Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/78139

TitleA dynamic neural field model of continuous input integration
Author(s)Wojtak, Weronika
Coombes, Stephen
Avitabile, Daniele
Bicho, Estela
Erlhagen, Wolfram
Keywordscognitive systems
dynamic neural fields
decision making
conservation law
localized states
stability
input integration
dynamic neural field
Issue date21-Aug-2021
PublisherSpringer
JournalBiological Cybernetics
CitationWojtak, W., Coombes, S., Avitabile, D., Bicho, E., & Erlhagen, W. (2021, August 21). A dynamic neural field model of continuous input integration. Biological Cybernetics. Springer Science and Business Media LLC. http://doi.org/10.1007/s00422-021-00893-7
Abstract(s)The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a “bump” independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.
TypeArticle
DescriptionCode availability Example codes implemented in MATLAB are available at https://github.com/w-wojtak/A-dynamic-neural-field-model of-continuous-input-integrati
URIhttps://hdl.handle.net/1822/78139
DOI10.1007/s00422-021-00893-7
ISSN0340-1200
Publisher versionhttps://doi.org/10.1007/s00422-021-00893-7
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals
CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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