Log in

Relevant bibliographies by topics / STDP [Spike Timing Dependant Plasticity] / Journal articles

To see the other types of publications on this topic, follow the link: STDP [Spike Timing Dependant Plasticity].

Journal articles on the topic 'STDP [Spike Timing Dependant Plasticity]'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'STDP [Spike Timing Dependant Plasticity].'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

BADOUAL, MATHILDE, QUAN ZOU, ANDREW P. DAVISON, et al. "BIOPHYSICAL AND PHENOMENOLOGICAL MODELS OF MULTIPLE SPIKE INTERACTIONS IN SPIKE-TIMING DEPENDENT PLASTICITY." International Journal of Neural Systems 16, no. 02 (2006): 79–97. http://dx.doi.org/10.1142/s0129065706000524.

Full text

Abstract:

Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. We also make links with other well-known plasticity rules. A simplified phenomenological model is also derived, which should be useful for fast numerical simulation and analytic

APA, Harvard, Vancouver, ISO, and other styles

2

Uramoto, Takumi, and Hiroyuki Torikai. "A Calcium-Based Simple Model of Multiple Spike Interactions in Spike-Timing-Dependent Plasticity." Neural Computation 25, no. 7 (2013): 1853–69. http://dx.doi.org/10.1162/neco_a_00462.

Full text

Abstract:

Spike-timing-dependent plasticity (STDP) is a form of synaptic modification that depends on the relative timings of presynaptic and postsynaptic spikes. In this letter, we proposed a calcium-based simple STDP model, described by an ordinary differential equation having only three state variables: one represents the density of intracellular calcium, one represents a fraction of open state NMDARs, and one represents the synaptic weight. We shown that in spite of its simplicity, the model can reproduce the properties of the plasticity that have been experimentally measured in various brain areas

APA, Harvard, Vancouver, ISO, and other styles

3

Dan, Yang, and Mu-Ming Poo. "Spike Timing-Dependent Plasticity: From Synapse to Perception." Physiological Reviews 86, no. 3 (2006): 1033–48. http://dx.doi.org/10.1152/physrev.00030.2005.

Full text

Abstract:

Information in the nervous system may be carried by both the rate and timing of neuronal spikes. Recent findings of spike timing-dependent plasticity (STDP) have fueled the interest in the potential roles of spike timing in processing and storage of information in neural circuits. Induction of long-term potentiation (LTP) and long-term depression (LTD) in a variety of in vitro and in vivo systems has been shown to depend on the temporal order of pre- and postsynaptic spiking. Spike timing-dependent modification of neuronal excitability and dendritic integration was also observed. Such STDP at

APA, Harvard, Vancouver, ISO, and other styles

4

Echeveste, Rodrigo, and Claudius Gros. "Two-Trace Model for Spike-Timing-Dependent Synaptic Plasticity." Neural Computation 27, no. 3 (2015): 672–98. http://dx.doi.org/10.1162/neco_a_00707.

Full text

Abstract:

We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the [Formula: see text] concentration in the dendritic spine of the postsynaptic neuron. This model intends to bridge the worlds of existing simplistic phenomenological rules and highly detailed models, thus constituting a practical tool for the study of the interplay of neural activity and synaptic plasticity in extended spiking neural networks. For isolated pairs of pre- and postsynaptic spikes, the standard pairwise S

APA, Harvard, Vancouver, ISO, and other styles

5

Florian, Răzvan V. "Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity." Neural Computation 19, no. 6 (2007): 1468–502. http://dx.doi.org/10.1162/neco.2007.19.6.1468.

Full text

Abstract:

The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spike-timing-dependent plasticity (STDP). Here we show that the modulation of STDP by a global reward signal leads to reinforcement learning. We first derive analytically learning rules involving reward-modulated spike-timing-dependent synaptic and intrinsic plasticity, by applying a reinforcement learning algorithm to the stochastic spike response model of spiking neurons. These rules have several features common to plasticity mechanisms experimentally

APA, Harvard, Vancouver, ISO, and other styles

6

Lightheart, Toby, Steven Grainger, and Tien-Fu Lu. "Spike-Timing-Dependent Construction." Neural Computation 25, no. 10 (2013): 2611–45. http://dx.doi.org/10.1162/neco_a_00501.

