Python for information theoretic analysis of neural data

Ince, R. A.A. , Petersen, R. S., Swan, D. C. and Panzeri, S. (2009) Python for information theoretic analysis of neural data. Frontiers in Neuroinformatics, 3(Art 4), (doi:10.3389/neuro.11.004.2009)

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Abstract

Information theory, the mathematical theory of communication in the presence of noise, is playing an increasingly important role in modern quantitative neuroscience. It makes it possible to treat neural systems as stochastic communication channels and gain valuable, quantitative insights into their sensory coding function. These techniques provide results on how neurons encode stimuli in a way which is independent of any specific assumptions on which part of the neuronal response is signal and which is noise, and they can be usefully applied even to highly non-linear systems where traditional techniques fail. In this article, we describe our work and experiences using Python for information theoretic analysis. We outline some of the algorithmic, statistical and numerical challenges in the computation of information theoretic quantities from neural data. In particular, we consider the problems arising from limited sampling bias and from calculation of maximum entropy distributions in the presence of constraints representing the effects of different orders of interaction in the system. We explain how and why using Python has allowed us to significantly improve the speed and domain of applicability of the information theoretic algorithms, allowing analysis of data sets characterized by larger numbers of variables. We also discuss how our use of Python is facilitating integration with collaborative databases and centralised computational resources.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Panzeri, Professor Stefano and Ince, Dr Robin
Authors: Ince, R. A.A., Petersen, R. S., Swan, D. C., 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:Frontiers in Neuroinformatics
Publisher:Frontiers Research Foundation
ISSN:1662-5196
ISSN (Online):1662-5196
Copyright Holders:Copyright © 2009 The Authors
First Published:First published in Frontiers in Human Neuroinformatics 3:Art 4
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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