Slow manifolds within network dynamics encode working memory efficiently and robustly.

Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic is...

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Autores principales:	Elham Ghazizadeh, ShiNung Ching
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2f274d172787454d833548af91935355
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id	oai:doaj.org-article:2f274d172787454d833548af91935355
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spelling	oai:doaj.org-article:2f274d172787454d833548af919353552021-12-02T19:57:48ZSlow manifolds within network dynamics encode working memory efficiently and robustly.1553-734X1553-735810.1371/journal.pcbi.1009366https://doaj.org/article/2f274d172787454d833548af919353552021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009366https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.Elham GhazizadehShiNung ChingPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009366 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Elham Ghazizadeh ShiNung Ching Slow manifolds within network dynamics encode working memory efficiently and robustly.
description	Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.
format	article
author	Elham Ghazizadeh ShiNung Ching
author_facet	Elham Ghazizadeh ShiNung Ching
author_sort	Elham Ghazizadeh
title	Slow manifolds within network dynamics encode working memory efficiently and robustly.
title_short	Slow manifolds within network dynamics encode working memory efficiently and robustly.
title_full	Slow manifolds within network dynamics encode working memory efficiently and robustly.
title_fullStr	Slow manifolds within network dynamics encode working memory efficiently and robustly.
title_full_unstemmed	Slow manifolds within network dynamics encode working memory efficiently and robustly.
title_sort	slow manifolds within network dynamics encode working memory efficiently and robustly.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2f274d172787454d833548af91935355
work_keys_str_mv	AT elhamghazizadeh slowmanifoldswithinnetworkdynamicsencodeworkingmemoryefficientlyandrobustly AT shinungching slowmanifoldswithinnetworkdynamicsencodeworkingmemoryefficientlyandrobustly
_version_	1718375768845713408

Slow manifolds within network dynamics encode working memory efficiently and robustly.

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