Full text

Abstract:

Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for app

APA, Harvard, Vancouver, ISO, and other styles

7

Lu, Hui, Hyungju Park, and Mu-Ming Poo. "Spike-timing-dependent BDNF secretion and synaptic plasticity." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1633 (2014): 20130132. http://dx.doi.org/10.1098/rstb.2013.0132.

Full text

Abstract:

In acute hippocampal slices, we found that the presence of extracellular brain-derived neurotrophic factor (BDNF) is essential for the induction of spike-timing-dependent long-term potentiation (tLTP). To determine whether BDNF could be secreted from postsynaptic dendrites in a spike-timing-dependent manner, we used a reduced system of dissociated hippocampal neurons in culture. Repetitive pairing of iontophoretically applied glutamate pulses at the dendrite with neuronal spikes could induce persistent alterations of glutamate-induced responses at the same dendritic site in a manner that mimic

APA, Harvard, Vancouver, ISO, and other styles

8

Leen, Todd K., and Robert Friel. "Stochastic Perturbation Methods for Spike-Timing-Dependent Plasticity." Neural Computation 24, no. 5 (2012): 1109–46. http://dx.doi.org/10.1162/neco_a_00267.

Full text

Abstract:

Online machine learning rules and many biological spike-timing-dependent plasticity (STDP) learning rules generate jump process Markov chains for the synaptic weights. We give a perturbation expansion for the dynamics that, unlike the usual approximation by a Fokker-Planck equation (FPE), is well justified. Our approach extends the related system size expansion by giving an expansion for the probability density as well as its moments. We apply the approach to two observed STDP learning rules and show that in regimes where the FPE breaks down, the new perturbation expansion agrees well with Mon

APA, Harvard, Vancouver, ISO, and other styles

9

Hunzinger, Jason F., Victor H. Chan, and Robert C. Froemke. "Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity." Journal of Neurophysiology 108, no. 2 (2012): 551–66. http://dx.doi.org/10.1152/jn.01150.2011.

Full text

Abstract:

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 netw

APA, Harvard, Vancouver, ISO, and other styles

10

Mendes, Alexandre, Gaetan Vignoud, Sylvie Perez, Elodie Perrin, Jonathan Touboul, and Laurent Venance. "Concurrent Thalamostriatal and Corticostriatal Spike-Timing-Dependent Plasticity and Heterosynaptic Interactions Shape Striatal Plasticity Map." Cerebral Cortex 30, no. 8 (2020): 4381–401. http://dx.doi.org/10.1093/cercor/bhaa024.

Full text

Abstract:

Abstract The striatum integrates inputs from the cortex and thalamus, which display concomitant or sequential activity. The striatum assists in forming memory, with acquisition of the behavioral repertoire being associated with corticostriatal (CS) plasticity. The literature has mainly focused on that CS plasticity, and little remains known about thalamostriatal (TS) plasticity rules or CS and TS plasticity interactions. We undertook here the study of these plasticity rules. We found bidirectional Hebbian and anti-Hebbian spike-timing-dependent plasticity (STDP) at the thalamic and cortical in

APA, Harvard, Vancouver, ISO, and other styles

11

Morrison, Abigail, Ad Aertsen, and Markus Diesmann. "Spike-Timing-Dependent Plasticity in Balanced Random Networks." Neural Computation 19, no. 6 (2007): 1437–67. http://dx.doi.org/10.1162/neco.2007.19.6.1437.

Full text

Abstract:

The balanced random network model attracts considerable interest because it explains the irregular spiking activity at low rates and large membrane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP). Confronted with the plethora of theoretical models for STDP available, we reexamine the experimental data. On this basis, we propose a novel STDP update rule, with a multiplicative dependence on the synaptic weight for depre

APA, Harvard, Vancouver, ISO, and other styles

12

Legenstein, Robert, Christian Naeger, and Wolfgang Maass. "What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?" Neural Computation 17, no. 11 (2005): 2337–82. http://dx.doi.org/10.1162/0899766054796888.

Full text

Abstract:

Spiking neurons are very flexible computational modules, which can implement with different values of their adjustable synaptic parameters an enormous variety of different transformations F from input spike trains to output spike trains. We examine in this letter the question to what extent a spiking neuron with biologically realistic models for dynamic synapses can be taught via spike-timing-dependent plasticity (STDP) to implement a given transformation F. We consider a supervised learning paradigm where during training, the output of the neuron is clamped to the target signal (teacher forci

APA, Harvard, Vancouver, ISO, and other styles

13

Appleby, Peter A., and Terry Elliott. "Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 18, no. 10 (2006): 2414–64. http://dx.doi.org/10.1162/neco.2006.18.10.2414.

