A novel analytical characterization for short-term plasticity parameters in spiking neural networks

Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both...

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Main Authors:	O'Brien, Michael J., Thibeault, Corey M., Srinivasa, Narayan
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4237058/

id	pubmed-4237058
recordtype	oai_dc
spelling	pubmed-42370582014-12-04 A novel analytical characterization for short-term plasticity parameters in spiking neural networks O'Brien, Michael J. Thibeault, Corey M. Srinivasa, Narayan Neuroscience Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search. Frontiers Media S.A. 2014-11-19 /pmc/articles/PMC4237058/ /pubmed/25477812 http://dx.doi.org/10.3389/fncom.2014.00148 Text en Copyright © 2014 HRL Laboratories LLC. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	O'Brien, Michael J. Thibeault, Corey M. Srinivasa, Narayan
spellingShingle	O'Brien, Michael J. Thibeault, Corey M. Srinivasa, Narayan A novel analytical characterization for short-term plasticity parameters in spiking neural networks
author_facet	O'Brien, Michael J. Thibeault, Corey M. Srinivasa, Narayan
author_sort	O'Brien, Michael J.
title	A novel analytical characterization for short-term plasticity parameters in spiking neural networks
title_short	A novel analytical characterization for short-term plasticity parameters in spiking neural networks
title_full	A novel analytical characterization for short-term plasticity parameters in spiking neural networks
title_fullStr	A novel analytical characterization for short-term plasticity parameters in spiking neural networks
title_full_unstemmed	A novel analytical characterization for short-term plasticity parameters in spiking neural networks
title_sort	novel analytical characterization for short-term plasticity parameters in spiking neural networks
description	Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.
publisher	Frontiers Media S.A.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4237058/
_version_	1613158547116261376

A novel analytical characterization for short-term plasticity parameters in spiking neural networks

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