Zednik, Carlos (2018) Computational Cognitive Neuroscience. [Preprint]

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Abstract

This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” (or reverse-engineering) 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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Item Type:	Preprint
Creators:	Creators Email ORCID Zednik, Carlos carlos.zednik@ovgu.de
Keywords:	Neuroscience, model, simulation, computational explanation, mechanistic explanation
Subjects:	Specific Sciences > Cognitive Science General Issues > Explanation General Issues > Models and Idealization Specific Sciences > Neuroscience
Depositing User:	Dr. Carlos Zednik
Date Deposited:	28 Mar 2018 14:36
Last Modified:	28 Mar 2018 14:36
Item ID:	14501
Subjects:	Specific Sciences > Cognitive Science General Issues > Explanation General Issues > Models and Idealization Specific Sciences > Neuroscience
Date:	2018
URI:	https://philsci-archive.pitt.edu/id/eprint/14501

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