Full text

Abstract:

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot

APA, Harvard, Vancouver, ISO, and other styles

14

Farries, Michael A., and Adrienne L. Fairhall. "Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity." Journal of Neurophysiology 98, no. 6 (2007): 3648–65. http://dx.doi.org/10.1152/jn.00364.2007.

Full text

Abstract:

Spike timing–dependent synaptic plasticity (STDP) has emerged as the preferred framework linking patterns of pre- and postsynaptic activity to changes in synaptic strength. Although synaptic plasticity is widely believed to be a major component of learning, it is unclear how STDP itself could serve as a mechanism for general purpose learning. On the other hand, algorithms for reinforcement learning work on a wide variety of problems, but lack an experimentally established neural implementation. Here, we combine these paradigms in a novel model in which a modified version of STDP achieves reinf

APA, Harvard, Vancouver, ISO, and other styles

15

Wang, W., G. Pedretti, V. Milo, et al. "Computing of temporal information in spiking neural networks with ReRAM synapses." Faraday Discussions 213 (2019): 453–69. http://dx.doi.org/10.1039/c8fd00097b.

Full text

Abstract:

This work addresses the methodology and implementation of a neuromorphic SNN system to compute the temporal information among neural spikes using ReRAM synapses capable of spike-timing dependent plasticity (STDP).

APA, Harvard, Vancouver, ISO, and other styles

16

Gidon, Albert, and Idan Segev. "Spike-Timing–Dependent Synaptic Plasticity and Synaptic Democracy in Dendrites." Journal of Neurophysiology 101, no. 6 (2009): 3226–34. http://dx.doi.org/10.1152/jn.91349.2008.

Full text

Abstract:

We explored in a computational study the effect of dendrites on excitatory synapses undergoing spike-timing–dependent plasticity (STDP), using both cylindrical dendritic models and reconstructed dendritic trees. We show that even if the initial strength, gpeak, of distal synapses is augmented in a location independent manner, the efficacy of distal synapses diminishes following STDP and proximal synapses would eventually dominate. Indeed, proximal synapses always win over distal synapses following linear STDP rule, independent of the initial synaptic strength distribution in the dendritic tree

APA, Harvard, Vancouver, ISO, and other styles

17

Tavanaei, Amirhossein, and Anthony Maida. "BP-STDP: Approximating backpropagation using spike timing dependent plasticity." Neurocomputing 330 (February 2019): 39–47. http://dx.doi.org/10.1016/j.neucom.2018.11.014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Rubin, Jonathan E., Richard C. Gerkin, Guo-Qiang Bi, and Carson C. Chow. "Calcium Time Course as a Signal for Spike-Timing–Dependent Plasticity." Journal of Neurophysiology 93, no. 5 (2005): 2600–2613. http://dx.doi.org/10.1152/jn.00803.2004.

Full text

Abstract:

Calcium has been proposed as a postsynaptic signal underlying synaptic spike-timing–dependent plasticity (STDP). We examine this hypothesis with computational modeling based on experimental results from hippocampal cultures, some of which are presented here, in which pairs and triplets of pre- and postsynaptic spikes induce potentiation and depression in a temporally asymmetric way. Specifically, we present a set of model biochemical detectors, based on plausible molecular pathways, which make direct use of the time course of the calcium signal to reproduce these experimental STDP results. Our

APA, Harvard, Vancouver, ISO, and other styles

19

Nobukawa, Sou, Haruhiko Nishimura, and Teruya Yamanishi. "Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity." Journal of Artificial Intelligence and Soft Computing Research 9, no. 4 (2019): 283–91. http://dx.doi.org/10.2478/jaiscr-2019-0009.

Full text

Abstract:

Abstract Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised le

APA, Harvard, Vancouver, ISO, and other styles

20

Gilson, Matthieu, Moritz Bürck, Anthony N. Burkitt, and J. Leo van Hemmen. "Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity." Neural Computation 24, no. 9 (2012): 2251–79. http://dx.doi.org/10.1162/neco_a_00331.

