Explanation in Computational Neuroscience: Causal and Non-causal

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. 1 Introduction1.1 Efficient coding explanation in computational neuroscience1.2 Defining non-causal explanation2 Case I: Hybrid Computation3 Case II: The Gabor Model Revisited4 Case III: A Dynamical Model of Prefrontal Cortex4.1 A new explanation of context-dependent computation4.2 Causal or non-causal?5 Causal and Non-causal: Does the Difference Matter?