 Lauren Ross (2015) and Mazviita Chirimuuta (2014) independently appeal to Robert Batterman’s account of minimal model explanation as an important kind of non-mechanistic explanation in neuroscience. Minimal models were developed initially to characterize a kind of explanation in the physical sciences (see, e.g., Batterman and Rice 2014). Batterman’s account distinguishes between two different kinds of scientific “why-questions”: why a phenomenon manifests in particular circumstances; and why a phenomenon manifests generally, or in a number of different circumstances. Mechanistic explanations answer the first type of why-question. Here a “more details the better” (MDB) assumption (Chirimuuta 2014), akin to Kaplan and Craver’s “all things being equal” assumption about better explanations (mentioned above), holds force. Minimal models, however, which minimize over the presented implantation details and hence violate MDB, are better able to answer the second type of scientific why-questions. Ross (2015), quoting from computational neuroscientists Rinzel and Ermentrout, insists that models containing more details than necessary can obscure identification of critical elements by leaving too many open possibilities, especially when one is trying to answer Batterman’s second kind of why-questions about a system’s behavior.