Applications of Information Theory to Analysis of Neural Data

TL;DR

This paper reviews how information theory provides a versatile framework for analyzing neural data, capturing complex relationships and information flow in the nervous system, with applications to single neurons and populations.

Contribution

It offers a comprehensive overview of applying information theoretic measures to neural encoding, highlighting its advantages over traditional linear methods.

Findings

Information theory captures nonlinear relationships in neural data.

It enables quantification of information gained from single-trial neural responses.

The review discusses various information-theoretic quantities used in neuroscience.

Abstract

Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it is a general measure sensitive to any relationship, not only linear effects; it has meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single trials, rather than in averages over multiple trials. A variety of information theoretic quantities are commonly used in neuroscience - (see entry "Definitions of Information-Theoretic Quantities"). In this entry we review some applications of information theory in neuroscience to study encoding of information in both single neurons and neuronal populations.