Continuous attractors with morphed/correlated maps.

Continuous attractor networks are used to model the storage and representation of analog quantities, such as position of a visual stimulus. The storage of multiple continuous attractors in the same network has previously been studied in the context of self-position coding. Several uncorrelated maps...

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Main Authors: Sandro Romani, Misha Tsodyks
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2010-01-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC2916844?pdf=render
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spelling doaj-6ba82d434c7041d5ad26e55e093bb8982020-11-25T02:05:18ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-01-016886186410.1371/journal.pcbi.1000869Continuous attractors with morphed/correlated maps.Sandro RomaniMisha TsodyksContinuous attractor networks are used to model the storage and representation of analog quantities, such as position of a visual stimulus. The storage of multiple continuous attractors in the same network has previously been studied in the context of self-position coding. Several uncorrelated maps of environments are stored in the synaptic connections, and a position in a given environment is represented by a localized pattern of neural activity in the corresponding map, driven by a spatially tuned input. Here we analyze networks storing a pair of correlated maps, or a morph sequence between two uncorrelated maps. We find a novel state in which the network activity is simultaneously localized in both maps. In this state, a fixed cue presented to the network does not determine uniquely the location of the bump, i.e. the response is unreliable, with neurons not always responding when their preferred input is present. When the tuned input varies smoothly in time, the neuronal responses become reliable and selective for the environment: the subset of neurons responsive to a moving input in one map changes almost completely in the other map. This form of remapping is a non-trivial transformation between the tuned input to the network and the resulting tuning curves of the neurons. The new state of the network could be related to the formation of direction selectivity in one-dimensional environments and hippocampal remapping. The applicability of the model is not confined to self-position representations; we show an instance of the network solving a simple delayed discrimination task.http://europepmc.org/articles/PMC2916844?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Sandro Romani
Misha Tsodyks
spellingShingle Sandro Romani
Misha Tsodyks
Continuous attractors with morphed/correlated maps.
PLoS Computational Biology
author_facet Sandro Romani
Misha Tsodyks
author_sort Sandro Romani
title Continuous attractors with morphed/correlated maps.
title_short Continuous attractors with morphed/correlated maps.
title_full Continuous attractors with morphed/correlated maps.
title_fullStr Continuous attractors with morphed/correlated maps.
title_full_unstemmed Continuous attractors with morphed/correlated maps.
title_sort continuous attractors with morphed/correlated maps.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2010-01-01
description Continuous attractor networks are used to model the storage and representation of analog quantities, such as position of a visual stimulus. The storage of multiple continuous attractors in the same network has previously been studied in the context of self-position coding. Several uncorrelated maps of environments are stored in the synaptic connections, and a position in a given environment is represented by a localized pattern of neural activity in the corresponding map, driven by a spatially tuned input. Here we analyze networks storing a pair of correlated maps, or a morph sequence between two uncorrelated maps. We find a novel state in which the network activity is simultaneously localized in both maps. In this state, a fixed cue presented to the network does not determine uniquely the location of the bump, i.e. the response is unreliable, with neurons not always responding when their preferred input is present. When the tuned input varies smoothly in time, the neuronal responses become reliable and selective for the environment: the subset of neurons responsive to a moving input in one map changes almost completely in the other map. This form of remapping is a non-trivial transformation between the tuned input to the network and the resulting tuning curves of the neurons. The new state of the network could be related to the formation of direction selectivity in one-dimensional environments and hippocampal remapping. The applicability of the model is not confined to self-position representations; we show an instance of the network solving a simple delayed discrimination task.
url http://europepmc.org/articles/PMC2916844?pdf=render
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