Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Deco, Gustavoca
- dc.contributor.author Hugues, Etienneca
- dc.date.accessioned 2016-02-17T16:57:43Z
- dc.date.available 2016-02-17T16:57:43Z
- dc.date.issued 2012
- dc.description.abstract Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural/nactivity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell/nrecordings show that attention reduces both the neural variability and correlations in the attended condition with respect/nto the non-attended one. This reduction of variability and redundancy enhances the information associated with the/ndetection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural/ncircuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such/ncircuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we/ndemonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced./nIn particular, we show that the network encoding sensitivity -as measured by the Fisher information- is maximized at the/nexact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of/nbalanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an/ninformation encoding standpoint.ca
- dc.description.sponsorship The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007- 2013) under grant agreements no. 269921(BrainScaleS) and no. 269459 (Coronet), from the Spanish Research Project SAF2010-16085 and from the CONSOLIDER-INGENIO 2010 Programme CSD2007-00012. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- dc.format.mimetype application/pdfca
- dc.identifier.citation Deco G, Hugues E. Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study. PLoS ONE. 2012;7(2):1-7. DOI: 10.1371/journal.pone.0030723
- dc.identifier.doi http://dx.doi.org/10.1371/journal.pone.0030723
- dc.identifier.issn 1932-6203
- dc.identifier.uri http://hdl.handle.net/10230/25864
- dc.language.iso engca
- dc.publisher Public Library of Scienceca
- dc.relation.ispartof PLoS ONE. 2012;7(2):1-7
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269921
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269459
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/SAF2010-16085
- dc.relation.projectID info:eu-repo/grantAgreement/ES/2PN/CSD2007-00012
- dc.rights © 2012 Deco, Hugues. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits/nunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.title Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical studyca
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/publishedVersionca