Full text

Abstract:

Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demons

APA, Harvard, Vancouver, ISO, and other styles

21

Pool, R. Rossi, and G. Mato. "Spike-Timing-Dependent Plasticity and Reliability Optimization: The Role of Neuron Dynamics." Neural Computation 23, no. 7 (2011): 1768–89. http://dx.doi.org/10.1162/neco_a_00140.

Full text

Abstract:

Plastic changes in synaptic efficacy can depend on the time ordering of presynaptic and postsynaptic spikes. This phenomenon is called spike-timing-dependent plasticity (STDP). One of the most striking aspects of this plasticity mechanism is that the STDP windows display a great variety of forms in different parts of the nervous system. We explore this issue from a theoretical point of view. We choose as the optimization principle the minimization of conditional entropy or maximization of reliability in the transmission of information. We apply this principle to two types of postsynaptic dynam

APA, Harvard, Vancouver, ISO, and other styles

22

Verhoog, Matthijs B., and Huibert D. Mansvelder. "Presynaptic Ionotropic Receptors Controlling and Modulating the Rules for Spike Timing-Dependent Plasticity." Neural Plasticity 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/870763.

Full text

Abstract:

Throughout life, activity-dependent changes in neuronal connection strength enable the brain to refine neural circuits and learn based on experience. In line with predictions made by Hebb, synapse strength can be modified depending on the millisecond timing of action potential firing (STDP). The sign of synaptic plasticity depends on the spike order of presynaptic and postsynaptic neurons. Ionotropic neurotransmitter receptors, such as NMDA receptors and nicotinic acetylcholine receptors, are intimately involved in setting the rules for synaptic strengthening and weakening. In addition, timing

APA, Harvard, Vancouver, ISO, and other styles

23

Robinson, Brian S., Theodore W. Berger, and Dong Song. "Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling." Neural Computation 28, no. 11 (2016): 2320–51. http://dx.doi.org/10.1162/neco_a_00883.

Full text

Abstract:

Characterization of long-term activity-dependent plasticity from behaviorally driven spiking activity is important for understanding the underlying mechanisms of learning and memory. In this letter, we present a computational framework for quantifying spike-timing-dependent plasticity (STDP) during behavior by identifying a functional plasticity rule solely from spiking activity. First, we formulate a flexible point-process spiking neuron model structure with STDP, which includes functions that characterize the stationary and plastic properties of the neuron. The STDP model includes a novel fu

APA, Harvard, Vancouver, ISO, and other styles

24

Guyonneau, Rudy, Rufin VanRullen, and Simon J. Thorpe. "Neurons Tune to the Earliest Spikes Through STDP." Neural Computation 17, no. 4 (2005): 859–79. http://dx.doi.org/10.1162/0899766053429390.

Full text

Abstract:

Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between input and output spikes. In many brains areas, temporal aspects of spike trains have been found to be highly reproducible. How will STDP affect a neuron's behavior when it is repeatedly presented with the same input spike pattern? We show in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic re

APA, Harvard, Vancouver, ISO, and other styles

25

SHIN, CHANG-WOO, and SEUNGHWAN KIM. "HIERARCHICAL MODULARITY OF THE FUNCTIONAL NEURAL NETWORK ORGANIZED BY SPIKE TIMING DEPENDENT SYNAPTIC PLASTICITY." International Journal of Modern Physics B 21, no. 23n24 (2007): 4124–29. http://dx.doi.org/10.1142/s021797920704530x.

Full text

Abstract:

We study the emergent functional neural network organized by synaptic reorganization by the spike timing dependent synaptic plasticity (STDP). We show that small-world and scale-free functional structures organized by STDP, in the case of synaptic balance, exhibit hierarchial modularity.

APA, Harvard, Vancouver, ISO, and other styles

26

Ocker, Gabriel Koch, and Brent Doiron. "Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing Plasticity." Cerebral Cortex 29, no. 3 (2018): 937–51. http://dx.doi.org/10.1093/cercor/bhy001.

Full text

Abstract:

Abstract The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectiv

APA, Harvard, Vancouver, ISO, and other styles

27

Elliott, Terry. "A Non-Markovian Random Walk Underlies a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 22, no. 5 (2010): 1180–230. http://dx.doi.org/10.1162/neco.2009.06-09-1038.

