the season. For this and other reasons, it seems unlikely that individual plasticity in the timing of nesting will offset the effects of climate change on sex ratios in this population, and we hypothesize that this conclusion applies to other populations with TSD.

Global Existence Results for Viscoplasticity at Finite Strain

NASA Astrophysics Data System (ADS)

Mielke, Alexander; Rossi, Riccarda; Savaré, Giuseppe

2018-01-01

We study a model for rate-dependent gradient plasticity at finite strain based on the multiplicative decomposition of the strain tensor, and investigate the existence of global-in-time solutions to the related PDE system. We reveal its underlying structure as a generalized gradient system, where the driving energy functional is highly nonconvex and features the geometric nonlinearities related to finite-strain elasticity as well as the multiplicative decomposition of finite-strain plasticity. Moreover, the dissipation potential depends on the left-invariant plastic rate, and thus depends on the plastic state variable. The existence theory is developed for a class of abstract, nonsmooth, and nonconvex gradient systems, for which we introduce suitable notions of solutions, namely energy-dissipation-balance and energy-dissipation-inequality solutions. Hence, we resort to the toolbox of the direct method of the calculus of variations to check that the specific energy and dissipation functionals for our viscoplastic models comply with the conditions of the general theory.

HEMP 3D: A finite difference program for calculating elastic-plastic flow, appendix B

NASA Astrophysics Data System (ADS)

Wilkins, Mark L.

1993-05-01

The HEMP 3D program can be used to solve problems in solid mechanics involving dynamic plasticity and time dependent material behavior and problems in gas dynamics. The equations of motion, the conservation equations, and the constitutive relations listed below are solved by finite difference methods following the format of the HEMP computer simulation program formulated in two space dimensions and time.

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

PubMed Central

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-01-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

PubMed

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-04-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Evaluation of a disposable plastic Neubauer counting chamber for semen analysis.

PubMed

Kirkman-Brown, Jackson; Björndahl, Lars

2009-02-01

To evaluate whether disposable plastic counting chambers effectively could replace nondisposable, time-consuming, and potentially dangerous glass hemocytometers. Evaluation of equipment in modern laboratory andrology. Comparison of results obtained with plastic chambers with results obtained with "gold-standard" glass hemocytometer counts. Diagnostic laboratory for andrology. Twenty-one patients undergoing investigation for infertility problems. No interventions with patients; sperm in diluted semen samples were used when patients had allowed the use for research and training. Sperm concentration, difference from results obtained with standard equipment. In the first three experimental series, with use of standard routine phase-contrast microscopy, significantly lower count results were obtained consistently from the plastic chambers than from standard chambers. In the fourth series, with use of specialized equipment, equivalent results were obtained but with a considerably greater time commitment because of difficulties in distinguishing sperm adjacent to the gridlines in the plastic chambers. The plastic disposable chamber type was not suitable for routine semen analysis because results are variable depending on the microscope used, and increased time is necessary to do the assessment accurately.

Cognitive plasticity as a moderator of functional dependency in elderly patients hospitalized for bone fractures.

PubMed

Calero-García, M J; Calero, M D; Navarro, E; Ortega, A R

2015-01-01

Bone fractures in older adults involve hospitalization and surgical intervention, aspects that have been related to loss of autonomy and independence. Several variables have been studied as moderators of how these patients recover. However, the implications of cognitive plasticity for functional recovery have not been studied to date. The present study analyzes the relationship between cognitive plasticity--defined as the capacity for learning or improved performance under conditions of training or performance optimization--and functional recovery in older adults hospitalized following a bone fracture. The study comprised 165 older adults who underwent surgery for bone fractures at a hospital in southern Spain. Participants were evaluated at different time points thereafter, with instruments that measure activities of daily life (ADL), namely the Barthel Index (BI) and the Lawton Index, as well as with a learning potential (cognitive plasticity) assessment test (Auditory Verbal Learning Test of Learning Potential, AVLT-LP). Results show that most of the participants have improved their level of independence 3 months after the intervention. However, some patients continue to have medium to high levels of dependency and this dependency is related to cognitive plasticity. The results of this study reveal the importance of the cognitive plasticity variable for evaluating older adults hospitalized for a fracture. They indicate a possible benefit to be obtained by implementing programs that reduce the degree of long-term dependency or decrease the likelihood of it arising.

Characterizing energy dependence and count rate performance of a dual scintillator fiber-optic detector for computed tomography

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

Hoerner, Matthew R., E-mail: mrh5038@ufl.edu; Stepusin, Elliott J.; Hyer, Daniel E.

Purpose: Kilovoltage (kV) x-rays pose a significant challenge for radiation dosimetry. In the kV energy range, even small differences in material composition can result in significant variations in the absorbed energy between soft tissue and the detector. In addition, the use of electronic systems in light detection has demonstrated measurement losses at high photon fluence rates incident to the detector. This study investigated the feasibility of using a novel dual scintillator detector and whether its response to changes in beam energy from scatter and hardening is readily quantified. The detector incorporates a tissue-equivalent plastic scintillator and a gadolinium oxysulfide scintillator,more » which has a higher sensitivity to scatter x-rays. Methods: The detector was constructed by coupling two scintillators: (1) small cylindrical plastic scintillator, 500 μm in diameter and 2 mm in length, and (2) 100 micron sheet of gadolinium oxysulfide 500 μm in diameter, each to a 2 m long optical fiber, which acts as a light guide to transmit scintillation photons from the sensitive element to a photomultiplier tube. Count rate linearity data were obtained from a wide range of exposure rates delivered from a radiological x-ray tube by adjusting the tube current. The data were fitted to a nonparalyzable dead time model to characterize the time response. The true counting rate was related to the reference free air dose air rate measured with a 0.6 cm{sup 3} Radcal{sup ®} thimble chamber as described in AAPM Report No. 111. Secondary electron and photon spectra were evaluated using Monte Carlo techniques to analyze ionization quenching and photon energy-absorption characteristics from free-in-air and in phantom measurements. The depth/energy dependence of the detector was characterized using a computed tomography dose index QA phantom consisting of nested adult head and body segments. The phantom provided up to 32 cm of acrylic with a compatible 0.6 cm{sup 3} calibrated ionization chamber to measure the reference air kerma. Results: Each detector exhibited counting losses of 5% when irradiated at a dose rate of 26.3 mGy/s (Gadolinium) and 324.3 mGy/s (plastic). The dead time of the gadolinium oxysulfide detector was determined to be 48 ns, while the dead time of the plastic scintillating detector was unable to accurately be calculated due to poor counting statistics from low detected count rates. Noticeable depth/energy dependence was observed for the plastic scintillator for depths greater than 16 cm of acrylic that was not present for measurements using the gadolinium oxysulfide scintillator, leading us to believe that quenching may play a larger role in the depth dependence of the plastic scintillator than the incident x-ray energy spectrum. When properly corrected for dead time effects, the energy response of the gadolinium oxysulfide scintillator is consistent with the plastic scintillator. Using the integrated dual detector method was superior to each detector individually as the depth-dependent measure of dose was correctable to less than 8% between 100 and 135 kV. Conclusions: The dual scintillator fiber-optic detector accommodates a methodology for energy dependent corrections of the plastic scintillator, improving the overall accuracy of the dosimeter across the range of diagnostic energies.« less

Characterizing energy dependence and count rate performance of a dual scintillator fiber-optic detector for computed tomography.

PubMed

Hoerner, Matthew R; Stepusin, Elliott J; Hyer, Daniel E; Hintenlang, David E

2015-03-01

Kilovoltage (kV) x-rays pose a significant challenge for radiation dosimetry. In the kV energy range, even small differences in material composition can result in significant variations in the absorbed energy between soft tissue and the detector. In addition, the use of electronic systems in light detection has demonstrated measurement losses at high photon fluence rates incident to the detector. This study investigated the feasibility of using a novel dual scintillator detector and whether its response to changes in beam energy from scatter and hardening is readily quantified. The detector incorporates a tissue-equivalent plastic scintillator and a gadolinium oxysulfide scintillator, which has a higher sensitivity to scatter x-rays. The detector was constructed by coupling two scintillators: (1) small cylindrical plastic scintillator, 500 μm in diameter and 2 mm in length, and (2) 100 micron sheet of gadolinium oxysulfide 500 μm in diameter, each to a 2 m long optical fiber, which acts as a light guide to transmit scintillation photons from the sensitive element to a photomultiplier tube. Count rate linearity data were obtained from a wide range of exposure rates delivered from a radiological x-ray tube by adjusting the tube current. The data were fitted to a nonparalyzable dead time model to characterize the time response. The true counting rate was related to the reference free air dose air rate measured with a 0.6 cm(3) Radcal(®) thimble chamber as described in AAPM Report No. 111. Secondary electron and photon spectra were evaluated using Monte Carlo techniques to analyze ionization quenching and photon energy-absorption characteristics from free-in-air and in phantom measurements. The depth/energy dependence of the detector was characterized using a computed tomography dose index QA phantom consisting of nested adult head and body segments. The phantom provided up to 32 cm of acrylic with a compatible 0.6 cm(3) calibrated ionization chamber to measure the reference air kerma. Each detector exhibited counting losses of 5% when irradiated at a dose rate of 26.3 mGy/s (Gadolinium) and 324.3 mGy/s (plastic). The dead time of the gadolinium oxysulfide detector was determined to be 48 ns, while the dead time of the plastic scintillating detector was unable to accurately be calculated due to poor counting statistics from low detected count rates. Noticeable depth/energy dependence was observed for the plastic scintillator for depths greater than 16 cm of acrylic that was not present for measurements using the gadolinium oxysulfide scintillator, leading us to believe that quenching may play a larger role in the depth dependence of the plastic scintillator than the incident x-ray energy spectrum. When properly corrected for dead time effects, the energy response of the gadolinium oxysulfide scintillator is consistent with the plastic scintillator. Using the integrated dual detector method was superior to each detector individually as the depth-dependent measure of dose was correctable to less than 8% between 100 and 135 kV. The dual scintillator fiber-optic detector accommodates a methodology for energy dependent corrections of the plastic scintillator, improving the overall accuracy of the dosimeter across the range of diagnostic energies.

Determination of the diffusion coefficient and solubility of radon in plastics.

PubMed

Pressyanov, D; Georgiev, S; Dimitrova, I; Mitev, K; Boshkova, T

2011-05-01

This paper describes a method for determination of the diffusion coefficient and the solubility of radon in plastics. The method is based on the absorption and desorption of radon in plastics. Firstly, plastic specimens are exposed for controlled time to referent (222)Rn concentrations. After exposure, the activity of the specimens is followed by HPGe gamma spectrometry. Using the mathematical algorithm described in this report and the decrease of activity as a function of time, the diffusion coefficient can be determined. In addition, if the referent (222)Rn concentration during the exposure is known, the solubility of radon can be determined. The algorithm has been experimentally applied for different plastics. The results show that this approach allows the specified quantities to be determined with a rather high accuracy-depending on the quality of the counting equipment, it can be better than 10 %.

Critical neural networks with short- and long-term plasticity.

PubMed

Michiels van Kessenich, L; Luković, M; de Arcangelis, L; Herrmann, H J

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1/f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Critical neural networks with short- and long-term plasticity

NASA Astrophysics Data System (ADS)

Michiels van Kessenich, L.; Luković, M.; de Arcangelis, L.; Herrmann, H. J.

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1 /f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Dependence of Plastic TATB Shock-Wave Sensitivity on Temperature, Density and Technology Factors

NASA Astrophysics Data System (ADS)

Vlasov, Yu. A.; Kosolapov, V. B.; Fomicheva, L. V.; Khabarov, I. P.

1999-06-01

Mixed TATB-based HE is the most perspective because of the manufacture and exploitation safety of its items. At the same time the safety of these explosive, at high temperatures, which take place at emergencies, causes the certain anxiety. Plastic TATB shock-wave sensitivity (SWS) researches has shown that temperature as one of the important factors of external influence is not always the determining reason of SWS change. It is known that density influence on SWS significantly. At the same time density depends on temperature and technology of details manufacturing. In this connection in this work the temperature dependence of plastic TATB SWS was studied in view of convertible and irreversible changes of density (p) under heating at -50[C up to 90[C . It is shown that during these influences the dependence of threshold pressure of initiation (P) from temperature is explained, first of all, by change of HE density, caused by its thermal expansion (compression), and also by irreversible changes of p and HE structure, arising at heating. It is found also that the share of irreversible change of density depends on technology of HE details manufacturing and is explained by relaxation of residual pressure in them. The mentioned relaxation is finished after the first cycles of thermal influence. The value of density change, caused by this factor, depends on temperature and duration of heating.

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.

Experimental and numerical characterisation of the elasto-plastic properties of bovine trabecular bone and a trabecular bone analogue.

PubMed

Kelly, Nicola; McGarry, J Patrick

2012-05-01

The inelastic pressure dependent compressive behaviour of bovine trabecular bone is investigated through experimental and computational analysis. Two loading configurations are implemented, uniaxial and confined compression, providing two distinct loading paths in the von Mises-pressure stress plane. Experimental results reveal distinctive yielding followed by a constant nominal stress plateau for both uniaxial and confined compression. Computational simulation of the experimental tests using the Drucker-Prager and Mohr-Coulomb plasticity models fails to capture the confined compression behaviour of trabecular bone. The high pressure developed during confined compression does not result in plastic deformation using these formulations, and a near elastic response is computed. In contrast, the crushable foam plasticity models provide accurate simulation of the confined compression tests, with distinctive yield and plateau behaviour being predicted. The elliptical yield surfaces of the crushable foam formulations in the von Mises-pressure stress plane accurately characterise the plastic behaviour of trabecular bone. Results reveal that the hydrostatic yield stress is equal to the uniaxial yield stress for trabecular bone, demonstrating the importance of accurate characterisation and simulation of the pressure dependent plasticity. It is also demonstrated in this study that a commercially available trabecular bone analogue material, cellular rigid polyurethane foam, exhibits similar pressure dependent yield behaviour, despite having a lower stiffness and strength than trabecular bone. This study provides a novel insight into the pressure dependent yield behaviour of trabecular bone, demonstrating the inadequacy of uniaxial testing alone. For the first time, crushable foam plasticity formulations are implemented for trabecular bone. The enhanced understanding of the inelastic behaviour of trabecular bone established in this study will allow for more realistic simulation of orthopaedic device implantation and failure. Copyright © 2011 Elsevier Ltd. All rights reserved.

Learning-Dependent Plasticity of the Barrel Cortex Is Impaired by Restricting GABA-Ergic Transmission.

PubMed

Posluszny, Anna; Liguz-Lecznar, Monika; Turzynska, Danuta; Zakrzewska, Renata; Bielecki, Maksymilian; Kossut, Malgorzata

2015-01-01

Experience-induced plastic changes in the cerebral cortex are accompanied by alterations in excitatory and inhibitory transmission. Increased excitatory drive, necessary for plasticity, precedes the occurrence of plastic change, while decreased inhibitory signaling often facilitates plasticity. However, an increase of inhibitory interactions was noted in some instances of experience-dependent changes. We previously reported an increase in the number of inhibitory markers in the barrel cortex of mice after fear conditioning engaging vibrissae, observed concurrently with enlargement of the cortical representational area of the row of vibrissae receiving conditioned stimulus (CS). We also observed that an increase of GABA level accompanied the conditioning. Here, to find whether unaltered GABAergic signaling is necessary for learning-dependent rewiring in the murine barrel cortex, we locally decreased GABA production in the barrel cortex or reduced transmission through GABAA receptors (GABAARs) at the time of the conditioning. Injections of 3-mercaptopropionic acid (3-MPA), an inhibitor of glutamic acid decarboxylase (GAD), into the barrel cortex prevented learning-induced enlargement of the conditioned vibrissae representation. A similar effect was observed after injection of gabazine, an antagonist of GABAARs. At the behavioral level, consistent conditioned response (cessation of head movements in response to CS) was impaired. These results show that appropriate functioning of the GABAergic system is required for both manifestation of functional cortical representation plasticity and for the development of a conditioned response.

Myelination: an overlooked mechanism of synaptic plasticity?

PubMed

Fields, R Douglas

2005-12-01

Myelination of the brain continues through childhood into adolescence and early adulthood--the question is, Why? Two new articles provide intriguing evidence that myelination may be an underappreciated mechanism of activity-dependent nervous system plasticity: one study reported increased myelination associated with extensive piano playing, another indicated that rats have increased myelination of the corpus callosum when raised in environments providing increased social interaction and cognitive stimulation. These articles make it clear that activity-dependent effects on myelination cannot be considered strictly a developmental event. They raise the question of whether myelination is an overlooked mechanism of activity-dependent plasticity, extending in humans until at least age 30. It has been argued that regulating the speed of conduction across long fiber tracts would have a major influence on synaptic response, by coordinating the timing of afferent input to maximize temporal summation. The increase in synaptic amplitude could be as large as neurotransmitter-based mechanisms of plasticity, such as LTP. These new findings raise a larger question: How did the oligodendrocytes know they were practicing the piano or that their environment was socially complex?

Leaf age dependent changes in within-canopy variation in leaf functional traits: a meta-analysis

PubMed Central

Niinemets, Ülo

2018-01-01

Within-canopy variation in leaf structural and photosynthetic characteristics is a major means by which whole canopy photosynthesis is maximized at given total canopy nitrogen. As key acclimatory modifications, leaf nitrogen content (NA) and photosynthetic capacity (AA) per unit area increase with increasing light availability in the canopy and these increases are associated with increases in leaf dry mass per unit area (MA) and/or nitrogen content per dry mass and/or allocation. However, leaf functional characteristics change with increasing leaf age during leaf development and aging, but the importance of these alterations for within-canopy trait gradients is unknown. I conducted a meta-analysis based on 71 canopies that were sampled at different time periods or, in evergreens, included measurements for different-aged leaves to understand how within-canopy variations in leaf traits (trait plasticity) depend on leaf age. The analysis demonstrated that in evergreen woody species, MA and NA plasticity decreased with increasing leaf age, but the change in AA plasticity was less suggesting a certain re-acclimation of AA to altered light. In deciduous woody species, MA and NA gradients in flush-type species increased during leaf development and were almost invariable through the rest of the season, while in continuously leaf-forming species, trait gradients increased constantly with increasing leaf age. In forbs, NA plasticity increased, while in grasses, NA plasticity decreased with increasing leaf age, reflecting life form differences in age-dependent changes in light availability and in nitrogen resorption for growth of generative organs. Although more work is needed to improve the coverage of age-dependent plasticity changes in some plant life forms, I argue that the age-dependent variation in trait plasticity uncovered in this study is large enough to warrant incorporation in simulations of canopy photosynthesis through the growing period. PMID:27033356

A rapid form of activity-dependent recovery from short-term synaptic depression in the intensity pathway of the auditory brainstem

PubMed Central

Horiuchi, Timothy K.

2011-01-01

Short-term synaptic plasticity acts as a time- and firing rate-dependent filter that mediates the transmission of information across synapses. In the avian auditory brainstem, specific forms of plasticity are expressed at different terminals of the same auditory nerve fibers and contribute to the divergence of acoustic timing and intensity information. To identify key differences in the plasticity properties, we made patch-clamp recordings from neurons in the cochlear nucleus responsible for intensity coding, nucleus angularis, and measured the time course of the recovery of excitatory postsynaptic currents following short-term synaptic depression. These synaptic responses showed a very rapid recovery, following a bi-exponential time course with a fast time constant of ~40 ms and a dependence on the presynaptic activity levels, resulting in a crossing over of the recovery trajectories following high-rate versus low-rate stimulation trains. We also show that the recorded recovery in the intensity pathway differs from similar recordings in the timing pathway, specifically the cochlear nucleus magnocellularis, in two ways: (1) a fast recovery that was not due to recovery from postsynaptic receptor desensitization and (2) a recovery trajectory that was characterized by a non-monotonic bump that may be due in part to facilitation mechanisms more prevalent in the intensity pathway. We tested whether a previously proposed model of synaptic transmission based on vesicle depletion and sequential steps of vesicle replenishment could account for the recovery responses, and found it was insufficient, suggesting an activity-dependent feedback mechanism is present. We propose that the rapid recovery following depression allows improved coding of natural auditory signals that often consist of sound bursts separated by short gaps. PMID:21409439

The Effect of Adhesion Interaction on the Mechanical Properties of Thermoplastic Basalt Plastics

NASA Astrophysics Data System (ADS)

Bashtannik, P. I.; Kabak, A. I.; Yakovchuk, Yu. Yu.

2003-01-01

The effect of temperature, adhesion time, and surface treatment of a reinforcing filler on the mechanical properties of thermoplastic basalt plastics based on a high-density polyethylene and a copolymer of 1,3,5-trioxane with 1,3-dioxolan is investigated. An extreme dependence for the adhesive strength in a thermoplastic-basalt fiber system is established and its effect on the mechanical properties of basalt plastics and the influence of the adhesion contact time on the adhesive strength in the system are clarified. The surface modification of basalt fibers in acidic and alkaline media intensifies the adhesion of thermoplastics to them owing to a more developed surface of the reinforcing fibers after etching. It is found that the treatment in the acidic medium is more efficient and considerably improves the mechanical properties of basalt plastics.

Sandia/Stanford Unified Creep Plasticity Damage Model for ANSYS

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

Pierce, David M.; Vianco, Paul T.; Fossum, Arlo F.

2006-09-03

A unified creep plasticity (UCP) model was developed, based upon the time-dependent and time-independent deformation properties of the 95.5Sn-3.9Ag-0.6Cu (wt.%) soldier that were measured at Sandia. Then, a damage parameter, D, was added to the equation to develop the unified creep plasticity damage (UCPD) model. The parameter, D, was parameterized, using data obtained at Sandia from isothermal fatigue experiments on a double-lap shear test. The softwae was validated against a BGA solder joint exposed to thermal cycling. The UCPD model was put into the ANSYS finite element as a subroutine. So, the softwae is the subroutine for ANSYS 8.1.

Quasi-static responses and variational principles in gradient plasticity

NASA Astrophysics Data System (ADS)

Nguyen, Quoc-Son

2016-12-01

Gradient models have been much discussed in the literature for the study of time-dependent or time-independent processes such as visco-plasticity, plasticity and damage. This paper is devoted to the theory of Standard Gradient Plasticity at small strain. A general and consistent mathematical description available for common time-independent behaviours is presented. Our attention is focussed on the derivation of general results such as the description of the governing equations for the global response and the derivation of related variational principles in terms of the energy and the dissipation potentials. It is shown that the quasi-static response under a loading path is a solution of an evolution variational inequality as in classical plasticity. The rate problem and the rate minimum principle are revisited. A time-discretization by the implicit scheme of the evolution equation leads to the increment problem. An increment of the response associated with a load increment is a solution of a variational inequality and satisfies also a minimum principle if the energy potential is convex. The increment minimum principle deals with stables solutions of the variational inequality. Some numerical methods are discussed in view of the numerical simulation of the quasi-static response.

Scattering by tilted plastic cylinders having curved ends and truncated plastic cones

NASA Astrophysics Data System (ADS)

Espana, Aubrey; Baik, Kyungmin; Marston, Philip L.

2005-04-01

In prior research an acoustic backscattering enhancement was demonstrated for a bluntly truncated plastic cylinder caused by a merged caustic [F. J. Blonigen and P. L. Marston, J. Acoust. Soc. Am. 107, 689-698 (2000)]. This was confirmed with analogous light scattering experiments [P. L. Marston, Y. B. Zhang, and D. B. Thiessen, Appl. Opt. 42, 412-417 (2003)]. In recent work a different backscattering enhancement associated with a caustic was identified for tilted plastic cylinders having curved ends. When the cylinder is tilted so as to focus a shear wave at the point of internal specular reflection, the curvature of the outgoing acoustic wavefront vanishes orthogonal to the meridional plane. This was verified with analogous light scattering experiments. The flatness of the outgoing wavefront enhances the scattering. Backscattering by truncated plastic cones as a function of tilt also shows enhancements associated with the composition of the target. The time dependence of the backscattering envelope as a function of tilt reveals different features depending on whether the top or bottom of the cone is illuminated by tone bursts. [Work supported by the Office of Naval Research.

Role of the visual experience-dependent nascent proteome in neuronal plasticity

PubMed Central

Liu, Han-Hsuan; McClatchy, Daniel B; Schiapparelli, Lucio; Shen, Wanhua; Yates, John R

2018-01-01

Experience-dependent synaptic plasticity refines brain circuits during development. To identify novel protein synthesis-dependent mechanisms contributing to experience-dependent plasticity, we conducted a quantitative proteomic screen of the nascent proteome in response to visual experience in Xenopus optic tectum using bio-orthogonal metabolic labeling (BONCAT). We identified 83 differentially synthesized candidate plasticity proteins (CPPs). The CPPs form strongly interconnected networks and are annotated to a variety of biological functions, including RNA splicing, protein translation, and chromatin remodeling. Functional analysis of select CPPs revealed the requirement for eukaryotic initiation factor three subunit A (eIF3A), fused in sarcoma (FUS), and ribosomal protein s17 (RPS17) in experience-dependent structural plasticity in tectal neurons and behavioral plasticity in tadpoles. These results demonstrate that the nascent proteome is dynamic in response to visual experience and that de novo synthesis of machinery that regulates RNA splicing and protein translation is required for experience-dependent plasticity. PMID:29412139

Mapping Viscoelastic and Plastic Properties of Polymers and Polymer-Nanotube Composites using Instrumented Indentation

PubMed Central

Gayle, Andrew J.; Cook, Robert F.

2016-01-01

An instrumented indentation method is developed for generating maps of time-dependent viscoelastic and time-independent plastic properties of polymeric materials. The method is based on a pyramidal indentation model consisting of two quadratic viscoelastic Kelvin-like elements and a quadratic plastic element in series. Closed-form solutions for indentation displacement under constant load and constant loading-rate are developed and used to determine and validate material properties. Model parameters are determined by point measurements on common monolithic polymers. Mapping is demonstrated on an epoxy-ceramic interface and on two composite materials consisting of epoxy matrices containing multi-wall carbon nanotubes. A fast viscoelastic deformation process in the epoxy was unaffected by the inclusion of the nanotubes, whereas a slow viscoelastic process was significantly impeded, as was the plastic deformation. Mapping revealed considerable spatial heterogeneity in the slow viscoelastic and plastic responses in the composites, particularly in the material with a greater fraction of nanotubes. PMID:27563168

Dynamical model of long-term synaptic plasticity

PubMed Central

Abarbanel, Henry D. I.; Huerta, R.; Rabinovich, M. I.

2002-01-01

Long-term synaptic plasticity leading to enhancement in synaptic efficacy (long-term potentiation, LTP) or decrease in synaptic efficacy (long-term depression, LTD) is widely regarded as underlying learning and memory in nervous systems. LTP and LTD at excitatory neuronal synapses are observed to be induced by precise timing of pre- and postsynaptic events. Modification of synaptic transmission in long-term plasticity is a complex process involving many pathways; for example, it is also known that both forms of synaptic plasticity can be induced by various time courses of Ca2+ introduction into the postsynaptic cell. We present a phenomenological description of a two-component process for synaptic plasticity. Our dynamical model reproduces the spike time-dependent plasticity of excitatory synapses as a function of relative timing between pre- and postsynaptic events, as observed in recent experiments. The model accounts for LTP and LTD when the postsynaptic cell is voltage clamped and depolarized (LTP) or hyperpolarized (LTD) and no postsynaptic action potentials are evoked. We are also able to connect our model with the Bienenstock, Cooper, and Munro rule. We give model predictions for changes in synaptic strength when periodic spike trains of varying frequency and Poisson distributed spike trains with varying average frequency are presented pre- and postsynaptically. When the frequency of spike presentation exceeds ≈30–40 Hz, only LTP is induced. PMID:12114531

Nicotinic modulation of hippocampal cell signaling and associated effects on learning and memory.

PubMed

Kutlu, Munir Gunes; Gould, Thomas J

2016-03-01

The hippocampus is a key brain structure involved in synaptic plasticity associated with long-term declarative memory formation. Importantly, nicotine and activation of nicotinic acetylcholine receptors (nAChRs) can alter hippocampal plasticity and these changes may occur through modulation of hippocampal kinases and transcription factors. Hippocampal kinases such as cAMP-dependent protein kinase (PKA), calcium/calmodulin-dependent protein kinases (CAMKs), extracellular signal-regulated kinases 1 and 2 (ERK1/2), and c-jun N-terminal kinase 1 (JNK1), and the transcription factor cAMP-response element-binding protein (CREB) that are activated either directly or indirectly by nicotine may modulate hippocampal plasticity and in parallel hippocampus-dependent learning and memory. Evidence suggests that nicotine may alter hippocampus-dependent learning by changing the time and magnitude of activation of kinases and transcription factors normally involved in learning and by recruiting additional cell signaling molecules. Understanding how nicotine alters learning and memory will advance basic understanding of the neural substrates of learning and aid in understanding mental disorders that involve cognitive and learning deficits. Copyright © 2015 Elsevier Inc. All rights reserved.

Categorizing experience-based foraging plasticity in mites: age dependency, primacy effects and memory persistence

PubMed Central

Davaasambuu, Undarmaa; Saussure, Stéphanie; Christiansen, Inga C.

2018-01-01

Behavioural plasticity can be categorized into activational (also termed contextual) and developmental plasticity. Activational plasticity allows immediate contextual behavioural changes, whereas developmental plasticity is characterized by time-lagged changes based on memory of previous experiences (learning). Behavioural plasticity tends to decline with age but whether this holds true for both plasticity categories and the effects of first-in-life experiences is poorly understood. We tackled this issue by assessing the foraging plasticity of plant-inhabiting predatory mites, Amblyseius swirskii, on thrips and spider mites following age-dependent prey experience, i.e. after hatching or after reaching maturity. Juvenile and young adult predator females were alternately presented thrips and spider mites, for establishing 1st and 2nd prey-in-life experiences, and tested, as gravid females, for their foraging plasticity when offered both prey species. Prey experience by juvenile predators resulted in clear learning effects, which were evident in likelier and earlier attacks on familiar prey, and higher proportional inclusion of familiar prey in total diet. First prey-in-life experience by juvenile but not adult predators resulted in primacy effects regarding attack latency. Prey experience by adult predators resulted mainly in prey-unspecific physiological changes, with easy-to-grasp spider mites providing higher net energy gains than difficult-to-grasp thrips. Prey experience by juvenile, but not adult, predators was adaptive, which was evident in a negative correlation between attack latencies and egg production. Overall, our study provides key evidence that similar experiences by juvenile and adult predators, including first-in-life experiences, may be associated with different types of behavioural plasticity, i.e. developmental and activational plasticity. PMID:29765663

Categorizing experience-based foraging plasticity in mites: age dependency, primacy effects and memory persistence.

PubMed

Schausberger, Peter; Davaasambuu, Undarmaa; Saussure, Stéphanie; Christiansen, Inga C

2018-04-01

Behavioural plasticity can be categorized into activational (also termed contextual) and developmental plasticity. Activational plasticity allows immediate contextual behavioural changes, whereas developmental plasticity is characterized by time-lagged changes based on memory of previous experiences (learning). Behavioural plasticity tends to decline with age but whether this holds true for both plasticity categories and the effects of first-in-life experiences is poorly understood. We tackled this issue by assessing the foraging plasticity of plant-inhabiting predatory mites, Amblyseius swirskii , on thrips and spider mites following age-dependent prey experience, i.e. after hatching or after reaching maturity. Juvenile and young adult predator females were alternately presented thrips and spider mites, for establishing 1st and 2nd prey-in-life experiences, and tested, as gravid females, for their foraging plasticity when offered both prey species. Prey experience by juvenile predators resulted in clear learning effects, which were evident in likelier and earlier attacks on familiar prey, and higher proportional inclusion of familiar prey in total diet. First prey-in-life experience by juvenile but not adult predators resulted in primacy effects regarding attack latency. Prey experience by adult predators resulted mainly in prey-unspecific physiological changes, with easy-to-grasp spider mites providing higher net energy gains than difficult-to-grasp thrips. Prey experience by juvenile, but not adult, predators was adaptive, which was evident in a negative correlation between attack latencies and egg production. Overall, our study provides key evidence that similar experiences by juvenile and adult predators, including first-in-life experiences, may be associated with different types of behavioural plasticity, i.e. developmental and activational plasticity.

The Thermal and Microstructural Effect of Plasticizing HMX-Nitrocellulose Composites

DOE PAGES

Yeager, John David; Watkins, Erik Benjamin; Duque, Amanda Lynn; ...

2017-03-15

Thermal ignition via self-heating (cook-off) of cyclotetramethylene-tetranitramine (HMX)-containing plastic-bonded explosives (PBXs) is driven by the β → δ phase transition in the HMX, which is affected if not dominated by microstructure. Here, we studied the HMX-binder interface and phase transition for several variations of PBX 9404 (HMX with plasticized nitrocellulose [NC] binder). Neutron reflectometry was used to examine the interface under several conditions—pristine, after aging, and after thermal treatment. The initial interfacial structure depended on the plasticizer, but the interface homogenized over time. Thermal and optical analyses showed that all formulated materials had higher transition temperatures than neat HMX. Thismore » effect increased with NC content.« less

The Thermal and Microstructural Effect of Plasticizing HMX-Nitrocellulose Composites

NASA Astrophysics Data System (ADS)

Yeager, John D.; Watkins, Erik B.; Higginbotham Duque, Amanda L.; Majewski, Jaroslaw

2018-01-01

Thermal ignition via self-heating (cook-off) of cyclotetramethylene-tetranitramine (HMX)-containing plastic-bonded explosives (PBXs) is driven by the β → δ phase transition in the HMX, which is affected if not dominated by microstructure. Here, the HMX-binder interface and phase transition were studied for several variations of PBX 9404 (HMX with plasticized nitrocellulose [NC] binder). Neutron reflectometry was used to examine the interface under several conditions-pristine, after aging, and after thermal treatment. The initial interfacial structure depended on the plasticizer, but the interface homogenized over time. Thermal and optical analyses showed that all formulated materials had higher transition temperatures than neat HMX. This effect increased with NC content.

The expression of plasticity-related genes in an acute model of stress is modulated by chronic desipramine in a time-dependent manner within medial prefrontal cortex.

PubMed

Nava, Nicoletta; Treccani, Giulia; Müller, Heidi Kaastrup; Popoli, Maurizio; Wegener, Gregers; Elfving, Betina

2017-01-01

It is well established that stress plays a major role in the pathogenesis of neuropsychiatric diseases. Stress-induced alteration of synaptic plasticity has been hypothesized to underlie the morphological changes observed by neuroimaging in psychiatric patients in key regions such as hippocampus and prefrontal cortex (PFC). We have recently shown that a single acute stress exposure produces significant short-term alterations of structural plasticity within medial PFC. These alterations were partially prevented by previous treatment with chronic desipramine (DMI). In the present study we evaluated the effects of acute Foot-shock (FS)-stress and pre-treatment with the traditional antidepressant DMI on the gene expression of key regulators of synaptic plasticity and structure. Expression of Homer, Shank, Spinophilin, Densin-180, and the small RhoGTPase related gene Rac1 and downstream target genes, Limk1, Cofilin1 and Rock1 were investigated 1 day (1d), 7 d and 14d after FS-stress exposure. We found that DMI specifically increases the short-term expression of Spinophilin, as well as Homer and Shank family genes, and that both acute stress and DMI exert significant long-term effects on mRNA levels of genes involved in spine plasticity. These findings support the knowledge that acute FS stress and antidepressant treatment induce both rapid and sustained time-dependent alterations in structural components of synaptic plasticity in rodent medial PFC. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

PubMed Central

Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon

2011-01-01

Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800

A study on emission of phthalate esters from plastic materials using a passive flux sampler

NASA Astrophysics Data System (ADS)

Fujii, M.; Shinohara, N.; Lim, A.; Otake, T.; Kumagai, K.; Yanagisawa, Y.

Phthalate esters are used as plasticizer in many plastics, and several studies have shown their toxicity. Phthalate esters are gradually emitted over time, and so it is conceivable that they pose a significant health risk. This study aims to investigate the temperature dependence of the emissions of various phthalate esters and to estimate the health risks of these emissions at various temperatures. A passive-type sampler was developed to measure the flux of phthalate esters from the surface of plastic materials. With this sampler, we examined three widely used plastic materials: synthetic leather, wallpaper and vinyl flooring. The observed maximum emissions of diethyl phthalate, dibutyl phthalate, and diethylhexyl phthalate (DEHP) from these materials at 20°C were 0.89, 0.77, and 14 μg m -2 h -1, respectively. Emissions at 80°C were 2.8, 4.5×10 2, and 1.5×10 3 μg m -2 h -1, respectively. The results showed this temperature dependence is determined primarily by the type of phthalate ester and less so by the type of material. The estimation from the results of temperature dependence indicated the concentration of DEHP in a vehicle left out in the sunshine during the day can exceed the recommended levels of Japan Ministry of Health, Labour and Welfare.

Plasticity of Neuron-Glial Transmission: Equipping Glia for Long-Term Integration of Network Activity.

PubMed

Croft, Wayne; Dobson, Katharine L; Bellamy, Tomas C

2015-01-01

The capacity of synaptic networks to express activity-dependent changes in strength and connectivity is essential for learning and memory processes. In recent years, glial cells (most notably astrocytes) have been recognized as active participants in the modulation of synaptic transmission and synaptic plasticity, implicating these electrically nonexcitable cells in information processing in the brain. While the concept of bidirectional communication between neurons and glia and the mechanisms by which gliotransmission can modulate neuronal function are well established, less attention has been focussed on the computational potential of neuron-glial transmission itself. In particular, whether neuron-glial transmission is itself subject to activity-dependent plasticity and what the computational properties of such plasticity might be has not been explored in detail. In this review, we summarize current examples of plasticity in neuron-glial transmission, in many brain regions and neurotransmitter pathways. We argue that induction of glial plasticity typically requires repetitive neuronal firing over long time periods (minutes-hours) rather than the short-lived, stereotyped trigger typical of canonical long-term potentiation. We speculate that this equips glia with a mechanism for monitoring average firing rates in the synaptic network, which is suited to the longer term roles proposed for astrocytes in neurophysiology.

Repeated Structural Imaging Reveals Nonlinear Progression of Experience-Dependent Volume Changes in Human Motor Cortex.

PubMed

Wenger, Elisabeth; Kühn, Simone; Verrel, Julius; Mårtensson, Johan; Bodammer, Nils Christian; Lindenberger, Ulman; Lövdén, Martin

2017-05-01

Evidence for experience-dependent structural brain change in adult humans is accumulating. However, its time course is not well understood, as intervention studies typically consist of only 2 imaging sessions (before vs. after training). We acquired up to 18 structural magnetic resonance images over a 7-week period while 15 right-handed participants practiced left-hand writing and drawing. After 4 weeks, we observed increases in gray matter of both left and right primary motor cortices relative to a control group; 3 weeks later, these differences were no longer reliable. Time-series analyses revealed that gray matter in the primary motor cortices expanded during the first 4 weeks and then partially renormalized, in particular in the right hemisphere, despite continued practice and increasing task proficiency. Similar patterns of expansion followed by partial renormalization are also found in synaptogenesis, cortical map plasticity, and maturation, and may qualify as a general principle of structural plasticity. Research on human brain plasticity needs to encompass more than 2 measurement occasions to capture expansion and potential renormalization processes over time. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mechanical and time-dependent behavior of wood-plastic composites subjected to bending

Treesearch

S. E. Hamel; John Hermanson; S. M. Cramer

2015-01-01

The most popular use of wood–plastic composite (WPC) members in the United States has been as outdoor decking material in residential construction. If the use of these products expands into more structural applications, such as beams and joists, it is imperative that the material’s mechanical behavior be understood. Since most of the potential structural uses of this...

Contribution of plastic waste recovery to greenhouse gas (GHG) savings in Spain.

PubMed

Sevigné-Itoiz, Eva; Gasol, Carles M; Rieradevall, Joan; Gabarrell, Xavier

2015-12-01

This paper concentrates on the quantification of greenhouse gas (GHG) emissions of post-consumer plastic waste recovery (material or energy) by considering the influence of the plastic waste quality (high or low), the recycled plastic applications (virgin plastic substitution or non-plastic substitution) and the markets of recovered plastic (regional or global). The aim is to quantify the environmental consequences of different alternatives in order to evaluate opportunities and limitations to select the best and most feasible plastic waste recovery option to decrease the GHG emissions. The methodologies of material flow analysis (MFA) for a time period of thirteen years and consequential life cycle assessment (CLCA) have been integrated. The study focuses on Spain as a representative country for Europe. The results show that to improve resource efficiency and avoid more GHG emissions, the options for plastic waste management are dependent on the quality of the recovered plastic. The results also show that there is an increasing trend of exporting plastic waste for recycling, mainly to China, that reduces the GHG benefits from recycling, suggesting that a new focus should be introduced to take into account the split between local recycling and exporting. Copyright © 2015 Elsevier Ltd. All rights reserved.

DYCAST: A finite element program for the crash analysis of structures

NASA Technical Reports Server (NTRS)

Pifko, A. B.; Winter, R.; Ogilvie, P.

1987-01-01

DYCAST is a nonlinear structural dynamic finite element computer code developed for crash simulation. The element library contains stringers, beams, membrane skin triangles, plate bending triangles and spring elements. Changing stiffnesses in the structure are accounted for by plasticity and very large deflections. Material nonlinearities are accommodated by one of three options: elastic-perfectly plastic, elastic-linear hardening plastic, or elastic-nonlinear hardening plastic of the Ramberg-Osgood type. Geometric nonlinearities are handled in an updated Lagrangian formulation by reforming the structure into its deformed shape after small time increments while accumulating deformations, strains, and forces. The nonlinearities due to combined loadings are maintained, and stiffness variation due to structural failures are computed. Numerical time integrators available are fixed-step central difference, modified Adams, Newmark-beta, and Wilson-theta. The last three have a variable time step capability, which is controlled internally by a solution convergence error measure. Other features include: multiple time-load history tables to subject the structure to time dependent loading; gravity loading; initial pitch, roll, yaw, and translation of the structural model with respect to the global system; a bandwidth optimizer as a pre-processor; and deformed plots and graphics as post-processors.

Correlation between elastic and plastic deformations of partially cured epoxy networks

NASA Astrophysics Data System (ADS)

Müller, Michael; Böhm, Robert; Geller, Sirko; Kupfer, Robert; Jäger, Hubert; Gude, Maik

2018-05-01

The thermo-mechanical behavior of polymer matrix materials is strongly dependent on the curing reaction as well as temperature and time. To date, investigations of epoxy resins and their composites mainly focused on the elastic domain because plastic deformation of cross-linked polymer networks was considered as irrelevant or not feasible. This paper presents a novel approach which combines both elastic and plastic domain. Based on an analytical framework describing the storage modulus, analogous parameter combinations are defined in order to reduce complexity when variations in temperature, strain rate and degree of cure are encountered.

Muscarinic acetylcholine receptors control baseline activity and Hebbian stimulus timing-dependent plasticity in fusiform cells of the dorsal cochlear nucleus.

PubMed

Stefanescu, Roxana A; Shore, Susan E

2017-03-01

Cholinergic modulation contributes to adaptive sensory processing by controlling spontaneous and stimulus-evoked neural activity and long-term synaptic plasticity. In the dorsal cochlear nucleus (DCN), in vitro activation of muscarinic acetylcholine receptors (mAChRs) alters the spontaneous activity of DCN neurons and interacts with N -methyl-d-aspartate (NMDA) and endocannabinoid receptors to modulate the plasticity of parallel fiber synapses onto fusiform cells by converting Hebbian long-term potentiation to anti-Hebbian long-term depression. Because noise exposure and tinnitus are known to increase spontaneous activity in fusiform cells as well as alter stimulus timing-dependent plasticity (StTDP), it is important to understand the contribution of mAChRs to in vivo spontaneous activity and plasticity in fusiform cells. In the present study, we blocked mAChRs actions by infusing atropine, a mAChR antagonist, into the DCN fusiform cell layer in normal hearing guinea pigs. Atropine delivery leads to decreased spontaneous firing rates and increased synchronization of fusiform cell spiking activity. Consistent with StTDP alterations observed in tinnitus animals, atropine infusion induced a dominant pattern of inversion of StTDP mean population learning rule from a Hebbian to an anti-Hebbian profile. Units preserving their initial Hebbian learning rules shifted toward more excitatory changes in StTDP, whereas units with initial suppressive learning rules transitioned toward a Hebbian profile. Together, these results implicate muscarinic cholinergic modulation as a factor in controlling in vivo fusiform cell baseline activity and plasticity, suggesting a central role in the maladaptive plasticity associated with tinnitus pathology. NEW & NOTEWORTHY This study is the first to use a novel method of atropine infusion directly into the fusiform cell layer of the dorsal cochlear nucleus coupled with simultaneous recordings of neural activity to clarify the contribution of muscarinic acetylcholine receptors (mAChRs) to in vivo fusiform cell baseline activity and auditory-somatosensory plasticity. We have determined that blocking the mAChRs increases the synchronization of spiking activity across the fusiform cell population and induces a dominant pattern of inversion in their stimulus timing-dependent plasticity. These modifications are consistent with similar changes established in previous tinnitus studies, suggesting that mAChRs might have a critical contribution in mediating the maladaptive alterations associated with tinnitus pathology. Blocking mAChRs also resulted in decreased fusiform cell spontaneous firing rates, which is in contrast with their tinnitus hyperactivity, suggesting that changes in the interactions between the cholinergic and GABAergic systems might also be an underlying factor in tinnitus pathology. Copyright © 2017 the American Physiological Society.

