Multiscale representations of community structures in attractor neural networks.

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit m...

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Autores principales:	Tatsuya Haga, Tomoki Fukai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/15f45421887a456e8e04392b725d9cd9
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id	oai:doaj.org-article:15f45421887a456e8e04392b725d9cd9
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spelling	oai:doaj.org-article:15f45421887a456e8e04392b725d9cd92021-12-02T19:58:02ZMultiscale representations of community structures in attractor neural networks.1553-734X1553-735810.1371/journal.pcbi.1009296https://doaj.org/article/15f45421887a456e8e04392b725d9cd92021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009296https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.Tatsuya HagaTomoki FukaiPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009296 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Tatsuya Haga Tomoki Fukai Multiscale representations of community structures in attractor neural networks.
description	Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.
format	article
author	Tatsuya Haga Tomoki Fukai
author_facet	Tatsuya Haga Tomoki Fukai
author_sort	Tatsuya Haga
title	Multiscale representations of community structures in attractor neural networks.
title_short	Multiscale representations of community structures in attractor neural networks.
title_full	Multiscale representations of community structures in attractor neural networks.
title_fullStr	Multiscale representations of community structures in attractor neural networks.
title_full_unstemmed	Multiscale representations of community structures in attractor neural networks.
title_sort	multiscale representations of community structures in attractor neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/15f45421887a456e8e04392b725d9cd9
work_keys_str_mv	AT tatsuyahaga multiscalerepresentationsofcommunitystructuresinattractorneuralnetworks AT tomokifukai multiscalerepresentationsofcommunitystructuresinattractorneuralnetworks
_version_	1718375765224980480

Multiscale representations of community structures in attractor neural networks.

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