Full text

Abstract:

A stochastic model of spike-timing-dependent plasticity (STDP) proposes that spike timing influences the probability but not the amplitude of synaptic strength change at single synapses. The classic, biphasic STDP profile emerges as a spatial average over many synapses presented with a single spike pair or as a temporal average over a single synapse presented with many spike pairs. We have previously shown that the model accounts for a variety of experimental data, including spike triplet results, and has a number of desirable theoretical properties, including being entirely self-stabilizing i

APA, Harvard, Vancouver, ISO, and other styles

28

Karmarkar, Uma R., and Dean V. Buonomano. "A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors?" Journal of Neurophysiology 88, no. 1 (2002): 507–13. http://dx.doi.org/10.1152/jn.2002.88.1.507.

Full text

Abstract:

In spike-timing dependent plasticity (STDP), synapses exhibit LTD or LTP depending on the order of activity in the presynaptic and postsynaptic cells. LTP occurs when a single presynaptic spike precedes a postsynaptic one (a positive interspike interval, or ISI), while the reverse order of activity (a negative ISI) produces LTD. A fundamental question is whether the “standard model” of plasticity in which moderate increases in Ca2+ influx through the N-methyl-d-aspartate (NMDA) channels induce LTD and large increases induce LTP, can account for the order and interval sensitivity of STDP. To ex

APA, Harvard, Vancouver, ISO, and other styles

29

Bohte, Sander M., and Michael C. Mozer. "Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity." Neural Computation 19, no. 2 (2007): 371–403. http://dx.doi.org/10.1162/neco.2007.19.2.371.

Full text

Abstract:

Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as spike-timing dependent plasticity (STDP). We derive STDP from a simple computational principle: synapses adapt so as to minimize the postsynaptic neuron's response variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an objective f

APA, Harvard, Vancouver, ISO, and other styles

30

Shen, Xi, Xiaobin Lin, and Philippe De Wilde. "Oscillations and Spiking Pairs: Behavior of a Neuronal Model with STDP Learning." Neural Computation 20, no. 8 (2008): 2037–69. http://dx.doi.org/10.1162/neco.2008.08-06-317.

Full text

Abstract:

In a biologically plausible but computationally simplified integrate-and-fire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models. The effects of spike-timing-dependent plasticity have been known to play a distinct role in information processing in the central nervous system for several years. In this letter, neuronal models with dynamical synapses are constructed, and we analyze the effect of STDP on co

APA, Harvard, Vancouver, ISO, and other styles

31

Zaitsev, Aleksey V., and Roger Anwyl. "Inhibition of the slow afterhyperpolarization restores the classical spike timing-dependent plasticity rule obeyed in layer 2/3 pyramidal cells of the prefrontal cortex." Journal of Neurophysiology 107, no. 1 (2012): 205–15. http://dx.doi.org/10.1152/jn.00452.2011.

Full text

Abstract:

The induction of long-term potentiation (LTP) and long-term depression (LTD) of excitatory postsynaptic currents was investigated in proximal synapses of layer 2/3 pyramidal cells of the rat medial prefrontal cortex. The spike timing-dependent plasticity (STDP) induction protocol of negative timing, with postsynaptic leading presynaptic stimulation of action potentials (APs), induced LTD as expected from the classical STDP rule. However, the positive STDP protocol of presynaptic leading postsynaptic stimulation of APs predominantly induced a presynaptically expressed LTD rather than the expect

APA, Harvard, Vancouver, ISO, and other styles

32

Inglebert, Yanis, Johnatan Aljadeff, Nicolas Brunel, and Dominique Debanne. "Synaptic plasticity rules with physiological calcium levels." Proceedings of the National Academy of Sciences 117, no. 52 (2020): 33639–48. http://dx.doi.org/10.1073/pnas.2013663117.

Full text

Abstract:

Spike-timing–dependent plasticity (STDP) is considered as a primary mechanism underlying formation of new memories during learning. Despite the growing interest in activity-dependent plasticity, it is still unclear whether synaptic plasticity rules inferred from in vitro experiments are correct in physiological conditions. The abnormally high calcium concentration used in in vitro studies of STDP suggests that in vivo plasticity rules may differ significantly from in vitro experiments, especially since STDP depends strongly on calcium for induction. We therefore studied here the influence of e

APA, Harvard, Vancouver, ISO, and other styles

33

Câteau, Hideyuki, and Tomoki Fukai. "A Stochastic Method to Predict the Consequence of Arbitrary Forms of Spike-Timing-Dependent Plasticity." Neural Computation 15, no. 3 (2003): 597–620. http://dx.doi.org/10.1162/089976603321192095.

