Explanation in Computational Neuroscience: Causal and Non-causal

British Journal for the Philosophy of Science 69 (3):849-880 (2018)
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Abstract

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, Woodward, and Lange. 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_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter?

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Mazviita Chirimuuta
University of Pittsburgh

References found in this work

Depth: An Account of Scientific Explanation.Michael Strevens - 2008 - Cambridge, Mass.: Harvard University Press.
Thinking about mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
Explaining the Brain.Carl F. Craver - 2007 - Oxford, GB: Oxford University Press.

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