Estimating Information-Theoretic Quantities

TL;DR

This paper discusses the application of information theory to neuroscience, highlighting its usefulness in quantifying neural information flow and the challenges in accurately estimating these quantities from experimental data.

Contribution

It provides an overview of information-theoretic measures used in neuroscience and addresses the difficulties in their unbiased estimation from neurophysiological data.

Findings

Information theory offers a versatile framework for studying neural information flow.

Estimating information-theoretic quantities from real data is challenging and requires careful methods.

These measures enable comparison of neural responses across different experiments.

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 in common use in neuroscience - (see entry "Summary of Information-Theoretic Quantities"). Estimating these quantities in an accurate and unbiased way from real neurophysiological data frequently presents challenges, which are explained in this entry.