Explanation in computational neuroscience: Causal and non-causal

Abstract

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman ([2002]), Woodward ([2003]), and Lange ([2013]). By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to shed light on the dispute over the interpretation of dynamical models of the brain.