Start date should be earlier than end date. OK Please enter valid date format.

Original Research ARTICLE

This article is part of a Research Topic

Python for information theoretic analysis of neural data

Robin A. A. Ince¹*, Rasmus S. Petersen¹, Daniel C. Swan² and Stefano Panzeri^1,3*

Faculty of Life Sciences, University of Manchester, Manchester, UK

Bioinformatics Support Unit, Institute of Cell and Molecular Biosciences, Newcastle University, Newcastle upon Tyne, UK

Robotics, Brain and Cognitive Sciences Department, Italian Institute of Technology, Genoa, Italy

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.

Keywords:

Python, information theory, neural coding, entropy, maximum entropy, bias, e-science

Citation:

Ince RAA, Petersen RS, Swan DC and Panzeri S (2009). Python for information theoretic analysis of neural data. Front. Neuroinform. 3:4. doi: 10.3389/neuro.11.004.2009

Received:

21 September 2008;

Paper pending published:

20 November 2008;

Accepted:

27 January 2009;

Published online:

11 February 2009.

Edited by:

Rolf Kötter, Radboud University Nijmegen, Netherlands

Reviewed by:

Osvaldo A. Rosso, The University of Newcastle, Australia
Pietro Berkes, Brandeis University, USA
John M. Beggs, Indiana University, USA

© 2009 Ince, Petersen, Swan and Panzeri. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

*Correspondence:

Robin A. A. Ince, Faculty of Life Sciences, 3.614 Stopford Building, Oxford Road, Manchester, M13 9PT, UK. e-mail: robin.ince@postgrad.manchester.ac.uk
Stefano Panzeri, Robotics, Brain and Cognitive Sciences Department, Italian Institute of Technology, Via Morego, 30, 16163 Genoa, Italy. e-mail: stefano.panzeri@iit.it