In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge (forthcoming)

Carlos Zednik
Eindhoven University of Technology
This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory.
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References found in this work BETA

Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).

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Citations of this work BETA

The Computational Theory of Mind.Steven Horst - 2005 - Stanford Encyclopedia of Philosophy.
Models and Mechanisms in Network Neuroscience.Carlos Zednik - 2018 - Philosophical Psychology 32 (1):23-51.

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Similar books and articles

The Semantic Challenge to Computational Neuroscience.Rick Grush - 2001 - In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press. pp. 155--172.
The Cambridge Handbook of Computational Psychology.Ron Sun (ed.) - 2008 - Cambridge University Press.
Models and Mechanisms in Network Neuroscience.Carlos Zednik - 2018 - Philosophical Psychology 32 (1):23-51.


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