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dc.contributor.author
Echeveste, Rodrigo Sebastián  
dc.contributor.author
Aitchison, Laurence  
dc.contributor.author
Hennequin, Guillaume  
dc.contributor.author
Lengyel, Máté  
dc.date.available
2020-09-15T15:09:43Z  
dc.date.issued
2020-08  
dc.identifier.citation
Echeveste, Rodrigo Sebastián; Aitchison, Laurence; Hennequin, Guillaume; Lengyel, Máté; Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference; Nature Publishing Group; Nature Neuroscience.; 23; 9; 8-2020; 1138-1149  
dc.identifier.issn
1097-6256  
dc.identifier.uri
http://hdl.handle.net/11336/114008  
dc.description.abstract
Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory?inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function?fast sampling-based inference?and predict further properties of these motifs that can be tested in future experiments.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Nature Publishing Group  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Neural Networks  
dc.subject
Cortical Dynamics  
dc.subject
Bayesian Inference  
dc.subject
Optimization  
dc.subject.classification
Otras Ciencias Naturales y Exactas  
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Otras Ciencias Naturales y Exactas  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-09-03T19:19:16Z  
dc.journal.volume
23  
dc.journal.number
9  
dc.journal.pagination
1138-1149  
dc.journal.pais
Reino Unido  
dc.journal.ciudad
Londres  
dc.description.fil
Fil: Echeveste, Rodrigo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina  
dc.description.fil
Fil: Aitchison, Laurence. University of Cambridge; Reino Unido  
dc.description.fil
Fil: Hennequin, Guillaume. University of Cambridge; Estados Unidos  
dc.description.fil
Fil: Lengyel, Máté. University of Cambridge; Reino Unido  
dc.journal.title
Nature Neuroscience.  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41593-020-0671-1  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/s41593-020-0671-1