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Journal of Mathematical Psychology, in press A Tutorial on Computational Cognitive Neuroscience

Summary: Journal of Mathematical Psychology, in press
A Tutorial on Computational Cognitive Neuroscience:
Modeling the Neurodynamics of Cognition
F. Gregory Ashby & Sebastien Helie
University of California, Santa Barbara
Computational Cognitive Neuroscience (CCN) is a new field that lies at the intersection of
computational neuroscience, machine learning, and neural network theory (i.e., connectionism). The
ideal CCN model should not make any assumptions that are known to contradict the current
neuroscience literature and at the same time provide good accounts of behavior and at least some
neuroscience data (e.g., single-neuron activity, fMRI data). Furthermore, once set, the architecture of
the CCN network and the models of each individual unit should remain fixed throughout all
applications. Because of the greater weight they place on biological accuracy, CCN models differ
substantially from traditional neural network models in how each individual unit is modeled, how
learning is modeled, and how behavior is generated from the network. A variety of CCN solutions to
these three problems are described. A real example of this approach is described, and some advantages
and limitations of the CCN approach are discussed.
Keywords: computational cognitive neuroscience, neural network modeling, neuroscience
1. Introduction
The emerging new field of Computational Cognitive
Neuroscience (CCN).


Source: Ashby, F. Gregory - Department of Psychology, University of California at Santa Barbara


Collections: Biology and Medicine; Computer Technologies and Information Sciences