Information-theoretic methods for studying population codes

Ince, R. A.A. , Senatore, R., Arabzadeh, E., Montani, F., Diamond, M. E. and Panzeri, S. (2010) Information-theoretic methods for studying population codes. Neural Networks, 23(6), pp. 713-727. (doi:10.1016/j.neunet.2010.05.008)

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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.

Item Type:Articles
Additional Information:NOTICE: this is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks 23(6):713-727 (August 2010) DOI:10.1016/j.neunet.2010.05.008
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Panzeri, Professor Stefano and Ince, Dr Robin
Authors: Ince, R. A.A., Senatore, R., Arabzadeh, E., Montani, F., Diamond, M. E., and Panzeri, S.
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
College of Science and Engineering > School of Psychology
Journal Name:Neural Networks
Publisher:Elsevier Ltd.
ISSN:0893-6080
ISSN (Online):1879-2782
Copyright Holders:Copyright © 2010 Elsevier Ltd.
First Published:First published in Neural Networks 23(6):713-727
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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