Information-theoretic methods for studying population codes
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Ince, Robin A; Senatore, Riccardo; Arabzadeh, Ehsan; Montani, Fernando; Diamond, Mathew E; Panzeri, Stefano
Description
Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental...[Show more]
dc.contributor.author | Ince, Robin A | |
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dc.contributor.author | Senatore, Riccardo | |
dc.contributor.author | Arabzadeh, Ehsan | |
dc.contributor.author | Montani, Fernando | |
dc.contributor.author | Diamond, Mathew E | |
dc.contributor.author | Panzeri, Stefano | |
dc.date.accessioned | 2015-12-08T22:36:26Z | |
dc.identifier.issn | 0893-6080 | |
dc.identifier.uri | http://hdl.handle.net/1885/35258 | |
dc.description.abstract | Population coding is the quantitative study of which algorithms or representations are used by the brain to combine together and evaluate the messages carried by different neurons. Here, we review an information-theoretic approach to population coding. We first discuss how to compute the information carried by simultaneously recorded neural populations, and in particular how to reduce the limited sampling bias which affects the calculation of information from a limited amount of experimental data. We then discuss how to quantify the contribution of individual members of the population, or the interaction between them, to the overall information encoded by the considered group of neurons. We focus in particular on evaluating what is the contribution of interactions up to any given order to the total information. We illustrate this formalism with applications to simulated data with realistic neuronal statistics and to real simultaneous recordings of multiple spike trains. | |
dc.publisher | Pergamon Press | |
dc.source | Neural Networks | |
dc.subject | Keywords: Experimental data; Information-theoretic approach; Information-theoretic methods; Mutual informations; Neural populations; Population coding; Quantitative study; Simulated data; Simultaneous recording; Somatosensory cortex; Spike train; Information theory Mutual information; Population coding; Sampling bias; Somatosensory cortex | |
dc.title | Information-theoretic methods for studying population codes | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 23 | |
dc.date.issued | 2010 | |
local.identifier.absfor | 110906 - Sensory Systems | |
local.identifier.ariespublication | u4693331xPUB122 | |
local.type.status | Published Version | |
local.contributor.affiliation | Ince, Robin A, University of Manchester | |
local.contributor.affiliation | Senatore, Riccardo, University of Manchester | |
local.contributor.affiliation | Arabzadeh, Ehsan, College of Medicine, Biology and Environment, ANU | |
local.contributor.affiliation | Montani, Fernando, Italian Institute of Technology | |
local.contributor.affiliation | Diamond, Mathew E, International School for Advanced Studies | |
local.contributor.affiliation | Panzeri, Stefano, Italian Institute of Technology | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.issue | 6 | |
local.bibliographicCitation.startpage | 713 | |
local.bibliographicCitation.lastpage | 727 | |
local.identifier.doi | 10.1016/j.neunet.2010.05.008 | |
dc.date.updated | 2016-02-24T11:17:45Z | |
local.identifier.scopusID | 2-s2.0-77953812601 | |
Collections | ANU Research Publications |
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