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

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Academic literature on the topic 'STDP [Spike Timing Dependant Plasticity]'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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

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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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