Minimal models and canonical neural computations: : the distinctness of computational explanation in neuroscience

• Autores: M. Chirimuuta
• Localización: Synthese, ISSN-e 1573-0964, Vol. 191, Nº. 2, 2014, págs. 127-153
• Idioma: inglés
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• Dialnet Métricas: 4 Citas
• Resumen

  • In a recent paper, Kaplan (Synthese 183:339�373, 2011) takes up the task of extending Craver�s (Explaining the brain, 2007) 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, 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. 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.