Investigation of BCF-12 Plastic Scintillating Coherent Fiber Bundle Timing Properties

DTIC Science & Technology

2012-03-22

spread in transit time. The shape of the voltage pulse produced at the anode of a PM following a scin - tillation event depends on the time constant of...Choong, G. Hull, and B.W. Reutter. “ Scin - tillator Non-Proportionality: Present Understanding and Future Challenges”. Nuclear Science, IEEE

An investigation of the inelastic behaviour of trabecular bone during the press-fit implantation of a tibial component in total knee arthroplasty.

PubMed

Kelly, N; Cawley, D T; Shannon, F J; McGarry, J P

2013-11-01

The stress distribution and plastic deformation of peri-prosthetic trabecular bone during press-fit tibial component implantation in total knee arthroplasty is investigated using experimental and finite element techniques. It is revealed that the computed stress distribution, implantation force and plastic deformation in the trabecular bone is highly dependent on the plasticity formulation implemented. By incorporating pressure dependent yielding using a crushable foam plasticity formulation to simulate the trabecular bone during implantation, highly localised stress concentrations and plastic deformation are computed at the bone-implant interface. If the pressure dependent yield is neglected using a traditional von Mises plasticity formulation, a significantly different stress distribution and implantation force is computed in the peri-prosthetic trabecular bone. The results of the study highlight the importance of: (i) simulating the insertion process of press-fit stem implantation; (ii) implementing a pressure dependent plasticity formulation, such as the crushable foam plasticity formulation, for the trabecular bone; (iii) incorporating friction at the implant-bone interface during stem insertion. Simulation of the press-fit implantation process with an appropriate pressure dependent plasticity formulation should be implemented in the design and assessment of arthroplasty prostheses. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Variables that affect the mechanism of drug release from osmotic pumps coated with acrylate/methacrylate copolymer latexes.

PubMed

Jensen, J L; Appel, L E; Clair, J H; Zentner, G M

1995-05-01

The feasibility of using modified Eudragit acrylic latexes as microporous coatings for osmotic devices was investigated. Potassium chloride tablets were coated with mixtures of Eudragit RS30D and RL30D acrylic latexes that also contained a plasticizer (triethyl citrate or acetyl tributyl citrate) and a pore-forming agent (urea). A 2(5-1) fractional factorial experimental design was employed to determine the effect of five formulation variables (RS30D:RL30D polymer ratio plasticizer type, plasticizer level, urea level, and cure) on the in vitro release rate of KCl in deionized water (di water), lag time, and coat burst strength. The RS30D:RL30D polymer ratio had the greatest effect on the release rate, and both lag time and burst strength were most affected by the urea level. Statistical optimization was performed, and a coat formulation with predicted desirable in vitro performance was prepared and tested. The in vitro release rate (di water), lag time, and coat burst strength agreed well with the prediction. Dissolutions were also performed in phosphate buffered saline (PBS; pH 7.4); several formulations released markedly slower in PBS than in di water. This discrepancy was dependent on the type of plasticizer and the amount of pore former. Only those coat formulations containing acetyl tributyl citrate as the plasticizer and a 100% urea [(g urea/g polymer solids) x 100] level exhibited similar release rates in di water and PBS. The mechanism of release from these devices was primarily osmotic, whereas the release from devices coated with a formulation containing triethyl citrate and 50% urea was not dependent on the osmotic pressure difference. Devices with an osmotic release mechanism behaved similarly in vivo and in vitro.(ABSTRACT TRUNCATED AT 250 WORDS)

Progresses in Polystyrene Biodegradation and Prospects for Solutions to Plastic Waste Pollution

NASA Astrophysics Data System (ADS)

Yang, S. S.; Brandon, A. M.; Xing, D. F.; Yang, J.; Pang, J. W.; Criddle, C. S.; Ren, N. Q.; Wu, W. M.

2018-05-01

Petroleum-based plastic pollution has been a global environmental concern for decades. The obvious contrast between the remarkable durability of the plastics and their short service time leads to the increasing accumulation of plastic wastes in the environment. A cost-effective, sustainable strategy to solve the problem should focus on source control and clean up. Polystyrene (PS) wastes, a recalcitrant plastic polymer, are among the wide spread man-made plastic pollutants. Destruction of PS wastes can be achieved using various abiotic methods such as incineration but such methods release potential air pollution and generation of hazardous by-products. Biodegradation and bioremediation has been proposed for years. Since the 1970’s, the microbial biodegradation of plastics, including PS, has been evaluated with mixed and isolated cultures from different sources such as activated sludge, trash, soil, and manure. To date, PS biodegradation by these microbial cultures is still quite slow. Recently, the larvae of yellow mealworms (Tenebrio molitor Linnaeus) have demonstrated promising PS biodegradation performance. Mealworms have demonstrated the ability to chew and ingest PS foam as food and are capable of degrading and mineralizing PS into CO2 via microbe-dependent activities within the gut in less than the 12-15 hrs gut retention time. These research results have revealed a potential for microbial biodegradation and bioremediation of plastic pollutants.

On a phase field approach for martensitic transformations in a crystal plastic material at a loaded surface

NASA Astrophysics Data System (ADS)

Schmitt, Regina; Kuhn, Charlotte; Müller, Ralf

2017-07-01

A continuum phase field model for martensitic transformations is introduced, including crystal plasticity with different slip systems for the different phases. In a 2D setting, the transformation-induced eigenstrain is taken into account for two martensitic orientation variants. With aid of the model, the phase transition and its dependence on the volume change, crystal plastic material behavior, and the inheritance of plastic deformations from austenite to martensite are studied in detail. The numerical setup is motivated by the process of cryogenic turning. The resulting microstructure qualitatively coincides with an experimentally obtained martensite structure. For the numerical calculations, finite elements together with global and local implicit time integration scheme are employed.

Homeostatic plasticity for single node delay-coupled reservoir computing.

PubMed

Toutounji, Hazem; Schumacher, Johannes; Pipa, Gordon

2015-06-01

Supplementing a differential equation with delays results in an infinite-dimensional dynamical system. This property provides the basis for a reservoir computing architecture, where the recurrent neural network is replaced by a single nonlinear node, delay-coupled to itself. Instead of the spatial topology of a network, subunits in the delay-coupled reservoir are multiplexed in time along one delay span of the system. The computational power of the reservoir is contingent on this temporal multiplexing. Here, we learn optimal temporal multiplexing by means of a biologically inspired homeostatic plasticity mechanism. Plasticity acts locally and changes the distances between the subunits along the delay, depending on how responsive these subunits are to the input. After analytically deriving the learning mechanism, we illustrate its role in improving the reservoir's computational power. To this end, we investigate, first, the increase of the reservoir's memory capacity. Second, we predict a NARMA-10 time series, showing that plasticity reduces the normalized root-mean-square error by more than 20%. Third, we discuss plasticity's influence on the reservoir's input-information capacity, the coupling strength between subunits, and the distribution of the readout coefficients.

Blocking c-Fos Expression Reveals the Role of Auditory Cortex Plasticity in Sound Frequency Discrimination Learning.

PubMed

de Hoz, Livia; Gierej, Dorota; Lioudyno, Victoria; Jaworski, Jacek; Blazejczyk, Magda; Cruces-Solís, Hugo; Beroun, Anna; Lebitko, Tomasz; Nikolaev, Tomasz; Knapska, Ewelina; Nelken, Israel; Kaczmarek, Leszek

2018-05-01

The behavioral changes that comprise operant learning are associated with plasticity in early sensory cortices as well as with modulation of gene expression, but the connection between the behavioral, electrophysiological, and molecular changes is only partially understood. We specifically manipulated c-Fos expression, a hallmark of learning-induced synaptic plasticity, in auditory cortex of adult mice using a novel approach based on RNA interference. Locally blocking c-Fos expression caused a specific behavioral deficit in a sound discrimination task, in parallel with decreased cortical experience-dependent plasticity, without affecting baseline excitability or basic auditory processing. Thus, c-Fos-dependent experience-dependent cortical plasticity is necessary for frequency discrimination in an operant behavioral task. Our results connect behavioral, molecular and physiological changes and demonstrate a role of c-Fos in experience-dependent plasticity and learning.

Spike timing analysis in neural networks with unsupervised synaptic plasticity

NASA Astrophysics Data System (ADS)

Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

2013-01-01

The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

Functional recovery of the dentate gyrus after a focal lesion is accompanied by structural reorganization in the adult rat.

PubMed

Zepeda, Angélica; Aguilar-Arredondo, Andrea; Michel, Gabriela; Ramos-Languren, Laura Elisa; Escobar, Martha L; Arias, Clorinda

2013-03-01

The adult brain is highly plastic and tends to undergo substantial reorganization after injury to compensate for the lesion effects. It has been shown that such reorganization mainly relies on anatomical and biochemical modifications of the remaining cells which give rise to a network rewiring without reinstating the original morphology of the damaged region. However, few studies have analyzed the neurorepair potential of a neurogenic structure. Thus, the aim of this work was to analyze if the DG could restore its original morphology after a lesion and to establish if the structural reorganization is accompanied by behavioral and electrophysiological recovery. Using a subepileptogenic injection of kainic acid (KA), we induced a focal lesion in the DG and assessed in time (1) the loss and recovery of dependent and non dependent DG cognitive functions, (2) the anatomical reorganization of the DG using a stereological probe and immunohistochemical markers for different neuronal maturation stages and, (3) synaptic plasticity as assessed through the induction of in vivo long-term potentiation (LTP) in the mossy fiber pathway (CA3-DG). Our results show that a DG focal lesion with KA leads to a well delimited region of neuronal loss, disorganization of the structure, the loss of associated mnemonic functions and the impairment to elicit LTP. However, behavioral and synaptic plasticity expression occurs in a time dependent fashion and occurs along the morphological restoration of the DG. These results provide novel information on neural plasticity events associated to functional reorganization after damage.

Effects of flow rate on the migration of different plasticizers from PVC infusion medical devices

PubMed Central

Eljezi, Teuta; Clauson, Hélène; Lambert, Céline; Bouattour, Yassine; Chennell, Philip; Pereira, Bruno; Sautou, Valérie

2018-01-01

Infusion medical devices (MDs) used in hospitals are often made of plasticized polyvinylchloride (PVC). These plasticizers may leach out into infused solutions during clinical practice, especially during risk-situations, e.g multiple infusions in Intensive Care Units and thus may enter into contact with the patients. The migrability of the plasticizers is dependent of several clinical parameters such as temperature, contact time, nature of the simulant, etc… However, no data is available about the influence of the flow rate at which drug solutions are administrated. In this study, we evaluated the impact of different flow rates on the release of the different plasticizers during an infusion procedure in order to assess if they could expose the patients to more toxic amounts of plasticizers. Migration assays with different PVC infusion sets and extension lines were performed with different flow rates that are used in clinical practice during 1h, 2h, 4h, 8h and 24h, using a lipophilic drug simulant. From a clinical point of view, the results showed that, regardless of the plasticizer, the faster the flow rate, the higher the infused volume and the higher the quantities of plasticizers released, both from infusion sets and extension lines, leading to higher patient exposure. However, physically, there was no significant difference of the migration kinetics linked to the flow rate for a same medical device, reflecting complex interactions between the PVC matrix and the simulant. The migration was especially dependent on the nature and the composition of the medical device. PMID:29474357

The Development and Activity-Dependent Expression of Aggrecan in the Cat Visual Cortex

PubMed Central

Sengpiel, F.; Beaver, C. J.; Crocker-Buque, A.; Kelly, G. M.; Matthews, R. T.; Mitchell, D. E.

2013-01-01

The Cat-301 monoclonal antibody identifies aggrecan, a chondroitin sulfate proteoglycan in the cat visual cortex and dorsal lateral geniculate nucleus (dLGN). During development, aggrecan expression increases in the dLGN with a time course that matches the decline in plasticity. Moreover, examination of tissue from selectively visually deprived cats shows that expression is activity dependent, suggesting a role for aggrecan in the termination of the sensitive period. Here, we demonstrate for the first time that the onset of aggrecan expression in area 17 also correlates with the decline in experience-dependent plasticity in visual cortex and that this expression is experience dependent. Dark rearing until 15 weeks of age dramatically reduced the density of aggrecan-positive neurons in the extragranular layers, but not in layer IV. This effect was reversible as dark-reared animals that were subsequently exposed to light showed normal numbers of Cat-301-positive cells. The reduction in aggrecan following certain early deprivation regimens is the first biochemical correlate of the functional changes to the γ-aminobutyric acidergic system that have been reported following early deprivation in cats. PMID:22368089

Algorithms for elasto-plastic-creep postbuckling

NASA Technical Reports Server (NTRS)

Padovan, J.; Tovichakchaikul, S.

1984-01-01

This paper considers the development of an improved constrained time stepping scheme which can efficiently and stably handle the pre-post-buckling behavior of general structure subject to high temperature environments. Due to the generality of the scheme, the combined influence of elastic-plastic behavior can be handled in addition to time dependent creep effects. This includes structural problems exhibiting indefinite tangent properties. To illustrate the capability of the procedure, several benchmark problems employing finite element analyses are presented. These demonstrate the numerical efficiency and stability of the scheme. Additionally, the potential influence of complex creep histories on the buckling characteristics is considered.

Learning enhances intrinsic excitability in a subset of lateral amygdala neurons

PubMed Central

Sehgal, Megha; Ehlers, Vanessa L.; Moyer, James R.

2014-01-01

Learning-induced modulation of neuronal intrinsic excitability is a metaplasticity mechanism that can impact the acquisition of new memories. Although the amygdala is important for emotional learning and other behaviors, including fear and anxiety, whether learning alters intrinsic excitability within the amygdala has received very little attention. Fear conditioning was combined with intracellular recordings to investigate the effects of learning on the intrinsic excitability of lateral amygdala (LA) neurons. To assess time-dependent changes, brain slices were prepared either immediately or 24-h post-conditioning. Fear conditioning significantly enhanced excitability of LA neurons, as evidenced by both decreased afterhyperpolarization (AHP) and increased neuronal firing. These changes were time-dependent such that reduced AHPs were evident at both time points whereas increased neuronal firing was only observed at the later (24-h) time point. Moreover, these changes occurred within a subset (32%) of LA neurons. Previous work also demonstrated that learning-related changes in synaptic plasticity are also evident in less than one-third of amygdala neurons, suggesting that the neurons undergoing intrinsic plasticity may be critical for fear memory. These data may be clinically relevant as enhanced LA excitability following fear learning could influence future amygdala-dependent behaviors. PMID:24554670

Deficient plasticity in the primary visual cortex of alpha-calcium/calmodulin-dependent protein kinase II mutant mice.

PubMed

Gordon, J A; Cioffi, D; Silva, A J; Stryker, M P

1996-09-01

The recent characterization of plasticity in the mouse visual cortex permits the use of mutant mice to investigate the cellular mechanisms underlying activity-dependent development. As calcium-dependent signaling pathways have been implicated in neuronal plasticity, we examined visual cortical plasticity in mice lacking the alpha-isoform of calcium/calmodulin-dependent protein kinase II (alpha CaMKII). In wild-type mice, brief occlusion of vision in one eye during a critical period reduces responses in the visual cortex. In half of the alpha CaMKII-deficient mice, visual cortical responses developed normally, but visual cortical plasticity was greatly diminished. After intensive training, spatial learning in the Morris water maze was severely impaired in a similar fraction of mutant animals. These data indicate that loss of alpha CaMKII results in a severe but variable defect in neuronal plasticity.

Mean-field theory of a plastic network of integrate-and-fire neurons.

PubMed

Chen, Chun-Chung; Jasnow, David

2010-01-01

We consider a noise-driven network of integrate-and-fire neurons. The network evolves as result of the activities of the neurons following spike-timing-dependent plasticity rules. We apply a self-consistent mean-field theory to the system to obtain the mean activity level for the system as a function of the mean synaptic weight, which predicts a first-order transition and hysteresis between a noise-dominated regime and a regime of persistent neural activity. Assuming Poisson firing statistics for the neurons, the plasticity dynamics of a synapse under the influence of the mean-field environment can be mapped to the dynamics of an asymmetric random walk in synaptic-weight space. Using a master equation for small steps, we predict a narrow distribution of synaptic weights that scales with the square root of the plasticity rate for the stationary state of the system given plausible physiological parameter values describing neural transmission and plasticity. The dependence of the distribution on the synaptic weight of the mean-field environment allows us to determine the mean synaptic weight self-consistently. The effect of fluctuations in the total synaptic conductance and plasticity step sizes are also considered. Such fluctuations result in a smoothing of the first-order transition for low number of afferent synapses per neuron and a broadening of the synaptic-weight distribution, respectively.

Discrete dislocation plasticity analysis of loading rate-dependent static friction.

PubMed

Song, H; Deshpande, V S; Van der Giessen, E

2016-08-01

From a microscopic point of view, the frictional force associated with the relative sliding of rough surfaces originates from deformation of the material in contact, by adhesion in the contact interface or both. We know that plastic deformation at the size scale of micrometres is not only dependent on the size of the contact, but also on the rate of deformation. Moreover, depending on its physical origin, adhesion can also be size and rate dependent, albeit different from plasticity. We present a two-dimensional model that incorporates both discrete dislocation plasticity inside a face-centred cubic crystal and adhesion in the interface to understand the rate dependence of friction caused by micrometre-size asperities. The friction strength is the outcome of the competition between adhesion and discrete dislocation plasticity. As a function of contact size, the friction strength contains two plateaus: at small contact length [Formula: see text], the onset of sliding is fully controlled by adhesion while for large contact length [Formula: see text], the friction strength approaches the size-independent plastic shear yield strength. The transition regime at intermediate contact size is a result of partial de-cohesion and size-dependent dislocation plasticity, and is determined by dislocation properties, interfacial properties as well as by the loading rate.

Improving the performance of the amblyopic visual system

PubMed Central

Levi, Dennis M.; Li, Roger W.

2008-01-01

Experience-dependent plasticity is closely linked with the development of sensory function; however, there is also growing evidence for plasticity in the adult visual system. This review re-examines the notion of a sensitive period for the treatment of amblyopia in the light of recent experimental and clinical evidence for neural plasticity. One recently proposed method for improving the effectiveness and efficiency of treatment that has received considerable attention is ‘perceptual learning’. Specifically, both children and adults with amblyopia can improve their perceptual performance through extensive practice on a challenging visual task. The results suggest that perceptual learning may be effective in improving a range of visual performance and, importantly, the improvements may transfer to visual acuity. Recent studies have sought to explore the limits and time course of perceptual learning as an adjunct to occlusion and to investigate the neural mechanisms underlying the visual improvement. These findings, along with the results of new clinical trials, suggest that it might be time to reconsider our notions about neural plasticity in amblyopia. PMID:19008199

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.

Creep of Ni(3)Al in the temperature regime of anomalous flow behavior

NASA Astrophysics Data System (ADS)

Uchic, Michael David

Much attention has been paid to understanding the dynamics of dislocation motion and substructure formation in Ni3Al in the anomalous flow regime. However, most of the experimental work that has been performed in the lowest temperatures of the anomalous flow regime has been under constant-strain-rate conditions. An alternative and perhaps more fundamental way to probe the plastic behavior of materials is a monotonic creep test, in which the stress and temperature are held constant while the time-dependent strain is measured. The aim of this study is to use constant-stress experiments to further explore the plastic flow anomaly in L12 alloys at low temperatures. Tension creep experiments have been carried out on <123> oriented single crystals of Ni75Al24Ta1 at temperatures between 293 and 473 K. We have observed primary creep leading to exhaustion at all temperatures and stresses, with creep rates declining faster than predicted by the logarithmic creep law. The total strain and creep strain have an anomalous dependence on temperature, which is consistent with the flow stress anomaly. We have also observed other unusual behavior in our creep experiments; for example, the reinitiation of plastic flow at low temperatures after a modest increment in applied stress shows a sigmoidal response, i.e., there is a significant time delay before the plastic strain rate accelerates to a maximum value. We also examined the ability to reinitiate plastic flow in samples that have been crept to exhaustion by simply lowering the test temperature. In addition, we have also performed conventional constant-displacement-rate experiments in the same temperature range. From these experiments, we have discovered that unlike most metals, Ni3Al displays a negative dependence of the work hardening rate (WHR) with increasing strain rate. For tests at intermediate temperatures (373 and 423 K), the WHRs of crystals tested at moderately high strain rates (10-2 s-1) are half the WHRs of crystals tested at conventional strain rates (10 -5 s-1), and this anomalous dependence has also been shown to be reversible with changes in strain rate. The implications of all results are discussed in light of our efforts to model plastic deformation in these alloys.

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

Yeager, John David; Watkins, Erik Benjamin; Duque, Amanda Lynn

Thermal ignition via self-heating (cook-off) of cyclotetramethylene-tetranitramine (HMX)-containing plastic-bonded explosives (PBXs) is driven by the β → δ phase transition in the HMX, which is affected if not dominated by microstructure. Here, we studied the HMX-binder interface and phase transition for several variations of PBX 9404 (HMX with plasticized nitrocellulose [NC] binder). Neutron reflectometry was used to examine the interface under several conditions—pristine, after aging, and after thermal treatment. The initial interfacial structure depended on the plasticizer, but the interface homogenized over time. Thermal and optical analyses showed that all formulated materials had higher transition temperatures than neat HMX. Thismore » effect increased with NC content.« less

Targeted, activity-dependent spinal stimulation produces long-lasting motor recovery in chronic cervical spinal cord injury

PubMed Central

McPherson, Jacob G.; Miller, Robert R.; Perlmutter, Steve I.

2015-01-01

Use-dependent movement therapies can lead to partial recovery of motor function after neurological injury. We attempted to improve recovery by developing a neuroprosthetic intervention that enhances movement therapy by directing spike timing-dependent plasticity in spared motor pathways. Using a recurrent neural–computer interface in rats with a cervical contusion of the spinal cord, we synchronized intraspinal microstimulation below the injury with the arrival of functionally related volitional motor commands signaled by muscle activity in the impaired forelimb. Stimulation was delivered during physical retraining of a forelimb behavior and throughout the day for 3 mo. Rats receiving this targeted, activity-dependent spinal stimulation (TADSS) exhibited markedly enhanced recovery compared with animals receiving targeted but open-loop spinal stimulation and rats receiving physical retraining alone. On a forelimb reach and grasp task, TADSS animals recovered 63% of their preinjury ability, more than two times the performance level achieved by the other therapy groups. Therapeutic gains were maintained for 3 additional wk without stimulation. The results suggest that activity-dependent spinal stimulation can induce neural plasticity that improves behavioral recovery after spinal cord injury. PMID:26371306

Prentice Award Lecture 2011: Removing the Brakes on Plasticity in the Amblyopic Brain

PubMed Central

Levi, Dennis M.

2012-01-01

Experience-dependent plasticity is closely linked with the development of sensory function. Beyond this sensitive period, developmental plasticity is actively limited; however, new studies provide growing evidence for plasticity in the adult visual system. The amblyopic visual system is an excellent model for examining the “brakes” that limit recovery of function beyond the critical period. While amblyopia can often be reversed when treated early, conventional treatment is generally not undertaken in older children and adults. However new clinical and experimental studies in both animals and humans provide evidence for neural plasticity beyond the critical period. The results suggest that perceptual learning and video game play may be effective in improving a range of visual performance measures and importantly the improvements may transfer to better visual acuity and stereopsis. These findings, along with the results of new clinical trials, suggest that it might be time to re-consider our notions about neural plasticity in amblyopia. PMID:22581119

Plastic Organic Scintillator Chemistry

NASA Astrophysics Data System (ADS)

Brightwell, C. R.; Temanson, E. S.; Febbraro, M. T.

2017-09-01

Due to their high light output, quick decay time, affordability, durability and ability to be molded, plastic organic scintillators are increasingly becoming a more viable method of particle detection. Since the plastic is composed entirely of single molecular chains with repeating units, scintillating properties remain stable despite changes in experimental conditions. Different scintillating plastics can be modified and tailored to suit specific experiments depending on a variety of requirements such as light output, scintillating wavelength, and PMT compatibility. The synthesis chemistry of a recent but well-known scintillating polyester, polyethylene naphthalate (PEN) will be presented to demonstrate how plastic organic scintillators can be modified for different particle detection experiments. PEN has been successfully synthesized at ORNL, and procedures are currently being investigated to modify PEN using different reactants and catalysts. The goal is to achieve a transparent scintillating plastic with an incorporated wavelength shifter in the chain that scintillates with a wavelength around 440 nm. The status of this project will be presented. This research is supported by the U. S. Department of Energy Office of Science.

Repeated Exposure to Ketamine-Xylazine during Early Development Impairs Motor Learning-dependent Dendritic Spine Plasticity in Adulthood

PubMed Central

Huang, Lianyan; Yang, Guang

2014-01-01

Background Recent studies in rodents suggest that repeated and prolonged anesthetic exposure at early stages of development leads to cognitive and behavioral impairments later in life. However, the underlying mechanism remains unknown. In this study, we tested whether exposure to general anesthesia during early development will disrupt the maturation of synaptic circuits and compromise learning-related synaptic plasticity later in life. Methods Mice received ketamine/xylazine (20/3 mg/kg) anesthesia for one or three times, starting at either early [postnatal day 14 (P14)] or late (P21) stages of development (n=105). Control mice received saline injections (n=34). At P30, mice were subjected to rotarod motor training and fear conditioning. Motor learning-induced synaptic remodeling was examined in vivo by repeatedly imaging fluorescently-labeled postsynaptic dendritic spines in the primary motor cortex before and after training using two-photon microscopy. Results Three exposures to ketamine/xylazine anesthesia between P14–18 impair the animals’ motor learning and learning-dependent dendritic spine plasticity [new spine formation, 8.4 ± 1.3% (mean ± SD) versus 13.4 ± 1.8%, P = 0.002] without affecting fear memory and cell apoptosis. One exposure at P14 or three exposures between P21–25 has no effects on the animals’ motor learning or spine plasticity. Finally, enriched motor experience ameliorates anesthesia-induced motor learning impairment and synaptic deficits. Conclusion Our study demonstrates that repeated exposures to ketamine/xylazine during early development impair motor learning and learning-dependent dendritic spine plasticity later in life. The reduction in synaptic structural plasticity may underlie anesthesia-induced behavioral impairment. PMID:25575163

Genetic deletion of melanin-concentrating hormone neurons impairs hippocampal short-term synaptic plasticity and hippocampal-dependent forms of short-term memory.

PubMed

Le Barillier, Léa; Léger, Lucienne; Luppi, Pierre-Hervé; Fort, Patrice; Malleret, Gaël; Salin, Paul-Antoine

2015-11-01

The cognitive role of melanin-concentrating hormone (MCH) neurons, a neuronal population located in the mammalian postero-lateral hypothalamus sending projections to all cortical areas, remains poorly understood. Mainly activated during paradoxical sleep (PS), MCH neurons have been implicated in sleep regulation. The genetic deletion of the only known MCH receptor in rodent leads to an impairment of hippocampal dependent forms of memory and to an alteration of hippocampal long-term synaptic plasticity. By using MCH/ataxin3 mice, a genetic model characterized by a selective deletion of MCH neurons in the adult, we investigated the role of MCH neurons in hippocampal synaptic plasticity and hippocampal-dependent forms of memory. MCH/ataxin3 mice exhibited a deficit in the early part of both long-term potentiation and depression in the CA1 area of the hippocampus. Post-tetanic potentiation (PTP) was diminished while synaptic depression induced by repetitive stimulation was enhanced suggesting an alteration of pre-synaptic forms of short-term plasticity in these mice. Behaviorally, MCH/ataxin3 mice spent more time and showed a higher level of hesitation as compared to their controls in performing a short-term memory T-maze task, displayed retardation in acquiring a reference memory task in a Morris water maze, and showed a habituation deficit in an open field task. Deletion of MCH neurons could thus alter spatial short-term memory by impairing short-term plasticity in the hippocampus. Altogether, these findings could provide a cellular mechanism by which PS may facilitate memory encoding. Via MCH neuron activation, PS could prepare the day's learning by increasing and modulating short-term synaptic plasticity in the hippocampus. © 2015 Wiley Periodicals, Inc.

Container effects on the physicochemical properties of parenteral lipid emulsions.

PubMed

Gonyon, Thomas; Carter, Phillip W; Dahlem, Olivier; Denet, Anne-Rose; Owen, Heather; Trouilly, Jean-Luc

2008-01-01

We evaluated the effects of glass and plastic containers on the physicochemical properties of parenteral nutrition lipid emulsions and total nutrient admixtures with an emphasis on globule size distribution and colloidal stability. A commercial lipid emulsion, 20% ClinOleic, was separated into glass (type II soda-lime-silica) and plastic (polypropylene multilayer) containers, sterilized, and then stored for 16 wk at 40 degrees C. Globule size distribution, pH, and zeta potential measurements were made every 4 wk. Admixtures derived from parent lipid emulsions were tested after admixing (t = 0), storage for 7 d at 5 degrees C plus 24 h at 25 degrees C (t = 7 + 1), and then after an additional 3 d at 25 degrees C (t = 7 + 4). The parent lipid emulsions in glass and plastic containers exhibited identical time-dependent behavior with respect to mean globule size, percentage of oil droplets >or=5 mum, pH, and zeta potential measurements. The percentages of oil droplets >or=5 mum of all test conditions remained well below the United States Pharmacopeia <729> limits of 0.05%. The total nutrient admixture time-dependent physicochemical characteristics were also found to be independent of the parent lipid emulsion container type. Plastic and glass containers were found to be suitable, safe, and indistinguishable with respect to physicochemical stability of a representative parenteral nutrition lipid emulsion and total nutrient admixtures derived from the parent lipid emulsion.

Dosage-dependent non-linear effect of L-dopa on human motor cortex plasticity.

PubMed

Monte-Silva, Katia; Liebetanz, David; Grundey, Jessica; Paulus, Walter; Nitsche, Michael A

2010-09-15

The neuromodulator dopamine affects learning and memory formation and their likely physiological correlates, long-term depression and potentiation, in animals and humans. It is known from animal experiments that dopamine exerts a dosage-dependent, inverted U-shaped effect on these functions. However, this has not been explored in humans so far. In order to reveal a non-linear dose-dependent effect of dopamine on cortical plasticity in humans, we explored the impact of 25, 100 and 200 mg of L-dopa on transcranial direct current (tDCS)-induced plasticity in twelve healthy human subjects. The primary motor cortex served as a model system, and plasticity was monitored by motor evoked potential amplitudes elicited by transcranial magnetic stimulation. As compared to placebo medication, low and high dosages of L-dopa abolished facilitatory as well as inhibitory plasticity, whereas the medium dosage prolonged inhibitory plasticity, and turned facilitatory plasticity into inhibition. Thus the results show clear non-linear, dosage-dependent effects of dopamine on both facilitatory and inhibitory plasticity, and support the assumption of the importance of a specific dosage of dopamine optimally suited to improve plasticity. This might be important for the therapeutic application of dopaminergic agents, especially for rehabilitative purposes, and explain some opposing results in former studies.

Plasticity in the Rat Prefrontal Cortex: Linking Gene Expression and an Operant Learning with a Computational Theory

PubMed Central

Rapanelli, Maximiliano; Lew, Sergio Eduardo; Frick, Luciana Romina; Zanutto, Bonifacio Silvano

2010-01-01

The plasticity in the medial Prefrontal Cortex (mPFC) of rodents or lateral prefrontal cortex in non human primates (lPFC), plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF), cAMP response element binding (CREB), Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression. PMID:20111591

Equalization of Synaptic Efficacy by Synchronous Neural Activity

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Choi, M. Y.

2007-11-01

It is commonly believed that spike timings of a postsynaptic neuron tend to follow those of the presynaptic neuron. Such orthodromic firing may, however, cause a conflict with the functional integrity of complex neuronal networks due to asymmetric temporal Hebbian plasticity. We argue that reversed spike timing in a synapse is a typical phenomenon in the cortex, which has a stabilizing effect on the neuronal network structure. We further demonstrate how the firing causality in a synapse is perturbed by synchronous neural activity and how the equilibrium property of spike-timing dependent plasticity is determined principally by the degree of synchronization. Remarkably, even noise-induced activity and synchrony of neurons can result in equalization of synaptic efficacy.

Viscoelastic behavior and lifetime (durability) predictions. [for laminated fiber reinforced plastics

NASA Technical Reports Server (NTRS)

Brinson, R. F.

1985-01-01

A method for lifetime or durability predictions for laminated fiber reinforced plastics is given. The procedure is similar to but not the same as the well known time-temperature-superposition principle for polymers. The method is better described as an analytical adaptation of time-stress-super-position methods. The analytical constitutive modeling is based upon a nonlinear viscoelastic constitutive model developed by Schapery. Time dependent failure models are discussed and are related to the constitutive models. Finally, results of an incremental lamination analysis using the constitutive and failure model are compared to experimental results. Favorable results between theory and predictions are presented using data from creep tests of about two months duration.

Toxicity of leachate from weathering plastics: An exploratory screening study with Nitocra spinipes.

PubMed

Bejgarn, Sofia; MacLeod, Matthew; Bogdal, Christian; Breitholtz, Magnus

2015-08-01

Between 60% and 80% of all marine litter is plastic. Leachate from plastics has previously been shown to cause acute toxicity in the freshwater species Daphnia magna. Here, we present an initial screening of the marine environmental hazard properties of leachates from weathering plastics to the marine harpacticoid copepod [Crustacea] Nitocra spinipes. Twenty-one plastic products made of different polymeric materials were leached and irradiated with artificial sunlight. Eight of the twenty-one plastics (38%) produced leachates that caused acute toxicity. Differences in toxicity were seen for different plastic products, and depending on the duration of irradiation. There was no consistent trend in how toxicity of leachate from plastics changed as a function of irradiation time. Leachate from four plastics became significantly more toxic after irradiation, two became significantly less toxic and two did not change significantly. Analysis of leachates from polyvinyl chloride (PVC) by liquid chromatography coupled to a full-scan high-resolution mass spectrometer showed that the leachates were a mixture of substances, but did not show evidence of degradation of the polymer backbone. This screening study demonstrates that leachates from different plastics differ in toxicity to N. spinipes and that the toxicity varies under simulated weathering. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Oxygen transport in congenital heart disease: influence of fetal hemoglobin, red cell pH, and 2,3-diphosphoglycerate.

PubMed

Versmold, H T; Linderkamp, C; Döhlemann, C; Riegel, K P

1976-06-01

In 48 individuals (age 1 day to 13 years) with congenital heart disease, blood oxygen transport function was studied in order to evaluate adaptive changes in shunt hypoxemia and to investigate the in vivo regulation of erythrocyte 2, 3-diphosphoglycerate concentration (RBC 2, 3-DPG) in the presence of fetal hemoglobin (HbF). Arterial pO2 and oxygen content, oxygen capacity, acid base status, oxygen affinity, HbF fraction, plasma pH, red cell pH, and RBC 2, 3-DPG were determined. During the first 50 days of life values of standard P50 (stdP50) (37, pH 7.4), actual in vivo P50 (actP50), RBC 2, 3-DPG, O2 capacity, arterial plasma pH, and red cell pH were scattered around the normal range, although tending to low values for stdP50 and arterial plasma pH and to high values for O2 capacity. After the third month, stdP50 actP50, RBC 2, 3-DPG, O2 capacity, and red cell pH were found to be elevated. Plasma pH and actP50 were scattered around the normal range (Figs. 1 and 2). Intraerythrocytic pH in hypoxemic infants was increased compared with normal children when related to plasma pH (Fig. 3). A close to normal intraerythrocytic pH was therefore found in the hypoxemic infants with low plasma pH, and an increased intraerythrocytic pH in the hypoxemic children with normal plasma pH (Fig. 1). A significant negative correlation exists between erythrocyte H+ ion and 2, 3-DPG concentration (Fig. 5); regression constants derived from data at high (mean 47%) and low (mean 9%) fractions of HbF are not significantly different (Regression Equations 8 and 11 in Table 1). Thus, the known difference in 2, 3-DPG binding to fetal or adult deoxyhemoglobin does not measurably influence the erythrocyte 2, 3-DPG concentration, indicating that in vivo the 2, 3-DPG synthesis in hypoxia is virtually regulated by the erythrocyte pH, which in turn is determined by plasma pH and the oxygenation state of hemoglobin.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

PubMed

Pecevski, Dejan; Maass, Wolfgang

2016-01-01

Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

PubMed Central

Pecevski, Dejan

2016-01-01

Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

On a phase diagram for random neural networks with embedded spike timing dependent plasticity.

PubMed

Turova, Tatyana S; Villa, Alessandro E P

2007-01-01

This paper presents an original mathematical framework based on graph theory which is a first attempt to investigate the dynamics of a model of neural networks with embedded spike timing dependent plasticity. The neurons correspond to integrate-and-fire units located at the vertices of a finite subset of 2D lattice. There are two types of vertices, corresponding to the inhibitory and the excitatory neurons. The edges are directed and labelled by the discrete values of the synaptic strength. We assume that there is an initial firing pattern corresponding to a subset of units that generate a spike. The number of activated externally vertices is a small fraction of the entire network. The model presented here describes how such pattern propagates throughout the network as a random walk on graph. Several results are compared with computational simulations and new data are presented for identifying critical parameters of the model.

Constitutive Models Based on Compressible Plastic Flows

NASA Technical Reports Server (NTRS)

Rajendran, A. M.

1983-01-01

The need for describing materials under time or cycle dependent loading conditions has been emphasized in recent years by several investigators. In response to the need, various constitutive models describing the nonlinear behavior of materials under creep, fatigue, or other complex loading conditions were developed. The developed models for describing the fully dense (non-porous) materials were mostly based on uncoupled plasticity theory. The improved characterization of materials provides a better understanding of the structual response under complex loading conditions. The pesent studies demonstrate that the rate or time dependency of the response of a porous aggregate can be incorporated into the nonlinear constitutive behavior of a porous solid by appropriately modeling the incompressible matrix behavior. It is also sown that the yield function which wads determined by a continuum mechanics approach must be verified by appropriate experiments on void containing sintered materials in order to obtain meaningful numbers for the constants that appear in the yield function.

Viscoplastic Characterization of Ti-6-4: Experiments

NASA Technical Reports Server (NTRS)

Lerch, Bradley A.; Arnold, Steven M.

2016-01-01

As part of a continued effort to improve the understanding of material time-dependent response, a series of mechanical tests have been conducted on the titanium alloy, Ti-6Al-4V. Tensile, creep, and stress relaxation tests were performed over a wide range of temperatures and strain rates to engage various amounts of time-dependent behavior. Additional tests were conducted that involved loading steps, overloads, dwell periods, and block loading segments to characterize the interaction between plasticity and time-dependent behavior. These data will be used to characterize a recently developed, viscoelastoplastic constitutive model with a goal toward better estimates of aerospace component behavior, resulting in improved safety.

Gap effects on leaf traits of tropical rainforest trees differing in juvenile light requirement.

PubMed

Houter, Nico C; Pons, Thijs L

2014-05-01

The relationships of 16 leaf traits and their plasticity with the dependence of tree species on gaps for regeneration (gap association index; GAI) were examined in a Neotropical rainforest. Young saplings of 24 species with varying GAI were grown under a closed canopy, in a medium-sized and in a large gap, thus capturing the full range of plasticity with respect to canopy openness. Structural, biomechanical, chemical and photosynthetic traits were measured. At the chloroplast level, the chlorophyll a/b ratio and plasticity in this variable were not related to the GAI. However, plasticity in total carotenoids per unit chlorophyll was larger in shade-tolerant species. At the leaf level, leaf mass per unit area (LMA) decreased with the GAI under the closed canopy and in the medium gap, but did not significantly decrease with the GAI in the large gap. This was a reflection of the larger plasticity in LMA and leaf thickness of gap-dependent species. The well-known opposite trends in LMA for adaptation and acclimation to high irradiance in evergreen tropical trees were thus not invariably found. Although leaf strength was dependent on LMA and thickness, plasticity in this trait was not related to the GAI. Photosynthetic capacity expressed on each basis increased with the GAI, but the large plasticity in these traits was not clearly related to the GAI. Although gap-dependent species tended to have a greater plasticity overall, as evident from a principle component analysis, leaf traits of gap-dependent species are thus not invariably more phenotypically plastic.

Superresolution imaging reveals activity-dependent plasticity of axon morphology linked to changes in action potential conduction velocity.

PubMed

Chéreau, Ronan; Saraceno, G Ezequiel; Angibaud, Julie; Cattaert, Daniel; Nägerl, U Valentin

2017-02-07

Axons convey information to nearby and distant cells, and the time it takes for action potentials (APs) to reach their targets governs the timing of information transfer in neural circuits. In the unmyelinated axons of hippocampus, the conduction speed of APs depends crucially on axon diameters, which vary widely. However, it is not known whether axon diameters are dynamic and regulated by activity-dependent mechanisms. Using time-lapse superresolution microscopy in brain slices, we report that axons grow wider after high-frequency AP firing: synaptic boutons undergo a rapid enlargement, which is mostly transient, whereas axon shafts show a more delayed and progressive increase in diameter. Simulations of AP propagation incorporating these morphological dynamics predicted bidirectional effects on AP conduction speed. The predictions were confirmed by electrophysiological experiments, revealing a phase of slowed down AP conduction, which is linked to the transient enlargement of the synaptic boutons, followed by a sustained increase in conduction speed that accompanies the axon shaft widening induced by high-frequency AP firing. Taken together, our study outlines a morphological plasticity mechanism for dynamically fine-tuning AP conduction velocity, which potentially has wide implications for the temporal transfer of information in the brain.

Increased gray matter density in the parietal cortex of mathematicians: a voxel-based morphometry study.

PubMed

Aydin, K; Ucar, A; Oguz, K K; Okur, O O; Agayev, A; Unal, Z; Yilmaz, S; Ozturk, C

2007-01-01

The training to acquire or practicing to perform a skill, which may lead to structural changes in the brain, is called experience-dependent structural plasticity. The main purpose of this cross-sectional study was to investigate the presence of experience-dependent structural plasticity in mathematicians' brains, which may develop after long-term practice of mathematic thinking. Twenty-six volunteer mathematicians, who have been working as academicians, were enrolled in the study. We applied an optimized method of voxel-based morphometry in the mathematicians and the age- and sex-matched control subjects. We assessed the gray and white matter density differences in mathematicians and the control subjects. Moreover, the correlation between the cortical density and the time spent as an academician was investigated. We found that cortical gray matter density in the left inferior frontal and bilateral inferior parietal lobules of the mathematicians were significantly increased compared with the control subjects. Furthermore, increase in gray matter density in the right inferior parietal lobule of the mathematicians was strongly correlated with the time spent as an academician (r = 0.84; P < .01). Left-inferior frontal and bilateral parietal regions are involved in arithmetic processing. Inferior parietal regions are also involved in high-level mathematic thinking, which requires visuospatial imagery, such as mental creation and manipulation of 3D objects. The voxel-based morphometric analysis of mathematicians' brains revealed increased gray matter density in the cortical regions related to mathematic thinking. The correlation between cortical density increase and the time spent as an academician suggests experience-dependent structural plasticity in mathematicians' brains.

Synaptic plasticity and neuronal refractory time cause scaling behaviour of neuronal avalanches

NASA Astrophysics Data System (ADS)

Michiels van Kessenich, L.; de Arcangelis, L.; Herrmann, H. J.

2016-08-01

Neuronal avalanches measured in vitro and in vivo in different cortical networks consistently exhibit power law behaviour for the size and duration distributions with exponents typical for a mean field self-organized branching process. These exponents are also recovered in neuronal network simulations implementing various neuronal dynamics on different network topologies. They can therefore be considered a very robust feature of spontaneous neuronal activity. Interestingly, this scaling behaviour is also observed on regular lattices in finite dimensions, which raises the question about the origin of the mean field behavior observed experimentally. In this study we provide an answer to this open question by investigating the effect of activity dependent plasticity in combination with the neuronal refractory time in a neuronal network. Results show that the refractory time hinders backward avalanches forcing a directed propagation. Hebbian plastic adaptation plays the role of sculpting these directed avalanche patterns into the topology of the network slowly changing it into a branched structure where loops are marginal.

Synaptic plasticity and neuronal refractory time cause scaling behaviour of neuronal avalanches.

PubMed

Michiels van Kessenich, L; de Arcangelis, L; Herrmann, H J

2016-08-18

Neuronal avalanches measured in vitro and in vivo in different cortical networks consistently exhibit power law behaviour for the size and duration distributions with exponents typical for a mean field self-organized branching process. These exponents are also recovered in neuronal network simulations implementing various neuronal dynamics on different network topologies. They can therefore be considered a very robust feature of spontaneous neuronal activity. Interestingly, this scaling behaviour is also observed on regular lattices in finite dimensions, which raises the question about the origin of the mean field behavior observed experimentally. In this study we provide an answer to this open question by investigating the effect of activity dependent plasticity in combination with the neuronal refractory time in a neuronal network. Results show that the refractory time hinders backward avalanches forcing a directed propagation. Hebbian plastic adaptation plays the role of sculpting these directed avalanche patterns into the topology of the network slowly changing it into a branched structure where loops are marginal.

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.

Interplay of multiple synaptic plasticity features in filamentary memristive devices for neuromorphic computing

NASA Astrophysics Data System (ADS)

La Barbera, Selina; Vincent, Adrien F.; Vuillaume, Dominique; Querlioz, Damien; Alibart, Fabien

2016-12-01

Bio-inspired computing represents today a major challenge at different levels ranging from material science for the design of innovative devices and circuits to computer science for the understanding of the key features required for processing of natural data. In this paper, we propose a detail analysis of resistive switching dynamics in electrochemical metallization cells for synaptic plasticity implementation. We show how filament stability associated to joule effect during switching can be used to emulate key synaptic features such as short term to long term plasticity transition and spike timing dependent plasticity. Furthermore, an interplay between these different synaptic features is demonstrated for object motion detection in a spike-based neuromorphic circuit. System level simulation presents robust learning and promising synaptic operation paving the way to complex bio-inspired computing systems composed of innovative memory devices.

Plasticity in the Developing Auditory Cortex: Evidence from Children with Sensorineural Hearing Loss and Auditory Neuropathy Spectrum Disorder

PubMed Central

Cardon, Garrett; Campbell, Julia; Sharma, Anu

2013-01-01

The developing auditory cortex is highly plastic. As such, the cortex is both primed to mature normally and at risk for re-organizing abnormally, depending upon numerous factors that determine central maturation. From a clinical perspective, at least two major components of development can be manipulated: 1) input to the cortex and 2) the timing of cortical input. Children with sensorineural hearing loss (SNHL) and auditory neuropathy spectrum disorder (ANSD) have provided a model of early deprivation of sensory input to the cortex, and demonstrated the resulting plasticity and development that can occur upon introduction of stimulation. In this article, we review several fundamental principles of cortical development and plasticity and discuss the clinical applications in children with SNHL and ANSD who receive intervention with hearing aids and/or cochlear implants. PMID:22668761

Focal Stroke in the Developing Rat Motor Cortex Induces Age- and Experience-Dependent Maladaptive Plasticity of Corticospinal System

PubMed Central

Gennaro, Mariangela; Mattiello, Alessandro; Mazziotti, Raffaele; Antonelli, Camilla; Gherardini, Lisa; Guzzetta, Andrea; Berardi, Nicoletta; Cioni, Giovanni; Pizzorusso, Tommaso

2017-01-01

Motor system development is characterized by an activity-dependent competition between ipsilateral and contralateral corticospinal tracts (CST). Clinical evidence suggests that age is crucial for developmental stroke outcome, with early lesions inducing a “maladaptive” strengthening of ipsilateral projections from the healthy hemisphere and worse motor impairment. Here, we investigated in developing rats the relation between lesion timing, motor outcome and CST remodeling pattern. We induced a focal ischemia into forelimb motor cortex (fM1) at two distinct pre-weaning ages: P14 and P21. We compared long-term motor outcome with changes in axonal sprouting of contralesional CST at red nucleus and spinal cord level using anterograde tracing. We found that P14 stroke caused a more severe long-term motor impairment than at P21, and induced a strong and aberrant contralesional CST sprouting onto denervated spinal cord and red nucleus. The mistargeted sprouting of CST, and the worse motor outcome of the P14 stroke rats were reversed by an early skilled motor training, underscoring the potential of early activity-dependent plasticity in modulating lesion outcome. Thus, changes in the mechanisms controlling CST plasticity occurring during the third postnatal week are associated with age-dependent regulation of the motor outcome after stroke. PMID:28706475

Focal Stroke in the Developing Rat Motor Cortex Induces Age- and Experience-Dependent Maladaptive Plasticity of Corticospinal System.

PubMed

Gennaro, Mariangela; Mattiello, Alessandro; Mazziotti, Raffaele; Antonelli, Camilla; Gherardini, Lisa; Guzzetta, Andrea; Berardi, Nicoletta; Cioni, Giovanni; Pizzorusso, Tommaso

2017-01-01

Motor system development is characterized by an activity-dependent competition between ipsilateral and contralateral corticospinal tracts (CST). Clinical evidence suggests that age is crucial for developmental stroke outcome, with early lesions inducing a "maladaptive" strengthening of ipsilateral projections from the healthy hemisphere and worse motor impairment. Here, we investigated in developing rats the relation between lesion timing, motor outcome and CST remodeling pattern. We induced a focal ischemia into forelimb motor cortex (fM1) at two distinct pre-weaning ages: P14 and P21. We compared long-term motor outcome with changes in axonal sprouting of contralesional CST at red nucleus and spinal cord level using anterograde tracing. We found that P14 stroke caused a more severe long-term motor impairment than at P21, and induced a strong and aberrant contralesional CST sprouting onto denervated spinal cord and red nucleus. The mistargeted sprouting of CST, and the worse motor outcome of the P14 stroke rats were reversed by an early skilled motor training, underscoring the potential of early activity-dependent plasticity in modulating lesion outcome. Thus, changes in the mechanisms controlling CST plasticity occurring during the third postnatal week are associated with age-dependent regulation of the motor outcome after stroke.

Spatiotemporal Computations of an Excitable and Plastic Brain: Neuronal Plasticity Leads to Noise-Robust and Noise-Constructive Computations

PubMed Central

Toutounji, Hazem; Pipa, Gordon

2014-01-01

It is a long-established fact that neuronal plasticity occupies the central role in generating neural function and computation. Nevertheless, no unifying account exists of how neurons in a recurrent cortical network learn to compute on temporally and spatially extended stimuli. However, these stimuli constitute the norm, rather than the exception, of the brain's input. Here, we introduce a geometric theory of learning spatiotemporal computations through neuronal plasticity. To that end, we rigorously formulate the problem of neural representations as a relation in space between stimulus-induced neural activity and the asymptotic dynamics of excitable cortical networks. Backed up by computer simulations and numerical analysis, we show that two canonical and widely spread forms of neuronal plasticity, that is, spike-timing-dependent synaptic plasticity and intrinsic plasticity, are both necessary for creating neural representations, such that these computations become realizable. Interestingly, the effects of these forms of plasticity on the emerging neural code relate to properties necessary for both combating and utilizing noise. The neural dynamics also exhibits features of the most likely stimulus in the network's spontaneous activity. These properties of the spatiotemporal neural code resulting from plasticity, having their grounding in nature, further consolidate the biological relevance of our findings. PMID:24651447

Auditory cortical plasticity induced by intracortical microstimulation under pharmacological blockage of inhibitory synapses.

PubMed

Yokota, R; Takahashi, H; Funamizu, A; Uchihara, M; Suzurikawa, J; Kanzaki, R

2006-01-01

Electrical stimulation that can reorganize our neural system has a potential for promising neurorehabilitation. We previously demonstrated that temporally controlled intracortical microstimulation (ICMS) could induce the spike time-dependant plasticity and modify tuning properties of cortical neurons as desired. A 'pairing' ICMS following tone-induced excitatory post-synaptic potentials (EPSPs) produced potentiation in response to the paired tones, while an 'anti-pairing' ICMS preceding the tone-induced EPSPs resulted in depression. However, the conventional ICMS affected both excitatory and inhibitory synapses, and thereby could not quantify net excitatory synaptic effects. In the present work, we evaluated the ICMS effects under a pharmacological blockage of inhibitory inputs. The pharmacological blockage enhanced the ICMS effects, suggesting that inhibitory inputs determine a plastic degree of the neural system. Alternatively, the conventional ICMS had an inadequate timing to control excitatory synaptic inputs, because inhibitory synapse determined the latency of total neural inputs.

miR-191 and miR-135 are required for long-lasting spine remodelling associated with synaptic long-term depression

NASA Astrophysics Data System (ADS)

Hu, Zhonghua; Yu, Danni; Gu, Qin-Hua; Yang, Yanqin; Tu, Kang; Zhu, Jun; Li, Zheng

2014-02-01

Activity-dependent modification of dendritic spines, subcellular compartments accommodating postsynaptic specializations in the brain, is an important cellular mechanism for brain development, cognition and synaptic pathology of brain disorders. NMDA receptor-dependent long-term depression (NMDAR-LTD), a prototypic form of synaptic plasticity, is accompanied by prolonged remodelling of spines. The mechanisms underlying long-lasting spine remodelling in NMDAR-LTD, however, are largely unclear. Here we show that LTD induction causes global changes in miRNA transcriptomes affecting many cellular activities. Specifically, we show that expression changes of miR-191 and miR-135 are required for maintenance but not induction of spine restructuring. Moreover, we find that actin depolymerization and AMPA receptor exocytosis are regulated for extended periods of time by miRNAs to support long-lasting spine plasticity. These findings reveal a miRNA-mediated mechanism and a role for AMPA receptor exocytosis in long-lasting spine plasticity, and identify a number of candidate miRNAs involved in LTD.

Sleep, Plasticity and the Pathophysiology of Neurodevelopmental Disorders: The Potential Roles of Protein Synthesis and Other Cellular Processes

PubMed Central

Picchioni, Dante; Reith, R. Michelle; Nadel, Jeffrey L.; Smith, Carolyn B.

2014-01-01

Sleep is important for neural plasticity, and plasticity underlies sleep-dependent memory consolidation. It is widely appreciated that protein synthesis plays an essential role in neural plasticity. Studies of sleep-dependent memory and sleep-dependent plasticity have begun to examine alterations in these functions in populations with neurological and psychiatric disorders. Such an approach acknowledges that disordered sleep may have functional consequences during wakefulness. Although neurodevelopmental disorders are not considered to be sleep disorders per se, recent data has revealed that sleep abnormalities are among the most prevalent and common symptoms and may contribute to the progression of these disorders. The main goal of this review is to highlight the role of disordered sleep in the pathology of neurodevelopmental disorders and to examine some potential mechanisms by which sleep-dependent plasticity may be altered. We will also briefly attempt to extend the same logic to the other end of the developmental spectrum and describe a potential role of disordered sleep in the pathology of neurodegenerative diseases. We conclude by discussing ongoing studies that might provide a more integrative approach to the study of sleep, plasticity, and neurodevelopmental disorders. PMID:24839550

Crash simulation of hybrid structures considering the stress and strain rate dependent material behavior of thermoplastic materials

NASA Astrophysics Data System (ADS)

Hopmann, Ch.; Schöngart, M.; Weber, M.; Klein, J.

2015-05-01

Thermoplastic materials are more and more used as a light weight replacement for metal, especially in the automotive industry. Since these materials do not provide the mechanical properties, which are required to manufacture supporting elements like an auto body or a cross bearer, plastics are combined with metals in so called hybrid structures. Normally, the plastics components are joined to the metal structures using different technologies like welding or screwing. Very often, the hybrid structures are made of flat metal parts, which are stiffened by a reinforcement structure made of thermoplastic materials. The loads on these structures are very often impulsive, for example in the crash situation of an automobile. Due to the large stiffness variation of metal and thermoplastic materials, complex states of stress and very high local strain rates occur in the contact zone under impact conditions. Since the mechanical behavior of thermoplastic materials is highly dependent on these types of load, the crash failure of metal plastic hybrid parts is very complex. The problem is that the normally used strain rate dependent elastic/plastic material models are not capable to simulate the mechanical behavior of thermoplastic materials depended on the state of stress. As part of a research project, a method to simulate the mechanical behavior of hybrid structures under impact conditions is developed at the IKV. For this purpose, a specimen for the measurement of mechanical properties dependet on the state of stress and a method for the strain rate depended characterization of thermoplastic materials were developed. In the second step impact testing is performed. A hybrid structure made from a metal sheet and a reinforcement structure of a Polybutylenterephthalat Polycarbonate blend is tested under impact conditions. The measured stress and strain rate depended material data are used to simulate the mechanical behavior of the hybrid structure under highly dynamic load with impact velocities up to 5 m/s. The mechanical behavior of the plastics structure is simulated using a quadratic yield surface, which takes the state of stress and the strain rate into account. The FE model is made from mid surface elements to reduce the computing time.

Context-Dependent Plastic Response during Egg-Laying in a Widespread Newt Species

PubMed Central

Tóth, Zoltán

2015-01-01

Previous research on predator-induced phenotypic plasticity mostly focused on responses in morphology, developmental time and/or behaviour during early life stages, but the potential significance of anticipatory parental responses has been investigated less often. In this study I examined behavioural and maternal responses of gravid female smooth newts, Lissotriton vulgaris, in the presence of chemical cues originating from invertebrate predators, Acilius sulcatus water beetles and Aeshna cyanea dragonfly larvae. More specifically, I tested the extent of oviposition preference, plasticity in egg-wrapping behaviour and plasticity in egg size when females had the possibility to lay eggs at oviposition sites with and without predator cues during overnight trials. I found that individuals did not avoid laying eggs in the environment with predator cues; however, individuals that deposited eggs into both environments adjusted the size of the laid eggs to the perceived environment. Females deposited larger eggs earlier in the season but egg size decreased with time in the absence of predator cues, whereas individuals laid eggs of average size throughout the investigated reproductive period when such cues were present. Also, egg size was found to be positively related to hatching success. Individuals did not adjust their wrapping behaviour to the presence of predator cues, but females differed in the extent of egg-wrapping between ponds. Females’ body mass and tail depth were also different between ponds, whereas their body size was positively associated with egg size. According to these results, female smooth newts have the potential to exhibit activational plasticity and invest differently into eggs depending on temporal and environmental factors. Such an anticipatory response may contribute to the success of this caudate species under a wide range of predator regimes at its natural breeding habitats. PMID:26291328

Regulating Critical Period Plasticity: Insight from the Visual System to Fear Circuitry for Therapeutic Interventions

PubMed Central

Nabel, Elisa M.; Morishita, Hirofumi

2013-01-01

Early temporary windows of heightened brain plasticity called critical periods developmentally sculpt neural circuits and contribute to adult behavior. Regulatory mechanisms of visual cortex development – the preeminent model of experience-dependent critical period plasticity-actively limit adult plasticity and have proved fruitful therapeutic targets to reopen plasticity and rewire faulty visual system connections later in life. Interestingly, these molecular mechanisms have been implicated in the regulation of plasticity in other functions beyond vision. Applying mechanistic understandings of critical period plasticity in the visual cortex to fear circuitry may provide a conceptual framework for developing novel therapeutic tools to mitigate aberrant fear responses in post traumatic stress disorder. In this review, we turn to the model of experience-dependent visual plasticity to provide novel insights for the mechanisms regulating plasticity in the fear system. Fear circuitry, particularly fear memory erasure, also undergoes age-related changes in experience-dependent plasticity. We consider the contributions of molecular brakes that halt visual critical period plasticity to circuitry underlying fear memory erasure. A major molecular brake in the visual cortex, perineuronal net formation, recently has been identified in the development of fear systems that are resilient to fear memory erasure. The roles of other molecular brakes, myelin-related Nogo receptor signaling and Lynx family proteins – endogenous inhibitors for nicotinic acetylcholine receptor, are explored in the context of fear memory plasticity. Such fear plasticity regulators, including epigenetic effects, provide promising targets for therapeutic interventions. PMID:24273519

Elastic, not plastic species: frozen plasticity theory and the origin of adaptive evolution in sexually reproducing organisms.

PubMed

Flegr, Jaroslav

2010-01-13

Darwin's evolutionary theory could easily explain the evolution of adaptive traits (organs and behavioral patterns) in asexual but not in sexual organisms. Two models, the selfish gene theory and frozen plasticity theory were suggested to explain evolution of adaptive traits in sexual organisms in past 30 years. The frozen plasticity theory suggests that sexual species can evolve new adaptations only when their members are genetically uniform, i.e. only after a portion of the population of the original species had split off, balanced on the edge of extinction for several generations, and then undergone rapid expansion. After a short period of time, estimated on the basis of paleontological data to correspond to 1-2% of the duration of the species, polymorphism accumulates in the gene pool due to frequency-dependent selection; and thus, in each generation, new mutations occur in the presence of different alleles and therefore change their selection coefficients from generation to generation. The species ceases to behave in an evolutionarily plastic manner and becomes evolutionarily elastic on a microevolutionary time-scale and evolutionarily frozen on a macroevolutionary time-scale. It then exists in this state until such changes accumulate in the environment that the species becomes extinct. Frozen plasticity theory, which includes the Darwinian model of evolution as a special case--the evolution of species in a plastic state, not only offers plenty of new predictions to be tested, but also provides explanations for a much broader spectrum of known biological phenomena than classic evolutionary theories. This article was reviewed by Rob Knight, Fyodor Kondrashov and Massimo Di Giulio (nominated by David H. Ardell).

Combinations of stroke neurorehabilitation to facilitate motor recovery: perspectives on Hebbian plasticity and homeostatic metaplasticity

PubMed Central

Takeuchi, Naoyuki; Izumi, Shin-Ichi

2015-01-01

Motor recovery after stroke involves developing new neural connections, acquiring new functions, and compensating for impairments. These processes are related to neural plasticity. Various novel stroke rehabilitation techniques based on basic science and clinical studies of neural plasticity have been developed to aid motor recovery. Current research aims to determine whether using combinations of these techniques can synergistically improve motor recovery. When different stroke neurorehabilitation therapies are combined, the timing of each therapeutic program must be considered to enable optimal neural plasticity. Synchronizing stroke rehabilitation with voluntary neural and/or muscle activity can lead to motor recovery by targeting Hebbian plasticity. This reinforces the neural connections between paretic muscles and the residual motor area. Homeostatic metaplasticity, which stabilizes the activity of neurons and neural circuits, can either augment or reduce the synergic effect depending on the timing of combination therapy and types of neurorehabilitation that are used. Moreover, the possibility that the threshold and degree of induced plasticity can be altered after stroke should be noted. This review focuses on the mechanisms underlying combinations of neurorehabilitation approaches and their future clinical applications. We suggest therapeutic approaches for cortical reorganization and maximal functional gain in patients with stroke, based on the processes of Hebbian plasticity and homeostatic metaplasticity. Few of the possible combinations of stroke neurorehabilitation have been tested experimentally; therefore, further studies are required to determine the appropriate combination for motor recovery. PMID:26157374

Mathematical characterization of mechanical behavior of porous frictional granular media

NASA Technical Reports Server (NTRS)

Chung, T. J.; Lee, J. K.

1972-01-01

A new definition of loading and unloading along the yield surface of Roscoe and Burland is introduced. This is achieved by noting that the strain-hardening parameter in the plastic potential function is deduced from the yield locus equation of Roscoe and Burland. The analytical results are compared with the experimental results for plate-bearing and cone-penetrometer problems and close agreements are demonstrated. The wheel-soil interaction is studied under dynamic loading. The rate-dependent plasticity or viscoelastoplastic behavior is considered. This is accomplished by the internal (hidden) variables associated with time-dependent viscous properties directly superimposed with inelastic behavior governed by the yield criteria of Roscoe and Burland. Effects of inertia and energy dissipation are properly accounted for. Example problems are presented.

Human vulnerability to stress depends on amygdala's predisposition and hippocampal plasticity

PubMed Central

Admon, Roee; Lubin, Gad; Stern, Orit; Rosenberg, Keren; Sela, Lee; Ben-Ami, Haim; Hendler, Talma

2009-01-01

Variations in people's vulnerability to stressful life events may rise from a predated neural sensitivity as well as from differential neural modifications in response to the event. Because the occurrence of a stressful life event cannot be foreseen, characterizing the temporal trajectory of its neural manifestations in humans has been a real challenge. The current prospective study examined the emotional experience and brain responses of 50 a priori healthy new recruits to the Israeli Defense Forces at 2 time points: before they entered their mandatory military service and after their subsequent exposure to stressful events while deployed in combat units. Over time, soldiers reported on increase in stress symptoms that was correlated with greater amygdala and hippocampus responsiveness to stress-related content. However, these closely situated core limbic regions exhibited different temporal trajectories with regard to the stress effect; whereas amygdala's reactivity before stress predicted the increase in stress symptoms, the hippocampal change in activation over time correlated with the increase in such symptoms. Hippocampal plasticity was also reflected by a modification over time of its functional coupling with the ventromedial prefrontal cortex, and this coupling magnitude was again predicted by predated amygdala reactivity. Together, these findings suggest that variations in human's likelihood to develop symptomatic phenomena following stressful life events may depend on a balanced interplay between their amygdala's predisposing reactivity and hippocampal posteriori intra- and interregional plasticity. Accordingly, an individually tailored therapeutic approach for trauma survivors should target these 2 neural probes while considering their unique temporal prints. PMID:19666562

The Role of Neuromodulators in Cortical Plasticity. A Computational Perspective

PubMed Central

Pedrosa, Victor; Clopath, Claudia

2017-01-01

Neuromodulators play a ubiquitous role across the brain in regulating plasticity. With recent advances in experimental techniques, it is possible to study the effects of diverse neuromodulatory states in specific brain regions. Neuromodulators are thought to impact plasticity predominantly through two mechanisms: the gating of plasticity and the upregulation of neuronal activity. However, the consequences of these mechanisms are poorly understood and there is a need for both experimental and theoretical exploration. Here we illustrate how neuromodulatory state affects cortical plasticity through these two mechanisms. First, we explore the ability of neuromodulators to gate plasticity by reshaping the learning window for spike-timing-dependent plasticity. Using a simple computational model, we implement four different learning rules and demonstrate their effects on receptive field plasticity. We then compare the neuromodulatory effects of upregulating learning rate versus the effects of upregulating neuronal activity. We find that these seemingly similar mechanisms do not yield the same outcome: upregulating neuronal activity can lead to either a broadening or a sharpening of receptive field tuning, whereas upregulating learning rate only intensifies the sharpening of receptive field tuning. This simple model demonstrates the need for further exploration of the rich landscape of neuromodulator-mediated plasticity. Future experiments, coupled with biologically detailed computational models, will elucidate the diversity of mechanisms by which neuromodulatory state regulates cortical plasticity. PMID:28119596

Laminar distribution of cholinergic- and serotonergic-dependent plasticity within kitten visual cortex.

PubMed

Kojic, L; Gu, Q; Douglas, R M; Cynader, M S

2001-02-28

Both cholinergic and serotonergic modulatory projections to mammalian striate cortex have been demonstrated to be involved in the regulation of postnatal plasticity, and a striking alteration in the number and intracortical distribution of cholinergic and serotonergic receptors takes place during the critical period for cortical plasticity. As well, agonists of cholinergic and serotonergic receptors have been demonstrated to facilitate induction of long-term synaptic plasticity in visual cortical slices supporting their involvement in the control of activity-dependent plasticity. We recorded field potentials from layers 4 and 2/3 in visual cortex slices of 60--80 day old kittens after white matter stimulation, before and after a period of high frequency stimulation (HFS), in the absence or presence of either cholinergic or serotonergic agonists. At these ages, the HFS protocol alone almost never induced long-term changes of synaptic plasticity in either layers 2/3 or 4. In layer 2/3, agonist stimulation of m1 receptors facilitated induction of long-term potentiation (LTP) with HFS stimulation, while the activation of serotonergic receptors had only a modest effect. By contrast, a strong serotonin-dependent LTP facilitation and insignificant muscarinic effects were observed after HFS within layer 4. The results show that receptor-dependent laminar stratification of synaptic modifiability occurs in the cortex at these ages. This plasticity may underly a control system gating the experience-dependent changes of synaptic organization within developing visual cortex.

Interaction of rate- and size-effect using a dislocation density based strain gradient viscoplasticity model

NASA Astrophysics Data System (ADS)

Nguyen, Trung N.; Siegmund, Thomas; Tomar, Vikas; Kruzic, Jamie J.

2017-12-01

Size effects occur in non-uniform plastically deformed metals confined in a volume on the scale of micrometer or sub-micrometer. Such problems have been well studied using strain gradient rate-independent plasticity theories. Yet, plasticity theories describing the time-dependent behavior of metals in the presence of size effects are presently limited, and there is no consensus about how the size effects vary with strain rates or whether there is an interaction between them. This paper introduces a constitutive model which enables the analysis of complex load scenarios, including loading rate sensitivity, creep, relaxation and interactions thereof under the consideration of plastic strain gradient effects. A strain gradient viscoplasticity constitutive model based on the Kocks-Mecking theory of dislocation evolution, namely the strain gradient Kocks-Mecking (SG-KM) model, is established and allows one to capture both rate and size effects, and their interaction. A formulation of the model in the finite element analysis framework is derived. Numerical examples are presented. In a special virtual creep test with the presence of plastic strain gradients, creep rates are found to diminish with the specimen size, and are also found to depend on the loading rate in an initial ramp loading step. Stress relaxation in a solid medium containing cylindrical microvoids is predicted to increase with decreasing void radius and strain rate in a prior ramp loading step.

Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites

PubMed Central

Schiess, Mathieu; Urbanczik, Robert; Senn, Walter

2016-01-01

In the last decade dendrites of cortical neurons have been shown to nonlinearly combine synaptic inputs by evoking local dendritic spikes. It has been suggested that these nonlinearities raise the computational power of a single neuron, making it comparable to a 2-layer network of point neurons. But how these nonlinearities can be incorporated into the synaptic plasticity to optimally support learning remains unclear. We present a theoretically derived synaptic plasticity rule for supervised and reinforcement learning that depends on the timing of the presynaptic, the dendritic and the postsynaptic spikes. For supervised learning, the rule can be seen as a biological version of the classical error-backpropagation algorithm applied to the dendritic case. When modulated by a delayed reward signal, the same plasticity is shown to maximize the expected reward in reinforcement learning for various coding scenarios. Our framework makes specific experimental predictions and highlights the unique advantage of active dendrites for implementing powerful synaptic plasticity rules that have access to downstream information via backpropagation of action potentials. PMID:26841235

Learning to learn – intrinsic plasticity as a metaplasticity mechanism for memory formation

PubMed Central

Sehgal, Megha; Song, Chenghui; Ehlers, Vanessa L.; Moyer, James R.

2013-01-01

“Use it or lose it” is a popular adage often associated with use-dependent enhancement of cognitive abilities. Much research has focused on understanding exactly how the brain changes as a function of experience. Such experience-dependent plasticity involves both structural and functional alterations that contribute to adaptive behaviors, such as learning and memory, as well as maladaptive behaviors, including anxiety disorders, phobias, and posttraumatic stress disorder. With the advancing age of our population, understanding how use-dependent plasticity changes across the lifespan may also help to promote healthy brain aging. A common misconception is that such experience-dependent plasticity (e.g., associative learning) is synonymous with synaptic plasticity. Other forms of plasticity also play a critical role in shaping adaptive changes within the nervous system, including intrinsic plasticity – a change in the intrinsic excitability of a neuron. Intrinsic plasticity can result from a change in the number, distribution or activity of various ion channels located throughout the neuron. Here, we review evidence that intrinsic plasticity is an important and evolutionarily conserved neural correlate of learning. Intrinsic plasticity acts as a metaplasticity mechanism by lowering the threshold for synaptic changes. Thus, learning-related intrinsic changes can facilitate future synaptic plasticity and learning. Such intrinsic changes can impact the allocation of a memory trace within a brain structure, and when compromised, can contribute to cognitive decline during the aging process. This unique role of intrinsic excitability can provide insight into how memories are formed and, more interestingly, how neurons that participate in a memory trace are selected. Most importantly, modulation of intrinsic excitability can allow for regulation of learning ability – this can prevent or provide treatment for cognitive decline not only in patients with clinical disorders but also in the aging population. PMID:23871744

Maladaptive spinal plasticity opposes spinal learning and recovery in spinal cord injury

PubMed Central

Ferguson, Adam R.; Huie, J. Russell; Crown, Eric D.; Baumbauer, Kyle M.; Hook, Michelle A.; Garraway, Sandra M.; Lee, Kuan H.; Hoy, Kevin C.; Grau, James W.

2012-01-01

Synaptic plasticity within the spinal cord has great potential to facilitate recovery of function after spinal cord injury (SCI). Spinal plasticity can be induced in an activity-dependent manner even without input from the brain after complete SCI. A mechanistic basis for these effects is provided by research demonstrating that spinal synapses have many of the same plasticity mechanisms that are known to underlie learning and memory in the brain. In addition, the lumbar spinal cord can sustain several forms of learning and memory, including limb-position training. However, not all spinal plasticity promotes recovery of function. Central sensitization of nociceptive (pain) pathways in the spinal cord may emerge in response to various noxious inputs, demonstrating that plasticity within the spinal cord may contribute to maladaptive pain states. In this review we discuss interactions between adaptive and maladaptive forms of activity-dependent plasticity in the spinal cord below the level of SCI. The literature demonstrates that activity-dependent plasticity within the spinal cord must be carefully tuned to promote adaptive spinal training. Prior work from our group has shown that stimulation that is delivered in a limb position-dependent manner or on a fixed interval can induce adaptive plasticity that promotes future spinal cord learning and reduces nociceptive hyper-reactivity. On the other hand, stimulation that is delivered in an unsynchronized fashion, such as randomized electrical stimulation or peripheral skin injuries, can generate maladaptive spinal plasticity that undermines future spinal cord learning, reduces recovery of locomotor function, and promotes nociceptive hyper-reactivity after SCI. We review these basic phenomena, how these findings relate to the broader spinal plasticity literature, discuss the cellular and molecular mechanisms, and finally discuss implications of these and other findings for improved rehabilitative therapies after SCI. PMID:23087647

ZIP2DL: An Elastic-Plastic, Large-Rotation Finite-Element Stress Analysis and Crack-Growth Simulation Program

NASA Technical Reports Server (NTRS)

Deng, Xiaomin; Newman, James C., Jr.

1997-01-01

ZIP2DL is a two-dimensional, elastic-plastic finte element program for stress analysis and crack growth simulations, developed for the NASA Langley Research Center. It has many of the salient features of the ZIP2D program. For example, ZIP2DL contains five material models (linearly elastic, elastic-perfectly plastic, power-law hardening, linear hardening, and multi-linear hardening models), and it can simulate mixed-mode crack growth for prescribed crack growth paths under plane stress, plane strain and mixed state of stress conditions. Further, as an extension of ZIP2D, it also includes a number of new capabilities. The large-deformation kinematics in ZIP2DL will allow it to handle elastic problems with large strains and large rotations, and elastic-plastic problems with small strains and large rotations. Loading conditions in terms of surface traction, concentrated load, and nodal displacement can be applied with a default linear time dependence or they can be programmed according to a user-defined time dependence through a user subroutine. The restart capability of ZIP2DL will make it possible to stop the execution of the program at any time, analyze the results and/or modify execution options and resume and continue the execution of the program. This report includes three sectons: a theoretical manual section, a user manual section, and an example manual secton. In the theoretical secton, the mathematics behind the various aspects of the program are concisely outlined. In the user manual section, a line-by-line explanation of the input data is given. In the example manual secton, three types of examples are presented to demonstrate the accuracy and illustrate the use of this program.

Beneficial effects of benzodiazepine diazepam on chronic stress-induced impairment of hippocampal structural plasticity and depression-like behavior in mice.

PubMed

Zhao, Yunan; Wang, Zhongli; Dai, Jianguo; Chen, Lin; Huang, Yufang; Zhan, Zhen

2012-03-17

Whether benzodiazepines (BZDs) have beneficial effects on the progress of chronic stress-induced impairment of hippocampal structural plasticity and major depression is uncertain. The present study designed four preclinical experiments to determine the effects of BZDs using chronic unpredictable stress model. In Experiment 1, several time course studies on behavior and hippocampus response to stress were conducted using the forced swim and tail suspension tests (FST and TST) as well as hippocampal structural plasticity markers. Chronic stress induced depression-like behavior in the FST and TST as well as decreased hippocampal structural plasticity that returned to normal within 3 wk. In Experiment 2, mice received p.o. administration of three diazepam dosages prior to each variate stress session for 4 wk. This treatment significantly antagonized the elevation of stress-induced corticosterone levels. Only low- (0.5mg/kg) and medium-dose (1mg/kg) diazepam blocked the detrimental effects of chronic stress. In Experiment 3, after 7 wk of stress sessions, daily p.o. diazepam administration during 1 wk recovery phase dose-dependently accelerated the recovery of stressed mice. In Experiment 4, 1 wk diazepam administration to control mice enhanced significantly hippocampal structural plasticity and induced an antidepressant-like behavioral effect, whereas 4 wk diazepam administration produced opposite effects. Hence, diazepam can slow the progress of chronic stress-induced detrimental consequences by normalizing glucocorticoid hormones. Considering the adverse effect of long-term diazepam administration on hippocampal plasticity, the preventive effects of diazepam may depend on the proper dose. Short-term diazepam treatment enhances hippocampal structural plasticity and is beneficial to recovery following chronic stress. Copyright © 2011 Elsevier B.V. All rights reserved.

Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement

PubMed Central

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes. PMID:21765890

Should I stay or should I go? A habitat-dependent dispersal kernel improves prediction of movement.

PubMed

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes.

A study of microindentation hardness tests by mechanism-based strain gradient plasticity

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

Huang, Y.; Xue, Z.; Gao, H.

2000-08-01

We recently proposed a theory of mechanism-based strain gradient (MSG) plasticity to account for the size dependence of plastic deformation at micron- and submicron-length scales. The MSG plasticity theory connects micron-scale plasticity to dislocation theories via a multiscale, hierarchical framework linking Taylor's dislocation hardening model to strain gradient plasticity. Here we show that the theory of MSG plasticity, when used to study micro-indentation, indeed reproduces the linear dependence observed in experiments, thus providing an important self-consistent check of the theory. The effects of pileup, sink-in, and the radius of indenter tip have been taken into account in the indentation model.more » In accomplishing this objective, we have generalized the MSG plasticity theory to include the elastic deformation in the hierarchical framework. (c) 2000 Materials Research Society.« less

Endocrine regulation of predator-induced phenotypic plasticity.

PubMed

Dennis, Stuart R; LeBlanc, Gerald A; Beckerman, Andrew P

2014-11-01

Elucidating the developmental and genetic control of phenotypic plasticity remains a central agenda in evolutionary ecology. Here, we investigate the physiological regulation of phenotypic plasticity induced by another organism, specifically predator-induced phenotypic plasticity in the model ecological and evolutionary organism Daphnia pulex. Our research centres on using molecular tools to test among alternative mechanisms of developmental control tied to hormone titres, receptors and their timing in the life cycle. First, we synthesize detail about predator-induced defenses and the physiological regulation of arthropod somatic growth and morphology, leading to a clear prediction that morphological defences are regulated by juvenile hormone and life-history plasticity by ecdysone and juvenile hormone. We then show how a small network of genes can differentiate phenotype expression between the two primary developmental control pathways in arthropods: juvenoid and ecdysteroid hormone signalling. Then, by applying an experimental gradient of predation risk, we show dose-dependent gene expression linking predator-induced plasticity to the juvenoid hormone pathway. Our data support three conclusions: (1) the juvenoid signalling pathway regulates predator-induced phenotypic plasticity; (2) the hormone titre (ligand), rather than receptor, regulates predator-induced developmental plasticity; (3) evolution has favoured the harnessing of a major, highly conserved endocrine pathway in arthropod development to regulate the response to cues about changing environments (risk) from another organism (predator).

Charge carrier dynamics in PMMA-LiClO4 based polymer electrolytes plasticized with different plasticizers

NASA Astrophysics Data System (ADS)

Pal, P.; Ghosh, A.

2017-07-01

We have studied the charge carrier dynamics in poly(methylmethacrylate)-LiClO4 polymer electrolytes plasticized with different plasticizers such as ethylene carbonate (EC), propylene carbonate (PC), polyethylene glycol (PEG), and dimethyl carbonate (DMC). We have measured the broadband complex conductivity spectra of these electrolytes in the frequency range of 0.01 Hz-3 GHz and in the temperature range of 203 K-363 K and analyzed the conductivity spectra in the framework of the random barrier model by taking into account the contribution of the electrode polarization observed at low frequencies and/or at high temperatures. It is observed that the temperature dependences of the ionic conductivity and relaxation time follow the Vogel-Tammann-Fulcher relation for all plasticized electrolytes. We have also performed the scaling of the conductivity spectra, which indicates that the charge carrier dynamics is almost independent of temperature and plasticizers in a limited frequency range. The existence of nearly constant loss in these electrolytes has been observed at low temperatures and/or high frequencies. We have studied the dielectric relaxation in these electrolytes using electric modulus formalism and obtained the stretched exponent and the decay function. We have observed less cooperative ion dynamics in electrolytes plasticized with DMC compared to electrolytes plasticized with EC, PC, and PEG.

Genetic evolution, plasticity, and bet-hedging as adaptive responses to temporally autocorrelated fluctuating selection: A quantitative genetic model.

PubMed

Tufto, Jarle

2015-08-01

Adaptive responses to autocorrelated environmental fluctuations through evolution in mean reaction norm elevation and slope and an independent component of the phenotypic variance are analyzed using a quantitative genetic model. Analytic approximations expressing the mutual dependencies between all three response modes are derived and solved for the joint evolutionary outcome. Both genetic evolution in reaction norm elevation and plasticity are favored by slow temporal fluctuations, with plasticity, in the absence of microenvironmental variability, being the dominant evolutionary outcome for reasonable parameter values. For fast fluctuations, tracking of the optimal phenotype through genetic evolution and plasticity is limited. If residual fluctuations in the optimal phenotype are large and stabilizing selection is strong, selection then acts to increase the phenotypic variance (bet-hedging adaptive). Otherwise, canalizing selection occurs. If the phenotypic variance increases with plasticity through the effect of microenvironmental variability, this shifts the joint evolutionary balance away from plasticity in favor of genetic evolution. If microenvironmental deviations experienced by each individual at the time of development and selection are correlated, however, more plasticity evolves. The adaptive significance of evolutionary fluctuations in plasticity and the phenotypic variance, transient evolution, and the validity of the analytic approximations are investigated using simulations. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Genetic variation of transgenerational plasticity of offspring germination in response to salinity stress and the seed transcriptome of Medicago truncatula.

PubMed

Vu, Wendy T; Chang, Peter L; Moriuchi, Ken S; Friesen, Maren L

2015-04-01

Transgenerational plasticity provides phenotypic variation that contributes to adaptation. For plants, the timing of seed germination is critical for offspring survival in stressful environments, as germination timing can alter the environmental conditions a seedling experiences. Stored seed transcripts are important determinants of seed germination, but have not previously been linked with transgenerational plasticity of germination behavior. In this study we used RNAseq and growth chamber experiments of the model legume M. trucantula to test whether parental exposure to salinity stress influences the expression of stored seed transcripts and early offspring traits and test for genetic variation. We detected genotype-dependent parental environmental effects (transgenerational plasticity) on the expression levels of stored seed transcripts, seed size, and germination behavior of four M. truncatula genotypes. More than 50% of the transcripts detected in the mature, ungerminated seed transcriptome were annotated as regulating seed germination, some of which are involved in abiotic stress response and post-embryonic development. Some genotypes showed increased seed size in response to parental exposure to salinity stress, but no parental environmental influence on germination timing. In contrast, other genotypes showed no seed size differences across contrasting parental conditions but displayed transgenerational plasticity for germimation timing, with significantly delayed germination in saline conditions when parental plants were exposed to salinity. In genotypes that show significant transgenerational plastic germination response, we found significant coexpression networks derived from salt responsive transcripts involved in post-transcriptional regulation of the germination pathway. Consistent with the delayed germination response to saline conditions in these genotypes, we found genes associated with dormancy and up-regulation of abscisic acid (ABA). Our results demonstrate genetic variation in transgenerational plasticity within M. truncatula and show that parental exposure to salinity stress influences the expression of stored seed transcripts, seed weight, and germination behavior. Furthermore, we show that the parental environment influences gene expression to modulate biological pathways that are likely responsible for offspring germination responses to salinity stress.

Neuromodulatory influence of norepinephrine during developmental experience-dependent plasticity.

PubMed

Golovin, Randall M; Ward, Nicholas J

2016-07-01

Critical periods represent phases of development during which neuronal circuits and their responses can be readily shaped by stimuli. Experience-dependent plasticity that occurs within these critical periods can be influenced in many ways; however, Shepard et al. (J Neurosci 35: 2432-2437, 2015) recently singled out norepinephrine as an essential driver of this plasticity within the auditory cortex. This work provides novel insight into the mechanisms of critical period plasticity and challenges previous conceptions that a functional redundancy exists between noradrenergic and cholinergic influences on cortical plasticity. Copyright © 2016 the American Physiological Society.

Cocaine Promotes Coincidence Detection and Lowers Induction Threshold during Hebbian Associative Synaptic Potentiation in Prefrontal Cortex.

PubMed

Ruan, Hongyu; Yao, Wei-Dong

2017-01-25

Addictive drugs usurp neural plasticity mechanisms that normally serve reward-related learning and memory, primarily by evoking changes in glutamatergic synaptic strength in the mesocorticolimbic dopamine circuitry. Here, we show that repeated cocaine exposure in vivo does not alter synaptic strength in the mouse prefrontal cortex during an early period of withdrawal, but instead modifies a Hebbian quantitative synaptic learning rule by broadening the temporal window and lowers the induction threshold for spike-timing-dependent LTP (t-LTP). After repeated, but not single, daily cocaine injections, t-LTP in layer V pyramidal neurons is induced at +30 ms, a normally ineffective timing interval for t-LTP induction in saline-exposed mice. This cocaine-induced, extended-timing t-LTP lasts for ∼1 week after terminating cocaine and is accompanied by an increased susceptibility to potentiation by fewer pre-post spike pairs, indicating a reduced t-LTP induction threshold. Basal synaptic strength and the maximal attainable t-LTP magnitude remain unchanged after cocaine exposure. We further show that the cocaine facilitation of t-LTP induction is caused by sensitized D1-cAMP/protein kinase A dopamine signaling in pyramidal neurons, which then pathologically recruits voltage-gated l-type Ca 2+ channels that synergize with GluN2A-containing NMDA receptors to drive t-LTP at extended timing. Our results illustrate a mechanism by which cocaine, acting on a key neuromodulation pathway, modifies the coincidence detection window during Hebbian plasticity to facilitate associative synaptic potentiation in prefrontal excitatory circuits. By modifying rules that govern activity-dependent synaptic plasticity, addictive drugs can derail the experience-driven neural circuit remodeling process important for executive control of reward and addiction. It is believed that addictive drugs often render an addict's brain reward system hypersensitive, leaving the individual more susceptible to relapse. We found that repeated cocaine exposure alters a Hebbian associative synaptic learning rule that governs activity-dependent synaptic plasticity in the mouse prefrontal cortex, characterized by a broader temporal window and a lower threshold for spike-timing-dependent LTP (t-LTP), a cellular form of learning and memory. This rule change is caused by cocaine-exacerbated D1-cAMP/protein kinase A dopamine signaling in pyramidal neurons that in turn pathologically recruits l-type Ca 2+ channels to facilitate coincidence detection during t-LTP induction. Our study provides novel insights on how cocaine, even with only brief exposure, may prime neural circuits for subsequent experience-dependent remodeling that may underlie certain addictive behavior. Copyright © 2017 the authors 0270-6474/17/370986-12$15.00/0.

Ionic conductivity and dielectric permittivity of polymer electrolyte plasticized with polyethylene glycol

NASA Astrophysics Data System (ADS)

Das, S.; Ghosh, A.

2016-05-01

We have studied ionic conductivity and dielectric permittivity of PEO-LiClO4 solid polymer electrolyte plasticized with polyethylene glycol (PEG). The temperature dependence of the ionic conductivity has been well interpreted using Vogel-Tamman-Fulcher equation. The maximum dielectric constant is observed for 30 wt. % of PEG content. To get further insights into the ion dynamics, the complex dielectric permittivity has been studied with Havriliak-Negami function. The variation of relaxation time with inverse temperature obtained from HN formalism follows VTF nature.

Softening and Hardening of Alloys of the Al - Zn System Under Plastic Deformation

NASA Astrophysics Data System (ADS)

Skvortsov, A. I.; Polev, V. V.

