Information theoretic analysis of computational models as a tool to understand the neural basis of behaviors

TL;DR

This paper advocates for using information theoretic analysis of computational models as a powerful method to understand how neural, bodily, and environmental interactions give rise to behavior, complementing experimental approaches.

Contribution

It introduces and discusses how information theory applied to computational models can generate hypotheses and provide insights into neural mechanisms underlying behavior.

Findings

Information theory helps analyze brain-body-environment models.

Computational models allow for controlled interventions and measurements.

This approach complements experimental neuroscience methods.

Abstract

One of the greatest research challenges of this century is to understand the neural basis for how behavior emerges in brain-body-environment systems. To this end, research has flourished along several directions but have predominantly focused on the brain. While there is in an increasing acceptance and focus on including the body and environment in studying the neural basis of behavior, animal researchers are often limited by technology or tools. Computational models provide an alternative framework within which one can study model systems where ground-truth can be measured and interfered with. These models act as a hypothesis generation framework that would in turn guide experimentation. Furthermore, the ability to intervene as we please, allows us to conduct in-depth analysis of these models in a way that cannot be performed in natural systems. For this purpose, information theory is emerging as a powerful tool that can provide insights into the operation of these brain-body-environment models. In this work, I provide an introduction, a review and discussion to make a case for how information theoretic analysis of computational models is a potent research methodology to help us better understand the neural basis of behavior.