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Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model

Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in th...

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Autores principales: Luz, Yotam, Shamir, Maoz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833372/
https://www.ncbi.nlm.nih.gov/pubmed/27082118
http://dx.doi.org/10.1371/journal.pcbi.1004878
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author Luz, Yotam
Shamir, Maoz
author_facet Luz, Yotam
Shamir, Maoz
author_sort Luz, Yotam
collection PubMed
description Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single post-synaptic cell. The pre-synaptic neural population is assumed to be oscillating at the same frequency, albeit with different phases, such that the net activity of the pre-synaptic population is constant in time. Thus, in the homogeneous case in which all synapses are equal, the post-synaptic neuron receives constant input and hence does not oscillate. To investigate the transition to oscillatory activity, we develop a mean-field Fokker-Planck approximation of the synaptic dynamics. We analyze the conditions causing the homogeneous solution to lose its stability. The findings show that oscillatory activity appears through a mechanism of spontaneous symmetry breaking. However, in the general case the homogeneous solution is unstable, and the synaptic dynamics does not converge to a different fixed point, but rather to a limit cycle. We show how the temporal structure of the STDP rule determines the stability of the homogeneous solution and the drift velocity of the limit cycle.
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spelling pubmed-48333722016-04-22 Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model Luz, Yotam Shamir, Maoz PLoS Comput Biol Research Article Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single post-synaptic cell. The pre-synaptic neural population is assumed to be oscillating at the same frequency, albeit with different phases, such that the net activity of the pre-synaptic population is constant in time. Thus, in the homogeneous case in which all synapses are equal, the post-synaptic neuron receives constant input and hence does not oscillate. To investigate the transition to oscillatory activity, we develop a mean-field Fokker-Planck approximation of the synaptic dynamics. We analyze the conditions causing the homogeneous solution to lose its stability. The findings show that oscillatory activity appears through a mechanism of spontaneous symmetry breaking. However, in the general case the homogeneous solution is unstable, and the synaptic dynamics does not converge to a different fixed point, but rather to a limit cycle. We show how the temporal structure of the STDP rule determines the stability of the homogeneous solution and the drift velocity of the limit cycle. Public Library of Science 2016-04-15 /pmc/articles/PMC4833372/ /pubmed/27082118 http://dx.doi.org/10.1371/journal.pcbi.1004878 Text en © 2016 Luz, Shamir http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Luz, Yotam
Shamir, Maoz
Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model
title Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model
title_full Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model
title_fullStr Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model
title_full_unstemmed Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model
title_short Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model
title_sort oscillations via spike-timing dependent plasticity in a feed-forward model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833372/
https://www.ncbi.nlm.nih.gov/pubmed/27082118
http://dx.doi.org/10.1371/journal.pcbi.1004878
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