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Entropy 2013, 15(5), 1587-1608; doi:10.3390/e15051587

Function Identification in Neuron Populations via Information Bottleneck

School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, BC 106, Station 14, CH-1015 Lausanne, Switzerland
Department of EECS, University of California, Berkeley, CA 94720-1770, USA
Helen Wills Neuroscience Institute and the UCB/UCSF Joint Graduate Group in Bioengineering, University of California, Berkeley, CA 94720, USA
Author to whom correspondence should be addressed.
Received: 26 February 2013 / Revised: 27 March 2013 / Accepted: 22 April 2013 / Published: 6 May 2013
(This article belongs to the Special Issue The Information Bottleneck Method)
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It is plausible to hypothesize that the spiking responses of certain neurons represent functions of the spiking signals of other neurons. A natural ensuing question concerns how to use experimental data to infer what kind of a function is being computed. Model-based approaches typically require assumptions on how information is represented. By contrast, information measures are sensitive only to relative behavior: information is unchanged by applying arbitrary invertible transformations to the involved random variables. This paper develops an approach based on the information bottleneck method that attempts to find such functional relationships in a neuron population. Specifically, the information bottleneck method is used to provide appropriate compact representations which can then be parsed to infer functional relationships. In the present paper, the parsing step is specialized to the case of remapped-linear functions. The approach is validated on artificial data and then applied to recordings from the motor cortex of a macaque monkey performing an arm-reaching task. Functional relationships are identified and shown to exhibit some degree of persistence across multiple trials of the same experiment.
Keywords: information theory; information bottleneck method; neuroscience information theory; information bottleneck method; neuroscience
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Buddha, S.K.; So, K.; Carmena, J.M.; Gastpar, M.C. Function Identification in Neuron Populations via Information Bottleneck. Entropy 2013, 15, 1587-1608.

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