  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.
