David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Studies in History and Philosophy of Science Part A 41 (3):271-279 (2010)
Computational neuroscientists not only employ computer models and simulations in studying brain functions. They also view the modeled nervous system itself as computing. What does it mean to say that the brain computes? And what is the utility of the ‘brain-as-computer’ assumption in studying brain functions? In previous work, I have argued that a structural conception of computation is not adequate to address these questions. Here I outline an alternative conception of computation, which I call the analog-model. The term ‘analog-model’ does not mean continuous, non-discrete or non-digital. It means that the functional performance of the system simulates mathematical relations in some other system, between what is being represented. The brain-as-computer view is invoked to demonstrate that the internal cellular activity is appropriate for the pertinent information-processing task.Keywords: Computation; Computational neuroscience; Analog computers; Representation; Simulation
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Citations of this work BETA
Worth Boone & Gualtiero Piccinini (2016). The Cognitive Neuroscience Revolution. Synthese 193 (5):1509-1534.
Gualtiero Piccinini & Carl Craver (2011). Integrating Psychology and Neuroscience: Functional Analyses as Mechanism Sketches. Synthese 183 (3):283-311.
Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.
Eric Hochstein (2016). One Mechanism, Many Models: A Distributed Theory of Mechanistic Explanation. Synthese 193 (5):1387-1407.
Kenneth Aizawa (2010). Computation in Cognitive Science: It is Not All About Turing-Equivalent Computation. Studies in History and Philosophy of Science Part A 41 (3):227-236.
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