Full text

Abstract:

Synapses in various neural preparations exhibit spike-timing-dependent plasticity (STDP) with a variety of learning window functions. The window functions determine the magnitude and the polarity of synaptic change according to the time difference of pre- and postsynaptic spikes. Numerical experiments revealed that STDP learning with a single-expo nential window function resulted in a bimodal distribution of synaptic conductances as a consequence of competition between synapses. A slightly modified window function, however, resulted in a unimodal distribution rather than a bimodal distribution

APA, Harvard, Vancouver, ISO, and other styles

34

Prokin, Ilya, Ivan Tyukin, and Victor Kazantsev. "Phase Selective Oscillations in Two Noise Driven Synaptically Coupled Spiking Neurons." International Journal of Bifurcation and Chaos 25, no. 07 (2015): 1540005. http://dx.doi.org/10.1142/s0218127415400052.

Full text

Abstract:

The work investigates the influence of spike-timing dependent plasticity (STDP) mechanisms on the dynamics of two synaptically coupled neurons driven by additive external noise. In this setting, the noise signal models synaptic inputs that the pair receives from other neurons in a larger network. We show that in the absence of STDP feedbacks the pair of neurons exhibit oscillations and intermittent synchronization. When the synapse connecting the neurons is supplied with a phase selective feedback mechanism simulating STDP, induced dynamics of spikes in the coupled system resembles a phase loc

APA, Harvard, Vancouver, ISO, and other styles

35

Bar Ilan, Lital, Albert Gidon, and Idan Segev. "Interregional synaptic competition in neurons with multiple STDP-inducing signals." Journal of Neurophysiology 105, no. 3 (2011): 989–98. http://dx.doi.org/10.1152/jn.00612.2010.

Full text

Abstract:

Neocortical layer 5 (L5) pyramidal cells have at least two spike initiation zones: Na+ spikes are generated near the soma, and Ca2+ spikes at the apical dendritic tuft. These spikes interact with each other and serve as signals for synaptic plasticity. The present computational study explores the implications of having two spike-timing-dependent plasticity (STDP) signals in a neuron, each with its respective regional population of synaptic “pupils.” In a detailed model of an L5 pyramidal neuron, competition emerges between synapses belonging to different regions, on top of the competition amon

APA, Harvard, Vancouver, ISO, and other styles

36

Hosaka, Ryosuke, Osamu Araki, and Tohru Ikeguchi. "STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns." Neural Computation 20, no. 2 (2008): 415–35. http://dx.doi.org/10.1162/neco.2007.11-05-043.

Full text

Abstract:

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory

APA, Harvard, Vancouver, ISO, and other styles

37

Franosch, Jan-Moritz P., Sebastian Urban, and J. Leo van Hemmen. "Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions." Neural Computation 25, no. 12 (2013): 3113–30. http://dx.doi.org/10.1162/neco_a_00520.

Full text

Abstract:

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

APA, Harvard, Vancouver, ISO, and other styles

38

Cameron, K., V. Boonsobhak, A. Murray, and D. Renshaw. "Spike Timing Dependent Plasticity (STDP) can Ameliorate Process Variations in Neuromorphic VLSI." IEEE Transactions on Neural Networks 16, no. 6 (2005): 1626–37. http://dx.doi.org/10.1109/tnn.2005.852238.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Elliott, Terry. "Discrete States of Synaptic Strength in a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 22, no. 1 (2010): 244–72. http://dx.doi.org/10.1162/neco.2009.07-08-814.

Full text

Abstract:

A stochastic model of spike-timing-dependent plasticity (STDP) postulates that single synapses presented with a single spike pair exhibit all-or-none quantal jumps in synaptic strength. The amplitudes of the jumps are independent of spiking timing, but their probabilities do depend on spiking timing. By making the amplitudes of both upward and downward transitions equal, synapses then occupy only a discrete set of states of synaptic strength. We explore the impact of a finite, discrete set of strength states on our model, finding three principal results. First, a finite set of strength states

APA, Harvard, Vancouver, ISO, and other styles

40

Conde, Virginia, Henning Vollmann, Marco Taubert, et al. "Reversed timing-dependent associative plasticity in the human brain through interhemispheric interactions." Journal of Neurophysiology 109, no. 9 (2013): 2260–71. http://dx.doi.org/10.1152/jn.01004.2012.