2017-11-01

The proportion of hardening and softening under plastic deformation at room temperature in metals and alloys of the Al - Zn system has been studied as dependent on the regime of preliminary heat treatment. The influence of the strain rate on the dependence of alloy hardness on the degree of plastic deformation is estimated.

Arc restores juvenile plasticity in adult mouse visual cortex

PubMed Central

Jenks, Kyle R.; Kim, Taekeun; Pastuzyn, Elissa D.; Okuno, Hiroyuki; Taibi, Andrew V.; Bear, Mark F.

2017-01-01

The molecular basis for the decline in experience-dependent neural plasticity over age remains poorly understood. In visual cortex, the robust plasticity induced in juvenile mice by brief monocular deprivation during the critical period is abrogated by genetic deletion of Arc, an activity-dependent regulator of excitatory synaptic modification. Here, we report that augmenting Arc expression in adult mice prolongs juvenile-like plasticity in visual cortex, as assessed by recordings of ocular dominance (OD) plasticity in vivo. A distinguishing characteristic of juvenile OD plasticity is the weakening of deprived-eye responses, believed to be accounted for by the mechanisms of homosynaptic long-term depression (LTD). Accordingly, we also found increased LTD in visual cortex of adult mice with augmented Arc expression and impaired LTD in visual cortex of juvenile mice that lack Arc or have been treated in vivo with a protein synthesis inhibitor. Further, we found that although activity-dependent expression of Arc mRNA does not change with age, expression of Arc protein is maximal during the critical period and declines in adulthood. Finally, we show that acute augmentation of Arc expression in wild-type adult mouse visual cortex is sufficient to restore juvenile-like plasticity. Together, our findings suggest a unifying molecular explanation for the age- and activity-dependent modulation of synaptic sensitivity to deprivation. PMID:28790183

History Dependence of the Microstructure on Time-Dependent Deformation During In-Situ Cooling of a Nickel-Based Single-Crystal Superalloy

NASA Astrophysics Data System (ADS)

Panwisawas, Chinnapat; D'Souza, Neil; Collins, David M.; Bhowmik, Ayan; Roebuck, Bryan

2018-05-01

Time-dependent plastic deformation through stress relaxation and creep deformation during in-situ cooling of the as-cast single-crystal superalloy CMSX-4® has been studied via neutron diffraction, transmission electron microscopy, electro-thermal miniature testing, and analytical modeling across two temperature regimes. Between 1000 °C and 900 °C, stress relaxation prevails and gives rise to softening as evidenced by a decreased dislocation density and the presence of long segment stacking faults in γ phase. Lattice strains decrease in both the γ matrix and γ' precipitate phases. A constitutive viscoplastic law derived from in-situ isothermal relaxation test under-estimates the equivalent plastic strain in the prediction of the stress and strain evolution during cooling in this case. It is thereby shown that the history dependence of the microstructure needs to be taken into account while deriving a constitutive law and which becomes even more relevant at high temperatures approaching the solvus. Higher temperature cooling experiments have also been carried out between 1300 °C and 1150 °C to measure the evolution of stress and plastic strain close to the γ' solvus temperature. In-situ cooling of samples using ETMT shows that creep dominates during high-temperature deformation between 1300 °C and 1220 °C, but below a threshold temperature, typically 1220 °C work hardening begins to prevail from increasing γ' fraction and resulting in a rapid increase in stress. The history dependence of prior accumulated deformation is also confirmed in the flow stress measurements using a single sample while cooling. The saturation stresses in the flow stress experiments show very good agreement with the stresses measured in the cooling experiments when viscoplastic deformation is dominant. This study demonstrates that experimentation during high-temperature deformation as well as the history dependence of the microstructure during cooling plays a key role in deriving an accurate viscoplastic constitutive law for the thermo-mechanical process during cooling from solidification.

Mecp2 Mediates Experience-Dependent Transcriptional Upregulation of Ryanodine Receptor Type-3.

PubMed

Torres, Rodrigo F; Hidalgo, Cecilia; Kerr, Bredford

2017-01-01

Mecp2 is a DNA methylation reader that plays a critical role in experience-dependent plasticity. Increasing evidence supports a role for epigenetic modifications in activity-induced gene expression. Hence, candidate genes related to such phenomena are of great interest. Ryanodine receptors are intracellular calcium channels that contribute to hippocampal synaptic plasticity, dendritic spine remodeling, and participate in learning and memory processes. Here we exposed mice to the enriched environment (EE) paradigm, which through increased stimulation induces experience dependent-plasticity, to explore a role for methyl-cytosines, and Mecp2 in directing Ryanodine receptor 3 ( Ryr3 ) transcriptional activity. EE induced a hippocampal-specific increase in the methylation of discrete cytosines located at a Ryr3 isoform promoter; chromatin immunoprecipitation experiments revealed that EE increased Mecp2 binding to this Ryr3 isoform promoter. Interestingly, the experimental paradigm induced robust Ryr3 upregulation, accompanied by miR132 -dependent suppression of p250GAP , a pathway driving synaptogenesis. In contrast to WT mice, Mecp2-null mice showed diminished levels of Ryr3 and displayed impaired EE-induced Ryr3 upregulation, compromising miR132 dependent suppression of p250GAP and experience-dependent structural plasticity. Based on these results, we propose that Mecp2 acts as a transcriptional activator of Ryr3 , contributing to experience-dependent plasticity.

Temporal course of gene expression during motor memory formation in primary motor cortex of rats.

PubMed

Hertler, B; Buitrago, M M; Luft, A R; Hosp, J A

2016-12-01

Motor learning is associated with plastic reorganization of neural networks in primary motor cortex (M1) that depends on changes in gene expression. Here, we investigate the temporal profile of these changes during motor memory formation in response to a skilled reaching task in rats. mRNA-levels were measured 1h, 7h and 24h after the end of a training session using microarray technique. To assure learning specificity, trained animals were compared to a control group. In response to motor learning, genes are sequentially regulated with high time-point specificity and a shift from initial suppression to later activation. The majority of regulated genes can be linked to learning-related plasticity. In the gene-expression cascade following motor learning, three different steps can be defined: (1) an initial suppression of genes influencing gene transcription. (2) Expression of genes that support translation of mRNA in defined compartments. (3) Expression of genes that immediately mediates plastic changes. Gene expression peaks after 24h - this is a much slower time-course when compared to hippocampus-dependent learning, where peaks of gene-expression can be observed 6-12h after training ended. Copyright © 2016 Elsevier Inc. All rights reserved.

Effective viscoelastic properties of shales.

NASA Astrophysics Data System (ADS)

Cornet, Jan; Dabrowski, Marcin; Schmid, Daniel

2017-04-01

Shales are often characterized as being elasto-plastic: they deform elastically for stresses below a certain yield and plastically at the limit. This approach dismisses any time dependent behavior that occurs in nature. Our goal is to better understand this time dependency by considering the visco-elastic behavior of shales before plasticity is reached. Shales are also typically heterogeneous and the question arises as to how to derive their effective properties in order to model them as a homogeneous medium. We model shales using inclusion based models due to their versatility and their ability to represent the microstructure. The inclusions represent competent quartz or calcite grains which are set in a viscous matrix made of clay minerals. Our approach relies on both numerical and analytical results in two dimension and we use them to cross check each other. The numerical results are obtained using MILAMIN, a fast-finite element solver for large problems, while the analytical solutions are based on the correspondence principle of linear viscoelasticity. This principle allows us to use the results on effective properties already derived for elastic bodies and to adapt them to viscoelastic bodies. We start by revisiting the problem of a single inclusion in an infinite medium and then move on to consider many inclusions.

The beneficial effects of treadmill step training on activity-dependent synaptic and cellular plasticity markers after complete spinal cord injury.

PubMed

Ilha, Jocemar; Centenaro, Lígia A; Broetto Cunha, Núbia; de Souza, Daniela F; Jaeger, Mariane; do Nascimento, Patrícia S; Kolling, Janaína; Ben, Juliana; Marcuzzo, Simone; Wyse, Angela T S; Gottfried, Carmem; Achaval, Matilde

2011-06-01

Several studies have shown that treadmill training improves neurological outcomes and promotes plasticity in lumbar spinal cord of spinal animals. The morphological and biochemical mechanisms underlying these phenomena remain unclear. The purpose of this study was to provide evidence of activity-dependent plasticity in spinal cord segment (L5) below a complete spinal cord transection (SCT) at T8-9 in rats in which the lower spinal cord segments have been fully separated from supraspinal control and that subsequently underwent treadmill step training. Five days after SCT, spinal animals started a step-training program on a treadmill with partial body weight support and manual step help. Hindlimb movements were evaluated over time and scored on the basis of the open-field BBB scale and were significantly improved at post-injury weeks 8 and 10 in trained spinal animals. Treadmill training also showed normalization of withdrawal reflex in trained spinal animals, which was significantly different from the untrained animals at post-injury weeks 8 and 10. Additionally, compared to controls, spinal rats had alpha motoneuronal soma size atrophy and reduced synaptophysin protein expression and Na(+), K(+)-ATPase activity in lumbar spinal cord. Step-trained rats had motoneuronal soma size, synaptophysin expression and Na(+), K(+)-ATPase activity similar to control animals. These findings suggest that treadmill step training can promote activity-dependent neural plasticity in lumbar spinal cord, which may lead to neurological improvements without supraspinal descending control after complete spinal cord injury.

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

Plasticity-related genes in brain development and amygdala-dependent learning.

PubMed

Ehrlich, D E; Josselyn, S A

2016-01-01

Learning about motivationally important stimuli involves plasticity in the amygdala, a temporal lobe structure. Amygdala-dependent learning involves a growing number of plasticity-related signaling pathways also implicated in brain development, suggesting that learning-related signaling in juveniles may simultaneously influence development. Here, we review the pleiotropic functions in nervous system development and amygdala-dependent learning of a signaling pathway that includes brain-derived neurotrophic factor (BDNF), extracellular signaling-related kinases (ERKs) and cyclic AMP-response element binding protein (CREB). Using these canonical, plasticity-related genes as an example, we discuss the intersection of learning-related and developmental plasticity in the immature amygdala, when aversive and appetitive learning may influence the developmental trajectory of amygdala function. We propose that learning-dependent activation of BDNF, ERK and CREB signaling in the immature amygdala exaggerates and accelerates neural development, promoting amygdala excitability and environmental sensitivity later in life. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Transcriptional and Translational Plasticity in Rodent Urinary Bladder TRP Channels with Urinary Bladder Inflammation, Bladder Dysfunction or Postnatal Maturation

PubMed Central

Merrill, Liana; Girard, Beatrice M.; May, Victor; Vizzard, Margaret A.

2013-01-01

These studies examined transcriptional and translational plasticity of three transient receptor potential (TRP) channels (TRPA1, TRPV1, TRPV4) with established neuronal and non-neuronal expression and functional roles in the lower urinary tract. Mechanosensor and nociceptor roles in either physiological or pathological lower urinary tract states have been suggested for TRPA1, TRPV1 and TRPV4. We have previously demonstrated neurochemical, organizational and functional plasticity in micturition reflex pathways following induction of urinary bladder inflammation using the antineoplastic agent, cyclophosphamide (CYP). More recently, we have characterized similar plasticity in micturition reflex pathways in a transgenic mouse model with chronic urothelial overexpression (OE) of nerve growth factor (NGF) and in a transgenic mouse model with deletion of vasoactive intestinal polypeptide (VIP). In addition, the micturition reflex undergoes postnatal maturation that may also reflect plasticity in urinary bladder TRP channel expression. Thus, we examined plasticity in urinary bladder TRP channel expression in diverse contexts using a combination of quantitative, real-time PCR and western blotting approaches. We demonstrate transcriptional and translational plasticity of urinary bladder TRPA1, TRPV1 and TRVP4 expression. Although the functional significance of urinary bladder TRP channel plasticity awaits further investigation, these studies demonstrate context-(inflammation, postnatal development, NGF-OE, VIP deletion) and tissue-dependent (urothelium + suburothelium, detrusor) plasticity. PMID:22865090

Fragile X Mental Retardation Protein Is Required to Maintain Visual Conditioning-Induced Behavioral Plasticity by Limiting Local Protein Synthesis

PubMed Central

Liu, Han-Hsuan

2016-01-01

Fragile X mental retardation protein (FMRP) is thought to regulate neuronal plasticity by limiting dendritic protein synthesis, but direct demonstration of a requirement for FMRP control of local protein synthesis during behavioral plasticity is lacking. Here we tested whether FMRP knockdown in Xenopus optic tectum affects local protein synthesis in vivo and whether FMRP knockdown affects protein synthesis-dependent visual avoidance behavioral plasticity. We tagged newly synthesized proteins by incorporation of the noncanonical amino acid azidohomoalanine and visualized them with fluorescent noncanonical amino acid tagging (FUNCAT). Visual conditioning and FMRP knockdown produce similar increases in FUNCAT in tectal neuropil. Induction of visual conditioning-dependent behavioral plasticity occurs normally in FMRP knockdown animals, but plasticity degrades over 24 h. These results indicate that FMRP affects visual conditioning-induced local protein synthesis and is required to maintain the visual conditioning-induced behavioral plasticity. SIGNIFICANCE STATEMENT Fragile X syndrome (FXS) is the most common form of inherited intellectual disability. Exaggerated dendritic protein synthesis resulting from loss of fragile X mental retardation protein (FMRP) is thought to underlie cognitive deficits in FXS, but no direct evidence has demonstrated that FMRP-regulated dendritic protein synthesis affects behavioral plasticity in intact animals. Xenopus tadpoles exhibit a visual avoidance behavior that improves with visual conditioning in a protein synthesis-dependent manner. We showed that FMRP knockdown and visual conditioning dramatically increase protein synthesis in neuronal processes. Furthermore, induction of visual conditioning-dependent behavioral plasticity occurs normally after FMRP knockdown, but performance rapidly deteriorated in the absence of FMRP. These studies show that FMRP negatively regulates local protein synthesis and is required to maintain visual conditioning-induced behavioral plasticity in vivo. PMID:27383604

Fragile X Mental Retardation Protein Is Required to Maintain Visual Conditioning-Induced Behavioral Plasticity by Limiting Local Protein Synthesis.

PubMed

Liu, Han-Hsuan; Cline, Hollis T

2016-07-06

Fragile X mental retardation protein (FMRP) is thought to regulate neuronal plasticity by limiting dendritic protein synthesis, but direct demonstration of a requirement for FMRP control of local protein synthesis during behavioral plasticity is lacking. Here we tested whether FMRP knockdown in Xenopus optic tectum affects local protein synthesis in vivo and whether FMRP knockdown affects protein synthesis-dependent visual avoidance behavioral plasticity. We tagged newly synthesized proteins by incorporation of the noncanonical amino acid azidohomoalanine and visualized them with fluorescent noncanonical amino acid tagging (FUNCAT). Visual conditioning and FMRP knockdown produce similar increases in FUNCAT in tectal neuropil. Induction of visual conditioning-dependent behavioral plasticity occurs normally in FMRP knockdown animals, but plasticity degrades over 24 h. These results indicate that FMRP affects visual conditioning-induced local protein synthesis and is required to maintain the visual conditioning-induced behavioral plasticity. Fragile X syndrome (FXS) is the most common form of inherited intellectual disability. Exaggerated dendritic protein synthesis resulting from loss of fragile X mental retardation protein (FMRP) is thought to underlie cognitive deficits in FXS, but no direct evidence has demonstrated that FMRP-regulated dendritic protein synthesis affects behavioral plasticity in intact animals. Xenopus tadpoles exhibit a visual avoidance behavior that improves with visual conditioning in a protein synthesis-dependent manner. We showed that FMRP knockdown and visual conditioning dramatically increase protein synthesis in neuronal processes. Furthermore, induction of visual conditioning-dependent behavioral plasticity occurs normally after FMRP knockdown, but performance rapidly deteriorated in the absence of FMRP. These studies show that FMRP negatively regulates local protein synthesis and is required to maintain visual conditioning-induced behavioral plasticity in vivo. Copyright © 2016 the authors 0270-6474/16/367325-15$15.00/0.

Mechanical characteristics of plastic base Ports and impact on flushing efficacy.

PubMed

Guiffant, Gérard; Flaud, Patrice; Royon, Laurent; Burnet, Espérie; Merckx, Jacques

2017-01-01

Three types of totally implantable venous access devices, Ports, are currently in use: titanium, plastic (polyoxymethylene, POM), and mixed (titanium base with a POM shell). Physics theory suggests that the interaction between a non-coring needle (NCN, made of stainless steel) and a plastic base would lead to the stronger material (steel) altering the more malleable material (plastic). To investigate whether needle impacts can alter a plastic base's surface, thus potentially reducing flushing efficacy. A Port made of POM was punctured 200 times with a 19-gauge NCN. Following the existing guidelines, the needle tip pricked the base with each puncture. The Port's base was then examined using a two-dimensional optical instrument, and a bi-dimensional numerical simulation using COMSOL ® was performed to investigate potential surface irregularities and their impact on fluid flow. Each needle impact created a hole (mean depth, 0.12 mm) with a small bump beside it (mean height, 0.02 mm) the Reynolds number Re k ≈10. A numerical simulation of the one hole/bump set showed that the flushing efficacy was 60% that of flushing along a flat surface. In clinical practice, the number of times a Port is punctured depends on patient and treatment characteristics, but each needle impact on the plastic base may increase the risk of decreased flushing effectiveness. Therefore, the more a plastic Port is accessed, the greater the risk of microorganisms, blood products, and medication accumulation. Multiple needle impacts created an irregular surface on the Port's base, which decreased flushing efficacy. Clinical investigation is needed to determine whether plastic base Ports are associated with an increased risk of Port infection and occlusion compared to titanium base Ports.

Loss of Tsc1 in vivo impairs hippocampal mGluR-LTD and increases excitatory synaptic function.

PubMed

Bateup, Helen S; Takasaki, Kevin T; Saulnier, Jessica L; Denefrio, Cassandra L; Sabatini, Bernardo L

2011-06-15

The autism spectrum disorder tuberous sclerosis complex (TSC) is caused by mutations in the Tsc1 or Tsc2 genes, whose protein products form a heterodimeric complex that negatively regulates mammalian target of rapamycin-dependent protein translation. Although several forms of synaptic plasticity, including metabotropic glutamate receptor (mGluR)-dependent long-term depression (LTD), depend on protein translation at the time of induction, it is unknown whether these forms of plasticity require signaling through the Tsc1/2 complex. To examine this possibility, we postnatally deleted Tsc1 in vivo in a subset of hippocampal CA1 neurons using viral delivery of Cre recombinase in mice. We found that hippocampal mGluR-LTD was abolished by loss of Tsc1, whereas a protein synthesis-independent form of NMDA receptor-dependent LTD was preserved. Additionally, AMPA and NMDA receptor-mediated EPSCs and miniature spontaneous EPSC frequency were enhanced in Tsc1 KO neurons. These changes in synaptic function occurred in the absence of alterations in spine density, morphology, or presynaptic release probability. Our findings indicate that signaling through Tsc1/2 is required for the expression of specific forms of hippocampal synaptic plasticity as well as the maintenance of normal excitatory synaptic strength. Furthermore, these data suggest that perturbations of synaptic signaling may contribute to the pathogenesis of TSC.

Modulatory role of androgenic and estrogenic neurosteroids in determining the direction of synaptic plasticity in the CA1 hippocampal region of male rats

PubMed Central

Pettorossi, Vito Enrico; Di Mauro, Michela; Scarduzio, Mariangela; Panichi, Roberto; Tozzi, Alessandro; Calabresi, Paolo; Grassi, Silvarosa

2013-01-01

Abstract Estrogenic and androgenic neurosteroids can rapidly modulate synaptic plasticity in the brain through interaction with membrane receptors for estrogens (ERs) and androgens (ARs). We used electrophysiological recordings in slices of young and adolescent male rats to explore the influence of sex neurosteroids on synaptic plasticity in the CA1 hippocampal region, by blocking ARs or ERs during induction of long‐term depression (LTD) and depotentiation (DP) by low‐frequency stimulation (LFS) and long‐term potentiation (LTP) by high‐frequency stimulation (HFS). We found that LTD and DP depend on ARs, while LTP on ERs in both age groups. Accordingly, the AR blocker flutamide affected induction of LTD reverting it into LTP, and prevented DP, while having no effect on HFS‐dependent LTP. Conversely, ER blockade with ICI 182,780 (ICI) markedly reduced LTP, but did not influence LTD and DP. However, the receptor blockade did not affect the maintenance of either LTD or LTP. Moreover, we found that similar to LTP and LTD induced in control condition, the LTP unveiled by flutamide during LFS and residual LTP induced by HFS under ICI depended on N‐methyl‐d aspartate receptor (NMDAR) activation. Furthermore, as the synaptic paired‐pulse facilitation (PPF) was not affected by either AR or ER blockade, we suggest that sex neurosteroids act primarily at a postsynaptic level. This study demonstrates for the first time the crucial role of estrogenic and androgenic neurosteroids in determining the sign of hippocampal synaptic plasticity in male rat and the activity‐dependent recruitment of androgenic and estrogenic pathways leading to LTD and LTP, respectively. PMID:24744863

Modulatory role of androgenic and estrogenic neurosteroids in determining the direction of synaptic plasticity in the CA1 hippocampal region of male rats.

PubMed

Pettorossi, Vito Enrico; Di Mauro, Michela; Scarduzio, Mariangela; Panichi, Roberto; Tozzi, Alessandro; Calabresi, Paolo; Grassi, Silvarosa

2013-12-01

Estrogenic and androgenic neurosteroids can rapidly modulate synaptic plasticity in the brain through interaction with membrane receptors for estrogens (ERs) and androgens (ARs). We used electrophysiological recordings in slices of young and adolescent male rats to explore the influence of sex neurosteroids on synaptic plasticity in the CA1 hippocampal region, by blocking ARs or ERs during induction of long-term depression (LTD) and depotentiation (DP) by low-frequency stimulation (LFS) and long-term potentiation (LTP) by high-frequency stimulation (HFS). We found that LTD and DP depend on ARs, while LTP on ERs in both age groups. Accordingly, the AR blocker flutamide affected induction of LTD reverting it into LTP, and prevented DP, while having no effect on HFS-dependent LTP. Conversely, ER blockade with ICI 182,780 (ICI) markedly reduced LTP, but did not influence LTD and DP. However, the receptor blockade did not affect the maintenance of either LTD or LTP. Moreover, we found that similar to LTP and LTD induced in control condition, the LTP unveiled by flutamide during LFS and residual LTP induced by HFS under ICI depended on N-methyl-d aspartate receptor (NMDAR) activation. Furthermore, as the synaptic paired-pulse facilitation (PPF) was not affected by either AR or ER blockade, we suggest that sex neurosteroids act primarily at a postsynaptic level. This study demonstrates for the first time the crucial role of estrogenic and androgenic neurosteroids in determining the sign of hippocampal synaptic plasticity in male rat and the activity-dependent recruitment of androgenic and estrogenic pathways leading to LTD and LTP, respectively.

The microglial fractalkine receptor is not required for activity-dependent plasticity in the mouse visual system.

PubMed

Lowery, Rebecca L; Tremblay, Marie-Eve; Hopkins, Brittany E; Majewska, Ania K

2017-11-01

Microglia have recently been implicated as key regulators of activity-dependent plasticity, where they contribute to the removal of inappropriate or excess synapses. However, the molecular mechanisms that mediate this microglial function are still not well understood. Although multiple studies have implicated fractalkine signaling as a mediator of microglia-neuron communications during synaptic plasticity, it is unclear whether this is a universal signaling mechanism or whether its role is limited to specific brain regions and stages of the lifespan. Here, we examined whether fractalkine signaling mediates microglial contributions to activity-dependent plasticity in the developing and adolescent visual system. Using genetic ablation of fractalkine's cognate receptor, CX 3 CR1, and both ex vivo characterization and in vivo imaging in mice, we examined whether fractalkine signaling is required for microglial dynamics and modulation of synapses, as well as activity-dependent plasticity in the visual system. We did not find a role for fractalkine signaling in mediating microglial properties during visual plasticity. Ablation of CX 3 CR1 had no effect on microglial density, distribution, morphology, or motility, in either adolescent or young adult mice across brain regions that include the visual cortex. Ablation of CX 3 CR1 also had no effect on baseline synaptic turnover or contact dynamics between microglia and neurons. Finally, we found that fractalkine signaling is not required for either early or late forms of activity-dependent visual system plasticity. These findings suggest that fractalkine is not a universal regulator of synaptic plasticity, but rather has heterogeneous roles in specific brain regions and life stages. © 2017 Wiley Periodicals, Inc.

The influence of chronic hypoxia upon chemoreception

PubMed Central

Powell, Frank L.

2007-01-01

Carotid body chemoreceptors are essential for time-dependent changes in ventilatory control during chronic hypoxia. Early theories of ventilatory acclimatization to hypoxia focused on time-dependent changes in known ventilatory stimuli, such as small changes in arterial pH that may play a significant role in some species. However, plasticity in the cellular and molecular mechanisms of carotid body chemoreception play a major role in ventilatory acclimatization to hypoxia in all species studied. Chronic hypoxia causes changes in (a) ion channels (potassium, sodium, calcium) to increase glomus cell excitability, and (b) neurotransmitters (dopamine, acetylcholine, ATP) and neuromodulators (endothelin-1) to increase carotid body afferent activity for a given PO2 and optimize O2-sensitivity. O2-sensing heme-containing molecules in the carotid body have not been studied in chronic hypoxia. Plasticity in medullary respiratory centers processing carotid body afferent input also contributes to ventilatory acclimatization to hypoxia. It is not known if the same mechanisms occur in patients with chronic hypoxemia from lung disease or high altitude natives. PMID:17291837

Gender Authorship Trends of Plastic Surgery Research in the United States.

PubMed

Silvestre, Jason; Wu, Liza C; Lin, Ines C; Serletti, Joseph M

2016-07-01

An increasing number of women are entering the medical profession, but plastic surgery remains a male-dominated profession, especially within academia. As academic aspirations and advancement depend largely on research productivity, the authors assessed the number of articles authored by women published in the journal Plastic and Reconstructive Surgery. Original articles in Plastic and Reconstructive Surgery published during the years 1970, 1980, 1990, 2000, 2004, and 2014 were analyzed. First and senior authors with an M.D. degree and U.S. institutional affiliation were categorized by gender. Authorship trends were compared with those from other specialties. Findings were placed in the context of gender trends among plastic surgery residents in the United States. The percentage of female authors in Plastic and Reconstructive Surgery increased from 2.4 percent in 1970 to 13.3 percent in 2014. Over the same time period, the percentage of female plastic surgery residents increased from 2.6 percent to 32.5 percent. By 2014, there were more female first authors (19.1 percent) than senior authors (7.7 percent) (p < 0.001). As a field, plastic surgery had fewer female authors than other medical specialties including pediatrics, obstetrics and gynecology, general surgery, internal medicine, and radiation oncology (p < 0.05). The increase in representation of female authors in plastic surgery is encouraging but lags behind advances in other specialties. Understanding reasons for these trends may help improve gender equity in academic plastic surgery.

Selection in a fluctuating environment and the evolution of sexual dimorphism in the seed beetle Callosobruchus maculatus.

PubMed

Hallsson, L R; Björklund, M

2012-08-01

Temperature changes in the environment, which realistically include environmental fluctuations, can create both plastic and evolutionary responses of traits. Sexes might differ in either or both of these responses for homologous traits, which in turn has consequences for sexual dimorphism and its evolution. Here, we investigate both immediate changes in and the evolution of sexual dimorphism in response to a changing environment (with and without fluctuations) using the seed beetle Callosobruchus maculatus. We investigate sex differences in plasticity and also the genetic architecture of body mass and developmental time dimorphism to test two existing hypotheses on sex differences in plasticity (adaptive canalization hypothesis and condition dependence hypothesis). We found a decreased sexual size dimorphism in higher temperature and that females responded more plastically than males, supporting the condition dependence hypothesis. However, selection in a fluctuating environment altered sex-specific patterns of genetic and environmental variation, indicating support for the adaptive canalization hypothesis. Genetic correlations between sexes (r(MF) ) were affected by fluctuating selection, suggesting facilitated independent evolution of the sexes. Thus, the selective past of a population is highly important for the understanding of the evolutionary dynamics of sexual dimorphism. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

3D inelastic analysis methods for hot section components

NASA Technical Reports Server (NTRS)

Dame, L. T.; Chen, P. C.; Hartle, M. S.; Huang, H. T.

1985-01-01

The objective is to develop analytical tools capable of economically evaluating the cyclic time dependent plasticity which occurs in hot section engine components in areas of strain concentration resulting from the combination of both mechanical and thermal stresses. Three models were developed. A simple model performs time dependent inelastic analysis using the power law creep equation. The second model is the classical model of Professors Walter Haisler and David Allen of Texas A and M University. The third model is the unified model of Bodner, Partom, et al. All models were customized for linear variation of loads and temperatures with all material properties and constitutive models being temperature dependent.

Superplastic Creep of Metal Nanowires From Rate-Dependent Plasticity Transition

DOE PAGES

Tao, Weiwei; Cao, Penghui; Park, Harold S.

2018-04-30

Understanding the time-dependent mechanical behavior of nanomaterials such as nanowires is essential to predict their reliability in nanomechanical devices. This understanding is typically obtained using creep tests, which are the most fundamental loading mechanism by which the time dependent deformation of materials is characterized. However, due to existing challenges facing both experimentalists and theorists, the time dependent mechanical response of nanowires is not well-understood. Here, we use atomistic simulations that can access experimental time scales to examine the creep of single-crystal face-centered cubic metal (Cu, Ag, Pt) nanowires. Here, we report that both Cu and Ag nanowires show significantly increasedmore » ductility and superplasticity under low creep stresses, where the superplasticity is driven by a rate-dependent transition in defect nucleation from twinning to trailing partial dislocations at the micro- or millisecond time scale. The transition in the deformation mechanism also governs a corresponding transition in the stress-dependent creep time at the microsecond (Ag) and millisecond (Cu) time scales. Overall, this work demonstrates the necessity of accessing time scales that far exceed those seen in conventional atomistic modeling for accurate insights into the time-dependent mechanical behavior and properties of nanomaterials.« less

Superplastic Creep of Metal Nanowires from Rate-Dependent Plasticity Transition.

PubMed

Tao, Weiwei; Cao, Penghui; Park, Harold S

2018-05-22

Understanding the time-dependent mechanical behavior of nanomaterials such as nanowires is essential to predict their reliability in nanomechanical devices. This understanding is typically obtained using creep tests, which are the most fundamental loading mechanism by which the time-dependent deformation of materials is characterized. However, due to existing challenges facing both experimentalists and theorists, the time-dependent mechanical response of nanowires is not well-understood. Here, we use atomistic simulations that can access experimental time scales to examine the creep of single-crystal face-centered cubic metal (Cu, Ag, Pt) nanowires. We report that both Cu and Ag nanowires show significantly increased ductility and superplasticity under low creep stresses, where the superplasticity is driven by a rate-dependent transition in defect nucleation from twinning to trailing partial dislocations at the micro- or millisecond time scale. The transition in the deformation mechanism also governs a corresponding transition in the stress-dependent creep time at the microsecond (Ag) and millisecond (Cu) time scales. Overall, this work demonstrates the necessity of accessing time scales that far exceed those seen in conventional atomistic modeling for accurate insights into the time-dependent mechanical behavior and properties of nanomaterials.

Superplastic Creep of Metal Nanowires From Rate-Dependent Plasticity Transition

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

Tao, Weiwei; Cao, Penghui; Park, Harold S.

Understanding the time-dependent mechanical behavior of nanomaterials such as nanowires is essential to predict their reliability in nanomechanical devices. This understanding is typically obtained using creep tests, which are the most fundamental loading mechanism by which the time dependent deformation of materials is characterized. However, due to existing challenges facing both experimentalists and theorists, the time dependent mechanical response of nanowires is not well-understood. Here, we use atomistic simulations that can access experimental time scales to examine the creep of single-crystal face-centered cubic metal (Cu, Ag, Pt) nanowires. Here, we report that both Cu and Ag nanowires show significantly increasedmore » ductility and superplasticity under low creep stresses, where the superplasticity is driven by a rate-dependent transition in defect nucleation from twinning to trailing partial dislocations at the micro- or millisecond time scale. The transition in the deformation mechanism also governs a corresponding transition in the stress-dependent creep time at the microsecond (Ag) and millisecond (Cu) time scales. Overall, this work demonstrates the necessity of accessing time scales that far exceed those seen in conventional atomistic modeling for accurate insights into the time-dependent mechanical behavior and properties of nanomaterials.« less

A Spiking Neural Network Model of the Medial Superior Olive Using Spike Timing Dependent Plasticity for Sound Localization

PubMed Central

Glackin, Brendan; Wall, Julie A.; McGinnity, Thomas M.; Maguire, Liam P.; McDaid, Liam J.

2010-01-01

Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz–1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of ±10° is used. For angular resolutions down to 2.5°, it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance. PMID:20802855

Impacts of environmental variability on desiccation rate, plastic responses and population dynamics of Glossina pallidipes.

PubMed

Kleynhans, E; Clusella-Trullas, S; Terblanche, J S

2014-02-01

Physiological responses to transient conditions may result in costly responses with little fitness benefits, and therefore, a trade-off must exist between the speed of response and the duration of exposure to new conditions. Here, using the puparia of an important insect disease vector, Glossina pallidipes, we examine this potential trade-off using a novel combination of an experimental approach and a population dynamics model. Specifically, we explore and dissect the interactions between plastic physiological responses, treatment-duration and -intensity using an experimental approach. We then integrate these experimental results from organismal water-balance data and their plastic responses into a population dynamics model to examine the potential relative fitness effects of simulated transient weather conditions on population growth rates. The results show evidence for the predicted trade-off for plasticity of water loss rate (WLR) and the duration of new environmental conditions. When altered environmental conditions lasted for longer durations, physiological responses could match the new environmental conditions, and this resulted in a lower WLR and lower rates of population decline. At shorter time-scales however, a mismatch between acclimation duration and physiological responses was reflected by reduced overall population growth rates. This may indicate a potential fitness cost due to insufficient time for physiological adjustments to take place. The outcomes of this work therefore suggest plastic water balance responses have both costs and benefits, and these depend on the time-scale and magnitude of variation in environmental conditions. These results are significant for understanding the evolution of plastic physiological responses and changes in population abundance in the context of environmental variability. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Manipulation of BDNF signaling modifies the experience-dependent plasticity induced by pure tone exposure during the critical period in the primary auditory cortex.

PubMed

Anomal, Renata; de Villers-Sidani, Etienne; Merzenich, Michael M; Panizzutti, Rogerio

2013-01-01

Sensory experience powerfully shapes cortical sensory representations during an early developmental "critical period" of plasticity. In the rat primary auditory cortex (A1), the experience-dependent plasticity is exemplified by significant, long-lasting distortions in frequency representation after mere exposure to repetitive frequencies during the second week of life. In the visual system, the normal unfolding of critical period plasticity is strongly dependent on the elaboration of brain-derived neurotrophic factor (BDNF), which promotes the establishment of inhibition. Here, we tested the hypothesis that BDNF signaling plays a role in the experience-dependent plasticity induced by pure tone exposure during the critical period in the primary auditory cortex. Elvax resin implants filled with either a blocking antibody against BDNF or the BDNF protein were placed on the A1 of rat pups throughout the critical period window. These pups were then exposed to 7 kHz pure tone for 7 consecutive days and their frequency representations were mapped. BDNF blockade completely prevented the shaping of cortical tuning by experience and resulted in poor overall frequency tuning in A1. By contrast, BDNF infusion on the developing A1 amplified the effect of 7 kHz tone exposure compared to control. These results indicate that BDNF signaling participates in the experience-dependent plasticity induced by pure tone exposure during the critical period in A1.

Neuralized1 activates CPEB3: a function for nonproteolytic ubiquitin in synaptic plasticity and memory storage.

PubMed

Pavlopoulos, Elias; Trifilieff, Pierre; Chevaleyre, Vivien; Fioriti, Luana; Zairis, Sakellarios; Pagano, Andrew; Malleret, Gaël; Kandel, Eric R

2011-12-09

The cytoplasmic polyadenylation element-binding protein 3 (CPEB3), a regulator of local protein synthesis, is the mouse homolog of ApCPEB, a functional prion protein in Aplysia. Here, we provide evidence that CPEB3 is activated by Neuralized1, an E3 ubiquitin ligase. In hippocampal cultures, CPEB3 activated by Neuralized1-mediated ubiquitination leads both to the growth of new dendritic spines and to an increase of the GluA1 and GluA2 subunits of AMPA receptors, two CPEB3 targets essential for synaptic plasticity. Conditional overexpression of Neuralized1 similarly increases GluA1 and GluA2 and the number of spines and functional synapses in the hippocampus and is reflected in enhanced hippocampal-dependent memory and synaptic plasticity. By contrast, inhibition of Neuralized1 reduces GluA1 and GluA2 levels and impairs hippocampal-dependent memory and synaptic plasticity. These results suggest a model whereby Neuralized1-dependent ubiquitination facilitates hippocampal plasticity and hippocampal-dependent memory storage by modulating the activity of CPEB3 and CPEB3-dependent protein synthesis and synapse formation. Copyright © 2011 Elsevier Inc. All rights reserved.

Effective temperature dynamics of shear bands in metallic glasses

NASA Astrophysics Data System (ADS)

Daub, Eric G.; Klaumünzer, David; Löffler, Jörg F.

2014-12-01

We study the plastic deformation of bulk metallic glasses with shear transformation zone (STZ) theory, a physical model for plasticity in amorphous systems, and compare it with experimental data. In STZ theory, plastic deformation occurs when localized regions rearrange due to applied stress and the density of these regions is determined by a dynamically evolving effective disorder temperature. We compare the predictions of STZ theory to experiments that explore the low-temperature deformation of Zr-based bulk metallic glasses via shear bands at various thermal temperatures and strain rates. By following the evolution of effective temperature with time, strain rate, and temperature through a series of approximate and numerical solutions to the STZ equations, we successfully model a suite of experimentally observed phenomena, including shear-band aging as apparent from slide-hold-slide tests, a temperature-dependent steady-state flow stress, and a strain-rate- and temperature-dependent transition from stick-slip (serrated flow) to steady-sliding (nonserrated flow). We find that STZ theory quantitatively matches the observed experimental data and provides a framework for relating the experimentally measured energy scales to different types of atomic rearrangements.

Life stage dependent responses to desiccation risk in the annual killifish Nothobranchius wattersi.

PubMed

Grégoir, A F; Philippe, C; Pinceel, T; Reniers, J; Thoré, E S J; Vanschoenwinkel, B; Brendonck, L

2017-09-01

To assess whether the annual killifish Nothobranchius wattersi responds plastically to a desiccation risk and whether this response is life stage dependent, life-history traits such as maturation time, fecundity and life span were experimentally measured in N. wattersi that were subjected to a drop in water level either as juveniles, as adults or both as juveniles and adults. Fish that were exposed to simulated pool drying as juveniles did not show changes in reproductive output or life span. Adults reacted by doubling short term egg deposition at the cost of a shorter lifespan. Overall, these results suggest that annual fish species can use phenotypic plasticity to maximize their reproductive output when faced with early pond drying, but this response appears to be life-stage specific. In addition to frogs and aquatic insects, phenotypic plasticity induced by forthcoming drought is now also confirmed in annual fishes and could well be a common feature of the limited number of fish taxa that manage to survive in this extreme environment. © 2017 The Fisheries Society of the British Isles.

Computational modeling of neural plasticity for self-organization of neural networks.

PubMed

Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Gamma-band activation predicts both associative memory and cortical plasticity

PubMed Central

Headley, Drew B.; Weinberger, Norman M.

2011-01-01

Gamma-band oscillations are a ubiquitous phenomenon in the nervous system and have been implicated in multiple aspects of cognition. In particular, the strength of gamma oscillations at the time a stimulus is encoded predicts its subsequent retrieval, suggesting that gamma may reflect enhanced mnemonic processing. Likewise, activity in the gamma-band can modulate plasticity in vitro. However, it is unclear whether experience-dependent plasticity in vivo is also related to gamma-band activation. The aim of the present study is to determine whether gamma activation in primary auditory cortex modulates both the associative memory for an auditory stimulus during classical conditioning and its accompanying specific receptive field plasticity. Rats received multiple daily sessions of single tone/shock trace and two-tone discrimination conditioning, during which local field potentials and multiunit discharges were recorded from chronically implanted electrodes. We found that the strength of tone-induced gamma predicted the acquisition of associative memory 24 h later, and ceased to predict subsequent performance once asymptote was reached. Gamma activation also predicted receptive field plasticity that specifically enhanced representation of the signal tone. This concordance provides a long-sought link between gamma oscillations, cortical plasticity and the formation of new memories. PMID:21900554

Stress relaxation study of fillers for directly compressed tablets

PubMed Central

Rehula, M.; Adamek, R.; Spacek, V.

2012-01-01

It is possible to assess viscoelastic properties of materials by means of the stress relaxation test. This method records the decrease in pressing power in a tablet at its constant height. The cited method was used to evaluate the time-dependent deformation for six various materials: microcrystalline cellulose, cellulose powder, hydroxypropyl methylcellulose, mannitol, lactose monohydrate, and hydrogen phosphate monohydrate. The decrease in pressing powering of a tablet during a 180 s period was described mathematically by the parameters of three exponential equations, where the whole course of the stress relaxation is divided into three individual processes (instant elastic deformation, retarded elastic deformation and permanent plastic deformation). Three values of the moduli of plasticity and elasticity were calculated for each compound. The values of elastic parameters ATi have a strong relationship with bulk density. The plastic parameters PTi represent particle tendency to form bonds. The values of plasticity in the third process PT3 ranged from 400 to 600 MPas. Mannitol had higher plasticity and lactose monohydrate on the contrary reduced plasticity. A linear relation exists between AT3 and PT3 for the third process. No similar interpretation of moduli calculated on the basis of three exponential equations has been realized yet. PMID:24850972

Mecp2 Mediates Experience-Dependent Transcriptional Upregulation of Ryanodine Receptor Type-3

PubMed Central

Torres, Rodrigo F.; Hidalgo, Cecilia; Kerr, Bredford

2017-01-01

Mecp2 is a DNA methylation reader that plays a critical role in experience-dependent plasticity. Increasing evidence supports a role for epigenetic modifications in activity-induced gene expression. Hence, candidate genes related to such phenomena are of great interest. Ryanodine receptors are intracellular calcium channels that contribute to hippocampal synaptic plasticity, dendritic spine remodeling, and participate in learning and memory processes. Here we exposed mice to the enriched environment (EE) paradigm, which through increased stimulation induces experience dependent-plasticity, to explore a role for methyl-cytosines, and Mecp2 in directing Ryanodine receptor 3 (Ryr3) transcriptional activity. EE induced a hippocampal-specific increase in the methylation of discrete cytosines located at a Ryr3 isoform promoter; chromatin immunoprecipitation experiments revealed that EE increased Mecp2 binding to this Ryr3 isoform promoter. Interestingly, the experimental paradigm induced robust Ryr3 upregulation, accompanied by miR132-dependent suppression of p250GAP, a pathway driving synaptogenesis. In contrast to WT mice, Mecp2-null mice showed diminished levels of Ryr3 and displayed impaired EE-induced Ryr3 upregulation, compromising miR132 dependent suppression of p250GAP and experience-dependent structural plasticity. Based on these results, we propose that Mecp2 acts as a transcriptional activator of Ryr3, contributing to experience-dependent plasticity. PMID:28659760

BNDF heterozygosity is associated with memory deficits and alterations in cortical and hippocampal EEG power.

PubMed

Geist, Phillip A; Dulka, Brooke N; Barnes, Abigail; Totty, Michael; Datta, Subimal

2017-08-14

Brain derived neurotrophic factor (BDNF) plays a pivotal role in structural plasticity, learning, and memory. Electroencephalogram (EEG) spectral power in the cortex and hippocampus has also been correlated with learning and memory. In this study, we investigated the effect of globally reduced BDNF levels on learning behavior and EEG power via BDNF heterozygous (KO) rats. We employed several behavioral tests that are thought to depend on cortical and hippocampal plasticity to varying degrees: novel object recognition, a test that is reliant on a variety of cognitive systems; contextual fear, which is highly hippocampal-dependent; and cued fear, which has been shown to be amygdala-dependent. We also examined the effects of BDNF reduction on cortical and hippocampal EEG spectral power via chronically implanted electrodes in the motor cortex and dorsal hippocampus. We found that BDNF KO rats were impaired in novelty recognition and fear memory retention, while hippocampal EEG power was decreased in slow waves and increased in fast waves. Interestingly, our results, for the first time, show sexual dimorphism in each of our tests. These results support the hypothesis that BDNF drives both cognitive plasticity and coordinates EEG activity patterns, potentially serving as a link between the two. Copyright © 2017 Elsevier B.V. All rights reserved.

