The minimum information principle and its application to neural code analysis

doi:10.1073/pnas.0806782106

The minimum information principle and its application to neural code analysis

^aSchool of Computer Science and Engineering, The Hebrew University, Jerusalem 91904, Israel;
^bDepartment of Physiology, Hadassah Medical School, The Hebrew University, Jerusalem 91120, Israel; and
^cThe Interdisciplinary Center for Neural Computation, The Hebrew University, Jerusalem 91904, Israel

Edited by Raphael D. Levine, The Hebrew University, Jerusalem, Israel, and approved December 29, 2008 (received for review August 5, 2008)

Abstract

The study of complex information processing systems requires appropriate theoretical tools to help unravel their underlying design principles. Information theory is one such tool, and has been utilized extensively in the study of the neural code. Although much progress has been made in information theoretic methodology, there is still no satisfying answer to the question: “What is the information that a given property of the neural population activity (e.g., the responses of single cells within the population) carries about a set of stimuli?” Here, we answer such questions via the minimum mutual information (MinMI) principle. We quantify the information in any statistical property of the neural response by considering all hypothetical neuronal populations that have the given property and finding the one that contains the minimum information about the stimuli. All systems with higher information values necessarily contain additional information processing mechanisms and, thus, the minimum captures the information related to the given property alone. MinMI may be used to measure information in properties of the neural response, such as that conveyed by responses of small subsets of cells (e.g., singles or pairs) in a large population and cooperative effects between subunits in networks. We show how the framework can be used to study neural coding in large populations and to reveal properties that are not discovered by other information theoretic methods.

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Footnotes

¹To whom correspondence should be addressed. E-mail: gamir{at}cs.huji.ac.il
↵²Present address: Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Avenue, Newark, NJ 07102.
Author contributions: A.G., E.S., E.V., and N.T. designed research; A.G., E.S., E.V., and N.T. performed research; A.G., E.S., and N.T. contributed new reagents/analytic tools; A.G. and E.S. analyzed data; and A.G., E.S., and N.T. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/cgi/content/full/0806782106/DCSupplemental.