dc.contributor.author |
Albantakis, Larissa |
dc.contributor.author |
Deco, Gustavo |
dc.date |
2011 |
dc.identifier.citation |
Albantakis L, Deco G. Changes of mind in an attractor network of decision-making. PLoS Computational Biology. 2011;7(6):1-13. DOI: 10.1371/journal.pcbi.1002086. |
dc.identifier.citation |
1553-734X |
dc.identifier.citation |
http://dx.doi.org/10.1371/journal.pcbi.1002086. |
dc.identifier.uri |
http://hdl.handle.net/10230/25842 |
dc.format |
application/pdf |
dc.language.iso |
eng |
dc.publisher |
Public Library of Science |
dc.relation |
PLoS Computational Biology. 2011;7(6):1-13 |
dc.relation |
info:eu-repo/grantAgreement/ES/2PN/CSD2007-00012 |
dc.relation |
info:eu-repo/grantAgreement/ES/2PN/BFU2007-61710 |
dc.rights |
@ 2011 Albantakis, Deco. 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 |
Changes of mind in an attractor network of decision-making |
dc.type |
info:eu-repo/semantics/article |
dc.type |
info:eu-repo/semantics/publishedVersion |
dc.description.abstract |
Attractor networks successfully account for psychophysical and neurophysiological data in various decision-making tasks./nEspecially their ability to model persistent activity, a property of many neurons involved in decision-making, distinguishes/nthem from other approaches. Stable decision attractors are, however, counterintuitive to changes of mind. Here we/ndemonstrate that a biophysically-realistic attractor network with spiking neurons, in its itinerant transients towards the/nchoice attractors, can replicate changes of mind observed recently during a two-alternative random-dot motion (RDM) task./nBased on the assumption that the brain continues to evaluate available evidence after the initiation of a decision, the/nnetwork predicts neural activity during changes of mind and accurately simulates reaction times, performance and/npercentage of changes dependent on difficulty. Moreover, the model suggests a low decision threshold and high incoming/nactivity that drives the brain region involved in the decision-making process into a dynamical regime close to a bifurcation,/nwhich up to now lacked evidence for physiological relevance. Thereby, we further affirmed the general conformance of/nattractor networks with higher level neural processes and offer experimental predictions to distinguish nonlinear attractor/nfrom linear diffusion models. |
dc.description.abstract |
The authors were supported by the Spanish Research Project BFU2007-61710/BFI and CONSOLIDER-INGENIO 2010 Programme CSD2007-00012 (“Bilingualism and Cognitive Neuroscience”). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |