Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience. - Chirimuuta, M. (Synthese, 2014)
In a recent paper, Kaplan (Synthese 183:339-373, ) takes up the task of extending Craver's (Explaining the brain, ) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model's explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51-62, ) propose that many neural response properties can be understood in terms of canonical neural computations. These are 'standard computational modules that apply the same fundamental operations in a variety of contexts.' Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology.