dc.contributor.author |
Deco, Gustavo |
dc.contributor.author |
Hugues, Etienne |
dc.date |
2012 |
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.citation |
1932-6203 |
dc.identifier.citation |
http://dx.doi.org/10.1371/journal.pone.0030723. |
dc.identifier.uri |
http://hdl.handle.net/10230/25864 |
dc.format |
application/pdf |
dc.language.iso |
eng |
dc.publisher |
Public Library of Science |
dc.relation |
PLoS ONE. 2012;7(2):1-7 |
dc.relation |
info:eu-repo/grantAgreement/EC/FP7/269921 |
dc.relation |
info:eu-repo/grantAgreement/EC/FP7/269459 |
dc.relation |
info:eu-repo/grantAgreement/ES/3PN/SAF2010-16085 |
dc.relation |
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. |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by/4.0/ |
dc.title |
Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study |
dc.type |
info:eu-repo/semantics/article |
dc.type |
info:eu-repo/semantics/publishedVersion |
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. |
dc.description.abstract |
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. |