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Information-theoretic methods for studying population codes

✍ Scribed by Robin A.A. Ince; Riccardo Senatore; Ehsan Arabzadeh; Fernando Montani; Mathew E. Diamond; Stefano Panzeri

Book ID: 103853944
Publisher: Elsevier Science
Year: 2010
Tongue: English
Weight: 873 KB
Volume: 23
Category: Article
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2010.05.008

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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