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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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 |
work_keys_str_mv |
AT danielwalters pathintegrationofheaddirectionupdatingapacketofneuralactivityatthecorrectspeedusingaxonalconductiondelays AT simonstringer pathintegrationofheaddirectionupdatingapacketofneuralactivityatthecorrectspeedusingaxonalconductiondelays AT edmundrolls pathintegrationofheaddirectionupdatingapacketofneuralactivityatthecorrectspeedusingaxonalconductiondelays |
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