Bidirectional synaptic structural plasticity after chronic cocaine administration occurs through Rap1 small GTPase signaling

PubMed Central

Cahill, Michael E.; Bagot, Rosemary C.; Gancarz, Amy M.; Walker, Deena M.; Sun, HaoSheng; Wang, Zi-Jun; Heller, Elizabeth A.; Feng, Jian; Kennedy, Pamela J.; Koo, Ja Wook; Cates, Hannah M.; Neve, Rachael L.; Shen, Li; Dietz, David M.

2016-01-01

Summary Dendritic spines are the sites of most excitatory synapses in the CNS, and opposing alterations in the synaptic structure of medium spiny neurons (MSNs) of the nucleus accumbens, a primary brain reward region, are seen at early vs. late time points after cocaine administration. Here we investigate the time-dependent molecular and biochemical processes that regulate this bidirectional synaptic structural plasticity of NAc MSNs and associated changes in cocaine reward in response to chronic cocaine exposure. Our findings reveal key roles for the bidirectional synaptic expression of the Rap1b small GTPase and an associated local-synaptic protein translation network in this process. The transcriptional mechanisms and pathway-specific inputs to NAc that regulate Rap1b expression are also characterized. Collectively, these findings provide a precise mechanism by which nuclear to synaptic interactions induce “metaplasticity” in NAc MSNs, and we reveal the specific effects of this plasticity on reward behavior in a brain circuit-specific manner. PMID:26844834

Endocrine mediated phenotypic plasticity: condition-dependent effects of juvenile hormone on dominance and fertility of wasp queens.

PubMed

Tibbetts, Elizabeth A; Izzo, Amanda S

2009-11-01

There has been increasing interest in the mechanisms that mediate behavioral and physiological plasticity across individuals with similar genotypes. Some of the most dramatic plasticity is found within and between social insect castes. For example, Polistes wasp queens can nest alone, dominate a group of cooperative queens, or act as worker-like subordinates who rarely reproduce. Previous work suggests that condition-dependent endocrine responses may play a role in plasticity between castes in the hymenoptera. Here, we test whether condition-dependent endocrine responses influence plasticity within castes in the wasp Polistes dominulus. We experimentally manipulate juvenile hormone (JH) titers in nest-founding queens and assess whether JH mediates variation in behavior and physiology. JH generally increased dominance and fertility of queens, but JH's effects were not uniform across individuals. JH had a stronger effect on the dominance and fertility of large individuals and individuals with facial patterns advertising high quality than on the dominance and fertility of small individuals and those advertising low quality. These results demonstrate that JH has condition-dependent effects. As such, they clarify how JH can mediate different behaviors in well nourished queens and poorly nourished workers. Many Polistes queens nest cooperatively with other queens, so condition-dependent hormonal responses provide a mechanism for queens to adaptively allocate energy based on their probability of successfully becoming the dominant queen. Research on the endocrine basis of plasticity often focuses on variation in endocrine titers alone. However, differential endocrine responses are likely to be a widespread mechanism mediating behavioral and physiological plasticity.

Rebound mechanics of micrometre-scale, spherical particles in high-velocity impacts.

PubMed

Yildirim, Baran; Yang, Hankang; Gouldstone, Andrew; Müftü, Sinan

2017-08-01

The impact mechanics of micrometre-scale metal particles with flat metal surfaces is investigated for high-velocity impacts ranging from 50 m s -1 to more than 1 km s -1 , where impact causes predominantly plastic deformation. A material model that includes high strain rate and temperature effects on the yield stress, heat generation due to plasticity, material damage due to excessive plastic strain and heat transfer is used in the numerical analysis. The coefficient of restitution e is predicted by the classical work using elastic-plastic deformation analysis with quasi-static impact mechanics to be proportional to [Formula: see text] and [Formula: see text] for the low and moderate impact velocities that span the ranges of 0-10 and 10-100 m s -1 , respectively. In the elastic-plastic and fully plastic deformation regimes the particle rebound is attributed to the elastic spring-back that initiates at the particle-substrate interface. At higher impact velocities (0.1-1 km s -1 ) e is shown to be proportional to approximately [Formula: see text]. In this deeply plastic deformation regime various deformation modes that depend on plastic flow of the material including the time lag between the rebound instances of the top and bottom points of particle and the lateral spreading of the particle are identified. In this deformation regime, the elastic spring-back initiates subsurface, in the substrate.

Interaction of plasticity and circuit organization during the acquisition of cerebellum-dependent motor learning

PubMed Central

Yang, Yan; Lisberger, Stephen G

2013-01-01

Motor learning occurs through interactions between the cerebellar circuit and cellular plasticity at different sites. Previous work has established plasticity in brain slices and suggested plausible sites of behavioral learning. We now reveal what actually happens in the cerebellum during short-term learning. We monitor the expression of plasticity in the simple-spike firing of cerebellar Purkinje cells during trial-over-trial learning in smooth pursuit eye movements of monkeys. Our findings imply that: 1) a single complex-spike response driven by one instruction for learning causes short-term plasticity in a Purkinje cell’s mossy fiber/parallel-fiber input pathways; 2) complex-spike responses and simple-spike firing rate are correlated across the Purkinje cell population; and 3) simple-spike firing rate at the time of an instruction for learning modulates the probability of a complex-spike response, possibly through a disynaptic feedback pathway to the inferior olive. These mechanisms may participate in long-term motor learning. DOI: http://dx.doi.org/10.7554/eLife.01574.001 PMID:24381248

Learning-dependent plasticity in human auditory cortex during appetitive operant conditioning.

PubMed

Puschmann, Sebastian; Brechmann, André; Thiel, Christiane M

2013-11-01

Animal experiments provide evidence that learning to associate an auditory stimulus with a reward causes representational changes in auditory cortex. However, most studies did not investigate the temporal formation of learning-dependent plasticity during the task but rather compared auditory cortex receptive fields before and after conditioning. We here present a functional magnetic resonance imaging study on learning-related plasticity in the human auditory cortex during operant appetitive conditioning. Participants had to learn to associate a specific category of frequency-modulated tones with a reward. Only participants who learned this association developed learning-dependent plasticity in left auditory cortex over the course of the experiment. No differential responses to reward predicting and nonreward predicting tones were found in auditory cortex in nonlearners. In addition, learners showed similar learning-induced differential responses to reward-predicting and nonreward-predicting tones in the ventral tegmental area and the nucleus accumbens, two core regions of the dopaminergic neurotransmitter system. This may indicate a dopaminergic influence on the formation of learning-dependent plasticity in auditory cortex, as it has been suggested by previous animal studies. Copyright © 2012 Wiley Periodicals, Inc.

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.

The size effects upon shock plastic compression of nanocrystals

NASA Astrophysics Data System (ADS)

Malygin, G. A.; Klyavin, O. V.

2017-10-01

For the first time a theoretical analysis of scale effects upon the shock plastic compression of nanocrystals is implemented in the context of a dislocation kinetic approach based on the equations and relationships of dislocation kinetics. The yield point of crystals τy is established as a quantitative function of their cross-section size D and the rate of shock deformation as τy ɛ2/3 D. This dependence is valid in the case of elastic stress relaxation on account of emission of dislocations from single-pole Frank-Read sources near the crystal surface.

Time-Dependent Damage Investigation of Rock Mass in an In Situ Experimental Tunnel

PubMed Central

Jiang, Quan; Cui, Jie; Chen, Jing

2012-01-01

In underground tunnels or caverns, time-dependent deformation or failure of rock mass, such as extending cracks, gradual rock falls, etc., are a costly irritant and a major safety concern if the time-dependent damage of surrounding rock is serious. To understand the damage evolution of rock mass in underground engineering, an in situ experimental testing was carried out in a large belowground tunnel with a scale of 28.5 m in width, 21 m in height and 352 m in length. The time-dependent damage of rock mass was detected in succession by an ultrasonic wave test after excavation. The testing results showed that the time-dependent damage of rock mass could last a long time, i.e., nearly 30 days. Regression analysis of damage factors defined by wave velocity, resulted in the time-dependent evolutional damage equation of rock mass, which corresponded with logarithmic format. A damage viscoelastic-plastic model was developed to describe the exposed time-dependent deterioration of rock mass by field test, such as convergence of time-dependent damage, deterioration of elastic modules and logarithmic format of damage factor. Furthermore, the remedial measures for damaged surrounding rock were discussed based on the measured results and the conception of damage compensation, which provides new clues for underground engineering design.

A simplified orthotropic formulation of the viscoplasticity theory based on overstress

NASA Technical Reports Server (NTRS)

Sutcu, M.; Krempl, E.

1988-01-01

An orthotropic, small strain viscoplasticity theory based on overstress is presented. In each preferred direction the stress is composed of time (rate) independent (or plastic) and viscous (or rate dependent) contributions. Tension-compression asymmetry can depend on direction and is included in the model. Upon a proper choice of a material constant one preferred direction can exhibit linear elastic response while the other two deform in a viscoplastic manner.

The Learning Hippocampus: Education and Experience-Dependent Plasticity

ERIC Educational Resources Information Center

Wenger, Elisabeth; Lövdén, Martin

2016-01-01

The hippocampal formation of the brain plays a crucial role in declarative learning and memory while at the same time being particularly susceptible to environmental influences. Education requires a well-functioning hippocampus, but may also influence the development of this brain structure. Understanding these bidirectional influences may have…

Endocannabinoid signaling is required for development and critical period plasticity of the whisker map in somatosensory cortex

PubMed Central

Li, Lu; Bender, Kevin J.; Drew, Patrick J.; Jadhav, Shantanu P.; Sylwestrak, Emily; Feldman, Daniel E.

2009-01-01

Summary Type 1 cannabinoid (CB1) receptors mediate widespread synaptic plasticity, but how this contributes to systems-level plasticity and development in vivo is unclear. We tested whether CB1 signaling is required for development and plasticity of the whisker map in rat somatosensory cortex. Treatment with the CB1 antagonist AM251 during an early critical period for layer (L) 2/3 development (beginning postnatal day [P] 12–16) disrupted whisker map development, leading to inappropriate whisker tuning in L2/3 column edges and a blurred map. Early AM251 treatment also prevented experience-dependent plasticity in L2/3, including deprivation-induced synapse weakening and weakening of deprived whisker responses. CB1 blockade after P25 did not disrupt map development or plasticity. AM251 had no acute effect on sensory-evoked spiking, and only modestly affected field potentials, suggesting that plasticity effects were not secondary to gross activity changes. These findings implicate CB1-dependent plasticity in systems-level development and early postnatal plasticity of the whisker map. PMID:19945395

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

GluN2B-containing NMDA receptors blockade rescues bidirectional synaptic plasticity in the bed nucleus of the stria terminalis of cocaine self-administering rats.

PubMed

deBacker, Julian; Hawken, Emily R; Normandeau, Catherine P; Jones, Andrea A; Di Prospero, Cynthia; Mechefske, Elysia; Gardner Gregory, James; Hayton, Scott J; Dumont, Éric C

2015-01-01

Drugs of abuse have detrimental effects on homeostatic synaptic plasticity in the motivational brain network. Bidirectional plasticity at excitatory synapses helps keep neural circuits within a functional range to allow for behavioral flexibility. Therefore, impaired bidirectional plasticity of excitatory synapses may contribute to the behavioral hallmarks of addiction, yet this relationship remains unclear. Here we tracked excitatory synaptic strength in the oval bed nucleus of the stria terminalis (ovBNST) using whole-cell voltage-clamp recordings in brain slices from rats self-administering sucrose or cocaine. In the cocaine group, we measured both a persistent increase in AMPA to NMDA ratio (A:N) and slow decay time of NMDA currents throughout the self-administration period and after withdrawal from cocaine. In contrast, the sucrose group exhibited an early increase in A:N ratios (acquisition) that returned toward baseline values with continued self-administration (maintenance) and after withdrawal. The sucrose rats also displayed a decrease in NMDA current decay time with continued self-administration (maintenance), which normalized after withdrawal. Cocaine self-administering rats exhibited impairment in NMDA-dependent long-term depression (LTD) that could be rescued by GluN2B-containing NMDA receptor blockade. Sucrose self-administering rats demonstrated no impairment in NMDA-dependent LTD. During the maintenance period of self-administration, in vivo (daily intraperitoneally for 5 days) pharmacologic blockade of GluN2B-containing NMDA receptors did not reduce lever pressing for cocaine. However, in vivo GluN2B blockade did normalize A:N ratios in cocaine self-administrating rats, and dissociated the magnitude of ovBNST A:N ratios from drug-seeking behavior after protracted withdrawal. Altogether, our data demonstrate when and how bidirectional plasticity at ovBNST excitatory synapses becomes dysfunctional with cocaine self-administration and that NMDA-mediated potentiation of AMPA receptors in this region may be part of the neural circuits of drug relapse.

A Cyclic-Plasticity-Based Mechanistic Approach for Fatigue Evaluation of 316 Stainless Steel Under Arbitrary Loading

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

Barua, Bipul; Mohanty, Subhasish; Listwan, Joseph T.

In this paper, a cyclic-plasticity based fully mechanistic fatigue modeling approach is presented. This is based on time-dependent stress-strain evolution of the material over the entire fatigue life rather than just based on the end of live information typically used for empirical S~N curve based fatigue evaluation approaches. Previously we presented constant amplitude fatigue test based related material models for 316 SS base, 508 LAS base and 316 SS- 316 SS weld which are used in nuclear reactor components such as pressure vessels, nozzles, and surge line pipes. However, we found that constant amplitude fatigue data based models have limitationmore » in capturing the stress-strain evolution under arbitrary fatigue loading. To address the above mentioned limitation, in this paper, we present a more advanced approach that can be used for modeling the cyclic stress-strain evolution and fatigue life not only under constant amplitude but also under any arbitrary (random/variable) fatigue loading. The related material model and analytical model results are presented for 316 SS base metal. Two methodologies (either based on time/cycle or based on accumulated plastic strain energy) to track the material parameters at a given time/cycle are discussed and associated analytical model results are presented. From the material model and analytical cyclic plasticity model results, it is found that the proposed cyclic plasticity model can predict all the important stages of material behavior during the entire fatigue life of the specimens with more than 90% accuracy« less

A Cyclic-Plasticity-Based Mechanistic Approach for Fatigue Evaluation of 316 Stainless Steel Under Arbitrary Loading

DOE PAGES

Barua, Bipul; Mohanty, Subhasish; Listwan, Joseph T.; ...

2017-12-05

In this paper, a cyclic-plasticity based fully mechanistic fatigue modeling approach is presented. This is based on time-dependent stress-strain evolution of the material over the entire fatigue life rather than just based on the end of live information typically used for empirical S~N curve based fatigue evaluation approaches. Previously we presented constant amplitude fatigue test based related material models for 316 SS base, 508 LAS base and 316 SS- 316 SS weld which are used in nuclear reactor components such as pressure vessels, nozzles, and surge line pipes. However, we found that constant amplitude fatigue data based models have limitationmore » in capturing the stress-strain evolution under arbitrary fatigue loading. To address the above mentioned limitation, in this paper, we present a more advanced approach that can be used for modeling the cyclic stress-strain evolution and fatigue life not only under constant amplitude but also under any arbitrary (random/variable) fatigue loading. The related material model and analytical model results are presented for 316 SS base metal. Two methodologies (either based on time/cycle or based on accumulated plastic strain energy) to track the material parameters at a given time/cycle are discussed and associated analytical model results are presented. From the material model and analytical cyclic plasticity model results, it is found that the proposed cyclic plasticity model can predict all the important stages of material behavior during the entire fatigue life of the specimens with more than 90% accuracy« less

Prospective Coding by Spiking Neurons

PubMed Central

Brea, Johanni; Gaál, Alexisz Tamás; Senn, Walter

2016-01-01

Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood. Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds. The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron’s firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. We illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. We discuss the potential role of the learning mechanism in classical trace conditioning. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(λ). PMID:27341100

Age-Dependent Glutamate Induction of Synaptic Plasticity in Cultured Hippocampal Neurons

ERIC Educational Resources Information Center

Ivenshitz, Miriam; Segal, Menahem; Sapoznik, Stav

2006-01-01

A common denominator for the induction of morphological and functional plasticity in cultured hippocampal neurons involves the activation of excitatory synapses. We now demonstrate massive morphological plasticity in mature cultured hippocampal neurons caused by a brief exposure to glutamate. This plasticity involves a slow, 70%-80% increase in…

The brain-tumor related protein podoplanin regulates synaptic plasticity and hippocampus-dependent learning and memory.

PubMed

Cicvaric, Ana; Yang, Jiaye; Krieger, Sigurd; Khan, Deeba; Kim, Eun-Jung; Dominguez-Rodriguez, Manuel; Cabatic, Maureen; Molz, Barbara; Acevedo Aguilar, Juan Pablo; Milicevic, Radoslav; Smani, Tarik; Breuss, Johannes M; Kerjaschki, Dontscho; Pollak, Daniela D; Uhrin, Pavel; Monje, Francisco J

2016-12-01

Podoplanin is a cell-surface glycoprotein constitutively expressed in the brain and implicated in human brain tumorigenesis. The intrinsic function of podoplanin in brain neurons remains however uncharacterized. Using an established podoplanin-knockout mouse model and electrophysiological, biochemical, and behavioral approaches, we investigated the brain neuronal role of podoplanin. Ex-vivo electrophysiology showed that podoplanin deletion impairs dentate gyrus synaptic strengthening. In vivo, podoplanin deletion selectively impaired hippocampus-dependent spatial learning and memory without affecting amygdala-dependent cued fear conditioning. In vitro, neuronal overexpression of podoplanin promoted synaptic activity and neuritic outgrowth whereas podoplanin-deficient neurons exhibited stunted outgrowth and lower levels of p-Ezrin, TrkA, and CREB in response to nerve growth factor (NGF). Surface Plasmon Resonance data further indicated a physical interaction between podoplanin and NGF. This work proposes podoplanin as a novel component of the neuronal machinery underlying neuritogenesis, synaptic plasticity, and hippocampus-dependent memory functions. The existence of a relevant cross-talk between podoplanin and the NGF/TrkA signaling pathway is also for the first time proposed here, thus providing a novel molecular complex as a target for future multidisciplinary studies of the brain function in the physiology and the pathology. Key messages Podoplanin, a protein linked to the promotion of human brain tumors, is required in vivo for proper hippocampus-dependent learning and memory functions. Deletion of podoplanin selectively impairs activity-dependent synaptic strengthening at the neurogenic dentate-gyrus and hampers neuritogenesis and phospho Ezrin, TrkA and CREB protein levels upon NGF stimulation. Surface plasmon resonance data indicates a physical interaction between podoplanin and NGF. On these grounds, a relevant cross-talk between podoplanin and NGF as well as a role for podoplanin in plasticity-related brain neuronal functions is here proposed.

The repetition timing of high frequency afferent stimulation drives the bidirectional plasticity at central synapses in the rat medial vestibular nuclei.

PubMed

Scarduzio, M; Panichi, R; Pettorossi, V E; Grassi, S

2012-10-25

In this study we show that high frequency stimulation (HFS, 100Hz) of afferent fibers to the medial vestibular nucleus (MVN) can induce opposite long-term modifications of synaptic responses in the type B neurons depending upon the stimulation pattern. Long burst stimulation (LBS: 2s) and short burst stimulation (SBS: 0.55s) were applied with different burst number (BN) and inter-burst intervals (IBI). It results that both LBS and SBS can induce either N-methyl-d aspartate receptors (NMDARs)-mediated long-term potentiation (LTP) or long-term depression (LTD), depending on temporal organization of repetitive bursts. In particular, the IBI plays a relevant role in guiding the shift from LTP to LTD since by using both LBS and SBS LTP is induced by shorter IBI than LTD. By contrast, the sign of long-term effect does not depend on the mean impulse frequency evaluated within the entire stimulation period. Therefore, the patterns of repetitive vestibular activation with different ratios between periods of increased activity and periods of basal activity may lead to LTP or LTD probably causing different levels of postsynaptic Ca(2+). On the whole, this study demonstrates that glutamatergic vestibular synapse in the MVN can undergo NMDAR-dependent bidirectional plasticity and puts forward a new aspect for understanding the adaptive and compensatory plasticity of the oculomotor responses. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons.

PubMed

Rubinov, Mikail; Sporns, Olaf; Thivierge, Jean-Philippe; Breakspear, Michael

2011-06-01

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.

High Strain Rate and Shock-Induced Deformation in Metals

NASA Astrophysics Data System (ADS)

Ravelo, Ramon

2012-02-01

Large-scale non-equilibrium molecular Dynamics (MD) simulations are now commonly used to study material deformation at high strain rates (10^9-10^12 s-1). They can provide detailed information-- such as defect morphology, dislocation densities, and temperature and stress profiles, unavailable or hard to measure experimentally. Computational studies of shock-induced plasticity and melting in fcc and bcc single, mono-crystal metals, exhibit generic characteristics: high elastic limits, large directional anisotropies in the yield stress and pre-melting much below the equilibrium melt temperature for shock wave propagation along specific crystallographic directions. These generic features in the response of single crystals subjected to high strain rates of deformation can be explained from the changes in the energy landscape of the uniaxially compressed crystal lattice. For time scales relevant to dynamic shock loading, the directional-dependence of the yield strength in single crystals is shown to be due to the onset of instabilities in elastic-wave propagation velocities. The elastic-plastic transition threshold can accurately be predicted by a wave-propagation stability analysis. These strain-induced instabilities create incipient defect structures, which can be quite different from the ones, which characterize the long-time, asymptotic state of the compressed solid. With increase compression and strain rate, plastic deformation via extended defects gives way to amorphization associated with the loss in shear rigidity along specific deformation paths. The hot amorphous or (super-cooled liquid) metal re-crystallizes at rates, which depend on the temperature difference between the amorphous solid and the equilibrium melt line. This plastic-amorphous transition threshold can be computed from shear-waves stability analyses. Examples from selected fcc and bcc metals will be presented employing semi-empirical potentials of the embedded atom method (EAM) type as well as results from density functional theory calculations.

Erythropoietin improves object placement recognition memory in a time dependent manner in both, uninjured animals and fimbria-fornix-lesioned male rats.

PubMed

Almaguer-Melian, W; Mercerón-Martinez, D; Delgado-Ocaña, S; Alberti-Amador, E; Gonzalez-Gómez, R; Bergado, Jorge A

2018-04-01

An increasing number of reports sustain a possible role of erythropoietin (EPO) as neuroprotective agent. In two previous articles we have evaluated EPO as plasticity promoting agent, and to contribute the restoration of brain function affected by acquired damage. We have shown that EPO is able to induce an increased synaptic efficacy in vivo along with a plasticity promoting effect. In the Morris water maze EPO administration to fimbria-fornix lesioned male rats induces a significant improvement of their spatial memory, affected by the lesion. Singularly, EPO was only effective when administered shortly after training (10 min) but not after several hours (5 h), suggesting a specific EPO effect on time dependent plasticity process. In the present paper we have expanded this line of evidence using a low stress paradigm of object placement recognition in lesioned and healthy male rats. The memory trace in this model is short-lasting; animals could recognize the change in object position when tested 24 h after, but not 48 or 72 h after the acquisition session. EPO administration 10 min after acquisition significantly prolongs retention to, at least, 72 h in healthy rats. No effect was seen if EPO was administered 5 h after training, suggesting a specific EPO modulatory effect on the consolidation process. Remarkably, early EPO treatment to fimbria fornix lesioned animals reverts the memory deficit caused by the lesion. An increased expression of the plasticity related gene arc, was also confirmed in the hippocampus and the prefrontal cortex, that is likely to be involved in the behavioral improvement observed. Copyright © 2018 Elsevier Inc. All rights reserved.

Microrheology of highly crosslinked microtubule networks is dominated by force-induced crosslinker unbinding

PubMed Central

Yang, Yali; Bai, Mo; Klug, William S.; Levine, Alex J.

2012-01-01

We determine the time- and force-dependent viscoelastic responses of reconstituted networks of microtubules that have been strongly crosslinked by biotin-streptavidin bonds. To measure the microscale viscoelasticity of such networks, we use a magnetic tweezers device to apply localized forces. At short time scales, the networks respond nonlinearly to applied force, with stiffening at small forces, followed by a reduction in the stiffening response at high forces, which we attribute to the force-induced unbinding of crosslinks. At long time scales, force-induced bond unbinding leads to local network rearrangement and significant bead creep. Interestingly, the network retains its elastic modulus even under conditions of significant plastic flow, suggesting that crosslinker breakage is balanced by the formation of new bonds. To better understand this effect, we developed a finite element model of such a stiff filament network with labile crosslinkers obeying force-dependent Bell model unbinding dynamics. The coexistence of dissipation, due to bond breakage, and the elastic recovery of the network is possible because each filament has many crosslinkers. Recovery can occur as long as a sufficient number of the original crosslinkers are preserved under the loading period. When these remaining original crosslinkers are broken, plastic flow results. PMID:23577042

Enduring critical period plasticity visualized by transcranial flavoprotein imaging in mouse primary visual cortex.

PubMed

Tohmi, Manavu; Kitaura, Hiroki; Komagata, Seiji; Kudoh, Masaharu; Shibuki, Katsuei

2006-11-08

Experience-dependent plasticity in the visual cortex was investigated using transcranial flavoprotein fluorescence imaging in mice anesthetized with urethane. On- and off-responses in the primary visual cortex were elicited by visual stimuli. Fluorescence responses and field potentials elicited by grating patterns decreased similarly as contrasts of visual stimuli were reduced. Fluorescence responses also decreased as spatial frequency of grating stimuli increased. Compared with intrinsic signal imaging in the same mice, fluorescence imaging showed faster responses with approximately 10 times larger signal changes. Retinotopic maps in the primary visual cortex and area LM were constructed using fluorescence imaging. After monocular deprivation (MD) of 4 d starting from postnatal day 28 (P28), deprived eye responses were suppressed compared with nondeprived eye responses in the binocular zone but not in the monocular zone. Imaging faithfully recapitulated a critical period for plasticity with maximal effects of MD observed around P28 and not in adulthood even under urethane anesthesia. Visual responses were compared before and after MD in the same mice, in which the skull was covered with clear acrylic dental resin. Deprived eye responses decreased after MD, whereas nondeprived eye responses increased. Effects of MD during a critical period were tested 2 weeks after reopening of the deprived eye. Significant ocular dominance plasticity was observed in responses elicited by moving grating patterns, but no long-lasting effect was found in visual responses elicited by light-emitting diode light stimuli. The present results indicate that transcranial flavoprotein fluorescence imaging is a powerful tool for investigating experience-dependent plasticity in the mouse visual cortex.

Kinetics of Brominated Flame Retardant (BFR) Releases from Granules of Waste Plastics.

PubMed

Sun, Bingbing; Hu, Yuanan; Cheng, Hefa; Tao, Shu

2016-12-20

Plastic components of e-waste contain high levels of brominated flame retardants (BFRs), whose releases cause environmental and human health concerns. This study characterized the release kinetics of polybrominated diphenyl ethers (PBDEs) from millimeter-sized granules processed from the plastic exteriors of two scrap computer displays at environmentally relevant temperatures. The release rate of a substitute of PBDEs, 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE), from the waste plastics, was reported for the first time. Deca-BDE was the most abundant PBDE congeners in both materials (87-89%), while BTBPE was also present at relatively high contents. The release kinetics of BFRs could be modeled as one-dimensional diffusion, while the temperature dependence of diffusion coefficients was well described by the Arrhenius equation. The diffusion coefficients of BFRs (at 30 °C) in the plastic matrices were estimated to be in the range of 10 -27.16 to 10 -19.96 m 2 ·s -1 , with apparent activation energies between 88.4 and 154.2 kJ·mol -1 . The half-lives of BFR releases (i.e., 50% depletion) from the plastic granules ranged from thousands to tens of billions of years at ambient temperatures. These findings suggest that BFRs are released very slowly from the matrices of waste plastics through molecular diffusion, while their emissions can be significantly enhanced with wear-and-tear and pulverization.

Lock-and-key mechanisms of cerebellar memory recall based on rebound currents.

PubMed

Wetmore, Daniel Z; Mukamel, Eran A; Schnitzer, Mark J

2008-10-01

A basic question for theories of learning and memory is whether neuronal plasticity suffices to guide proper memory recall. Alternatively, information processing that is additional to readout of stored memories might occur during recall. We formulate a "lock-and-key" hypothesis regarding cerebellum-dependent motor memory in which successful learning shapes neural activity to match a temporal filter that prevents expression of stored but inappropriate motor responses. Thus, neuronal plasticity by itself is necessary but not sufficient to modify motor behavior. We explored this idea through computational studies of two cerebellar behaviors and examined whether deep cerebellar and vestibular nuclei neurons can filter signals from Purkinje cells that would otherwise drive inappropriate motor responses. In eyeblink conditioning, reflex acquisition requires the conditioned stimulus (CS) to precede the unconditioned stimulus (US) by >100 ms. In our biophysical models of cerebellar nuclei neurons this requirement arises through the phenomenon of postinhibitory rebound depolarization and matches longstanding behavioral data on conditioned reflex timing and reliability. Although CS-US intervals<100 ms may induce Purkinje cell plasticity, cerebellar nuclei neurons drive conditioned responses only if the CS-US training interval was >100 ms. This bound reflects the minimum time for deinactivation of rebound currents such as T-type Ca2+. In vestibulo-ocular reflex adaptation, hyperpolarization-activated currents in vestibular nuclei neurons may underlie analogous dependence of adaptation magnitude on the timing of visual and vestibular stimuli. Thus, the proposed lock-and-key mechanisms link channel kinetics to recall performance and yield specific predictions of how perturbations to rebound depolarization affect motor expression.

Kinetic Modeling of the Release of Ethylene Oxide from Sterilized Plastic Containers and its Interaction with Monoclonal Antibodies.

PubMed

Yu, Bryan Lei; Han, Jun; Hammond, Matthew; Wang, Xuemei; Zhang, Qingchun; Clausen, Andrew; Forster, Ronald; Eu, Mingda

Ethylene oxide (ETO) is commonly used to sterilize plastic containers, but the effects of residual amounts left after sterilization on protein therapeutics are still not well understood. Here we focus primarily on the factors that influence concentrations of ETO migrating from ETO-treated plastic containers into aqueous solution. A study was designed to investigate the kinetics of this process at various temperatures, and the kinetic data could be fit with a model based on a combination of Fickean diffusion and first-order chemical reaction (to account for observed hydrolysis of ETO). The diffusion and reaction rate constants thus obtained obey Arrhenius-like temperature dependence. These results indicate that for analytical methods involving extraction into water, measurements of residual ETO in a container must account for the effects of ETO hydrolysis. Further, the effects of salt concentration and pH of the fluid in the container on accumulated ETO levels were explored. Finally, interactions of ETO with anti-streptavidin (AntiSA) Immunoglobulin G1 (IgG1) and IgG2 antibodies were studied, with ETO adducts found on all methionine residues when incubated in solutions spiked with ETO at concentrations that could be reached (based on the kinetic studies) in ETO-treated plastic vials. Overall, the likelihood of observable ETO-protein modifications upon storage in ETO-sterilized containers will depend on a complex interplay of protein properties, formulation details, storage conditions, and amount of residual ETO initially in the container. Ethylene oxide (ETO) is commonly used to sterilize plastic containers, but the effects of residual amounts left after sterilization on protein therapeutics are still not well understood. Here we describe experiments exploring the factors that influence concentrations of ETO migrating from ETO-treated plastic containers into aqueous solution over time. Additionally, interactions of ETO with model antibodies were studied, with ETO adducts found on all methionine residues when incubated in solutions spiked with ETO at concentrations that could potentially be reached in ETO-treated plastic vials. Overall, the likelihood of observable ETO-protein modifications upon storage in ETO-sterilized containers will depend on a complex interplay of protein properties, formulation details, storage conditions, and amount of residual ETO initially in the container. © PDA, Inc. 2017.

Inelastic deformation of metal matrix composites: Plasticity and damage mechanisms, part 2

NASA Technical Reports Server (NTRS)

Majumdar, B. S.; Newaz, G. M.

1992-01-01

The inelastic deformation mechanisms for the SiC (SCS-6)/Ti-15-3 system were studied at 538 C (1000 F) using a combination of mechanical measurements and detailed microstructural examinations. The objectives were to evaluate the contributions of plasticity and damage to the overall MMC response, and to compare the room temperature and elevated temperature deformation behaviors. Four different laminates were studied: (0)8, (90)8,(+ or -45)2s, and (0/90)2s, with the primary emphasis on the unidirectional (0)8, and (90)8 systems. The elevated temperature responses were similar to those at room temperature, involving a two-stage elastic-plastic type of response for the (0)8 system, and a characteristic three-stage deformation response for the (90)8 and (+ or -45)2s systems. The primary effects of elevated temperatures included: (1) reduction in the 'yield' and failure strengths; (2) plasticity through diffused slip rather than concentrated planar slip (which occurred at room temperature); and (3) time-dependent deformation. The inelastic deformation mechanism for the (0)8 MMC was dominated by plasticity at both temperatures. For the (90)8 and (+ or -45)2s MMCs, a combination of damage and plasticity contributed to the deformation at both temperatures.

T-type calcium channels in synaptic plasticity

PubMed Central

Lambert, Régis C.

2017-01-01

ABSTRACT The role of T-type calcium currents is rarely considered in the extensive literature covering the mechanisms of long-term synaptic plasticity. This situation reflects the lack of suitable T-type channel antagonists that till recently has hampered investigations of the functional roles of these channels. However, with the development of new pharmacological and genetic tools, a clear involvement of T-type channels in synaptic plasticity is starting to emerge. Here, we review a number of studies showing that T-type channels participate to numerous homo- and hetero-synaptic plasticity mechanisms that involve different molecular partners and both pre- and post-synaptic modifications. The existence of T-channel dependent and independent plasticity at the same synapse strongly suggests a subcellular localization of these channels and their partners that allows specific interactions. Moreover, we illustrate the functional importance of T-channel dependent synaptic plasticity in neocortex and thalamus. PMID:27653665

State of the Science White Paper: Effects of Plastics Pollution on Aquatic Life and Aquatic-Dependent Wildlife

EPA Pesticide Factsheets

This document is a state-of-the-science review – one that summarizes available scientific information on the effects of chemicals associated with plastic pollution and their potential impacts on aquatic life and aquatic-dependent wildlife.

FNIRS-based evaluation of cortical plasticity in children with cerebral palsy undergoing constraint-induced movement therapy

NASA Astrophysics Data System (ADS)

Cao, Jianwei; Khan, Bilal; Hervey, Nathan; Tian, Fenghua; Delgado, Mauricio R.; Clegg, Nancy J.; Smith, Linsley; Roberts, Heather; Tulchin-Francis, Kirsten; Shierk, Angela; Shagman, Laura; MacFarlane, Duncan; Liu, Hanli; Alexandrakis, George

2015-03-01

Sensorimotor cortex plasticity induced by constraint-induced movement therapy (CIMT) in six children (10.2 ± 2.1 years old) with hemiplegic cerebral palsy (CP) was assessed by functional near-infrared spectroscopy (fNIRS). The activation laterality index and time-to-peak/duration during a finger tapping task were quantified before, immediately after, and six months after CIMT. Five age-matched healthy children (9.8 ± 1.3 years old) were also imaged at the same time points to provide comparative activation metrics for normal controls. In children with CP the activation time-to-peak/duration for all sensorimotor centers displayed significant normalization immediately after CIMT that persisted six months later. In contrast to this longer term improvement in localized activation response, the laterality index that depended on communication between sensorimotor centers improved immediately after CIMT, but relapsed six months later.

Position- and Hippo signaling-dependent plasticity during lineage segregation in the early mouse embryo

PubMed Central

Posfai, Eszter; Petropoulos, Sophie; de Barros, Flavia Regina Oliveira; Schell, John Paul; Jurisica, Igor; Sandberg, Rickard; Lanner, Fredrik; Rossant, Janet

2017-01-01

The segregation of the trophectoderm (TE) from the inner cell mass (ICM) in the mouse blastocyst is determined by position-dependent Hippo signaling. However, the window of responsiveness to Hippo signaling, the exact timing of lineage commitment and the overall relationship between cell commitment and global gene expression changes are still unclear. Single-cell RNA sequencing during lineage segregation revealed that the TE transcriptional profile stabilizes earlier than the ICM and prior to blastocyst formation. Using quantitative Cdx2-eGFP expression as a readout of Hippo signaling activity, we assessed the experimental potential of individual blastomeres based on their level of Cdx2-eGFP expression and correlated potential with gene expression dynamics. We find that TE specification and commitment coincide and occur at the time of transcriptional stabilization, whereas ICM cells still retain the ability to regenerate TE up to the early blastocyst stage. Plasticity of both lineages is coincident with their window of sensitivity to Hippo signaling. DOI: http://dx.doi.org/10.7554/eLife.22906.001 PMID:28226240

Lubricant Rheology in Concentrated Contacts

NASA Technical Reports Server (NTRS)

Jacobson, B. O.

1984-01-01

Lubricant behavior in highly stressed situtations shows that a Newtonian model for lubricant rheology is insufficient for explanation of traction behavior. The oil film build up is predicted by using a Newtonian lubricant model except at high slide to roll ratios and at very high loads, where the nonNewtonian behavior starts to be important already outside the Hertzian contact area. Static and dynamic experiments are reported. In static experiments the pressure is applied to the lubricant more than a million times longer than in an EHD contact. Depending on the pressure-temperature history of the experiment the lubricant will become a crystallized or amorphous solid at high pressures. In dynamic experiments, the oil is in an amorphous solid state. Depending on the viscosity, time scale, elasticity of the oil and the bearing surfaces, the oil film pressure, shear strain rate and the type of lubricant, different properties of the oil are important for prediction of shear stresses in the oil. The different proposed models for the lubricant, which describe it to a Newtonian liquid, an elastic liquid, a plastic liquid and an elastic-plastic solid.

Pulse Responses of the Conducting Polymer Poly(3,4-ethylenedioxythiophene): Poly(styrenesulfonate)-Based Junctions

NASA Astrophysics Data System (ADS)

Zeng, Fei; Li, Xiaojun; Li, Sizhao; Chang, Chiating; Hu, Yuandong

2017-03-01

Pulse responses were studied for the heterojunctions within the structure of Ti/poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS)/Ti. The pulse response was found to resemble that of the action potential after the pulse width was modulated to a time scale of nanoseconds. Using the pulse as a stimulation protocol to simulate synaptic plasticity produced spike rate-dependent plasticity-like phenomena. Thus, the application scope of this conducting polymer-based memristor can be extended from a time scale of milliseconds to one of nanoseconds, depending on the requirement of neuromorphic circuits. Current oscillations were observed with a period within 100 ns. The mechanisms of the behavior response were analyzed according to memristor protocol. An interface barrier is thought to primarily account for the origin of the capacitive feature and the charge q, i.e., one of the basic characteristic of the memristor. The chain structure of this conducting polymer should primarily account for the origin of its inductive feature and the flux φ, i.e., another basic characteristic of the memristor.

Time- and temperature-dependent migration studies of Irganox 1076 from plastics into foods and food simulants.

PubMed

Beldì, G; Pastorelli, S; Franchini, F; Simoneau, C

2012-01-01

The study provides an exhaustive set of migration data for octadecyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate (Irganox 1076) from low-density polyethylene (LDPE) in several food matrices. Irganox 1076 was used as a model migrant because it represents one of the typical substances used as an antioxidant in food packaging polymers. Kinetic (time-dependent) migration studies of Irganox 1076 were performed for selected foodstuffs chosen with different physical-chemical properties and in relation to the actual European food consumption market. The effect of fat content and of the temperature of storage on the migration from plastic packaging was evaluated. The results show that migration increased with fat content and storage temperature. All data obtained from real foods were also compared with data obtained from simulants tested in the same conditions. In all studied cases, the kinetics in simulants were higher than those in foodstuffs. The work provides data valuable for the extension of the validation of migration model developed on simulants to foodstuffs themselves.

A Viscoplastic Constitutive Theory for Monolithic Ceramic Materials. Series 1

NASA Technical Reports Server (NTRS)

Janosik, Lesley A.; Duffy, Stephen F.

1997-01-01

With increasing use of ceramic materials in high temperature structural applications such as advanced heat engine components, the need arises to accurately predict thermomechanical behavior. This paper, which is the first of two in a series, will focus on inelastic deformation behavior associated with these service conditions by providing an overview of a viscoplastic constitutive model that accounts for time-dependent hereditary material deformation (e.g., creep, stress relaxation, etc.) in monolithic structural ceramics. Early work in the field of metal plasticity indicated that inelastic deformations are essentially unaffected by hydrostatic stress. This is not the case, however, for ceramic-based material systems, unless the ceramic is fully dense. The theory presented here allows for fully dense material behavior as a limiting case. In addition, ceramic materials exhibit different time-dependent behavior in tension and compression. Thus, inelastic deformation models for ceramics must be constructed in a fashion that admits both sensitivity to hydrostatic stress and differing behavior in tension and compression. A number of constitutive theories for materials that exhibit sensitivity to the hydrostatic component of stress have been proposed that characterize deformation using time-independent classical plasticity as a foundation. However, none of these theories allow different behavior in tension and compression. In addition, these theories are somewhat lacking in that they are unable to capture creep, relaxation, and rate-sensitive phenomena exhibited by ceramic materials at high temperature. When subjected to elevated service temperatures, ceramic materials exhibit complex thermomechanical behavior that is inherently time-dependent, and hereditary in the sense that current behavior depends not only on current conditions, but also on thermo-mechanical history. The objective of this work is to present the formulation of a macroscopic continuum theory that captures these time-dependent phenomena. Specifically, the overview contained in this paper focuses on the multiaxial derivation of the constitutive model, and examines the scalar threshold function and its attending geometrical implications.

Activity-dependent plasticity in spinal cord injury

PubMed Central

Lynskey, James V.; Belanger, Adam; Jung, Ranu

2008-01-01

The adult mammalian central nervous system (CNS) is capable of considerable plasticity, both in health and disease. After spinal neurotrauma, the degrees and extent of neuroplasticity and recovery depend on multiple factors, including the level and extent of injury, postinjury medical and surgical care, and rehabilitative interventions. Rehabilitation strategies focus less on repairing lost connections and more on influencing CNS plasticity for regaining function. Current evidence indicates that strategies for rehabilitation, including passive exercise, active exercise with some voluntary control, and use of neuroprostheses, can enhance sensorimotor recovery after spinal cord injury (SCI) by promoting adaptive structural and functional plasticity while mitigating maladaptive changes at multiple levels of the neuraxis. In this review, we will discuss CNS plasticity that occurs both spontaneously after SCI and in response to rehabilitative therapies. PMID:18566941

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.

Orientational order and rotational relaxation in the plastic crystal phase of tetrahedral molecules.

PubMed

Rey, Rossend

2008-01-17

A methodology recently introduced to describe orientational order in liquid carbon tetrachloride is extended to the plastic crystal phase of XY4 molecules. The notion that liquid and plastic crystal phases are germane regarding orientational order is confirmed for short intermolecular distances but is seen to fail beyond, as long range orientational correlations are found for the simulated solid phase. It is argued that, if real, such a phenomenon may not to be accessible with direct (diffraction) methods due to the high molecular symmetry. This behavior is linked to the existence of preferential orientation with respect to the fcc crystalline network defined by the centers of mass. It is found that the dominant class accounts, at most, for one-third of all configurations, with a feeble dependence on temperature. Finally, the issue of rotational relaxation is also addressed, with an excellent agreement with experimental measures. It is shown that relaxation is nonhomogeneous in the picosecond range, with a slight dispersion of decay times depending on the initial orientational class. The results reported mainly correspond to neopentane over a wide temperature range, although results for carbon tetrachloride are included, as well.

Thermal properties of light-weight concrete with waste polypropylene aggregate

NASA Astrophysics Data System (ADS)

Záleská, Martina; Pokorný, Jaroslav; Pavlíková, Milena; Pavlík, Zbyšek

2017-07-01

Thermal properties of a sustainable light-weight concrete incorporating high volume of waste polypropylene as partial substitution of natural aggregate were studied in the paper. Glass fiber reinforced polypropylene (GFPP), a by-product of PP tubes production, partially substituted fine natural silica aggregate in 10, 20, 30, 40, and 50 mass%. In order to quantify the effect of GFPP use on concrete properties, a reference concrete mix without plastic waste was studied as well. For the applied GFPP, bulk density, matrix density, and particle size distribution were measured. Specific attention was paid to thermal transport and storage properties of GFPP that were examined in dependence on compaction time. For the developed light-weight concrete, thermal properties were accessed using transient impulse technique, whereas the measurement was done in dependence on moisture content, from the dry state to fully water saturated state. Additionally, the investigated thermal properties were plotted as function of porosity. The tested light-weight concrete was found to be prospective construction material possessing improved thermal insulation function. Moreover, the reuse of waste plastics in concrete composition was beneficial both from the environmental and financial point of view considering plastics low biodegradability and safe disposal.

