A spike-timing pattern based neural network model for the study of memory dynamics.

It is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timi...

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Autores principales:	Jian K Liu, Zhen-Su She
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eda4b2be721448eabb421e2281471c18
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id	oai:doaj.org-article:eda4b2be721448eabb421e2281471c18
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spelling	oai:doaj.org-article:eda4b2be721448eabb421e2281471c182021-11-25T06:21:27ZA spike-timing pattern based neural network model for the study of memory dynamics.1932-620310.1371/journal.pone.0006247https://doaj.org/article/eda4b2be721448eabb421e2281471c182009-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19629179/?tool=EBIhttps://doaj.org/toc/1932-6203It is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.Jian K LiuZhen-Su ShePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 4, Iss 7, p e6247 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jian K Liu Zhen-Su She A spike-timing pattern based neural network model for the study of memory dynamics.
description	It is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.
format	article
author	Jian K Liu Zhen-Su She
author_facet	Jian K Liu Zhen-Su She
author_sort	Jian K Liu
title	A spike-timing pattern based neural network model for the study of memory dynamics.
title_short	A spike-timing pattern based neural network model for the study of memory dynamics.
title_full	A spike-timing pattern based neural network model for the study of memory dynamics.
title_fullStr	A spike-timing pattern based neural network model for the study of memory dynamics.
title_full_unstemmed	A spike-timing pattern based neural network model for the study of memory dynamics.
title_sort	spike-timing pattern based neural network model for the study of memory dynamics.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/eda4b2be721448eabb421e2281471c18
work_keys_str_mv	AT jiankliu aspiketimingpatternbasedneuralnetworkmodelforthestudyofmemorydynamics AT zhensushe aspiketimingpatternbasedneuralnetworkmodelforthestudyofmemorydynamics AT jiankliu spiketimingpatternbasedneuralnetworkmodelforthestudyofmemorydynamics AT zhensushe spiketimingpatternbasedneuralnetworkmodelforthestudyofmemorydynamics
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A spike-timing pattern based neural network model for the study of memory dynamics.

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