Explanation and description in computational neuroscience

Synthese 183 (3):339-373 (2011)
Abstract
The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists.
Keywords Explanation  Mechanism  Computational models  Computational neuroscience
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References found in this work BETA
Robert W. Batterman (2002). Asymptotics and the Role of Minimal Models. British Journal for the Philosophy of Science 53 (1):21-38.
William Bechtel (2005). Explanation: A Mechanist Alternative. Studies in History and Philosophy of Biol and Biomed Sci 36 (2):421--441.

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Citations of this work BETA
Cory D. Wright (2012). Mechanistic Explanation Without the Ontic Conception. European Journal of Philosophy of Science 2 (3):375-394.
Alexander Mebius (2014). A Weakened Mechanism is Still a Mechanism: On the Causal Role of Absences in Mechanistic Explanation. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 45:43-48.
Similar books and articles
Rick Grush (2001). The Semantic Challenge to Computational Neuroscience. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press. 155--172.
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