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Cited by 39Memory and Neural MechanismsNeural dynamics and brain functionNeural Networks and Applications

Continuous attractors with morphed/correlated maps

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Romani, Sandro, Tsodyks, Misha·2010·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1000869PMC PMC2916844
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Abstract

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.

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  • Romani and Tsodyks, 2010 studied the effects of adding correlation to spatial maps in forward-engineered multistable attractor networks.

    “Romani and Tsodyks, 2010 studied the effects of adding correlation to spatial maps in forward-engineered multistable attractor networks”

    From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Romani and Tsodyks, 2010

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Authors
Romani, S., & Tsodyks, M.
Affiliations
Weizmann Institute of Science (IL)
Corresponding author
Tsodyks, Misha
ORCID
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2010-08-05
Volume
6
Issue
8
Pages
e1000869
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1000869
PMC
PMC2916844
OpenAlex
W1988510861
Cited by
39
References
5
Citations by year
2026: 3 · 2025: 2 · 2024: 1 · 2023: 12 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 1 · 2014: 5 · 2013: 1 · 2012: 1
FWCI
1.16
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
continuous attractor networks, spatial representation, remapping, direction selectivity, tuning curves
MeSH terms
Animals, Computer Simulation, Discrimination, Psychological, Hippocampus, Models, Biological, Numerical Analysis, Computer-Assisted, Neural Networks, Computer

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