Temperature dependence of plastic scintillators

NASA Astrophysics Data System (ADS)

Peralta, L.

2018-03-01

Plastic scintillator detectors have been studied as dosimeters, since they provide a cost-effective alternative to conventional ionization chambers. Several articles have reported undesired response dependencies on beam energy and temperature, which provides the motivation to determine appropriate correction factors. In this work, we studied the light yield temperature dependency of four plastic scintillators, BCF-10, BCF-60, BC-404, RP-200A and two clear fibers, BCF-98 and SK-80. Measurements were made using a 50 kVp X-ray beam to produce the scintillation and/or radioluminescence signal. The 0 to 40 °C temperature range was scanned for each scintillator, and temperature coefficients were obtained.

Internal state variable plasticity-damage modeling of AISI 4140 steel including microstructure-property relations: temperature and strain rate effects

NASA Astrophysics Data System (ADS)

Nacif el Alaoui, Reda

Mechanical structure-property relations have been quantified for AISI 4140 steel. under different strain rates and temperatures. The structure-property relations were used. to calibrate a microstructure-based internal state variable plasticity-damage model for. monotonic tension, compression and torsion plasticity, as well as damage evolution. Strong stress state and temperature dependences were observed for the AISI 4140 steel. Tension tests on three different notched Bridgman specimens were undertaken to study. the damage-triaxiality dependence for model validation purposes. Fracture surface. analysis was performed using Scanning Electron Microscopy (SEM) to quantify the void. nucleation and void sizes in the different specimens. The stress-strain behavior exhibited. a fairly large applied stress state (tension, compression dependence, and torsion), a. moderate temperature dependence, and a relatively small strain rate dependence.

Synaptic Plasticity and Learning Behaviors Mimicked in Single Inorganic Synapses of Pt/HfOx/ZnOx/TiN Memristive System

NASA Astrophysics Data System (ADS)

Wang, Lai-Guo; Zhang, Wei; Chen, Yan; Cao, Yan-Qiang; Li, Ai-Dong; Wu, Di

2017-01-01

In this work, a kind of new memristor with the simple structure of Pt/HfOx/ZnOx/TiN was fabricated completely via combination of thermal-atomic layer deposition (TALD) and plasma-enhanced ALD (PEALD). The synaptic plasticity and learning behaviors of Pt/HfOx/ZnOx/TiN memristive system have been investigated deeply. Multilevel resistance states are obtained by varying the programming voltage amplitudes during the pulse cycling. The device conductance can be continuously increased or decreased from cycle to cycle with better endurance characteristics up to about 3 × 103 cycles. Several essential synaptic functions are simultaneously achieved in such a single double-layer of HfOx/ZnOx device, including nonlinear transmission properties, such as long-term plasticity (LTP), short-term plasticity (STP), and spike-timing-dependent plasticity. The transformation from STP to LTP induced by repetitive pulse stimulation is confirmed in Pt/HfOx/ZnOx/TiN memristive device. Above all, simple structure of Pt/HfOx/ZnOx/TiN by ALD technique is a kind of promising memristor device for applications in artificial neural network.

Time-Dependent Behavior of Diabase and a Nonlinear Creep Model

NASA Astrophysics Data System (ADS)

Yang, Wendong; Zhang, Qiangyong; Li, Shucai; Wang, Shugang

2014-07-01

Triaxial creep tests were performed on diabase specimens from the dam foundation of the Dagangshan hydropower station, and the typical characteristics of creep curves were analyzed. Based on the test results under different stress levels, a new nonlinear visco-elasto-plastic creep model with creep threshold and long-term strength was proposed by connecting an instantaneous elastic Hooke body, a visco-elasto-plastic Schiffman body, and a nonlinear visco-plastic body in series mode. By introducing the nonlinear visco-plastic component, this creep model can describe the typical creep behavior, which includes the primary creep stage, the secondary creep stage, and the tertiary creep stage. Three-dimensional creep equations under constant stress conditions were deduced. The yield approach index (YAI) was used as the criterion for the piecewise creep function to resolve the difficulty in determining the creep threshold value and the long-term strength. The expression of the visco-plastic component was derived in detail and the three-dimensional central difference form was given. An example was used to verify the credibility of the model. The creep parameters were identified, and the calculated curves were in good agreement with the experimental curves, indicating that the model is capable of replicating the physical processes.

Pathways for degradation of plastic polymers floating in the marine environment.

PubMed

Gewert, Berit; Plassmann, Merle M; MacLeod, Matthew

2015-09-01

Each year vast amounts of plastic are produced worldwide. When released to the environment, plastics accumulate, and plastic debris in the world's oceans is of particular environmental concern. More than 60% of all floating debris in the oceans is plastic and amounts are increasing each year. Plastic polymers in the marine environment are exposed to sunlight, oxidants and physical stress, and over time they weather and degrade. The degradation processes and products must be understood to detect and evaluate potential environmental hazards. Some attention has been drawn to additives and persistent organic pollutants that sorb to the plastic surface, but so far the chemicals generated by degradation of the plastic polymers themselves have not been well studied from an environmental perspective. In this paper we review available information about the degradation pathways and chemicals that are formed by degradation of the six plastic types that are most widely used in Europe. We extrapolate that information to likely pathways and possible degradation products under environmental conditions found on the oceans' surface. The potential degradation pathways and products depend on the polymer type. UV-radiation and oxygen are the most important factors that initiate degradation of polymers with a carbon-carbon backbone, leading to chain scission. Smaller polymer fragments formed by chain scission are more susceptible to biodegradation and therefore abiotic degradation is expected to precede biodegradation. When heteroatoms are present in the main chain of a polymer, degradation proceeds by photo-oxidation, hydrolysis, and biodegradation. Degradation of plastic polymers can lead to low molecular weight polymer fragments, like monomers and oligomers, and formation of new end groups, especially carboxylic acids.

Study of the character of the time dependence of the ratio of signals in the IR and visible channels of a radiometric apparatus when fragments of space junk are observed

NASA Astrophysics Data System (ADS)

Pavlov, N. I.; Él'Ts, E. É.

2006-01-01

A more accurate expression is derived for determining the specific load of fragments of space junk via the time dependence of the ratio of signals in the IR and visible channels of on-board radiometric observation apparatus. Results are presented of a calculation of the time behavior of this ratio when aluminum and plastic debris is observed on near-earth orbits. The cases considered here involve constant heating of the debris by solar radiation and the variation of this heating according to a harmonic law because the debris rotates around its center of mass.

The Role of CREB, SRF, and MEF2 in Activity-Dependent Neuronal Plasticity in the Visual Cortex.

PubMed

Pulimood, Nisha S; Rodrigues, Wandilson Dos Santos; Atkinson, Devon A; Mooney, Sandra M; Medina, Alexandre E

2017-07-12

The transcription factors CREB (cAMP response element binding factor), SRF (serum response factor), and MEF2 (myocyte enhancer factor 2) play critical roles in the mechanisms underlying neuronal plasticity. However, the role of the activation of these transcription factors in the different components of plasticity in vivo is not well known. In this study, we tested the role of CREB, SRF, and MEF2 in ocular dominance plasticity (ODP), a paradigm of activity-dependent neuronal plasticity in the visual cortex. These three proteins bind to the synaptic activity response element (SARE), an enhancer sequence found upstream of many plasticity-related genes (Kawashima et al., 2009; Rodríguez-Tornos et al., 2013), and can act cooperatively to express Arc , a gene required for ODP (McCurry et al., 2010). We used viral-mediated gene transfer to block the transcription function of CREB, SRF, and MEF2 in the visual cortex, and measured visually evoked potentials in awake male and female mice before and after a 7 d monocular deprivation, which allowed us to examine both the depression component (Dc-ODP) and potentiation component (Pc-ODP) of plasticity independently. We found that CREB, SRF, and MEF2 are all required for ODP, but have differential effects on Dc-ODP and Pc-ODP. CREB is necessary for both Dc-ODP and Pc-ODP, whereas SRF and MEF2 are only needed for Dc-ODP. This finding supports previous reports implicating SRF and MEF2 in long-term depression (required for Dc-ODP), and CREB in long-term potentiation (required for Pc-ODP). SIGNIFICANCE STATEMENT Activity-dependent neuronal plasticity is the cellular basis for learning and memory, and it is crucial for the refinement of neuronal circuits during development. Identifying the mechanisms of activity-dependent neuronal plasticity is crucial to finding therapeutic interventions in the myriad of disorders where it is disrupted, such as Fragile X syndrome, Rett syndrome, epilepsy, major depressive disorder, and autism spectrum disorder. Transcription factors are essential nuclear proteins that trigger the expression of gene programs required for long-term functional and structural plasticity changes. Our results elucidate the specific role of the transcription factors CREB, SRF, and MEF2 in the depression and potentiation components of ODP in vivo , therefore better informing future attempts to find therapeutic targets for diseases where activity-dependent plasticity is disrupted. Copyright © 2017 the authors 0270-6474/17/376628-10$15.00/0.

The Role of CREB, SRF, and MEF2 in Activity-Dependent Neuronal Plasticity in the Visual Cortex

PubMed Central

Rodrigues, Wandilson dos Santos; Mooney, Sandra M.

2017-01-01

The transcription factors CREB (cAMP response element binding factor), SRF (serum response factor), and MEF2 (myocyte enhancer factor 2) play critical roles in the mechanisms underlying neuronal plasticity. However, the role of the activation of these transcription factors in the different components of plasticity in vivo is not well known. In this study, we tested the role of CREB, SRF, and MEF2 in ocular dominance plasticity (ODP), a paradigm of activity-dependent neuronal plasticity in the visual cortex. These three proteins bind to the synaptic activity response element (SARE), an enhancer sequence found upstream of many plasticity-related genes (Kawashima et al., 2009; Rodríguez-Tornos et al., 2013), and can act cooperatively to express Arc, a gene required for ODP (McCurry et al., 2010). We used viral-mediated gene transfer to block the transcription function of CREB, SRF, and MEF2 in the visual cortex, and measured visually evoked potentials in awake male and female mice before and after a 7 d monocular deprivation, which allowed us to examine both the depression component (Dc-ODP) and potentiation component (Pc-ODP) of plasticity independently. We found that CREB, SRF, and MEF2 are all required for ODP, but have differential effects on Dc-ODP and Pc-ODP. CREB is necessary for both Dc-ODP and Pc-ODP, whereas SRF and MEF2 are only needed for Dc-ODP. This finding supports previous reports implicating SRF and MEF2 in long-term depression (required for Dc-ODP), and CREB in long-term potentiation (required for Pc-ODP). SIGNIFICANCE STATEMENT Activity-dependent neuronal plasticity is the cellular basis for learning and memory, and it is crucial for the refinement of neuronal circuits during development. Identifying the mechanisms of activity-dependent neuronal plasticity is crucial to finding therapeutic interventions in the myriad of disorders where it is disrupted, such as Fragile X syndrome, Rett syndrome, epilepsy, major depressive disorder, and autism spectrum disorder. Transcription factors are essential nuclear proteins that trigger the expression of gene programs required for long-term functional and structural plasticity changes. Our results elucidate the specific role of the transcription factors CREB, SRF, and MEF2 in the depression and potentiation components of ODP in vivo, therefore better informing future attempts to find therapeutic targets for diseases where activity-dependent plasticity is disrupted. PMID:28607167

Conceptualizing withdrawal-induced escalation of alcohol self-administration as a learned, plasticity-dependent process

PubMed Central

Walker, Brendan M.

2013-01-01

This article represents one of five contributions focusing on the topic “Plasticity and neuroadaptive responses within the extended amygdala in response to chronic or excessive alcohol exposure” that were developed by awardees participating in the Young Investigator Award Symposium at the “Alcoholism and Stress: A Framework for Future Treatment Strategies” conference in Volterra, Italy on May 3–6, 2011 that was organized/chaired by Drs. Antonio Noronha and Fulton Crews and sponsored by the National Institute on Alcohol Abuse and Alcoholism. This review discusses the dependence-induced neuroadaptations in affective systems that provide a basis for negative reinforcement learning and presents evidence demonstrating that escalated alcohol consumption during withdrawal is a learned, plasticity-dependent process. The review concludes by identifying changes within extended amygdala dynorphin/kappa-opioid receptor systems that could serve as the foundation for the occurrence of negative reinforcement processes. While some evidence contained herein may be specific to alcohol dependence-related learning and plasticity, much of the information will be of relevance to any addictive disorder involving negative reinforcement mechanisms. Collectively, the information presented within this review provides a framework to assess the negative reinforcing effects of alcohol in a manner that distinguishes neuroadaptations produced by chronic alcohol exposure from the actual plasticity that is associated with negative reinforcement learning in dependent organisms. PMID:22459874

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

Recycling of plastic: accounting of greenhouse gases and global warming contributions.

PubMed

Astrup, Thomas; Fruergaard, Thilde; Christensen, Thomas H

2009-11-01

Major greenhouse gas (GHG) emissions related to plastic waste recycling were evaluated with respect to three management alternatives: recycling of clean, single-type plastic, recycling of mixed/contaminated plastic, and use of plastic waste as fuel in industrial processes. Source-separated plastic waste was received at a material recovery facility (MRF) and processed for granulation and subsequent downstream use. In the three alternatives, plastic was assumed to be substituting virgin plastic in new products, wood in low-strength products (outdoor furniture, fences, etc.), and coal or fuel oil in the case of energy utilization. GHG accounting was organized in terms of indirect upstream emissions (e.g. provision of energy, fuels, and materials), direct emissions at the MRF (e.g. fuel combustion), and indirect downstream emissions (e.g. avoided emissions from production of virgin plastic, wood, or coal/oil). Combined, upstream and direct emissions were estimated to be roughly between 5 and 600 kg CO(2)-eq. tonne( -1) of plastic waste depending on treatment at the MRF and CO(2) emissions from electricity production. Potential downstream savings arising from substitution of virgin plastic, wood, and energy fuels were estimated to be around 60- 1600 kg CO(2)-eq. tonne( -1) of plastic waste depending on substitution ratios and CO(2) emissions from electricity production. Based on the reviewed data, it was concluded that substitution of virgin plastic should be preferred. If this is not viable due to a mixture of different plastic types and/or contamination, the plastic should be used for energy utilization. Recycling of plastic waste for substitution of other materials such as wood provided no savings with respect to global warming.

Why get big in the cold? Size-fecundity relationships explain the temperature-size rule in a pulmonate snail (Physa).

PubMed

Arendt, J

2015-01-01

Most ectotherms follow a pattern of size plasticity known as the temperature-size rule where individuals reared in cold environments are larger at maturation than those reared in warm environments. This pattern seems maladaptive because growth is slower in the cold so it takes longer to reach a large size. However, it may be adaptive if reaching a large size has a greater benefit in a cold than in a warm environment such as when size-dependent mortality or size-dependent fecundity depends on temperature. I present a theoretical model showing how a correlation between temperature and the size-fecundity relationship affects optimal size at maturation. I parameterize the model using data from a freshwater pulmonate snail from the genus Physa. Nine families were reared from hatching in one of three temperature regimes (daytime temperature of 22, 25 or 28 °C, night-time temperature of 22 °C, under a 12L:12D light cycle). Eight of the nine families followed the temperature-size rule indicating genetic variation for this plasticity. As predicted, the size-fecundity relationship depended upon temperature; fecundity increases steeply with size in the coldest treatment, less steeply in the intermediate treatment, and shows no relationship with size in the warmest treatment. Thus, following the temperature-size rule is adaptive for this species. Although rarely measured under multiple conditions, size-fecundity relationships seem to be sensitive to a number of environmental conditions in addition to temperature including local productivity, competition and predation. If this form of plasticity is as widespread as it appears to be, this model shows that such plasticity has the potential to greatly modify current life-history theory. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Morris Water Maze Training in Mice Elevates Hippocampal Levels of Transcription Factors Nuclear Factor (Erythroid-derived 2)-like 2 and Nuclear Factor Kappa B p65

PubMed Central

Snow, Wanda M.; Pahlavan, Payam S.; Djordjevic, Jelena; McAllister, Danielle; Platt, Eric E.; Alashmali, Shoug; Bernstein, Michael J.; Suh, Miyoung; Albensi, Benedict C.

2015-01-01

Research has identified several transcription factors that regulate activity-dependent plasticity and memory, with cAMP-response element binding protein (CREB) being the most well-studied. In neurons, CREB activation is influenced by the transcription factor nuclear factor kappa B (NF-κB), considered central to immunity but more recently implicated in memory. The transcription factor early growth response-2 (Egr-2), an NF-κB gene target, is also associated with learning and memory. Nuclear factor (erythroid-derived 2)-like 2 (Nrf2), an antioxidant transcription factor linked to NF-κB in pathological conditions, has not been studied in normal memory. Given that numerous transcription factors implicated in activity-dependent plasticity demonstrate connections to NF-κB, this study simultaneously evaluated protein levels of NF-κB, CREB, Egr-2, Nrf2, and actin in hippocampi from young (1 month-old) weanling CD1 mice after training in the Morris water maze, a hippocampal-dependent spatial memory task. After a 6-day acquisition period, time to locate the hidden platform decreased in the Morris water maze. Mice spent more time in the target vs. non-target quadrants of the maze, suggestive of recall of the platform location. Western blot data revealed a decrease in NF-κB p50 protein after training relative to controls, whereas NF-κB p65, Nrf2 and actin increased. Nrf2 levels were correlated with platform crosses in nearly all tested animals. These data demonstrate that training in a spatial memory task results in alterations in and associations with particular transcription factors in the hippocampus, including upregulation of NF-κB p65 and Nrf2. Training-induced increases in actin protein levels caution against its use as a loading control in immunoblot studies examining activity-dependent plasticity, learning, and memory. PMID:26635523

Life-history plasticity and sustainable exploitation: a theory of growth compensation applied to walleye management.

PubMed

Lester, Nigel P; Shuter, Brian J; Venturelli, Paul; Nadeau, Daniel

2014-01-01

A simple population model was developed to evaluate the role of plastic and evolutionary life-history changes on sustainable exploitation rates. Plastic changes are embodied in density-dependent compensatory adjustments to somatic growth rate and larval/juvenile survival, which can compensate for the reductions in reproductive lifetime and mean population fecundity that accompany the higher adult mortality imposed by exploitation. Evolutionary changes are embodied in the selective pressures that higher adult mortality imposes on age at maturity, length at maturity, and reproductive investment. Analytical development, based on a biphasic growth model, led to simple equations that show explicitly how sustainable exploitation rates are bounded by each of these effects. We show that density-dependent growth combined with a fixed length at maturity and fixed reproductive investment can support exploitation-driven mortality that is 80% of the level supported by evolutionary changes in maturation and reproductive investment. Sustainable fishing mortality is proportional to natural mortality (M) times the degree of density-dependent growth, as modified by both the degree of density-dependent early survival and the minimum harvestable length. We applied this model to estimate sustainable exploitation rates for North American walleye populations (Sander vitreus). Our analysis of demographic data from walleye populations spread across a broad latitudinal range indicates that density-dependent variation in growth rate can vary by a factor of 2. Implications of this growth response are generally consistent with empirical studies suggesting that optimal fishing mortality is approximately 0.75M for teleosts. This approach can be adapted to the management of other species, particularly when significant exploitation is imposed on many, widely distributed, but geographically isolated populations.

Morphofunctional Experience-Dependent Plasticity in the Honeybee Brain

ERIC Educational Resources Information Center

Andrione, Mara; Timberlake, Benjamin F.; Vallortigara, Giorgio; Antolini, Renzo; Haase, Albrecht

2017-01-01

Repeated or prolonged exposure to an odorant without any positive or negative reinforcement produces experience-dependent plasticity, which results in habituation and latent inhibition. In the honeybee ("Apis mellifera"), it has been demonstrated that, even if the absolute neural representation of an odor in the primary olfactory center,…

NR2B-dependent plasticity of adult-born granule cells is necessary for context discrimination.

PubMed

Kheirbek, Mazen A; Tannenholz, Lindsay; Hen, René

2012-06-20

Adult-generated granule cells (GCs) in the dentate gyrus (DG) exhibit a period of heightened plasticity 4-6 weeks postmitosis. However, the functional contribution of this critical window of plasticity to hippocampal neurogenesis and behavior remains unknown. Here, we show that deletion of NR2B-containing NMDA receptors from adult-born GCs impairs a neurogenesis-dependent form of LTP in the DG and reduces dendritic complexity of adult-born GCs, but does not impact their survival. Mice in which the NR2B-containing NMDA receptor was deleted from adult-born GCs did not differ from controls in baseline anxiety-like behavior or discrimination of very different contexts, but were impaired in discrimination of highly similar contexts. These results indicate that NR2B-dependent plasticity of adult-born GCs is necessary for fine contextual discrimination and is consistent with their proposed role in pattern separation.

Levitation of YBa2Cu3O(7-x) superconductor in a variable magnetic field

NASA Technical Reports Server (NTRS)

Terentiev, Alexander N.; Kuznetsov, Anatoliy A.

1992-01-01

The influence of both a linear alternating and rotational magnetic field component on the levitation behavior of a YBa2Cu3O(7-x) superconductor was examined. The transition from a plastic regime of levitation to an elastic one, induced by an alternating field component, was observed. An elastic regime in contrast to a plastic one is characterized by the unique position of stable levitation and field frequency dependence of relaxation time to this position. It was concluded that the vibrations of a magnet levitated above the superconductor can induce a transition from a plastic regime of levitation to an elastic one. It was found that a rotational magnetic field component induced rotations of a levitated superconductor. Rotational frictional motion of flux lines is likely to be an origin of torque developed. A prototype of a motor based on a levitated superconductor rotor is proposed.

The maternal environment interacts with genetic variation in regulating seed dormancy in Swedish Arabidopsis thaliana

PubMed Central

Nordborg, Magnus

2017-01-01

Seed dormancy is a complex adaptive trait that controls the timing of seed germination, one of the major fitness components in many plant species. Despite being highly heritable, seed dormancy is extremely plastic and influenced by a wide range of environmental cues. Here, using a set of 92 Arabidopsis thaliana lines from Sweden, we investigate the effect of seed maturation temperature on dormancy variation at the population level. The response to temperature differs dramatically between lines, demonstrating that genotype and the maternal environment interact in controlling the trait. By performing a genome-wide association study (GWAS), we identified several candidate genes that could presumably account for this plasticity, two of which are involved in the photoinduction of germination. Altogether, our results provide insight into both the molecular mechanisms and the evolution of dormancy plasticity, and can serve to improve our understanding of environmentally dependent life-history transitions. PMID:29281703

Nonlinear dielectric spectroscopy in a fragile plastic crystal

NASA Astrophysics Data System (ADS)

Michl, M.; Bauer, Th.; Lunkenheimer, P.; Loidl, A.

2016-03-01

In this work we provide a thorough examination of the nonlinear dielectric properties of a succinonitrile-glutaronitrile mixture, representing one of the rare examples of a plastic crystal with fragile glassy dynamics. The detected alteration of the complex dielectric permittivity under high fields can be explained considering the heterogeneous nature of glassy dynamics and a field-induced variation of entropy. While the first mechanism was also found in structural glass formers, the latter effect seems to be more pronounced in plastic crystals. Moreover, the third harmonic component of the dielectric susceptibility is reported, revealing a hump-like spectral shape as predicted, e.g., within a model considering cooperative molecular dynamics. If assuming the validity of this model, one can deduce the temperature dependence of the number of correlated molecules Ncorr from these data. In accord with the fragile nature of the glass transition in this plastic crystal, we obtain a relatively strong temperature dependence of Ncorr, in contrast to the much weaker temperature dependence in plastic-crystalline cyclo-octanol, whose glass transition is of strong nature.

Novel Experience Induces Persistent Sleep-Dependent Plasticity in the Cortex but not in the Hippocampus

PubMed Central

Ribeiro, Sidarta; Shi, Xinwu; Engelhard, Matthew; Zhou, Yi; Zhang, Hao; Gervasoni, Damien; Lin, Shi-Chieh; Wada, Kazuhiro; Lemos, Nelson A.M.

2007-01-01

Episodic and spatial memories engage the hippocampus during acquisition but migrate to the cerebral cortex over time. We have recently proposed that the interplay between slow-wave (SWS) and rapid eye movement (REM) sleep propagates recent synaptic changes from the hippocampus to the cortex. To test this theory, we jointly assessed extracellular neuronal activity, local field potentials (LFP), and expression levels of plasticity-related immediate-early genes (IEG) arc and zif-268 in rats exposed to novel spatio-tactile experience. Post-experience firing rate increases were strongest in SWS and lasted much longer in the cortex (hours) than in the hippocampus (minutes). During REM sleep, firing rates showed strong temporal dependence across brain areas: cortical activation during experience predicted hippocampal activity in the first post-experience hour, while hippocampal activation during experience predicted cortical activity in the third post-experience hour. Four hours after experience, IEG expression was specifically upregulated during REM sleep in the cortex, but not in the hippocampus. Arc gene expression in the cortex was proportional to LFP amplitude in the spindle-range (10–14 Hz) but not to firing rates, as expected from signals more related to dendritic input than to somatic output. The results indicate that hippocampo-cortical activation during waking is followed by multiple waves of cortical plasticity as full sleep cycles recur. The absence of equivalent changes in the hippocampus may explain its mnemonic disengagement over time. PMID:18982118

Evaluation of Endocrine Disrupting Compounds Migration in Household Food Containers under Domestic Use Conditions.

PubMed

Sáiz, Jorge; Gómara, Belén

2017-08-09

Plasticizers and plastic monomers are commonly used in packaging. Most of them act as endocrine disrupters and are susceptible to migrate from the packaging to the food. We evaluated the migration of endocrine disrupting compounds from three different household food containers to four food simulants under different domestic treatments and for different periods of time, with the aim of reproducing real domestic conditions. The results showed that the migration to the simulants increased with the storage time, up to more than 50 times in certain cases. The heating power seemed to increase the migration processes (up to more than 30 times), and reusing containers produced an increase or decrease of the concentrations depending on the container type and the simulant. The concentrations found were lower than other concentrations reported (always less than 4000 pg/mL, down to less than 20 pg/mL), which might be a consequence of the domestic conditions used.

Use-Dependent Dendritic Regrowth Is Limited after Unilateral Controlled Cortical Impact to the Forelimb Sensorimotor Cortex

PubMed Central

Jones, Theresa A.; Liput, Daniel J.; Maresh, Erin L.; Donlan, Nicole; Parikh, Toral J.; Marlowe, Dana

2012-01-01

Abstract Compensatory neural plasticity occurs in both hemispheres following unilateral cortical damage incurred by seizures, stroke, and focal lesions. Plasticity is thought to play a role in recovery of function, and is important for the utility of rehabilitation strategies. Such effects have not been well described in models of traumatic brain injury (TBI). We examined changes in immunoreactivity for neural structural and plasticity-relevant proteins in the area surrounding a controlled cortical impact (CCI) to the forelimb sensorimotor cortex (FL-SMC), and in the contralateral homotopic cortex over time (3–28 days). CCI resulted in considerable motor deficits in the forelimb contralateral to injury, and increased reliance on the ipsilateral forelimb. The density of dendritic processes, visualized with immunostaining for microtubule-associated protein-2 (MAP-2), were bilaterally decreased at all time points. Synaptophysin (SYN) immunoreactivity increased transiently in the injured hemisphere, but this reflected an atypical labeling pattern, and it was unchanged in the contralateral hemisphere compared to uninjured controls. The lack of compensatory neuronal structural plasticity in the contralateral homotopic cortex, despite behavioral asymmetries, is in contrast to previous findings in stroke models. In the cortex surrounding the injury (but not the contralateral cortex), decreases in dendrites were accompanied by neurodegeneration, as indicated by Fluoro-Jade B (FJB) staining, and increased expression of the growth-inhibitory protein Nogo-A. These studies indicate that, following unilateral CCI, the cortex undergoes neuronal structural degradation in both hemispheres out to 28 days post-injury, which may be indicative of compromised compensatory plasticity. This is likely to be an important consideration in designing therapeutic strategies aimed at enhancing plasticity following TBI. PMID:22352953

Transport of persistent organic pollutants by microplastics in estuarine conditions

NASA Astrophysics Data System (ADS)

Bakir, Adil; Rowland, Steven J.; Thompson, Richard C.

2014-03-01

Microplastics represent an increasing source of anthropogenic contamination in aquatic environments, where they may also act as scavengers and transporters of persistent organic pollutants. As estuaries are amongst the most productive aquatic systems, it is important to understand sorption behaviour and transport of persistent organic pollutants (POPs) by microplastics along estuarine gradients. The effects of salinity sorption equilibrium kinetics on the distribution coefficients (Kd) of phenanthrene (Phe) and 4,4‧-DDT, onto polyvinyl chloride (PVC) and onto polyethylene (PE) were therefore investigated. A salinity gradient representing freshwater, estuarine and marine conditions, with salinities corresponding to 0 (MilliQ water, 690 μS/cm), 8.8, 17.5, 26.3 and 35 was used. Salinity had no significant effect on the time required to reach equilibrium onto PVC or PE and neither did it affect desorption rates of contaminants from plastics. Although salinity had no effect on sorption capacity of Phe onto plastics, a slight decrease in sorption capacity was observed for DDT with salinity. Salinity had little effect on sorption behaviour and POP/plastic combination was shown to be a more important factor. Transport of Phe and DDT from riverine to brackish and marine waters by plastic is therefore likely to be much more dependent on the aqueous POP concentration than on salinity. The physical characteristics of the polymer and local environmental conditions (e.g. plastic density, particle residence time in estuaries) will affect the physical transport of contaminated plastics. A transport model of POPs by microplastics under estuarine conditions is proposed. Transport of Phe and DDT by PVC and PE from fresh and brackish water toward fully marine conditions was the most likely net direction for contaminant transport and followed the order: Phe-PE >> DDT-PVC = DDT-PE >> Phe-PVC.

Use-dependent dendritic regrowth is limited after unilateral controlled cortical impact to the forelimb sensorimotor cortex.

PubMed

Jones, Theresa A; Liput, Daniel J; Maresh, Erin L; Donlan, Nicole; Parikh, Toral J; Marlowe, Dana; Kozlowski, Dorothy A

2012-05-01

Compensatory neural plasticity occurs in both hemispheres following unilateral cortical damage incurred by seizures, stroke, and focal lesions. Plasticity is thought to play a role in recovery of function, and is important for the utility of rehabilitation strategies. Such effects have not been well described in models of traumatic brain injury (TBI). We examined changes in immunoreactivity for neural structural and plasticity-relevant proteins in the area surrounding a controlled cortical impact (CCI) to the forelimb sensorimotor cortex (FL-SMC), and in the contralateral homotopic cortex over time (3-28 days). CCI resulted in considerable motor deficits in the forelimb contralateral to injury, and increased reliance on the ipsilateral forelimb. The density of dendritic processes, visualized with immunostaining for microtubule-associated protein-2 (MAP-2), were bilaterally decreased at all time points. Synaptophysin (SYN) immunoreactivity increased transiently in the injured hemisphere, but this reflected an atypical labeling pattern, and it was unchanged in the contralateral hemisphere compared to uninjured controls. The lack of compensatory neuronal structural plasticity in the contralateral homotopic cortex, despite behavioral asymmetries, is in contrast to previous findings in stroke models. In the cortex surrounding the injury (but not the contralateral cortex), decreases in dendrites were accompanied by neurodegeneration, as indicated by Fluoro-Jade B (FJB) staining, and increased expression of the growth-inhibitory protein Nogo-A. These studies indicate that, following unilateral CCI, the cortex undergoes neuronal structural degradation in both hemispheres out to 28 days post-injury, which may be indicative of compromised compensatory plasticity. This is likely to be an important consideration in designing therapeutic strategies aimed at enhancing plasticity following TBI.

Rheological Characterization of Unusual DWPF Slurry Samples (U)

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

Koopman, D. C.

2005-09-01

A study was undertaken to identify and clarify examples of unusual rheological behavior in Defense Waste Processing Facility (DWPF) simulant slurry samples. Identification was accomplished by reviewing sludge, Sludge Receipt and Adjustment Tank (SRAT) product, and Slurry Mix Evaporator (SME) product simulant rheological results from the prior year. Clarification of unusual rheological behavior was achieved by developing and implementing new measurement techniques. Development of these new methods is covered in a separate report, WSRC-TR-2004-00334. This report includes a review of recent literature on unusual rheological behavior, followed by a summary of the rheological measurement results obtained on a set ofmore » unusual simulant samples. Shifts in rheological behavior of slurries as the wt. % total solids changed have been observed in numerous systems. The main finding of the experimental work was that the various unusual DWPF simulant slurry samples exhibit some degree of time dependent behavior. When a given shear rate is applied to a sample, the apparent viscosity of the slurry changes with time rather than remaining constant. These unusual simulant samples are more rheologically complex than Newtonian liquids or more simple slurries, neither of which shows significant time dependence. The study concludes that the unusual rheological behavior that has been observed is being caused by time dependent rheological properties in the slurries being measured. Most of the changes are due to the effect of time under shear, but SB3 SME products were also changing properties while stored in sample bottles. The most likely source of this shear-related time dependence for sludge is in the simulant preparation. More than a single source of time dependence was inferred for the simulant SME product slurries based on the range of phenomena observed. Rheological property changes were observed on the time-scale of a single measurement (minutes) as well as on a time scale of hours to weeks. The unusual shape of the slurry flow curves was not an artifact of the rheometric measurement. Adjusting the user-specified parameters in the rheometer measurement jobs can alter the shape of the flow curve of these time dependent samples, but this was not causing the unusual behavior. Variations in the measurement parameters caused the time dependence of a given slurry to manifest at different rates. The premise of the controlled shear rate flow curve measurement is that the dynamic response of the sample to a change in shear rate is nearly instantaneous. When this is the case, the data can be fitted to a time independent rheological equation, such as the Bingham plastic model. In those cases where this does not happen, interpretation of the data is difficult. Fitting time dependent data to time independent rheological equations, such as the Bingham plastic model, is also not appropriate.« less

Fragile X Mental Retardation Protein and Dendritic Local Translation of the Alpha Subunit of the Calcium/Calmodulin-Dependent Kinase II Messenger RNA Are Required for the Structural Plasticity Underlying Olfactory Learning.

PubMed

Daroles, Laura; Gribaudo, Simona; Doulazmi, Mohamed; Scotto-Lomassese, Sophie; Dubacq, Caroline; Mandairon, Nathalie; Greer, Charles August; Didier, Anne; Trembleau, Alain; Caillé, Isabelle

2016-07-15

In the adult brain, structural plasticity allowing gain or loss of synapses remodels circuits to support learning. In fragile X syndrome, the absence of fragile X mental retardation protein (FMRP) leads to defects in plasticity and learning deficits. FMRP is a master regulator of local translation but its implication in learning-induced structural plasticity is unknown. Using an olfactory learning task requiring adult-born olfactory bulb neurons and cell-specific ablation of FMRP, we investigated whether learning shapes adult-born neuron morphology during their synaptic integration and its dependence on FMRP. We used alpha subunit of the calcium/calmodulin-dependent kinase II (αCaMKII) mutant mice with altered dendritic localization of αCaMKII messenger RNA, as well as a reporter of αCaMKII local translation to investigate the role of this FMRP messenger RNA target in learning-dependent structural plasticity. Learning induces profound changes in dendritic architecture and spine morphology of adult-born neurons that are prevented by ablation of FMRP in adult-born neurons and rescued by an metabotropic glutamate receptor 5 antagonist. Moreover, dendritically translated αCaMKII is necessary for learning and associated structural modifications and learning triggers an FMRP-dependent increase of αCaMKII dendritic translation in adult-born neurons. Our results strongly suggest that FMRP mediates structural plasticity of olfactory bulb adult-born neurons to support olfactory learning through αCaMKII local translation. This reveals a new role for FMRP-regulated dendritic local translation in learning-induced structural plasticity. This might be of clinical relevance for the understanding of critical periods disruption in autism spectrum disorder patients, among which fragile X syndrome is the primary monogenic cause. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Development of a Pressure-Dependent Constitutive Model with Combined Multilinear Kinematic and Isotropic Hardening

NASA Technical Reports Server (NTRS)

Allen Phillip A.; Wilson, Christopher D.

2003-01-01

The development of a pressure-dependent constitutive model with combined multilinear kinematic and isotropic hardening is presented. The constitutive model is developed using the ABAQUS user material subroutine (UMAT). First the pressure-dependent plasticity model is derived. Following this, the combined bilinear and combined multilinear hardening equations are developed for von Mises plasticity theory. The hardening rule equations are then modified to include pressure dependency. The method for implementing the new constitutive model into ABAQUS is given.

The Current Status of Somatostatin-Interneurons in Inhibitory Control of Brain Function and Plasticity

PubMed Central

2016-01-01

The mammalian neocortex contains many distinct inhibitory neuronal populations to balance excitatory neurotransmission. A correct excitation/inhibition equilibrium is crucial for normal brain development, functioning, and controlling lifelong cortical plasticity. Knowledge about how the inhibitory network contributes to brain plasticity however remains incomplete. Somatostatin- (SST-) interneurons constitute a large neocortical subpopulation of interneurons, next to parvalbumin- (PV-) and vasoactive intestinal peptide- (VIP-) interneurons. Unlike the extensively studied PV-interneurons, acknowledged as key components in guiding ocular dominance plasticity, the contribution of SST-interneurons is less understood. Nevertheless, SST-interneurons are ideally situated within cortical networks to integrate unimodal or cross-modal sensory information processing and therefore likely to be important mediators of experience-dependent plasticity. The lack of knowledge on SST-interneurons partially relates to the wide variety of distinct subpopulations present in the sensory neocortex. This review informs on those SST-subpopulations hitherto described based on anatomical, molecular, or electrophysiological characteristics and whose functional roles can be attributed based on specific cortical wiring patterns. A possible role for these subpopulations in experience-dependent plasticity will be discussed, emphasizing on learning-induced plasticity and on unimodal and cross-modal plasticity upon sensory loss. This knowledge will ultimately contribute to guide brain plasticity into well-defined directions to restore sensory function and promote lifelong learning. PMID:27403348

Context-dependent plasticity in the subcortical encoding of linguistic pitch patterns

PubMed Central

Lau, Joseph C. Y.; Wong, Patrick C. M.

2016-01-01

We examined the mechanics of online experience-dependent auditory plasticity by assessing the influence of prior context on the frequency-following responses (FFRs), which reflect phase-locked responses from neural ensembles within the subcortical auditory system. FFRs were elicited to a Cantonese falling lexical pitch pattern from 24 native speakers of Cantonese in a variable context, wherein the falling pitch pattern randomly occurred in the context of two other linguistic pitch patterns; in a patterned context, wherein, the falling pitch pattern was presented in a predictable sequence along with two other pitch patterns, and in a repetitive context, wherein the falling pitch pattern was presented with 100% probability. We found that neural tracking of the stimulus pitch contour was most faithful and accurate when listening context was patterned and least faithful when the listening context was variable. The patterned context elicited more robust pitch tracking relative to the repetitive context, suggesting that context-dependent plasticity is most robust when the context is predictable but not repetitive. Our study demonstrates a robust influence of prior listening context that works to enhance online neural encoding of linguistic pitch patterns. We interpret these results as indicative of an interplay between contextual processes that are responsive to predictability as well as novelty in the presentation context. NEW & NOTEWORTHY Human auditory perception in dynamic listening environments requires fine-tuning of sensory signal based on behaviorally relevant regularities in listening context, i.e., online experience-dependent plasticity. Our finding suggests what partly underlie online experience-dependent plasticity are interplaying contextual processes in the subcortical auditory system that are responsive to predictability as well as novelty in listening context. These findings add to the literature that looks to establish the neurophysiological bases of auditory system plasticity, a central issue in auditory neuroscience. PMID:27832606

Context-dependent plasticity in the subcortical encoding of linguistic pitch patterns.

PubMed

Lau, Joseph C Y; Wong, Patrick C M; Chandrasekaran, Bharath

2017-02-01

We examined the mechanics of online experience-dependent auditory plasticity by assessing the influence of prior context on the frequency-following responses (FFRs), which reflect phase-locked responses from neural ensembles within the subcortical auditory system. FFRs were elicited to a Cantonese falling lexical pitch pattern from 24 native speakers of Cantonese in a variable context, wherein the falling pitch pattern randomly occurred in the context of two other linguistic pitch patterns; in a patterned context, wherein, the falling pitch pattern was presented in a predictable sequence along with two other pitch patterns, and in a repetitive context, wherein the falling pitch pattern was presented with 100% probability. We found that neural tracking of the stimulus pitch contour was most faithful and accurate when listening context was patterned and least faithful when the listening context was variable. The patterned context elicited more robust pitch tracking relative to the repetitive context, suggesting that context-dependent plasticity is most robust when the context is predictable but not repetitive. Our study demonstrates a robust influence of prior listening context that works to enhance online neural encoding of linguistic pitch patterns. We interpret these results as indicative of an interplay between contextual processes that are responsive to predictability as well as novelty in the presentation context. Human auditory perception in dynamic listening environments requires fine-tuning of sensory signal based on behaviorally relevant regularities in listening context, i.e., online experience-dependent plasticity. Our finding suggests what partly underlie online experience-dependent plasticity are interplaying contextual processes in the subcortical auditory system that are responsive to predictability as well as novelty in listening context. These findings add to the literature that looks to establish the neurophysiological bases of auditory system plasticity, a central issue in auditory neuroscience. Copyright © 2017 the American Physiological Society.

Mirror trends of plasticity and stability indicators in primate prefrontal cortex.

PubMed

García-Cabezas, Miguel Á; Joyce, Mary Kate P; John, Yohan J; Zikopoulos, Basilis; Barbas, Helen

2017-10-01

Research on plasticity markers in the cerebral cortex has largely focused on their timing of expression and role in shaping circuits during critical and normal periods. By contrast, little attention has been focused on the spatial dimension of plasticity-stability across cortical areas. The rationale for this analysis is based on the systematic variation in cortical structure that parallels functional specialization and raises the possibility of varying levels of plasticity. Here, we investigated in adult rhesus monkeys the expression of markers related to synaptic plasticity or stability in prefrontal limbic and eulaminate areas that vary in laminar structure. Our findings revealed that limbic areas are impoverished in three markers of stability: intracortical myelin, the lectin Wisteria floribunda agglutinin, which labels perineuronal nets, and parvalbumin, which is expressed in a class of strong inhibitory neurons. By contrast, prefrontal limbic areas were enriched in the enzyme calcium/calmodulin-dependent protein kinase II (CaMKII), known to enhance plasticity. Eulaminate areas have more elaborate laminar architecture than limbic areas and showed the opposite trend: they were enriched in markers of stability and had lower expression of the plasticity-related marker CaMKII. The expression of glial fibrillary acidic protein (GFAP), a marker of activated astrocytes, was also higher in limbic areas, suggesting that cellular stress correlates with the rate of circuit reshaping. Elevated markers of plasticity may endow limbic areas with flexibility necessary for learning and memory within an affective context, but may also render them vulnerable to abnormal structural changes, as seen in neurologic and psychiatric diseases. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Drosophila Fragile X Mental Retardation Protein Developmentally Regulates Activity-Dependent Axon Pruning

PubMed Central

Tessier, Charles R.; Broadie, Kendal

2014-01-01

Summary Fragile X Syndrome (FraX) is a broad-spectrum neurological disorder with symptoms ranging from hyperexcitability to mental retardation and autism. Loss of the fragile X mental retardation 1 (fmr1) gene product, the mRNA-binding translational regulator FMRP, causes structural over-elaboration of dendritic and axonal processes as well as functional alterations in synaptic plasticity at maturity. It is unclear, however, whether FraX is primarily a disease of development, a disease of plasticity or both; a distinction vital for engineering intervention strategies. To address this critical issue, we have used the Drosophila FraX model to investigate the developmental roles of Drosophila FMRP (dFMRP). dFMRP expression and regulation of chickadee/profilin coincides with a transient window of late brain development. During this time, dFMRP is positively regulated by sensory input activity, and required to limit axon growth and for efficient activity-dependent pruning of axon branches in the Mushroom Body learning/memory center. These results demonstrate that dFMRP has a primary role in activity-dependent neural circuit refinement in late brain development. PMID:18321984

Numerical implementation of non-local polycrystal plasticity using fast Fourier transforms

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

Lebensohn, Ricardo A.; Needleman, Alan

Here, we present the numerical implementation of a non-local polycrystal plasticity theory using the FFT-based formulation of Suquet and co-workers. Gurtin (2002) non-local formulation, with geometry changes neglected, has been incorporated in the EVP-FFT algorithm of Lebensohn et al. (2012). Numerical procedures for the accurate estimation of higher order derivatives of micromechanical fields, required for feedback into single crystal constitutive relations, are identified and applied. A simple case of a periodic laminate made of two fcc crystals with different plastic properties is first used to assess the soundness and numerical stability of the proposed algorithm and to study the influencemore » of different model parameters on the predictions of the non-local model. Different behaviors at grain boundaries are explored, and the one consistent with the micro-clamped condition gives the most pronounced size effect. The formulation is applied next to 3-D fcc polycrystals, illustrating the possibilities offered by the proposed numerical scheme to analyze the mechanical response of polycrystalline aggregates in three dimensions accounting for size dependence arising from plastic strain gradients with reasonable computing times.« less

Numerical implementation of non-local polycrystal plasticity using fast Fourier transforms

DOE PAGES

Lebensohn, Ricardo A.; Needleman, Alan

2016-03-28

Here, we present the numerical implementation of a non-local polycrystal plasticity theory using the FFT-based formulation of Suquet and co-workers. Gurtin (2002) non-local formulation, with geometry changes neglected, has been incorporated in the EVP-FFT algorithm of Lebensohn et al. (2012). Numerical procedures for the accurate estimation of higher order derivatives of micromechanical fields, required for feedback into single crystal constitutive relations, are identified and applied. A simple case of a periodic laminate made of two fcc crystals with different plastic properties is first used to assess the soundness and numerical stability of the proposed algorithm and to study the influencemore » of different model parameters on the predictions of the non-local model. Different behaviors at grain boundaries are explored, and the one consistent with the micro-clamped condition gives the most pronounced size effect. The formulation is applied next to 3-D fcc polycrystals, illustrating the possibilities offered by the proposed numerical scheme to analyze the mechanical response of polycrystalline aggregates in three dimensions accounting for size dependence arising from plastic strain gradients with reasonable computing times.« less

Neural plasticity and its initiating conditions in tinnitus.