Full text

Abstract:

Spike timing-dependent plasticity (STDP) has been proposed as one of the key mechanisms underlying learning and memory. Repetitive median nerve stimulation, followed by transcranial magnetic stimulation (TMS) of the contralateral primary motor cortex (M1), defined as paired-associative stimulation (PAS), has been used as an in vivo model of STDP in humans. PAS-induced excitability changes in M1 have been repeatedly shown to be time-dependent in a STDP-like fashion, since synchronous arrival of inputs within M1 induces long-term potentiation-like effects, whereas an asynchronous arrival induces

APA, Harvard, Vancouver, ISO, and other styles

41

Palmer, John H. C., and Pulin Gong. "Formation and Regulation of Dynamic Patterns in Two-Dimensional Spiking Neural Circuits with Spike-Timing-Dependent Plasticity." Neural Computation 25, no. 11 (2013): 2833–57. http://dx.doi.org/10.1162/neco_a_00511.

Full text

Abstract:

Spike-timing-dependent plasticity (STDP) is an important synaptic dynamics that is capable of shaping the complex spatiotemporal activity of neural circuits. In this study, we examine the effects of STDP on the spatiotemporal patterns of a spatially extended, two-dimensional spiking neural circuit. We show that STDP can promote the formation of multiple, localized spiking wave patterns or multiple spike timing sequences in a broad parameter space of the neural circuit. Furthermore, we illustrate that the formation of these dynamic patterns is due to the interaction between the dynamics of ongo

APA, Harvard, Vancouver, ISO, and other styles

42

Garrido, Jesús A., Niceto R. Luque, Silvia Tolu, and Egidio D’Angelo. "Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks." International Journal of Neural Systems 26, no. 05 (2016): 1650020. http://dx.doi.org/10.1142/s0129065716500209.

Full text

Abstract:

The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input

APA, Harvard, Vancouver, ISO, and other styles

43

Hwang, Sungmin, Hyungjin Kim, and Byung-Gook Park. "Quantized Weight Transfer Method Using Spike-Timing-Dependent Plasticity for Hardware Spiking Neural Network." Applied Sciences 11, no. 5 (2021): 2059. http://dx.doi.org/10.3390/app11052059.

Full text

Abstract:

A hardware-based spiking neural network (SNN) has attracted many researcher’s attention due to its energy-efficiency. When implementing the hardware-based SNN, offline training is most commonly used by which trained weights by a software-based artificial neural network (ANN) are transferred to synaptic devices. However, it is time-consuming to map all the synaptic weights as the scale of the neural network increases. In this paper, we propose a method for quantized weight transfer using spike-timing-dependent plasticity (STDP) for hardware-based SNN. STDP is an online learning algorithm for SN

APA, Harvard, Vancouver, ISO, and other styles

44

Masquelier, Timothée, Rudy Guyonneau, and Simon J. Thorpe. "Competitive STDP-Based Spike Pattern Learning." Neural Computation 21, no. 5 (2009): 1259–76. http://dx.doi.org/10.1162/neco.2008.06-08-804.

Full text

Abstract:

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, i

APA, Harvard, Vancouver, ISO, and other styles

45

ZHAO, GANG. "PROBABILITY OF ENTRAINMENT, SYNAPTIC MODIFICATION AND ENTRAINED PHASE ARE PHASE-DEPENDENT IN STDP." Journal of Biological Systems 18, no. 02 (2010): 479–93. http://dx.doi.org/10.1142/s0218339010003354.

Full text

Abstract:

Synaptic conductance can be modified in an activity dependent manner, in which the temporal relationship between pre- and post-synaptic spikes plays a major role. This spike timing dependent plasticity (STDP) has profound implications in neural coding, computation and functionality. Because the STDP learning curve is strongly nonlinear, initial state may have great impacts on the eventual state of the system. However, although this feature is intuitively clear, it has not been explored in detail before. This paper presents a preliminary numerical study in this direction. In a model of two pace

APA, Harvard, Vancouver, ISO, and other styles

46

Fuenzalida, Marco, David Fernández de Sevilla, Alejandro Couve, and Washington Buño. "Role of AMPA and NMDA Receptors and Back-Propagating Action Potentials in Spike Timing–Dependent Plasticity." Journal of Neurophysiology 103, no. 1 (2010): 47–54. http://dx.doi.org/10.1152/jn.00416.2009.

