Information-theoretic methods for studying population codes

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

doi:10.1016/j.neunet.2010.05.008

Information-theoretic methods for studying population codes

Robin A.A. Ince, Riccardo Senatore, Ehsan Arabzadeh, Fernando Montani, Mathew E. Diamond, Stefano Panzeri^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

45 Citations (Scopus)

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.

Original language	English
Pages (from-to)	713-727
Number of pages	15
Journal	Neural Networks
Volume	23
Issue number	6
DOIs	https://doi.org/10.1016/j.neunet.2010.05.008
Publication status	Published - Aug 2010
Externally published	Yes

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10.1016/j.neunet.2010.05.008

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title = "Information-theoretic methods for studying population codes",

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.",

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AU - Ince, Robin A.A.

AU - Senatore, Riccardo

AU - Arabzadeh, Ehsan

AU - Montani, Fernando

AU - Diamond, Mathew E.

AU - Panzeri, Stefano

PY - 2010/8

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N2 - 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.

AB - 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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JO - Neural Networks

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Information-theoretic methods for studying population codes

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