PubMed

Roberts, L E

2018-03-01

Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

Functional outcomes following lesions in visual cortex: Implications for plasticity of high-level vision.

PubMed

Liu, Tina T; Behrmann, Marlene

2017-10-01

Understanding the nature and extent of neural plasticity in humans remains a key challenge for neuroscience. Importantly, however, a precise characterization of plasticity and its underlying mechanism has the potential to enable new approaches for enhancing reorganization of cortical function. Investigations of the impairment and subsequent recovery of cognitive and perceptual functions following early-onset cortical lesions in humans provide a unique opportunity to elucidate how the brain changes, adapts, and reorganizes. Specifically, here, we focus on restitution of visual function, and we review the findings on plasticity and re-organization of the ventral occipital temporal cortex (VOTC) in published reports of 46 patients with a lesion to or resection of the visual cortex early in life. Findings reveal that a lesion to the VOTC results in a deficit that affects the visual recognition of more than one category of stimuli (faces, objects and words). In addition, the majority of pediatric patients show limited recovery over time, especially those in whom deficits in low-level vision also persist. Last, given that neither the equipotentiality nor the modularity view on plasticity was clearly supported, we suggest some intermediate possibilities in which some plasticity may be evident but that this might depend on the area that was affected, its maturational trajectory as well as its structural and functional connectivity constraints. Finally, we offer suggestions for future research that can elucidate plasticity further. Copyright © 2017 Elsevier Ltd. All rights reserved.

Contrasting roles for parvalbumin-expressing inhibitory neurons in two forms of adult visual cortical plasticity

PubMed Central

Kaplan, Eitan S; Cooke, Sam F; Komorowski, Robert W; Chubykin, Alexander A; Thomazeau, Aurore; Khibnik, Lena A; Gavornik, Jeffrey P; Bear, Mark F

2016-01-01

The roles played by cortical inhibitory neurons in experience-dependent plasticity are not well understood. Here we evaluate the participation of parvalbumin-expressing (PV+) GABAergic neurons in two forms of experience-dependent modification of primary visual cortex (V1) in adult mice: ocular dominance (OD) plasticity resulting from monocular deprivation and stimulus-selective response potentiation (SRP) resulting from enriched visual experience. These two forms of plasticity are triggered by different events but lead to a similar increase in visual cortical response. Both also require the NMDA class of glutamate receptor (NMDAR). However, we find that PV+ inhibitory neurons in V1 play a critical role in the expression of SRP and its behavioral correlate of familiarity recognition, but not in the expression of OD plasticity. Furthermore, NMDARs expressed within PV+ cells, reversibly inhibited by the psychotomimetic drug ketamine, play a critical role in SRP, but not in the induction or expression of adult OD plasticity. DOI: http://dx.doi.org/10.7554/eLife.11450.001 PMID:26943618

The limitations of staggered grid finite differences in plasticity problems

NASA Astrophysics Data System (ADS)

Pranger, Casper; Herrendörfer, Robert; Le Pourhiet, Laetitia

2017-04-01

Most crustal-scale applications operate at grid sizes much larger than those at which plasticity occurs in nature. As a consequence, plastic shear bands often localize to the scale of one grid cell, and numerical ploys — like introducing an artificial length scale — are needed to counter this. If for whatever reasons (good or bad) this is not done, we find that problems may arise due to the fact that in the staggered grid finite difference discretization, unknowns like components of the stress tensor and velocity vector are located in physically different positions. This incurs frequent interpolation, reducing the accuracy of the discretization. For purely stress-dependent plasticity problems the adverse effects might be contained because the magnitude of the stress discontinuity across a plastic shear band is limited. However, we find that when rate-dependence of friction is added in the mix, things become ugly really fast and the already hard-to-solve and highly nonlinear problem of plasticity incurs an extra penalty.

Indentation-derived elastic modulus of multilayer thin films: Effect of unloading induced plasticity

DOE PAGES

Jamison, Ryan Dale; Shen, Yu -Lin

2015-08-13

Nanoindentation is useful for evaluating the mechanical properties, such as elastic modulus, of multilayer thin film materials. A fundamental assumption in the derivation of the elastic modulus from nanoindentation is that the unloading process is purely elastic. In this work, the validity of elastic assumption as it applies to multilayer thin films is studied using the finite element method. The elastic modulus and hardness from the model system are compared to experimental results to show validity of the model. Plastic strain is shown to increase in the multilayer system during the unloading process. Additionally, the indentation-derived modulus of a monolayermore » material shows no dependence on unloading plasticity while the modulus of the multilayer system is dependent on unloading-induced plasticity. Lastly, the cyclic behavior of the multilayer thin film is studied in relation to the influence of unloading-induced plasticity. Furthermore, it is found that several cycles are required to minimize unloading-induced plasticity.« less

Integrative Analysis of Disease Signatures Shows Inflammation Disrupts Juvenile Experience-Dependent Cortical Plasticity

PubMed Central

Smith, Milo R.; Burman, Poromendro

2016-01-01

Throughout childhood and adolescence, periods of heightened neuroplasticity are critical for the development of healthy brain function and behavior. Given the high prevalence of neurodevelopmental disorders, such as autism, identifying disruptors of developmental plasticity represents an essential step for developing strategies for prevention and intervention. Applying a novel computational approach that systematically assessed connections between 436 transcriptional signatures of disease and multiple signatures of neuroplasticity, we identified inflammation as a common pathological process central to a diverse set of diseases predicted to dysregulate plasticity signatures. We tested the hypothesis that inflammation disrupts developmental cortical plasticity in vivo using the mouse ocular dominance model of experience-dependent plasticity in primary visual cortex. We found that the administration of systemic lipopolysaccharide suppressed plasticity during juvenile critical period with accompanying transcriptional changes in a particular set of molecular regulators within primary visual cortex. These findings suggest that inflammation may have unrecognized adverse consequences on the postnatal developmental trajectory and indicate that treating inflammation may reduce the burden of neurodevelopmental disorders. PMID:28101530

Neuron Morphology Influences Axon Initial Segment Plasticity.

PubMed

Gulledge, Allan T; Bravo, Jaime J

2016-01-01

In most vertebrate neurons, action potentials are initiated in the axon initial segment (AIS), a specialized region of the axon containing a high density of voltage-gated sodium and potassium channels. It has recently been proposed that neurons use plasticity of AIS length and/or location to regulate their intrinsic excitability. Here we quantify the impact of neuron morphology on AIS plasticity using computational models of simplified and realistic somatodendritic morphologies. In small neurons (e.g., dentate granule neurons), excitability was highest when the AIS was of intermediate length and located adjacent to the soma. Conversely, neurons having larger dendritic trees (e.g., pyramidal neurons) were most excitable when the AIS was longer and/or located away from the soma. For any given somatodendritic morphology, increasing dendritic membrane capacitance and/or conductance favored a longer and more distally located AIS. Overall, changes to AIS length, with corresponding changes in total sodium conductance, were far more effective in regulating neuron excitability than were changes in AIS location, while dendritic capacitance had a larger impact on AIS performance than did dendritic conductance. The somatodendritic influence on AIS performance reflects modest soma-to-AIS voltage attenuation combined with neuron size-dependent changes in AIS input resistance, effective membrane time constant, and isolation from somatodendritic capacitance. We conclude that the impact of AIS plasticity on neuron excitability will depend largely on somatodendritic morphology, and that, in some neurons, a shorter or more distally located AIS may promote, rather than limit, action potential generation.

Atomistic Simulation of the Rate-Dependent Ductile-to-Brittle Failure Transition in Bicrystalline Metal Nanowires.

PubMed

Tao, Weiwei; Cao, Penghui; Park, Harold S

2018-02-14

The mechanical properties and plastic deformation mechanisms of metal nanowires have been studied intensely for many years. One of the important yet unresolved challenges in this field is to bridge the gap in properties and deformation mechanisms reported for slow strain rate experiments (∼10 -2 s -1 ), and high strain rate molecular dynamics (MD) simulations (∼10 8 s -1 ) such that a complete understanding of strain rate effects on mechanical deformation and plasticity can be obtained. In this work, we use long time scale atomistic modeling based on potential energy surface exploration to elucidate the atomistic mechanisms governing a strain-rate-dependent incipient plasticity and yielding transition for face centered cubic (FCC) copper and silver nanowires. The transition occurs for both metals with both pristine and rough surfaces for all computationally accessible diameters (<10 nm). We find that the yield transition is induced by a transition in the incipient plastic event from Shockley partials nucleated on primary slip systems at MD strain rates to the nucleation of planar defects on non-Schmid slip planes at experimental strain rates, where multiple twin boundaries and planar stacking faults appear in copper and silver, respectively. Finally, we demonstrate that, at experimental strain rates, a ductile-to-brittle transition in failure mode similar to previous experimental studies on bicrystalline silver nanowires is observed, which is driven by differences in dislocation activity and grain boundary mobility as compared to the high strain rate case.

Neuron Morphology Influences Axon Initial Segment Plasticity123

PubMed Central

2016-01-01

In most vertebrate neurons, action potentials are initiated in the axon initial segment (AIS), a specialized region of the axon containing a high density of voltage-gated sodium and potassium channels. It has recently been proposed that neurons use plasticity of AIS length and/or location to regulate their intrinsic excitability. Here we quantify the impact of neuron morphology on AIS plasticity using computational models of simplified and realistic somatodendritic morphologies. In small neurons (e.g., dentate granule neurons), excitability was highest when the AIS was of intermediate length and located adjacent to the soma. Conversely, neurons having larger dendritic trees (e.g., pyramidal neurons) were most excitable when the AIS was longer and/or located away from the soma. For any given somatodendritic morphology, increasing dendritic membrane capacitance and/or conductance favored a longer and more distally located AIS. Overall, changes to AIS length, with corresponding changes in total sodium conductance, were far more effective in regulating neuron excitability than were changes in AIS location, while dendritic capacitance had a larger impact on AIS performance than did dendritic conductance. The somatodendritic influence on AIS performance reflects modest soma-to-AIS voltage attenuation combined with neuron size-dependent changes in AIS input resistance, effective membrane time constant, and isolation from somatodendritic capacitance. We conclude that the impact of AIS plasticity on neuron excitability will depend largely on somatodendritic morphology, and that, in some neurons, a shorter or more distally located AIS may promote, rather than limit, action potential generation. PMID:27022619

Subcritical crack propagation due to chemical rock weakening: macroscale chemo-plasticity and chemo-elasticity modeling

NASA Astrophysics Data System (ADS)

Hueckel, T.; Hu, M.

2015-12-01

Crack propagation in a subcritically stressed rock subject to chemically aggressive environment is analyzed and numerically simulated. Chemically induced weakening is often encountered in hydraulic fracturing of low-permeability oil/gas reservoirs and heat reservoirs, during storage of CO2 and nuclear waste corroding canisters, and other circumstances when rock matrix acidizing is involved. Upon acidizing, mineral mass dissolution is substantially enhanced weakening the rock and causing crack propagation and eventually permeability changes in the medium. The crack process zone is modeled mathematically via a chemo-plastic coupling and chemo-elastic coupling model. In plasticity a two-way coupling is postulated between mineral dissolution and a yield limit of rock matrix. The rate of dissolution is described by a rate law, but the mineral mass removal per unit volume is also a function of a variable internal specific surface area, which is in turn affected by the micro-cracking (treated as a plastic strain). The behavior of the rock matrix is modeled as rigid-plastic adding a chemical softening capacity to Cam-Clay model. Adopting the Extended Johnson's approximation of processes around the crack tip, the evolution of the stress field and deformation as a function of the chemically enhanced rock damage is modeled in a simplified way. In addition, chemical reactive transport is made dependent on plastic strain representing micro-cracking. Depending on mechanical and chemical boundary conditions, the area of enhanced chemical softening is near or somewhat away from the crack tip.In elasticity, chemo-mechanical effect is postulated via a chemical volumetric shrinkage strain proportional to mass removal variable, conceived analogously to thermal expansion. Two versions are considered: of constant coefficient of shrinkage and a variable one, coupled to deviatoric strain. Airy Potential approach used for linear elasticity is extended considering an extra term, which is uncoupled or coupled to strain. The later case requires iterations with solution of reactive transport equation. A decrease of stress intensity factor with time of reaction is well reproduced.

Changes in the quality of medicines during storage under LED lighting and consideration of countermeasures.

PubMed

Yamashita, Shuuji; Iguchi, Kazuhiro; Noguchi, Yoshihiro; Sakai, Chihiro; Yokoyama, Satoshi; Ino, Yoko; Hayashi, Hideki; Teramachi, Hitomi; Sako, Magoichi; Sugiyama, Tadashi

2018-01-01

In recent years, the popularity of LED lighting has rapidly increased, owing to its many advantages, including economic benefits. We examined the change in the quality of drugs during storage under LED and fluorescent lighting and found that some medicines exhibited a different degree of color change depending on the light source. The purpose of this study was to investigate the effects of different plastic storage bags on the color change over time when various medicines were stored under LED and fluorescent lighting conditions. Photostability tests were conducted on several types of target drugs. Subsequently, subjective evaluation by ten evaluators and objective evaluation by image analysis software were carried out regarding color change. A similar change in color tone was observed after all types of illumination. Subjective evaluation by 10 evaluators revealed that "change in color tone" occurred in the order of bulb-color LED lighting < daylight-color LED lighting < fluorescent lighting, regardless of the type of plastic bags. A similar tendency was observed also in objective evaluation. In this study, it was considered that a brown light-shielding plastic bag was more effective than a normal plastic bag for the prevention of the color change of medicines stored under LED lighting. The above results suggested that the most appropriate combination of plastic bag and light source for medicine storage was a brown light-shielding plastic bag and bulb-color LED lighting.

Neural plasticity and behavior - sixty years of conceptual advances.

PubMed

Sweatt, J David

2016-10-01

This brief review summarizes 60 years of conceptual advances that have demonstrated a role for active changes in neuronal connectivity as a controller of behavior and behavioral change. Seminal studies in the first phase of the six-decade span of this review firmly established the cellular basis of behavior - a concept that we take for granted now, but which was an open question at the time. Hebbian plasticity, including long-term potentiation and long-term depression, was then discovered as being important for local circuit refinement in the context of memory formation and behavioral change and stabilization in the mammalian central nervous system. Direct demonstration of plasticity of neuronal circuit function in vivo, for example, hippocampal neurons forming place cell firing patterns, extended this concept. However, additional neurophysiologic and computational studies demonstrated that circuit development and stabilization additionally relies on non-Hebbian, homoeostatic, forms of plasticity, such as synaptic scaling and control of membrane intrinsic properties. Activity-dependent neurodevelopment was found to be associated with cell-wide adjustments in post-synaptic receptor density, and found to occur in conjunction with synaptic pruning. Pioneering cellular neurophysiologic studies demonstrated the critical roles of transmembrane signal transduction, NMDA receptor regulation, regulation of neural membrane biophysical properties, and back-propagating action potential in critical time-dependent coincidence detection in behavior-modifying circuits. Concerning the molecular mechanisms underlying these processes, regulation of gene transcription was found to serve as a bridge between experience and behavioral change, closing the 'nature versus nurture' divide. Both active DNA (de)methylation and regulation of chromatin structure have been validated as crucial regulators of gene transcription during learning. The discovery of protein synthesis dependence on the acquisition of behavioral change was an influential discovery in the neurochemistry of behavioral modification. Higher order cognitive functions such as decision making and spatial and language learning were also discovered to hinge on neural plasticity mechanisms. The role of disruption of these processes in intellectual disabilities, memory disorders, and drug addiction has recently been clarified based on modern genetic techniques, including in the human. The area of neural plasticity and behavior has seen tremendous advances over the last six decades, with many of those advances being specifically in the neurochemistry domain. This review provides an overview of the progress in the area of neuroplasticity and behavior over the life-span of the Journal of Neurochemistry. To organize the broad literature base, the review collates progress into fifteen broad categories identified as 'conceptual advances', as viewed by the author. The fifteen areas are delineated in the figure above. This article is part of the 60th Anniversary special issue. © 2016 International Society for Neurochemistry.

Plasticizers effect on native biodegradable package materials

NASA Astrophysics Data System (ADS)

Cozar, Onuc; Cioica, Nicolae; Coţa, Constantin; Nagy, Elena Mihaela; Fechete, Radu

2017-01-01

Changes in intensity of some IR and Raman bands suggest the plasticizing - antiplasticizing effects of water and glycerol contents and a small increase of amorphous/crystalline ratio, too. The nuclear magnetic relaxation data show that the amorphous/crystalline ratio depends on amylose/amylopectin mobility and also by the place of their polymer chain segments. Thus the distributions of spin-spin (T2) relaxation times and the shift toward higher values of some T2 characteristic peaks show that the increasing of water and glycerol content in the starch package materials lead to the more mobile amylose and amylopectin polymer chain segments and the prevalence of amorphous regions in the prepared native corn starch samples.

Cavitation in Amorphous Solids

NASA Astrophysics Data System (ADS)

Guan, Pengfei; Lu, Shuo; Spector, Michael J. B.; Valavala, Pavan K.; Falk, Michael L.

2013-05-01

Molecular dynamics simulations of cavitation in a Zr50Cu50 metallic glass exhibit a waiting time dependent cavitation rate. On short time scales nucleation rates and critical cavity sizes are commensurate with a classical theory of nucleation that accounts for both the plastic dissipation during cavitation and the cavity size dependence of the surface energy. All but one parameter, the Tolman length, can be extracted directly from independent calculations or estimated from physical principles. On longer time scales strain aging in the form of shear relaxations results in a systematic decrease of cavitation rate. The high cavitation rates that arise due to the suppression of the surface energy in small cavities provide a possible explanation for the quasibrittle fracture observed in metallic glasses.

Elastic-Plastic Behavior of U6Nb Under Ramp Wave Loading

NASA Astrophysics Data System (ADS)

Hayes, D. B.; Hall, C.; Hixson, R. S.

2005-07-01

Prior shock experiments on the alloy uranium-niobium-6 wt.% (U6Nb) were absent an elastic precursor when one was expected (A. K. Zurek, et. al., Journal de Physique IV, 10 (#9) p677-682). This was later explained as a consequence of shear stress relaxation from time-dependent twinning that prevented sufficient shear stress for plastic yielding. (D. B. Hayes, et. al., Shock Compression of Condensed Matter-2003, p1177, American Institute of Physics 2004) Pressure was ramped to 13 GPa in 150-ns on eight U6Nb specimens with thicknesses from 0.5 -- 1.1-mm and the back surface velocities were measured with laser interferometry. This pressure load produces a stress wave with sufficiently fast rise time so that, according to the prior work, twins do not have time to form. Four of the U6Nb specimens had been cold-rolled which increased the yield stress. Each velocity history was analyzed with a backward integration analysis to give the stress-strain response of the U6Nb. Comparison of these results with prior Hugoniot measurements shows that the U6Nb in the present experiments responds as an elastic-plastic material and the deduced yield strength of the baseline and of the cold-rolled material agree with static results.

Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.

PubMed

Bichler, Olivier; Querlioz, Damien; Thorpe, Simon J; Bourgoin, Jean-Philippe; Gamrat, Christian

2012-08-01

A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience. Copyright © 2012 Elsevier Ltd. All rights reserved.

Time of Day Does Not Modulate Improvements in Motor Performance following a Repetitive Ballistic Motor Training Task

PubMed Central

Sale, Martin V.; Ridding, Michael C.; Nordstrom, Michael A.

2013-01-01

Repetitive performance of a task can result in learning. The neural mechanisms underpinning such use-dependent plasticity are influenced by several neuromodulators. Variations in neuromodulator levels may contribute to the variability in performance outcomes following training. Circulating levels of the neuromodulator cortisol change throughout the day. High cortisol levels inhibit neuroplasticity induced with a transcranial magnetic stimulation (TMS) paradigm that has similarities to use-dependent plasticity. The present study investigated whether performance changes following a motor training task are modulated by time of day and/or changes in endogenous cortisol levels. Motor training involving 30 minutes of repeated maximum left thumb abduction was undertaken by twenty-two participants twice, once in the morning (8 AM) and once in the evening (8 PM) on separate occasions. Saliva was assayed for cortisol concentration. Motor performance, quantified by measuring maximum left thumb abduction acceleration, significantly increased by 28% following training. Neuroplastic changes in corticomotor excitability of abductor pollicis brevis, quantified with TMS, increased significantly by 23% following training. Training-related motor performance improvements and neuroplasticity were unaffected by time of day and salivary cortisol concentration. Although similar neural elements and processes contribute to motor learning, training-induced neuroplasticity, and TMS-induced neuroplasticity, our findings suggest that the influence of time of day and cortisol differs for these three interventions. PMID:23577271

The Homeostatic Interaction Between Anodal Transcranial Direct Current Stimulation and Motor Learning in Humans is Related to GABAA Activity.

PubMed

Amadi, Ugwechi; Allman, Claire; Johansen-Berg, Heidi; Stagg, Charlotte J

2015-01-01

The relative timing of plasticity-induction protocols is known to be crucial. For example, anodal transcranial direct current stimulation (tDCS), which increases cortical excitability and typically enhances plasticity, can impair performance if it is applied before a motor learning task. Such timing-dependent effects have been ascribed to homeostatic plasticity, but the specific synaptic site of this interaction remains unknown. We wished to investigate the synaptic substrate, and in particular the role of inhibitory signaling, underpinning the behavioral effects of anodal tDCS in homeostatic interactions between anodal tDCS and motor learning. We used transcranial magnetic stimulation (TMS) to investigate cortical excitability and inhibitory signaling following tDCS and motor learning. Each subject participated in four experimental sessions and data were analyzed using repeated measures ANOVAs and post-hoc t-tests as appropriate. As predicted, we found that anodal tDCS prior to the motor task decreased learning rates. This worsening of learning after tDCS was accompanied by a correlated increase in GABAA activity, as measured by TMS-assessed short interval intra-cortical inhibition (SICI). This provides the first direct demonstration in humans that inhibitory synapses are the likely site for the interaction between anodal tDCS and motor learning, and further, that homeostatic plasticity at GABAA synapses has behavioral relevance in humans. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Dynamic Brains and the Changing Rules of Neuroplasticity: Implications for Learning and Recovery

PubMed Central

Voss, Patrice; Thomas, Maryse E.; Cisneros-Franco, J. Miguel; de Villers-Sidani, Étienne

2017-01-01

A growing number of research publications have illustrated the remarkable ability of the brain to reorganize itself in response to various sensory experiences. A traditional view of this plastic nature of the brain is that it is predominantly limited to short epochs during early development. Although examples showing that neuroplasticity exists outside of these finite time-windows have existed for some time, it is only recently that we have started to develop a fuller understanding of the different regulators that modulate and underlie plasticity. In this article, we will provide several lines of evidence indicating that mechanisms of neuroplasticity are extremely variable across individuals and throughout the lifetime. This variability is attributable to several factors including inhibitory network function, neuromodulator systems, age, sex, brain disease, and psychological traits. We will also provide evidence of how neuroplasticity can be manipulated in both the healthy and diseased brain, including recent data in both young and aged rats demonstrating how plasticity within auditory cortex can be manipulated pharmacologically and by varying the quality of sensory inputs. We propose that a better understanding of the individual differences that exist within the various mechanisms that govern experience-dependent neuroplasticity will improve our ability to harness brain plasticity for the development of personalized translational strategies for learning and recovery following brain injury or disease. PMID:29085312

Reelin Supplementation Enhances Cognitive Ability, Synaptic Plasticity, and Dendritic Spine Density

ERIC Educational Resources Information Center

Rogers, Justin T.; Rusiana, Ian; Trotter, Justin; Zhao, Lisa; Donaldson, Erika; Pak, Daniel T.S.; Babus, Lenard W.; Peters, Melinda; Banko, Jessica L.; Chavis, Pascale; Rebeck, G. William; Hoe, Hyang-Sook; Weeber, Edwin J.

2011-01-01

Apolipoprotein receptors belong to an evolutionarily conserved surface receptor family that has intimate roles in the modulation of synaptic plasticity and is necessary for proper hippocampal-dependent memory formation. The known lipoprotein receptor ligand Reelin is important for normal synaptic plasticity, dendritic morphology, and cognitive…

Neuronal plasticity and neurotrophic factors in drug responses

PubMed Central

Castrén, Eero; Antila, Hanna

2017-01-01

Neurotrophic factors, particularly brain-derived neurotrophic factor (BDNF) and other members of the neurotrophin family, are central mediators of the activity-dependent plasticity through which environmental experiences, such as sensory information are translated into the structure and function of neuronal networks. Synthesis, release and action of BDNF is regulated by neuronal activity and BDNF in turn leads to trophic effects such as formation, stabilization and potentiation of synapses through its high-affinity TrkB receptors. Several clinically available drugs directly activate neurotrophins and neuronal plasticity. In particular, antidepressant drugs rapidly activate TrkB signaling and gradually increase BDNF expression, and the behavioral effects of antidepressants are mediated by and dependent on BDNF signaling through TrkB at least in rodents. These findings indicate that antidepressants, widely used drugs, effectively act as TrkB activators. They further imply that neuronal plasticity is a central mechanism in the action of antidepressant drugs. Indeed, it was recently discovered that antidepressants reactivate a state of plasticity in the adult cerebral cortex that closely resembles the enhanced plasticity normally observed during postnatal critical periods. This state of induced plasticity, known as iPlasticity, allows environmental stimuli to beneficially reorganize networks abnormally wired during early life. iPlasticity has been observed in cortical as well as subcortical networks and is induced by several pharmacological and non-pharmacological treatments. iPlasticity is a new pharmacological principle where drug treatment and rehabilitation cooperate: the drug acts permissively to enhance plasticity and rehabilitation provides activity to guide the appropriate wiring of the plastic network. Optimization of iPlastic drug treatment with novel means of rehabilitation may help improve the efficacy of available drug treatments and expand the use of currently existing drugs into new indications. PMID:28397840

Antimony leaching from polyethylene terephthalate (PET) plastic used for bottled drinking water.

PubMed

Westerhoff, Paul; Prapaipong, Panjai; Shock, Everett; Hillaireau, Alice

2008-02-01

Antimony is a regulated contaminant that poses both acute and chronic health effects in drinking water. Previous reports suggest that polyethylene terephthalate (PET) plastics used for water bottles in Europe and Canada leach antimony, but no studies on bottled water in the United States have previously been conducted. Nine commercially available bottled waters in the southwestern US (Arizona) were purchased and tested for antimony concentrations as well as for potential antimony release by the plastics that compose the bottles. The southwestern US was chosen for the study because of its high consumption of bottled water and elevated temperatures, which could increase antimony leaching from PET plastics. Antimony concentrations in the bottled waters ranged from 0.095 to 0.521 ppb, well below the US Environmental Protection Agency (USEPA) maximum contaminant level (MCL) of 6 ppb. The average concentration was 0.195+/-0.116 ppb at the beginning of the study and 0.226+/-0.160 ppb 3 months later, with no statistical differences; samples were stored at 22 degrees C. However, storage at higher temperatures had a significant effect on the time-dependent release of antimony. The rate of antimony (Sb) release could be fit by a power function model (Sb(t)=Sb 0 x[Time, h]k; k=8.7 x 10(-6)x[Temperature ( degrees C)](2.55); Sb 0 is the initial antimony concentration). For exposure temperatures of 60, 65, 70, 75, 80, and 85 degrees C, the exposure durations necessary to exceed the 6 ppb MCL are 176, 38, 12, 4.7, 2.3, and 1.3 days, respectively. Summertime temperatures inside of cars, garages, and enclosed storage areas can exceed 65 degrees C in Arizona, and thus could promote antimony leaching from PET bottled waters. Microwave digestion revealed that the PET plastic used by one brand contained 213+/-35 mgSb/kg plastic; leaching of all the antimony from this plastic into 0.5L of water in a bottle could result in an antimony concentration of 376 ppb. Clearly, only a small fraction of the antimony in PET plastic bottles is released into the water. Still, the use of alternative types of plastics that do not leach antimony should be considered, especially for climates where exposure to extreme conditions can promote antimony release from PET plastics.

Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation after Brain Damage

ERIC Educational Resources Information Center

Kleim, Jeffrey A.; Jones, Theresa A.

2008-01-01

Purpose: This paper reviews 10 principles of experience-dependent neural plasticity and considerations in applying them to the damaged brain. Method: Neuroscience research using a variety of models of learning, neurological disease, and trauma are reviewed from the perspective of basic neuroscientists but in a manner intended to be useful for the…

The Resistance to Deformation and Facture of Magnesium MA2-1 Under Shock-Wave Loading at 293 K and 823 K of the Temperature

NASA Astrophysics Data System (ADS)

Garkushin, Gennady; Kanel, Gennady; Razorenov, Sergey

2011-06-01

The spall strength and elastic-plastic response have been measured with the VISAR for MA2-1 (94.2% Mg, 0.4 % Mn, 4.4% Al, 1% Zn) alloy at temperatures from 293 K to 823 K. The decay of elastic precursor wave at 293 K is approximately in reverse proportionality with the cubic root from the distance that corresponds to decrease of plastic strain rate from 5 ×105 s-1 at 0.25 mm (213 MPa of the shear stress) down to 5 ×103 s-1 at 10 mm (63 MPa shear stress). An analysis of the rise times of plastic shock waves shows by order of magnitude faster plastic strain rates at corresponding shear stresses than that at the HEL. The decay of elastic precursor wave is weaker and the dependence of initial plastic strain rate on the shear stress at HEL is stronger than that was observed for aluminum. Unlike to aluminum, the magnesium alloy does not exhibit anomalous thermal hardening: the HEL values at 823 K are close to the values at room temperatures. The temperature increase from 293 K to 823 K has led to significant decrease of the spall strength.

Experimental investigation of Rayleigh Taylor instability in elastic-plastic materials

NASA Astrophysics Data System (ADS)

Haley, Aaron Alan; Banerjee, Arindam

2010-11-01

The interface of an elastic-plastic plate accelerated by a fluid of lower density is Rayleigh Taylor (RT) unstable, the growth being mitigated by the mechanical strength of the plate. The instability is observed when metal plates are accelerated by high explosives, in explosive welding, and in volcanic island formation due to the strength of the inner crust. In contrast to the classical case involving Newtonian fluids, RT instability in accelerated solids is not well understood. The difficulties for constructing a theory for the linear growth phase in solids is essentially due to the character of elastic-plastic constitutive properties which has a nonlinear dependence on the magnitude of the rate of deformation. Experimental investigation of the phenomena is difficult due to the exceedingly small time scales (in high energy density experiments) and large measurement uncertainties of material properties. We performed experiments on our Two-Wheel facility to study the linear stage of the incompressible RT instability in elastic-plastic materials (yogurt) whose properties were well characterized. Rotation of the wheels imparted a constant centrifugal acceleration on the material interface that was cut with a small sinusoidal ripple. The controlled initial conditions and precise acceleration amplitudes are levied to investigate transition from elastic to plastic deformation and allow accurate and detailed measurements of flow properties.

Transgenerational plasticity and climate change experiments: Where do we go from here?

PubMed

Donelson, Jennifer M; Salinas, Santiago; Munday, Philip L; Shama, Lisa N S

2018-01-01

Phenotypic plasticity, both within and across generations, is an important mechanism that organisms use to cope with rapid climate change. While an increasing number of studies show that plasticity across generations (transgenerational plasticity or TGP) may occur, we have limited understanding of key aspects of TGP, such as the environmental conditions that may promote it, its relationship to within-generation plasticity (WGP) and its role in evolutionary potential. In this review, we consider how the detection of TGP in climate change experiments is affected by the predictability of environmental variation, as well as the timing and magnitude of environmental change cues applied. We also discuss the need to design experiments that are able to distinguish TGP from selection and TGP from WGP in multigenerational experiments. We conclude by suggesting future research directions that build on the knowledge to date and admit the limitations that exist, which will depend on the way environmental change is simulated and the type of experimental design used. Such an approach will open up this burgeoning area of research to a wider variety of organisms and allow better predictive capacity of the role of TGP in the response of organisms to future climate change. © 2017 John Wiley & Sons Ltd.

A Miniaturized Extruder to Prototype Amorphous Solid Dispersions: Selection of Plasticizers for Hot Melt Extrusion.

PubMed

Lauer, Matthias E; Maurer, Reto; Paepe, Anne T De; Stillhart, Cordula; Jacob, Laurence; James, Rajesh; Kojima, Yuki; Rietmann, Rene; Kissling, Tom; van den Ende, Joost A; Schwarz, Sabine; Grassmann, Olaf; Page, Susanne

2018-05-19

Hot-melt extrusion is an option to fabricate amorphous solid dispersions and to enhance oral bioavailability of poorly soluble compounds. The selection of suitable polymer carriers and processing aids determines the dissolution, homogeneity and stability performance of this solid dosage form. A miniaturized extrusion device (MinEx) was developed and Hypromellose acetate succinate type L (HPMCAS-L) based extrudates containing the model drugs neurokinin-1 (NK1) and cholesterylester transfer protein (CETP) were manufactured, plasticizers were added and their impact on dissolution and solid-state properties were assessed. Similar mixtures were manufactured with a lab-scale extruder, for face to face comparison. The properties of MinEx extrudates widely translated to those manufactured with a lab-scale extruder. Plasticizers, Polyethyleneglycol 4000 (PEG4000) and Poloxamer 188, were homogenously distributed but decreased the storage stability of the extrudates. Stearic acid was found condensed in ultrathin nanoplatelets which did not impact the storage stability of the system. Depending on their distribution and physicochemical properties, plasticizers can modulate storage stability and dissolution performance of extrudates. MinEx is a valuable prototyping-screening method and enables rational selection of plasticizers in a time and material sparing manner. In eight out of eight cases the properties of the extrudates translated to products manufactured in lab-scale extrusion trials.

Accelerated Physical Stability Testing of Amorphous Dispersions.

PubMed

Mehta, Mehak; Suryanarayanan, Raj

2016-08-01

The goal was to develop an accelerated physical stability testing method of amorphous dispersions. Water sorption is known to cause plasticization and may accelerate drug crystallization. In an earlier investigation, it was observed that both the increase in mobility and decrease in stability in amorphous dispersions was explained by the "plasticization" effect of water (Mehta et al. Mol. Pharmaceutics 2016, 13 (4), 1339-1346). In this work, the influence of water concentration (up to 1.8% w/w) on the correlation between mobility and crystallization in felodipine dispersions was investigated. With an increase in water content, the α-relaxation time as well as the time for 1% w/w felodipine crystallization decreased. The relaxation times of the systems, obtained with different water concentration, overlapped when the temperature was scaled (Tg/T). The temperature dependencies of the α-relaxation time as well as the crystallization time were unaffected by the water concentration. Thus, the value of the coupling coefficient, up to a water concentration of 1.8% w/w, was approximately constant. Based on these findings, the use of "water sorption" is proposed to build predictive models for crystallization in slow crystallizing dispersions.

The brain-tumor related protein podoplanin regulates synaptic plasticity and hippocampus-dependent learning and memory

PubMed Central

Cicvaric, Ana; Yang, Jiaye; Krieger, Sigurd; Khan, Deeba; Kim, Eun-Jung; Dominguez-Rodriguez, Manuel; Cabatic, Maureen; Molz, Barbara; Acevedo Aguilar, Juan Pablo; Milicevic, Radoslav; Smani, Tarik; Breuss, Johannes M.; Kerjaschki, Dontscho; Pollak, Daniela D.; Uhrin, Pavel; Monje, Francisco J.

2016-01-01

Abstract Introduction: Podoplanin is a cell-surface glycoprotein constitutively expressed in the brain and implicated in human brain tumorigenesis. The intrinsic function of podoplanin in brain neurons remains however uncharacterized. Materials and methods: Using an established podoplanin-knockout mouse model and electrophysiological, biochemical, and behavioral approaches, we investigated the brain neuronal role of podoplanin. Results: Ex-vivo electrophysiology showed that podoplanin deletion impairs dentate gyrus synaptic strengthening. In vivo, podoplanin deletion selectively impaired hippocampus-dependent spatial learning and memory without affecting amygdala-dependent cued fear conditioning. In vitro, neuronal overexpression of podoplanin promoted synaptic activity and neuritic outgrowth whereas podoplanin-deficient neurons exhibited stunted outgrowth and lower levels of p-Ezrin, TrkA, and CREB in response to nerve growth factor (NGF). Surface Plasmon Resonance data further indicated a physical interaction between podoplanin and NGF. Discussion: This work proposes podoplanin as a novel component of the neuronal machinery underlying neuritogenesis, synaptic plasticity, and hippocampus-dependent memory functions. The existence of a relevant cross-talk between podoplanin and the NGF/TrkA signaling pathway is also for the first time proposed here, thus providing a novel molecular complex as a target for future multidisciplinary studies of the brain function in the physiology and the pathology.Key messagesPodoplanin, a protein linked to the promotion of human brain tumors, is required in vivo for proper hippocampus-dependent learning and memory functions.Deletion of podoplanin selectively impairs activity-dependent synaptic strengthening at the neurogenic dentate-gyrus and hampers neuritogenesis and phospho Ezrin, TrkA and CREB protein levels upon NGF stimulation.Surface plasmon resonance data indicates a physical interaction between podoplanin and NGF. On these grounds, a relevant cross-talk between podoplanin and NGF as well as a role for podoplanin in plasticity-related brain neuronal functions is here proposed. PMID:27558977

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

PubMed Central

Wang, Jianxin; Chen, Bo; Wang, Yaqun; Wang, Ningtao; Garbey, Marc; Tran-Son-Tay, Roger; Berceli, Scott A.; Wu, Rongling

2013-01-01

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments. PMID:23470995

Experience-Dependent Synaptic Plasticity in V1 Occurs without Microglial CX3CR1

PubMed Central

Stevens, Beth

2017-01-01

Brief monocular deprivation (MD) shifts ocular dominance and reduces the density of thalamic synapses in layer 4 of the mouse primary visual cortex (V1). We found that microglial lysosome content is also increased as a result of MD. Previous studies have shown that the microglial fractalkine receptor CX3CR1 is involved in synaptic development and hippocampal plasticity. We therefore tested the hypothesis that neuron-to-microglial communication via CX3CR1 is an essential component of visual cortical development and plasticity in male mice. Our data show that CX3CR1 is not required for normal development of V1 responses to visual stimulation, multiple forms of experience-dependent plasticity, or the synapse loss that accompanies MD in layer 4. By ruling out an essential role for fractalkine signaling, our study narrows the search for understanding how microglia respond to active synapse modification in the visual cortex. SIGNIFICANCE STATEMENT Microglia in the visual cortex respond to monocular deprivation with increased lysosome content, but signaling through the fractalkine receptor CX3CR1 is not an essential component in the mechanisms of visual cortical development or experience-dependent synaptic plasticity. PMID:28951447

Ups and Downs in the Ocean: Effects of Biofouling on Vertical Transport of Microplastics.

PubMed

Kooi, Merel; Nes, Egbert H van; Scheffer, Marten; Koelmans, Albert A

2017-07-18

Recent studies suggest size-selective removal of small plastic particles from the ocean surface, an observation that remains unexplained. We studied one of the hypotheses regarding this size-selective removal: the formation of a biofilm on the microplastics (biofouling). We developed the first theoretical model that is capable of simulating the effect of biofouling on the fate of microplastic. The model is based on settling, biofilm growth, and ocean depth profiles for light, water density, temperature, salinity, and viscosity. Using realistic parameters, the model simulates the vertical transport of small microplastic particles over time, and predicts that the particles either float, sink to the ocean floor, or oscillate vertically, depending on the size and density of the particle. The predicted size-dependent vertical movement of microplastic particles results in a maximum concentration at intermediate depths. Consequently, relatively low abundances of small particles are predicted at the ocean surface, while at the same time these small particles may never reach the ocean floor. Our results hint at the fate of "lost" plastic in the ocean, and provide a start for predicting risks of exposure to microplastics for potentially vulnerable species living at these depths.

Fast-Spiking Interneurons Supply Feedforward Control of Bursting, Calcium, and Plasticity for Efficient Learning.

PubMed

Owen, Scott F; Berke, Joshua D; Kreitzer, Anatol C

2018-02-08

Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning. Copyright © 2018 Elsevier Inc. All rights reserved.

Gradient-type modeling of the effects of plastic recovery and surface passivation in thin films

NASA Astrophysics Data System (ADS)

Liu, Jinxing; Kah Soh, Ai

2016-08-01

The elasto-plastic responses of thin films subjected to cyclic tension-compression loading and bending are studied, with a focus on Bauschinger and size effects. For this purpose, a model is established by incorporating plastic recovery into the strain gradient plasticity theory we proposed recently. Elastic and plastic parts of strain and strain gradient, which are determined by the elasto-plastic decomposition according to the associative rule, are assumed to have a degree of material-dependent reversibility. Based on the above assumption, a dislocation reversibility-dependent rule is built to describe evolutions of different deformation components under cyclic loadings. Furthermore, a simple strategy is provided to implement the passivated boundary effects by introducing a gradual change to relevant material parameters in the yield function. Based on this theory, both bulge and bending tests under cyclic loading conditions are investigated. By comparing the present predictions with the existing experimental data, it is found that the yield function is able to exhibit the size effect, the Bauschinger effect, the influence of surface passivation and the hysteresis-loop phenomenon. Thus, the proposed model is deemed helpful in studying plastic deformations of micron-scale films.

H3 and H4 Lysine Acetylation Correlates with Developmental and Experimentally Induced Adult Experience-Dependent Plasticity in the Mouse Visual Cortex

PubMed Central

Vierci, Gabriela; Pannunzio, Bruno; Bornia, Natalia; Rossi, Francesco M.

2016-01-01

Histone posttranslational modifications play a fundamental role in orchestrating gene expression. In this work, we analyzed the acetylation of H3 and H4 histones (AcH3–AcH4) and its modulation by visual experience in the mouse visual cortex (VC) during normal development and in two experimental conditions that restore juvenile-like plasticity levels in adults (fluoxetine treatment and enriched environment). We found that AcH3–AcH4 declines with age and is upregulated by treatments restoring plasticity in the adult. We also found that visual experience modulates AcH3–AcH4 in young and adult plasticity-restored mice but not in untreated ones. Finally, we showed that the transporter vGAT is downregulated in adult plasticity-restored models. In summary, we identified a dynamic regulation of AcH3–AcH4, which is associated with high plasticity levels and enhanced by visual experience. These data, along with recent ones, indicate H3–H4 acetylation as a central hub in the control of experience-dependent plasticity in the VC. PMID:27891053

Leaf-trait plasticity and species vulnerability to climate change in a Mongolian steppe.