Full text

Abstract:

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; EPSPAMPA and EPSPNMDA) 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 EPSPAMPA with 7-nitro-2,3-dioxo-1,4-dihydroquinoxaline-6-carbonitrile (CNQX) during the STDP protocol prevented L

APA, Harvard, Vancouver, ISO, and other styles

47

Pedró, Marta, Javier Martín-Martínez, Marcos Maestro-Izquierdo, Rosana Rodríguez, and Montserrat Nafría. "Self-Organizing Neural Networks Based on OxRAM Devices under a Fully Unsupervised Training Scheme." Materials 12, no. 21 (2019): 3482. http://dx.doi.org/10.3390/ma12213482.

Full text

Abstract:

A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic architectures is provided in this work. We experimentally demonstrate spike-timing dependent plasticity (STDP) in Oxide-based Resistive Random Access Memory (OxRAM) devices, and propose a set of waveforms in order to induce symmetric conductivity changes. An empirical model is used to describe the observed plasticity. A neuromorphic system based on the tested devices is simulated, where the developed learning algorithm is tested, involving STDP as the local learning rule. The design of the system and learnin

APA, Harvard, Vancouver, ISO, and other styles

48

Burkitt, Anthony N., Hamish Meffin, and David B. Grayden. "Spike-Timing-Dependent Plasticity: The Relationship to Rate-Based Learning for Models with Weight Dynamics Determined by a Stable Fixed Point." Neural Computation 16, no. 5 (2004): 885–940. http://dx.doi.org/10.1162/089976604773135041.

Full text

Abstract:

Experimental evidence indicates that synaptic modification depends on the timing relationship between the presynaptic inputs and the output spikes that they generate. In this letter, results are presented for models of spike-timing-dependent plasticity (STDP) whose weight dynamics is determined by a stable fixed point. Four classes of STDP are identified on the basis of the time extent of their input-output interactions. The effect on the potentiation of synapses with different rates of input is investigated to elucidate the relationship of STDP with classical studies of long-term potentiation

APA, Harvard, Vancouver, ISO, and other styles

49

SAMURA, TOSHIKAZU, and MOTONOBU HATTORI. "HIPPOCAMPAL MEMORY MODIFICATION INDUCED BY PATTERN COMPLETION AND SPIKE-TIMING DEPENDENT SYNAPTIC PLASTICITY." International Journal of Neural Systems 15, no. 01n02 (2005): 13–22. http://dx.doi.org/10.1142/s0129065705000025.

Full text

Abstract:

One of the roles of the hippocampus is viewed as modifying episodic memory so that it can contribute to form semantic memory. In this paper, we show that pattern completion ability of the hippocampal CA3 and symmetric spike timing-dependent synaptic plasticity (STDP) induce memory modification so that the hippocampal CA3 can memorize invariable parts of repetitive episodes as essential elements and forget variable parts of them as unnecessary ones.

APA, Harvard, Vancouver, ISO, and other styles

50

Gangarossa, Giuseppe, Sylvie Perez, Yulia Dembitskaya, Ilya Prokin, Hugues Berry, and Laurent Venance. "BDNF Controls Bidirectional Endocannabinoid Plasticity at Corticostriatal Synapses." Cerebral Cortex 30, no. 1 (2019): 197–214. http://dx.doi.org/10.1093/cercor/bhz081.

Full text

Abstract:

AbstractThe dorsal striatum exhibits bidirectional corticostriatal synaptic plasticity, NMDAR and endocannabinoids (eCB) mediated, necessary for the encoding of procedural learning. Therefore, characterizing factors controlling corticostriatal plasticity is of crucial importance. Brain-derived neurotrophic factor (BDNF) and its receptor, the tropomyosine receptor kinase-B (TrkB), shape striatal functions, and their dysfunction deeply affects basal ganglia. BDNF/TrkB signaling controls NMDAR plasticity in various brain structures including the striatum. However, despite cross-talk between BDNF

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!