Accurate path integration in continuous attractor network models of grid cells.

Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid...

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Autores principales:	Yoram Burak, Ila R Fiete
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/507dfade45ba4a73a9cc40891c4791a9
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id	oai:doaj.org-article:507dfade45ba4a73a9cc40891c4791a9
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spelling	oai:doaj.org-article:507dfade45ba4a73a9cc40891c4791a92021-11-25T05:41:50ZAccurate path integration in continuous attractor network models of grid cells.1553-734X1553-735810.1371/journal.pcbi.1000291https://doaj.org/article/507dfade45ba4a73a9cc40891c4791a92009-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19229307/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets triggered by external sensory cues. Such inadequacies, shared by various models, cast doubt on the dead-reckoning potential of the grid cell system. Here we focus on the question of accurate path integration, specifically in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat's velocity and heading direction. We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model networks can accurately integrate velocity inputs over a maximum of approximately 10-100 meters and approximately 1-10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other.Yoram BurakIla R FietePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 2, p e1000291 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yoram Burak Ila R Fiete Accurate path integration in continuous attractor network models of grid cells.
description	Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets triggered by external sensory cues. Such inadequacies, shared by various models, cast doubt on the dead-reckoning potential of the grid cell system. Here we focus on the question of accurate path integration, specifically in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat's velocity and heading direction. We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model networks can accurately integrate velocity inputs over a maximum of approximately 10-100 meters and approximately 1-10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other.
format	article
author	Yoram Burak Ila R Fiete
author_facet	Yoram Burak Ila R Fiete
author_sort	Yoram Burak
title	Accurate path integration in continuous attractor network models of grid cells.
title_short	Accurate path integration in continuous attractor network models of grid cells.
title_full	Accurate path integration in continuous attractor network models of grid cells.
title_fullStr	Accurate path integration in continuous attractor network models of grid cells.
title_full_unstemmed	Accurate path integration in continuous attractor network models of grid cells.
title_sort	accurate path integration in continuous attractor network models of grid cells.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/507dfade45ba4a73a9cc40891c4791a9
work_keys_str_mv	AT yoramburak accuratepathintegrationincontinuousattractornetworkmodelsofgridcells AT ilarfiete accuratepathintegrationincontinuousattractornetworkmodelsofgridcells
_version_	1718414516462551040

Accurate path integration in continuous attractor network models of grid cells.

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