PubMed

Liancourt, Pierre; Boldgiv, Bazartseren; Song, Daniel S; Spence, Laura A; Helliker, Brent R; Petraitis, Peter S; Casper, Brenda B

2015-09-01

Climate change is expected to modify plant assemblages in ways that will have major consequences for ecosystem functions. How climate change will affect community composition will depend on how individual species respond, which is likely related to interspecific differences in functional traits. The extraordinary plasticity of some plant traits is typically neglected in assessing how climate change will affect different species. In the Mongolian steppe, we examined whether leaf functional traits under ambient conditions and whether plasticity in these traits under altered climate could explain climate-induced biomass responses in 12 co-occurring plant species. We experimentally created three probable climate change scenarios and used a model selection procedure to determine the set of baseline traits or plasticity values that best explained biomass response. Under all climate change scenarios, plasticity for at least one leaf trait correlated with change in species performance, while functional leaf-trait values in ambient conditions did not. We demonstrate that trait plasticity could play a critical role in vulnerability of species to a rapidly changing environment. Plasticity should be considered when examining how climate change will affect plant performance, species' niche spaces, and ecological processes that depend on plant community composition. © 2015 John Wiley & Sons Ltd.

Study the Cyclic Plasticity Behavior of 508 LAS under Constant, Variable and Grid-Load-Following Loading Cycles for Fatigue Evaluation of PWR Components

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

Mohanty, Subhasish; Barua, Bipul; Soppet, William K.

This report provides an update of an earlier assessment of environmentally assisted fatigue for components in light water reactors. This report is a deliverable in September 2016 under the work package for environmentally assisted fatigue under DOE’s Light Water Reactor Sustainability program. In an April 2016 report, we presented a detailed thermal-mechanical stress analysis model for simulating the stress-strain state of a reactor pressure vessel and its nozzles under grid-load-following conditions. In this report, we provide stress-controlled fatigue test data for 508 LAS base metal alloy under different loading amplitudes (constant, variable, and random grid-load-following) and environmental conditions (in airmore » or pressurized water reactor coolant water at 300°C). Also presented is a cyclic plasticity-based analytical model that can simultaneously capture the amplitude and time dependency of the component behavior under fatigue loading. Results related to both amplitude-dependent and amplitude-independent parameters are presented. The validation results for the analytical/mechanistic model are discussed. This report provides guidance for estimating time-dependent, amplitude-independent parameters related to material behavior under different service conditions. The developed mechanistic models and the reported material parameters can be used to conduct more accurate fatigue and ratcheting evaluation of reactor components.« less

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.

Incident angle dependence of proton response of CR-39 (TS-16) track detector

NASA Technical Reports Server (NTRS)

Oda, K.; Csige, I.; Yamauchi, T.; Miyake, H.; Benton, E. V.

1993-01-01

The proton response of the TS-16 type of CR-39 plastic nuclear track detector has been studied with accelerated and fast neutron induced protons in vacuum and in air. The diameters of etched tracks were measured as a function of etching time and the etch rate ratio and the etch induction layer were determined from the growth curve of the diameter using a variable etch rate ratio model. In the case of the accelerated protons in vacuum an anomalous incident angle dependence of the response is observed.

Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation

PubMed Central

Tetzlaff, Christian; Kolodziejski, Christoph; Timme, Marc; Tsodyks, Misha; Wörgötter, Florentin

2013-01-01

Memory storage in the brain relies on mechanisms acting on time scales from minutes, for long-term synaptic potentiation, to days, for memory consolidation. During such processes, neural circuits distinguish synapses relevant for forming a long-term storage, which are consolidated, from synapses of short-term storage, which fade. How time scale integration and synaptic differentiation is simultaneously achieved remains unclear. Here we show that synaptic scaling – a slow process usually associated with the maintenance of activity homeostasis – combined with synaptic plasticity may simultaneously achieve both, thereby providing a natural separation of short- from long-term storage. The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall. These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes. PMID:24204240

Synaptic scaling enables dynamically distinct short- and long-term memory formation.

PubMed

Tetzlaff, Christian; Kolodziejski, Christoph; Timme, Marc; Tsodyks, Misha; Wörgötter, Florentin

2013-10-01

Memory storage in the brain relies on mechanisms acting on time scales from minutes, for long-term synaptic potentiation, to days, for memory consolidation. During such processes, neural circuits distinguish synapses relevant for forming a long-term storage, which are consolidated, from synapses of short-term storage, which fade. How time scale integration and synaptic differentiation is simultaneously achieved remains unclear. Here we show that synaptic scaling - a slow process usually associated with the maintenance of activity homeostasis - combined with synaptic plasticity may simultaneously achieve both, thereby providing a natural separation of short- from long-term storage. The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall. These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes.

Spreading of triboelectrically charged granular matter

NASA Astrophysics Data System (ADS)

Kumar, Deepak; Sane, A.; Gohil, Smita.; Bandaru, P. R.; Bhattacharya, S.; Ghosh, Shankar

2014-06-01

We report on the spreading of triboelectrically charged glass particles on an oppositely charged surface of a plastic cylindrical container in the presence of a constant mechanical agitation. The particles spread via sticking, as a monolayer on the cylinder's surface. Continued agitation initiates a sequence of instabilities of this monolayer, which first forms periodic wavy-stripe-shaped transverse density modulation in the monolayer and then ejects narrow and long particle-jets from the tips of these stripes. These jets finally coalesce laterally to form a homogeneous spreading front that is layered along the spreading direction. These remarkable growth patterns are related to a time evolving frictional drag between the moving charged glass particles and the countercharges on the plastic container. The results provide insight into the multiscale time-dependent tribolelectric processes and motivates further investigation into the microscopic causes of these macroscopic dynamical instabilities and spatial structures.

GRASP1 regulates synaptic plasticity and learning through endosomal recycling of AMPA receptors

PubMed Central

Chiu, Shu-Ling; Diering, Graham Hugh; Ye, Bing; Takamiya, Kogo; Chen, Chih-Ming; Jiang, Yuwu; Niranjan, Tejasvi; Schwartz, Charles E.; Wang, Tao; Huganir, Richard L.

2017-01-01

Summary Learning depends on experience-dependent modification of synaptic efficacy and neuronal connectivity in the brain. We provide direct evidence for physiological roles of the recycling endosome protein GRASP1 in glutamatergic synapse function and animal behavior. Mice lacking GRASP1 showed abnormal excitatory synapse number, synaptic plasticity and hippocampal-dependent learning and memory due to a failure in learning-induced synaptic AMPAR incorporation. We identified two GRASP1 point mutations from intellectual disability (ID) patients that showed convergent disruptive effects on AMPAR recycling and glutamate uncaging-induced structural and functional plasticity. Wild-type GRASP1, but not ID mutants, rescues spine loss in hippocampal CA1 neurons of Grasp1 knockout mice. Together, these results demonstrate a requirement for normal recycling endosome function in AMPAR-dependent synaptic function and neuronal connectivity in vivo, and suggest a potential role for GRASP1 in the pathophysiology of human cognitive disorders. PMID:28285821

Timing and Frequency of Sublethal Exposure Modifies the Induction and Retention of Increased Insecticide Tolerance in Wood Frogs (Lithobates sylvaticus).

PubMed

Jones, Devin K; Yates, Erika K; Mattes, Brian M; Hintz, William D; Schuler, Matthew S; Relyea, Rick A

2018-05-22

While the paradigm for increased tolerance to pesticides has been by selection on constitutive (naïve) traits, recent research has shown it can also occur through phenotypic plasticity. However, the time period in which induction can occur, the duration of induced tolerance, and the influence of multiple induction events remain unknown. We hypothesized that the induction of increased pesticide tolerance is limited to early sensitive periods, the magnitude of induced tolerance depends on the number of exposures, and the retention of induced tolerance depends on the time elapsed after an exposure and the number of exposures. To test these hypotheses, we exposed wood frog tadpoles to either a no-carbaryl control (water) or 0.5 mg/L carbaryl at four time periods, and later tested their tolerance to carbaryl using time-to-death assays. We discovered that tadpoles induced increased tolerance early and midway, but not late, in our experiment and their constitutive tolerance increased with age. We found no difference in the magnitude of induced tolerance following one or two exposures. Lastly, induced pesticide tolerance was reversed within 6 d, but was retained only when tadpoles experienced all four consecutive exposures. Phenotypic plasticity provides an immediate response for sensitive amphibian larvae to early pesticide exposures and reduces phenotypic mismatches in aquatic environments contaminated by agrochemicals. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

The secret life of ground squirrels: accelerometry reveals sex-dependent plasticity in above-ground activity

PubMed Central

Wilsterman, Kathryn; Zhang, Victor; Moore, Jeanette; Barnes, Brian M.; Buck, C. Loren

2016-01-01

The sexes differ in how and when they allocate energy towards reproduction, but how this influences phenotypic plasticity in daily activity patterns is unclear. Here, we use collar-mounted light loggers and triaxial accelerometers to examine factors that affect time spent above ground and overall dynamic body acceleration (ODBA), an index of activity-specific energy expenditure, across the active season of free-living, semi-fossorial arctic ground squirrels (Urocitellus parryii). We found high day-to-day variability in time spent above ground and ODBA with most of the variance explained by environmental conditions known to affect thermal exchange. In both years, females spent more time below ground compared with males during parturition and early lactation; however, this difference was fourfold larger in the second year, possibly, because females were in better body condition. Daily ODBA positively correlated with time spent above ground in both sexes, but females were more active per unit time above ground. Consequently, daily ODBA did not differ between the sexes when females were early in lactation, even though females were above ground three to six fewer hours each day. Further, on top of having the additional burden of milk production, ODBA data indicate females also had fragmented rest patterns and were more active during late lactation. Our results indicate that sex differences in reproductive requirements can have a substantial influence on activity patterns, but the size of this effect may be dependent on capital resources accrued during gestation. PMID:27703706

Analytical, Numerical and Experimental Examination of Reinforced Composites Beams Covered with Carbon Fiber Reinforced Plastic

NASA Astrophysics Data System (ADS)

Kasimzade, A. A.; Tuhta, S.

2012-03-01

In the article, analytical, numerical (Finite Element Method) and experimental investigation results of beam that was strengthened with fiber reinforced plastic-FRP composite has been given as comparative, the effect of FRP wrapping number to the maximum load and moment capacity has been evaluated depending on this results. Carbon FRP qualitative dependences have been occurred between wrapping number and beam load and moment capacity for repair-strengthen the reinforced concrete beams with carbon fiber. Shown possibilities of application traditional known analysis programs, for the analysis of Carbon Fiber Reinforced Plastic (CFRP) strengthened structures.

How quickly do albatrosses and petrels digest plastic particles?

PubMed

Ryan, Peter G

2015-12-01

Understanding how rapidly seabirds excrete or regurgitate ingested plastic items is important for their use as monitors of marine debris. van Franeker and Law (2015) inferred that fulmarine petrels excrete ∼75% of plastic particles within a month of ingestion based on decreases in the amounts of plastic in the stomachs of adult petrels moving to relatively clean environments to breed. However, similar decreases occur among resident species due to adults passing plastic loads to their chicks. The few direct measures of wear rates and retention times of persistent stomach contents suggest longer plastic residence times in most albatrosses and petrels. Residence time presumably varies with item size, type of plastic, the amount and composition of other persistent stomach contents, and the size at which items are excreted, which may vary among taxa. Accurate measures of ingested plastic retention times are needed to better understand temporal and spatial patterns in ingested plastic loads within marine organisms, especially if they are to be used as indicators of plastic pollution trends. Copyright © 2015 Elsevier Ltd. All rights reserved.

Chronic Enhancement of Serotonin Facilitates Excitatory Transcranial Direct Current Stimulation-Induced Neuroplasticity.

PubMed

Kuo, Hsiao-I; Paulus, Walter; Batsikadze, Giorgi; Jamil, Asif; Kuo, Min-Fang; Nitsche, Michael A

2016-04-01

Serotonin affects memory formation via modulating long-term potentiation (LTP) and depression (LTD). Accordingly, acute selective serotonin reuptake inhibitor (SSRI) administration enhanced LTP-like plasticity induced by transcranial direct current stimulation (tDCS) in humans. However, it usually takes some time for SSRI to reduce clinical symptoms such as anxiety, negative mood, and related symptoms of depression and anxiety disorders. This might be related to an at least partially different effect of chronic serotonergic enhancement on plasticity, as compared with single-dose medication. Here we explored the impact of chronic application of the SSRI citalopram (CIT) on plasticity induced by tDCS in healthy humans in a partially double-blinded, placebo (PLC)-controlled, randomized crossover study. Furthermore, we explored the dependency of plasticity induction from the glutamatergic system via N-methyl-D-aspartate receptor antagonism. Twelve healthy subjects received PLC medication, combined with anodal or cathodal tDCS of the primary motor cortex. Afterwards, the same subjects took CIT (20 mg/day) consecutively for 35 days. During this period, four additional interventions were performed (CIT and PLC medication with anodal/cathodal tDCS, CIT and dextromethorphan (150 mg) with anodal/cathodal tDCS). Plasticity was monitored by motor-evoked potential amplitudes elicited by transcranial magnetic stimulation. Chronic application of CIT increased and prolonged the LTP-like plasticity induced by anodal tDCS for over 24 h, and converted cathodal tDCS-induced LTD-like plasticity into facilitation. These effects were abolished by dextromethorphan. Chronic serotonergic enhancement results in a strengthening of LTP-like glutamatergic plasticity, which might partially explain the therapeutic impact of SSRIs in depression and other neuropsychiatric diseases.

Deformation behavior of human enamel and dentin-enamel junction under compression.

PubMed

Zaytsev, Dmitry; Panfilov, Peter

2014-01-01

Deformation behavior under uniaxial compression of human enamel and dentin-enamel junction (DEJ) is considered in comparison with human dentin. This deformation scheme allows estimating the total response from all levels of the hierarchical composite material in contrast with the indentation, which are limited by the mesoscopic and microscopic scales. It was shown for the first time that dental enamel is the strength (up to 1850MPa) hard tissue, which is able to consider some elastic (up to 8%) and plastic (up to 5%) deformation under compression. In so doing, it is almost undeformable substance under the creep condition. Mechanical properties of human enamel depend on the geometry of sample. Human dentin exhibits the similar deformation behavior under compression, but the values of its elasticity (up to 40%) and plasticity (up to 18%) are much more, while its strength (up to 800MPa) is less in two times. Despite the difference in mechanical properties, human enamel is able to suppress the cracking alike dentin. Deformation behavior under the compression of the samples contained DEJ as the same to dentin. This feature allows a tooth to be elastic-plastic (as dentin) and wear resistible (as enamel), simultaneously. © 2013 Elsevier B.V. All rights reserved.

Temperature-dependent plastic hysteresis in highly confined polycrystalline Nb films

NASA Astrophysics Data System (ADS)

Waheed, S.; Hao, R.; Zheng, Z.; Wheeler, J. M.; Michler, J.; Balint, D. S.; Giuliani, F.

2018-02-01

In this study, the effect of temperature on the cyclic deformation behaviour of a confined polycrystalline Nb film is investigated. Micropillars encapsulating a thin niobium interlayer are deformed under cyclic axial compression at different test temperatures. A distinct plastic hysteresis is observed for samples tested at elevated temperatures, whereas negligible plastic hysteresis is observed for samples tested at room temperature. These results are interpreted using planar discrete dislocation plasticity incorporating slip transmission across grain boundaries. The effect of temperature-dependent grain boundary energy and dislocation mobility on dislocation penetration and, consequently, the size of plastic hysteresis is simulated to correlate with the experimental results. It is found that the decrease in grain boundary energy barrier caused by the increase in temperature does not lead to any appreciable change in the cyclic response. However, dislocation mobility significantly affects the size of plastic hysteresis, with high mobilities leading to a larger hysteresis. Therefore, it is postulated that the experimental observations are predominantly caused by an increase in dislocation mobility as the temperature is increased above the critical temperature of body-centred cubic niobium.

1000 to 1200 K time-dependent compressive deformation of single-crystalline and polycrystalline B2 Ni-40Al

NASA Technical Reports Server (NTRS)

Whittenberger, J. D.; Noebe, R. D.; Kumar, K. S.; Mannan, S. K.; Cullers, C. L.

1991-01-01

The 1000-K and 1200-K time-dependent deformation of 100-line-oriented and non-100-line-oriented single crystals of Ni-40Al (made by a modified Bridgman technique) was examined over a large range of strain rates (from 0.1 to 10 to the -7th per sec). The results were compared with those for polycrystalline Ni-40Al made by hot pressing XD synthesized powder. The results from measurements of slow-plastic-strain-rate properties of the two materials show that single crystals offer no strength advantage over polycrystalline material. Both forms were found to deform via a dislocation climb mechanism.

Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night

PubMed Central

Muller, Lyle; Piantoni, Giovanni; Koller, Dominik; Cash, Sydney S; Halgren, Eric; Sejnowski, Terrence J

2016-01-01

During sleep, the thalamus generates a characteristic pattern of transient, 11-15 Hz sleep spindle oscillations, which synchronize the cortex through large-scale thalamocortical loops. Spindles have been increasingly demonstrated to be critical for sleep-dependent consolidation of memory, but the specific neural mechanism for this process remains unclear. We show here that cortical spindles are spatiotemporally organized into circular wave-like patterns, organizing neuronal activity over tens of milliseconds, within the timescale for storing memories in large-scale networks across the cortex via spike-time dependent plasticity. These circular patterns repeat over hours of sleep with millisecond temporal precision, allowing reinforcement of the activity patterns through hundreds of reverberations. These results provide a novel mechanistic account for how global sleep oscillations and synaptic plasticity could strengthen networks distributed across the cortex to store coherent and integrated memories. DOI: http://dx.doi.org/10.7554/eLife.17267.001 PMID:27855061

Plastic scintillation detectors for dose monitoring in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Antunes, J.; Machado, J.; Peralta, L.; Matela, N.

2018-01-01

Plastic scintillators detectors (PSDs) have been studied as dosimeters, since they provide a cost-effective alternative to conventional ionization chambers. Measurement and analysis of energy dependency were performed on a Siemens Mammomat tomograph for two different peak kilovoltages: 26 kV and 35 kV. Both PSD displayed good linearity for each energy considered and almost no energy dependence.

Academic plastic surgery: a study of current issues and future challenges.

PubMed

Zetrenne, Eleonore; Kosins, Aaron M; Wirth, Garrett A; Bui, Albert; Evans, Gregory R D; Wells, James H

2008-06-01

The objectives of this study were (1) to evaluate the role of a full-time academic plastic surgeon, (2) to define the indicators predictive of a successful career in academic plastic surgery, and (3) to understand the current issues that will affect future trends in the practice of academic plastic surgery. A questionnaire was developed to evaluate the role of current full-time academic plastic surgeons and to understand the current issues and future challenges facing academic plastic surgery. Each plastic surgery program director in the United States was sent the survey for distribution among all full-time academic plastic surgeons. Over a 6-week period, responses from 143 full-time academic plastic surgeons (approximately 31%) were returned. Fifty-three percent of respondents had been academic plastic surgeons for longer than 10 years. Seventy-three percent of respondents defined academic plastic surgeons as clinicians who are teachers and researchers. However, 53% of respondents believed that academic plastic surgeons were not required to teach or practice within university hospitals/academic centers. The 3 factors reported most frequently as indicative of a successful career in academic plastic surgery were peer recognition, personal satisfaction, and program reputation. Dedication and motivation were the personal characteristics rated most likely to contribute to academic success. Forty-four percent of respondents were unable to identify future academic plastic surgeons from plastic surgery residency applicants, and 27% were not sure. Most (93%) of the respondents believed that academic surgery as practiced today will change. The overall job description of a full-time academic plastic surgeon remains unchanged (teacher and researcher). Whereas peer recognition, personal satisfaction, and program reputation were most frequently cited as indicative of a successful plastic surgery career, financial success was rated the least indicative. Similarly, whereas the personal characteristics of dedication and motivation were rated most likely to contribute to academic success, economic competence was rated least likely. Although the role of academic plastic surgeons remains constant, the practice of academic plastic surgery is evolving. As a result, the future clinical milieu of academic plastic surgeons and training programs is in question.

Motor Learning Enhances Use-Dependent Plasticity

PubMed Central

2017-01-01

Motor behaviors are shaped not only by current sensory signals but also by the history of recent experiences. For instance, repeated movements toward a particular target bias the subsequent movements toward that target direction. This process, called use-dependent plasticity (UDP), is considered a basic and goal-independent way of forming motor memories. Most studies consider movement history as the critical component that leads to UDP (Classen et al., 1998; Verstynen and Sabes, 2011). However, the effects of learning (i.e., improved performance) on UDP during movement repetition have not been investigated. Here, we used transcranial magnetic stimulation in two experiments to assess plasticity changes occurring in the primary motor cortex after individuals repeated reinforced and nonreinforced actions. The first experiment assessed whether learning a skill task modulates UDP. We found that a group that successfully learned the skill task showed greater UDP than a group that did not accumulate learning, but made comparable repeated actions. The second experiment aimed to understand the role of reinforcement learning in UDP while controlling for reward magnitude and action kinematics. We found that providing subjects with a binary reward without visual feedback of the cursor led to increased UDP effects. Subjects in the group that received comparable reward not associated with their actions maintained the previously induced UDP. Our findings illustrate how reinforcing consistent actions strengthens use-dependent memories and provide insight into operant mechanisms that modulate plastic changes in the motor cortex. SIGNIFICANCE STATEMENT Performing consistent motor actions induces use-dependent plastic changes in the motor cortex. This plasticity reflects one of the basic forms of human motor learning. Past studies assumed that this form of learning is exclusively affected by repetition of actions. However, here we showed that success-based reinforcement signals could affect the human use-dependent plasticity (UDP) process. Our results indicate that learning augments and interacts with UDP. This effect is important to the understanding of the interplay between the different forms of motor learning and suggests that reinforcement is not only important to learning new behaviors, but can shape our subsequent behavior via its interaction with UDP. PMID:28143961

Spectrum of Slip Processes on the Subduction Interface in a Continuum Framework Resolved by Rate-and State Dependent Friction and Adaptive Time Stepping

NASA Astrophysics Data System (ADS)

Herrendoerfer, R.; van Dinther, Y.; Gerya, T.

2015-12-01

To explore the relationships between subduction dynamics and the megathrust earthquake potential, we have recently developed a numerical model that bridges the gap between processes on geodynamic and earthquake cycle time scales. In a self-consistent, continuum-based framework including a visco-elasto-plastic constitutive relationship, cycles of megathrust earthquake-like ruptures were simulated through a purely slip rate-dependent friction, albeit with very low slip rates (van Dinther et al., JGR, 2013). In addition to much faster earthquakes, a range of aseismic slip processes operate at different time scales in nature. These aseismic processes likely accommodate a considerable amount of the plate convergence and are thus relevant in order to estimate the long-term seismic coupling and related hazard in subduction zones. To simulate and resolve this wide spectrum of slip processes, we innovatively implemented rate-and state dependent friction (RSF) and an adaptive time-stepping into our continuum framework. The RSF formulation, in contrast to our previous friction formulation, takes the dependency of frictional strength on a state variable into account. It thereby allows for continuous plastic yielding inside rate-weakening regions, which leads to aseismic slip. In contrast to the conventional RSF formulation, we relate slip velocities to strain rates and use an invariant formulation. Thus we do not require the a priori definition of infinitely thin, planar faults in a homogeneous elastic medium. With this new implementation of RSF, we succeed to produce consistent cycles of frictional instabilities. By changing the frictional parameter a, b, and the characteristic slip distance, we observe a transition from stable sliding to stick-slip behaviour. This transition is in general agreement with predictions from theoretical estimates of the nucleation size, thereby to first order validating our implementation. By incorporating adaptive time-stepping based on a fraction of characteristic slip distance over maximum slip velocity, we are able to resolve stick-slip events and increase computational speed. In this better resolved framework, we examine the role of aseismic slip on the megathrust cycle and its dependence on subduction velocity.

Training Alters the Resolution of Lexical Interference: Evidence for Plasticity of Competition and Inhibition

PubMed Central

Kapnoula, Efthymia C.; McMurray, Bob

2016-01-01

Language learning is generally described as a problem of acquiring new information (e.g., new words). However, equally important are changes in how the system processes known information. For example, a wealth of studies has suggested dramatic changes over development in how efficiently children recognize familiar words, but it is unknown what kind of experience-dependent mechanisms of plasticity give rise to such changes in real-time processing. We examined the plasticity of the language processing system by testing whether a fundamental aspect of spoken word recognition, lexical interference, can be altered by experience. Adult participants were trained on a set of familiar words over a series of 4 tasks. In the high-competition (HC) condition, tasks were designed to encourage coactivation of similar words (e.g., net and neck) and to require listeners to resolve this competition. Tasks were similar in the low-competition (LC) condition, but did not enhance this competition. Immediately after training, interlexical interference was tested using a visual world paradigm task. Participants in the HC group resolved interference to a fuller degree than those in the LC group, demonstrating that experience can shape the way competition between words is resolved. TRACE simulations showed that the observed late differences in the pattern of interference resolution can be attributed to differences in the strength of lexical inhibition. These findings inform cognitive models in many domains that involve competition/interference processes, and suggest an experience-dependent mechanism of plasticity that may underlie longer term changes in processing efficiency associated with both typical and atypical development. PMID:26709587

The Dependence of Portevin-Le Châtelier Effect on the γ' Precipitates in a Wrought Ni-Base Superalloy

NASA Astrophysics Data System (ADS)

Wang, Xinguang; Han, Guoming; Cui, Chuanyong; Guan, Shuai; Jin, Tao; Sun, Xiaofeng; Hu, Zhuangqi

2016-12-01

The dependence of Portevin-Le Châtelier (PLC) effect on the γ' precipitates of the Nimonic 263 alloy in different microstructural conditions has been studied by analyzing the parameters of the tensile curves and the deformation mechanisms. It is shown that the γ' precipitates with different sizes, edge-to-edge interprecipitate distance, and areal number density are obtained by altering the aging time. It is demonstrated that when the mean size of the γ' precipitates is less than 28 nm (aging less than 25 hours), the deformation mechanisms are dominated by APB-coupled a/2<101> dislocations shearing the small γ' precipitates and the slip bands continuously cutting the γ and γ' phases. When the γ' size is between 28 and 45 nm (aging time between 25 and 50 hours), the deformation mechanism is controlled by the APB-coupled a/2<101> dislocations shearing the small γ' precipitates, the a/6<112> Shockley partial dislocation continuously shearing the γ and γ' phases combined with matrix dislocations by-passing the γ' precipitates; If the γ' size over 45 nm (aging time more than 50 hours), Orowan by-passing becomes the main deformation mechanism. Moreover, with increasing the aging time, the critical plastic strain for the onset of the PLC effect increases and reaches a maximum after aging for 50 hours, and then gradually decreases. At last, the dependence of critical plastic strain on the deformation mechanisms is well explained by the elementary incremental strain (γ). The precipitation process of the γ' phase can directly influence the PLC effect by changing the interactions among solutes atoms, mobile dislocations, and forest dislocations.

Neuron-glia metabolic coupling and plasticity.

PubMed

Magistretti, Pierre J

2006-06-01

The coupling between synaptic activity and glucose utilization (neurometabolic coupling) is a central physiological principle of brain function that has provided the basis for 2-deoxyglucose-based functional imaging with positron emission tomography (PET). Astrocytes play a central role in neurometabolic coupling, and the basic mechanism involves glutamate-stimulated aerobic glycolysis; the sodium-coupled reuptake of glutamate by astrocytes and the ensuing activation of the Na-K-ATPase triggers glucose uptake and processing via glycolysis, resulting in the release of lactate from astrocytes. Lactate can then contribute to the activity-dependent fuelling of the neuronal energy demands associated with synaptic transmission. An operational model, the 'astrocyte-neuron lactate shuttle', is supported experimentally by a large body of evidence, which provides a molecular and cellular basis for interpreting data obtained from functional brain imaging studies. In addition, this neuron-glia metabolic coupling undergoes plastic adaptations in parallel with adaptive mechanisms that characterize synaptic plasticity. Thus, distinct subregions of the hippocampus are metabolically active at different time points during spatial learning tasks, suggesting that a type of metabolic plasticity, involving by definition neuron-glia coupling, occurs during learning. In addition, marked variations in the expression of genes involved in glial glycogen metabolism are observed during the sleep-wake cycle, with in particular a marked induction of expression of the gene encoding for protein targeting to glycogen (PTG) following sleep deprivation. These data suggest that glial metabolic plasticity is likely to be concomitant with synaptic plasticity.

Background sounds contribute to spectrotemporal plasticity in primary auditory cortex.

PubMed

Moucha, Raluca; Pandya, Pritesh K; Engineer, Navzer D; Rathbun, Daniel L; Kilgard, Michael P

2005-05-01

The mammalian auditory system evolved to extract meaningful information from complex acoustic environments. Spectrotemporal selectivity of auditory neurons provides a potential mechanism to represent natural sounds. Experience-dependent plasticity mechanisms can remodel the spectrotemporal selectivity of neurons in primary auditory cortex (A1). Electrical stimulation of the cholinergic nucleus basalis (NB) enables plasticity in A1 that parallels natural learning and is specific to acoustic features associated with NB activity. In this study, we used NB stimulation to explore how cortical networks reorganize after experience with frequency-modulated (FM) sweeps, and how background stimuli contribute to spectrotemporal plasticity in rat auditory cortex. Pairing an 8-4 kHz FM sweep with NB stimulation 300 times per day for 20 days decreased tone thresholds, frequency selectivity, and response latency of A1 neurons in the region of the tonotopic map activated by the sound. In an attempt to modify neuronal response properties across all of A1 the same NB activation was paired in a second group of rats with five downward FM sweeps, each spanning a different octave. No changes in FM selectivity or receptive field (RF) structure were observed when the neural activation was distributed across the cortical surface. However, the addition of unpaired background sweeps of different rates or direction was sufficient to alter RF characteristics across the tonotopic map in a third group of rats. These results extend earlier observations that cortical neurons can develop stimulus specific plasticity and indicate that background conditions can strongly influence cortical plasticity.

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

PubMed Central

Diehl, Peter U.; Cook, Matthew

2015-01-01

In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks. PMID:26941637

Regulation of expression of the ligand for CD40 on T helper lymphocytes.

PubMed

Castle, B E; Kishimoto, K; Stearns, C; Brown, M L; Kehry, M R

1993-08-15

Activated Th cells deliver contact-dependent signals to resting B lymphocytes that initiate and drive B cell proliferation. Recently, a ligand for the B lymphocyte membrane protein, CD40, has been identified that delivers contact-dependent Th cell signals to B cells. A dimeric soluble form of CD40 was produced and used to further characterize the regulation of expression of the CD40 ligand. Expression of the CD40 ligand was rapidly induced after Th lymphocyte activation, and its stability depended upon whether Th cells were activated with soluble or plastic-bound stimuli. Th cells activated with soluble stimuli rapidly turned over cell-surface CD40 ligand whereas Th cells activated with plastic-bound stimuli exhibited more stable CD40 ligand expression for up to 48 h. Removal of activated Th cells from the plastic-bound stimulus resulted in a rapid turnover of CD40 ligand, suggesting that continuous stimulation could maintain CD40 ligand expression. Ligation by soluble CD40 could also stabilize expression of CD40 ligand on the Th cell surface. Both CD40 ligand and IL-2 were transiently synthesized from 1 to 12 h after Th cell activation and had similar kinetics of synthesis. In Con A-activated Th cells newly synthesized CD40 ligand exhibited an initial high turnover (1.5 h t1/2) and after 5 h of Th cell activation became more stable (10-h t1/2). In Th cells activated with plastic-bound anti-CD3, CD40 ligand exhibited a similar biphasic turnover except that the rapid turnover phase began significantly later. This delay could allow more time for newly synthesized CD40 ligand to assemble or associate with other molecules and thus become stabilized on the cell surface. Newly synthesized CD40 ligand in Con A-activated Th cells appeared to not be efficient in delivering Th cell-dependent contact signals to resting B cells, implying the need for assembly or accessory proteins. Regulation of CD40 ligand expression was consistent with all the characteristics of Th cell-delivered contact signals to B cells and may contribute to the high degree of specificity in B cell responses.

Myelin Associated Inhibitors: A Link Between Injury-Induced and Experience-Dependent Plasticity

PubMed Central

Akbik, Feras; Cafferty, William B. J.; Strittmatter, Stephen M.

2011-01-01

SUMMARY In the adult, both neurologic recovery and anatomical growth after a CNS injury are limited. Two classes of growth inhibitors, myelin associated inhibitors (MAIs) and extracellular matrix associated inhibitors, limit both functional recovery and anatomical rearrangements in animal models of spinal cord injury. Here we focus on how MAIs limit a wide spectrum of growth that includes regeneration, sprouting, and plasticity in both the intact and lesioned CNS. Three classic myelin associated inhibitors, Nogo-A, MAG, and OMgp, signal through their common receptors, Nogo-66 Receptor-1 (NgR1) and Paired-Immunoglobulin-like-Receptor-1 (PirB), to regulate cytoskeletal dynamics and inhibit growth. Initially described as inhibitors of axonal regeneration, subsequent work has demonstrated that MAIs also limit activity and experience-dependent plasticity in the intact, adult CNS. MAIs therefore represent a point of convergence for plasticity that limits anatomical rearrangements regardless of the inciting stimulus, blurring the distinction between injury studies and more “basic” plasticity studies. PMID:21699896

Gap junction plasticity as a mechanism to regulate network-wide oscillations

PubMed Central

Nicola, Wilten; Clopath, Claudia

2018-01-01

Cortical oscillations are thought to be involved in many cognitive functions and processes. Several mechanisms have been proposed to regulate oscillations. One prominent but understudied mechanism is gap junction coupling. Gap junctions are ubiquitous in cortex between GABAergic interneurons. Moreover, recent experiments indicate their strength can be modified in an activity-dependent manner, similar to chemical synapses. We hypothesized that activity-dependent gap junction plasticity acts as a mechanism to regulate oscillations in the cortex. We developed a computational model of gap junction plasticity in a recurrent cortical network based on recent experimental findings. We showed that gap junction plasticity can serve as a homeostatic mechanism for oscillations by maintaining a tight balance between two network states: asynchronous irregular activity and synchronized oscillations. This homeostatic mechanism allows for robust communication between neuronal assemblies through two different mechanisms: transient oscillations and frequency modulation. This implies a direct functional role for gap junction plasticity in information transmission in cortex. PMID:29529034

[Skin and soft tissue complications after orthopedic interventions on tumors : interdisciplinary management].

PubMed

Radtke, C; Calliess, T; Windhagen, H; Vogt, P

2015-03-01

Interdisciplinary collaboration between orthopedic and plastic surgeons is indicated in reconstructive surgery of the extremities for both traumatic orthopedic fractures with extensive soft tissue damage and musculoskeletal tumor resection. We want to emphasize the need for close cooperation starting in the preoperative planning for reconstruction after tumor resection in order to discuss and establish a unified approach. This is particularly important to establish a joint approach with special consideration of possibly necessary adjuvant therapies. One collaborative approach is for the orthopedic surgeon to resect the tumor and the plastic surgeon to carry out the defect reconstruction for exclusive soft tissue coverage including flap surgery as well as for functional reconstruction depending on the location and extent of tumor resection. Thus, careful preoperative and postoperative communication on the precise location, extent of tumor resection and the therapy timing between the orthopedic surgeon and the plastic surgeon will allow the most effective subsequent repair of the resection site.

Sleep deprivation during a specific 3-hour time window post-training impairs hippocampal synaptic plasticity and memory

PubMed Central

Prince, Toni-Moi; Wimmer, Mathieu; Choi, Jennifer; Havekes, Robbert; Aton, Sara; Abel, Ted

2014-01-01

Sleep deprivation disrupts hippocampal function and plasticity. In particular, long-term memory consolidation is impaired by sleep deprivation, suggesting that a specific critical period exists following learning during which sleep is necessary. To elucidate the impact of sleep deprivation on long-term memory consolidation and synaptic plasticity, long-term memory was assessed when mice were sleep deprived following training in the hippocampus-dependent object place recognition task. We found that 3 hours of sleep deprivation significantly impaired memory when deprivation began 1 hour after training. In contrast, 3 hours of deprivation beginning immediately post-training did not impair spatial memory. Furthermore, a 3-hour sleep deprivation beginning 1 hour after training impaired hippocampal long-term potentiation (LTP), whereas sleep deprivation immediately after training did not affect LTP. Together, our findings define a specific 3-hour critical period, extending from 1 to 4 hours after training, during which sleep deprivation impairs hippocampal function. PMID:24380868

Scattering by truncated targets with and without boundary interactions

NASA Astrophysics Data System (ADS)

Marston, Philip L.; Baik, Kyungmin; Espana, Aubrey; Osterhoudt, Curtis F.; Morse, Scot F.; Hefner, Brian T.; Blonigen, Florian J.

2005-04-01

Ray methods have been applied to the scattering of various truncated targets having wavenumber-radius products as small as 10 [F. J. Blonigen and P. L. Marston, J. Acoust. Soc. Am. 107, 689-698 (2000); S. F. Morse and P. L. Marston, ibid. 112, 1318-1326 (2002); B. T. Hefner and P. L. Marston, ARLO 2, 55-60 (2001)]. Recent work emphasizes the exploration of scattering enhancements for other situations including plastic cylinders having curved ends, truncated plastic cones, partially exposed cylinders, and objects in simulated conditions for burial in a seabed. Enhanced scattering is often associated with a locally flat outgoing wavefront. For plastic targets it has been helpful to examine the time dependence of the backscattered envelope as a function of target tilt for targets illuminated by short tone bursts. For partially exposed objects it is helpful to examine the backscattering as a function of the target exposure. For simulated buried targets, it has been helpful to excite target resonances. [Work supported by the Office of Naval Research.

Temperature effect on refractive index sensing performance of a U-shape tapered plastic optical fiber

NASA Astrophysics Data System (ADS)

Teng, Chuanxin; Yu, Fangda; Jing, Ning; Zheng, Jie

2016-11-01

The temperature dependence of a refractive index (RI) sensing probe based on a U-shape tapered plastic optical fiber (POF) was investigated experimentally. The changes in light propagation loss in the probe induced by temperature are of the same order of magnitude as those induced by measured RI changes. The temperature dependence loss and temperature dependence RI deviation of the sensing probe were measured (at the wavelength of 635 nm) in temperature of 10-60 °C. By extracting pure temperature dependence of the sensing probe alone, the influence of temperature to the sensor was characterized.

The influence of temperature to a refractive index sensor based on a macro-bending tapered plastic optical fiber

NASA Astrophysics Data System (ADS)

Teng, Chuan-xin; Yu, Fang-da; Jing, Ning; Zheng, Jie

2016-09-01

The temperature influence to a refractive index (RI) sensor based on a macro-bending tapered plastic optical fiber (POF) was investigated experimentally. The total temperature dependence loss (TDLtotal) and total temperature dependence RI deviation (TDRtotal) were measured at different temperature (10-60 °C) over an RI range of 1.33-1.41. The temperature dependence RI deviation of the sensor itself was obtained by subtracting the temperature dependence RI of measured liquid from TDRtotal. Therefore, the influence of temperature variation to the sensor was characterized and corrected.

Fear, food and sexual ornamentation: plasticity of colour development in Trinidadian guppies

PubMed Central

Ruell, E. W.; Handelsman, C. A.; Hawkins, C. L.; Sofaer, H. R.; Ghalambor, C. K.; Angeloni, L.

2013-01-01

The evolution of male ornamentation often reflects compromises between sexual and natural selection, but it may also be influenced by phenotypic plasticity. We investigated the developmental plasticity of male colour ornamentation in Trinidadian guppies in response to two environmental variables that covary in nature: predation risk and food availability. We found that exposure to chemical predator cues delayed the development of pigment-based colour elements, which are conspicuous to visual-oriented predators. Predator cues also reduced the size of colour elements at the time of maturity and caused adult males to be less colourful. To the best of our knowledge, these findings provide the first example of a plastic reduction in the development of a sexually selected male ornament in response to predator cues. The influence of predator cues on ornamentation probably affects individual fitness by reducing conspicuousness to predators, but could reduce attractiveness to females. Reduced food availability during development caused males to delay the development of colour elements and mature later, probably reflecting a physiological constraint, but their coloration at maturity and later in adulthood was largely unaffected, suggesting that variation in food quantity without variation in quality does not contribute to condition dependence of the trait. PMID:23466982

Mechanisms of Translation Control Underlying Long-lasting Synaptic Plasticity and the Consolidation of Long-term Memory

PubMed Central

Santini, Emanuela; Huynh, Thu N.; Klann, Eric

2018-01-01

The complexity of memory formation and its persistence is a phenomenon that has been studied intensely for centuries. Memory exists in many forms and is stored in various brain regions. Generally speaking, memories are reorganized into broadly distributed cortical networks over time through systems level consolidation. At the cellular level, storage of information is believed to initially occur via altered synaptic strength by processes such as long-term potentiation (LTP). New protein synthesis is required for long-lasting synaptic plasticity as well as for the formation of long-term memory. The mammalian target of rapamycin complex 1 (mTORC1) is a critical regulator of cap-dependent protein synthesis and is required for numerous forms of long-lasting synaptic plasticity and long-term memory. As such, the study of mTORC1 and protein factors that control translation initiation and elongation have enhanced our understanding of how the process of protein synthesis is regulated during memory formation. Herein we will discuss the molecular mechanisms that regulate protein synthesis as well as pharmacological and genetic manipulations that demonstrate the requirement for proper translational control in long-lasting synaptic plasticity and long-term memory formation. PMID:24484700

Breathing Monitor Using Dye-Doped Optical Fiber

NASA Astrophysics Data System (ADS)

Muto, Shinzo; Fukasawa, Akihiko; Ogawa, Takayuki; Morisawa, Masayuki; Ito, Hiroshi

1990-08-01

A new monitoring system of human breathing using umbelliferon dye-doped plastic fiber has been studied. Under UV light pumping, the fiber which was used as a sensor head generates blue fluorescence depending on human expiration. By converting the light signal to electronic pulses, the counting of breathing and real-time monitoring of abnormal breathing such as a heavy cough or a cloggy sputum have easily been obtained.

Dysregulation of synaptic plasticity precedes appearance of morphological defects in a Pten conditional knockout mouse model of autism.

PubMed

Takeuchi, Koichi; Gertner, Michael J; Zhou, Jing; Parada, Luis F; Bennett, Michael V L; Zukin, R Suzanne

2013-03-19

The phosphoinositide signaling system is a crucial regulator of neural development, cell survival, and plasticity. Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) negatively regulates phosphatidylinositol 3-kinase signaling and downstream targets. Nse-Cre Pten conditional knockout mice, in which Pten is ablated in granule cells of the dentate gyrus and pyramidal neurons of the hippocampal CA3, but not CA1, recapitulate many of the symptoms of humans with inactivating PTEN mutations, including progressive hypertrophy of the dentate gyrus and deficits in hippocampus-based social and cognitive behaviors. However, the impact of Pten loss on activity-dependent synaptic plasticity in this clinically relevant mouse model of Pten inactivation remains unclear. Here, we show that two phosphatidylinositol 3-kinase- and protein synthesis-dependent forms of synaptic plasticity, theta burst-induced long-term potentiation and metabotropic glutamate receptor (mGluR)-dependent long-term depression, are dysregulated at medial perforant path-to-dentate gyrus synapses of young Nse-Cre Pten conditional knockout mice before the onset of visible morphological abnormalities. In contrast, long-term potentiation and mGluR-dependent long-term depression are normal at CA3-CA1 pyramidal cell synapses at this age. Our results reveal that deletion of Pten in dentate granule cells dysregulates synaptic plasticity, a defect that may underlie abnormal social and cognitive behaviors observed in humans with Pten inactivating mutations and potentially other autism spectrum disorders.

Plasticity of orientation preference maps in the visual cortex of adult cats.

PubMed

Godde, Ben; Leonhardt, Ralph; Cords, Sven M; Dinse, Hubert R

2002-04-30

In contrast to the high degree of experience-dependent plasticity usually exhibited by cortical representational maps, a number of experiments performed in visual cortex suggest that the basic layout of orientation preference maps is only barely susceptible to activity-dependent modifications. In fact, most of what we know about activity-dependent plasticity in adults comes from experiments in somatosensory, auditory, or motor cortex. Applying a stimulation protocol that has been proven highly effective in other cortical areas, we demonstrate here that enforced synchronous cortical activity induces major changes of orientation preference maps (OPMs) in adult cats. Combining optical imaging of intrinsic signals and electrophysiological single-cell recordings, we show that a few hours of intracortical microstimulation (ICMS) lead to an enlargement of the cortical representational zone at the ICMS site and an extensive restructuring of the entire OPM layout up to several millimeters away, paralleled by dramatic changes of pinwheel numbers and locations. At the single-cell level, we found that the preferred orientation was shifted toward the orientation of the ICMS site over a region of up to 4 mm. Our results show that manipulating the synchronicity of cortical activity locally without invoking training, attention, or reinforcement, OPMs undergo large-scale reorganization reminiscent of plastic changes observed for nonvisual cortical maps. However, changes were much more widespread and enduring. Such large-scale restructuring of the visual cortical networks indicates a substantial capability for activity-dependent plasticity of adult visual cortex and may provide the basis for cognitive learning processes.