M. Chirimuuta April 23. 2013 Minimal Models and Canonical Neural Computations In a recent paper, Kaplan (2011) takes up the task of extending Craver’s (2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience (i.e. research using applied mathematics and computer science to analyze and simulate neural systems). He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach to computational modeling in neuroscience is intended to replace earlier accounts which posited a level of computational analysis conceived as autonomous from underlying mechanistic details (Marr 1982). In this talk I will discuss work in computational neuroscience that creates difficulties for Kaplan’s project. Carandini and Heeger (2012) 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. Rather than advocate a return to Marr’s system of independent levels of analysis, I propose that Carandini and Heeger’s approach should be understood as an instance of minimal modeling, comparable to that described in other branches of science (Batterman 2002). 1