Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.

The head direction cell system is capable of accurately updating its current representation of head direction in the absence of visual input. This is known as the path integration of head direction. An important question is how the head direction cell system learns to perform accurate path integrati...

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Main Authors: Daniel Walters, Simon Stringer, Edmund Rolls
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3602583?pdf=render
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spelling doaj-89db63a606c840e29099ba36a57ac76c2020-11-25T02:55:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0183e5833010.1371/journal.pone.0058330Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.Daniel WaltersSimon StringerEdmund RollsThe head direction cell system is capable of accurately updating its current representation of head direction in the absence of visual input. This is known as the path integration of head direction. An important question is how the head direction cell system learns to perform accurate path integration of head direction. In this paper we propose a model of velocity path integration of head direction in which the natural time delay of axonal transmission between a linked continuous attractor network and competitive network acts as a timing mechanism to facilitate the correct speed of path integration. The model effectively learns a "look-up" table for the correct speed of path integration. In simulation, we show that the model is able to successfully learn two different speeds of path integration across two different axonal conduction delays, and without the need to alter any other model parameters. An implication of this model is that, by learning look-up tables for each speed of path integration, the model should exhibit a degree of robustness to damage. In simulations, we show that the speed of path integration is not significantly affected by degrading the network through removing a proportion of the cells that signal rotational velocity.http://europepmc.org/articles/PMC3602583?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Daniel Walters
Simon Stringer
Edmund Rolls
spellingShingle Daniel Walters
Simon Stringer
Edmund Rolls
Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.
PLoS ONE
author_facet Daniel Walters
Simon Stringer
Edmund Rolls
author_sort Daniel Walters
title Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.
title_short Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.
title_full Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.
title_fullStr Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.
title_full_unstemmed Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.
title_sort path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2013-01-01
description The head direction cell system is capable of accurately updating its current representation of head direction in the absence of visual input. This is known as the path integration of head direction. An important question is how the head direction cell system learns to perform accurate path integration of head direction. In this paper we propose a model of velocity path integration of head direction in which the natural time delay of axonal transmission between a linked continuous attractor network and competitive network acts as a timing mechanism to facilitate the correct speed of path integration. The model effectively learns a "look-up" table for the correct speed of path integration. In simulation, we show that the model is able to successfully learn two different speeds of path integration across two different axonal conduction delays, and without the need to alter any other model parameters. An implication of this model is that, by learning look-up tables for each speed of path integration, the model should exhibit a degree of robustness to damage. In simulations, we show that the speed of path integration is not significantly affected by degrading the network through removing a proportion of the cells that signal rotational velocity.
url http://europepmc.org/articles/PMC3602583?pdf=render
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AT simonstringer pathintegrationofheaddirectionupdatingapacketofneuralactivityatthecorrectspeedusingaxonalconductiondelays
AT edmundrolls pathintegrationofheaddirectionupdatingapacketofneuralactivityatthecorrectspeedusingaxonalconductiondelays
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