A Neural Approach to Cognition Based on Dynamic Field Theory

Jonas Lins () and Gregor Schöner ()
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Jonas Lins: Ruhr-Universität Bochum, Institute for Neural Computation
Gregor Schöner: Ruhr-Universität Bochum, Institute for Neural Computation

Chapter Chapter 12 in Neural Fields, 2014, pp 319-339 from Springer

Abstract: Abstract How may cognitive function emerge from the different dynamic properties, regimes, and solutions of neural field equations? To date, this question has received much less attention than the purely mathematical analysis of neural fields. Dynamic Field Theory Dynamic field theory (DFT) (DFT) aims to bridge the ensuing gap, by bringing together neural field dynamics with principles of neural representation and fundamentals of cognition Cognition . This chapter provides review of each of these aspects. We show how dynamic fields can be viewed as mathematical descriptions of activation patterns in neural populations that arise due to sensory and motor events; how field dynamics in DFT give rise to a set of stable states and associated instabilities that provide the elementary building blocks for cognitive processes; and how these properties can be brought to bear in the construction of neurally grounded process models of cognition Cognition . We conclude that DFT provides a valuable framework for linking mathematical descriptions of neural activity to actual sensory, motor, and cognitive functionality and behavioral signatures thereof.

Keywords: Tuning Curve; Neural Population; Local Excitation; Localize Input; Neural Field (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-54593-1_12

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DOI: 10.1007/978-3-642-54593-1_12

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