Cognitive Science 37 (3):453-488 (2013)
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Abstract |
We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation
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Keywords | Digital computation Neural computation Analog computation Spike trains Computational theory of cognition |
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DOI | 10.1111/cogs.12012 |
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References found in this work BETA
Computation and Cognition: Toward a Foundation for Cognitive Science.Zenon W. Pylyshyn - 1984 - MIT Press.
Mind in Life: Biology, Phenomenology, and the Sciences of Mind.Evan Thompson - 2007 - Harvard University Press.
View all 101 references / Add more references
Citations of this work BETA
The Cognitive Neuroscience Revolution.Worth Boone & Gualtiero Piccinini - 2016 - Synthese 193 (5):1509-1534.
Explanation in Computational Neuroscience: Causal and Non-Causal.M. Chirimuuta - 2018 - British Journal for the Philosophy of Science 69 (3):849-880.
Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience.M. Chirimuuta - 2014 - Synthese 191 (2):127-153.
The Cognitive Basis of Computation: Putting Computation in Its Place.Daniel D. Hutto, Erik Myin, Anco Peeters & Farid Zahnoun - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. London: Routledge. pp. 272-282.
From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.Marcin Miłkowski - 2018 - Minds and Machines 28 (3):515-541.
View all 36 citations / Add more citations
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