The timing of vision - how neural processing links to different temporal dynamics

In this review, we describe our recent attempts to model the neural correlates of visual perception with biologically inspired networks of spiking neurons, emphasizing the dynamical aspects. Experimental evidence suggests distinct processing modes depending on the type of task the visual system is e...

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Detalles Bibliográficos
Autores: Masquelier, Timothée, Albantakis, Larissa, Deco, Gustavo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2011
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/25800
Acceso en línea:http://hdl.handle.net/10230/25800
http://dx.doi.org/10.3389/fpsyg.2011.00151
Access Level:acceso abierto
Palabra clave:Vision
Attention
Spiking neurons
Neurodynamics
Oscillations
STDP
Neural coding
Decision making
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spelling The timing of vision - how neural processing links to different temporal dynamicsMasquelier, TimothéeAlbantakis, LarissaDeco, GustavoVisionAttentionSpiking neuronsNeurodynamicsOscillationsSTDPNeural codingDecision makingIn this review, we describe our recent attempts to model the neural correlates of visual perception with biologically inspired networks of spiking neurons, emphasizing the dynamical aspects. Experimental evidence suggests distinct processing modes depending on the type of task the visual system is engaged in. A first mode, crucial for object recognition, deals with rapidly extracting the glimpse of a visual scene in the first 100 ms after its presentation. The promptness of this process points to mainly feedforward processing, which relies on latency coding, and may be shaped by spike timing-dependent plasticity (STDP). Our simulations confirm the plausibility and efficiency of such a scheme. A second mode can be engaged whenever one needs to perform finer perceptual discrimination through evidence accumulation on the order of 400 ms and above. Here, our simulations, together with theoretical considerations, show how predominantly local recurrent connections and long neural time-constants enable the integration and build-up of firing rates on this timescale. In particular, we review how a non-linear model with attractor states induced by strong recurrent connectivity provides straightforward explanations for several recent experimental observations. A third mode, involving additional top-down attentional signals, is relevant for more complex visual scene processing. In the model, as in the brain, these top-down attentional signals shape visual processing by biasing the competition between different pools of neurons. The winning pools may not only have a higher firing rate, but also more synchronous oscillatory activity. This fourth mode, oscillatory activity, leads to faster reaction times and enhanced information transfers in the model. This has indeed been observed experimentally. Moreover, oscillatory activity can format spike times and encode information in the spike phases with respect to the oscillatory cycle. This phenomenon is referred to as “phase-of-firing coding,” and experimental evidence for it is accumulating in the visual system. Simulations show that this code can again be efficiently decoded by STDP. Future work should focus on continuous natural vision, bio-inspired hardware vision systems, and novel experimental paradigms to further distinguish current modeling approaches.The authors were supported by the Fyssen Foundation, the FP7 European Project Coronet, and the CONSOLIDER-INGENIO 2010 Programme CSD2007-00012.Frontiers Media201620162011info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/25800http://dx.doi.org/10.3389/fpsyg.2011.00151reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésFrontiers in psychology. 2011;151(2):1-14info:eu-repo/grantAgreement/ES/2PN/CSD2007-00012info:eu-repo/grantAgreement//EC/FP7© 2011 Masquelier, Albantakis and Deco. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.http://creativecommons.org/licenses/by/3.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/258002026-05-29T05:05:01Z
dc.title.none.fl_str_mv The timing of vision - how neural processing links to different temporal dynamics
title The timing of vision - how neural processing links to different temporal dynamics
spellingShingle The timing of vision - how neural processing links to different temporal dynamics
Masquelier, Timothée
Vision
Attention
Spiking neurons
Neurodynamics
Oscillations
STDP
Neural coding
Decision making
title_short The timing of vision - how neural processing links to different temporal dynamics
title_full The timing of vision - how neural processing links to different temporal dynamics
title_fullStr The timing of vision - how neural processing links to different temporal dynamics
title_full_unstemmed The timing of vision - how neural processing links to different temporal dynamics
title_sort The timing of vision - how neural processing links to different temporal dynamics
dc.creator.none.fl_str_mv Masquelier, Timothée
Albantakis, Larissa
Deco, Gustavo
author Masquelier, Timothée
author_facet Masquelier, Timothée
Albantakis, Larissa
Deco, Gustavo
author_role author
author2 Albantakis, Larissa
Deco, Gustavo
author2_role author
author
dc.subject.none.fl_str_mv Vision
Attention
Spiking neurons
Neurodynamics
Oscillations
STDP
Neural coding
Decision making
topic Vision
Attention
Spiking neurons
Neurodynamics
Oscillations
STDP
Neural coding
Decision making
description In this review, we describe our recent attempts to model the neural correlates of visual perception with biologically inspired networks of spiking neurons, emphasizing the dynamical aspects. Experimental evidence suggests distinct processing modes depending on the type of task the visual system is engaged in. A first mode, crucial for object recognition, deals with rapidly extracting the glimpse of a visual scene in the first 100 ms after its presentation. The promptness of this process points to mainly feedforward processing, which relies on latency coding, and may be shaped by spike timing-dependent plasticity (STDP). Our simulations confirm the plausibility and efficiency of such a scheme. A second mode can be engaged whenever one needs to perform finer perceptual discrimination through evidence accumulation on the order of 400 ms and above. Here, our simulations, together with theoretical considerations, show how predominantly local recurrent connections and long neural time-constants enable the integration and build-up of firing rates on this timescale. In particular, we review how a non-linear model with attractor states induced by strong recurrent connectivity provides straightforward explanations for several recent experimental observations. A third mode, involving additional top-down attentional signals, is relevant for more complex visual scene processing. In the model, as in the brain, these top-down attentional signals shape visual processing by biasing the competition between different pools of neurons. The winning pools may not only have a higher firing rate, but also more synchronous oscillatory activity. This fourth mode, oscillatory activity, leads to faster reaction times and enhanced information transfers in the model. This has indeed been observed experimentally. Moreover, oscillatory activity can format spike times and encode information in the spike phases with respect to the oscillatory cycle. This phenomenon is referred to as “phase-of-firing coding,” and experimental evidence for it is accumulating in the visual system. Simulations show that this code can again be efficiently decoded by STDP. Future work should focus on continuous natural vision, bio-inspired hardware vision systems, and novel experimental paradigms to further distinguish current modeling approaches.
publishDate 2011
dc.date.none.fl_str_mv 2011
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/25800
http://dx.doi.org/10.3389/fpsyg.2011.00151
url http://hdl.handle.net/10230/25800
http://dx.doi.org/10.3389/fpsyg.2011.00151
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Frontiers in psychology. 2011;151(2):1-14
info:eu-repo/grantAgreement/ES/2PN/CSD2007-00012
info:eu-repo/grantAgreement//EC/FP7
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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