Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain

MIT Press, 28 août 2000 - 532 pages
This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the computational cognitive neuroscience.

The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena.

The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software.


Table des matières

Introduction and Overview
HigherLevel Cognition
Perception and Attention
Individual Neurons
Networks of Neurons
Hebbian Model Learning
ErrorDriven Task Learning
HigherLevel Cognition
A Introduction to the PDP++ Simulation Environment
B Tutorial for Constructing Simulations in PDP++
Leabra Implementation Reference
Author Index

ErrorDriven Task Learning
Combined Model and Task Learning and Other Mechanisms
LargeScale Brain Area Organization and Cognitive Phenomena
Perception and Attention

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Expressions et termes fréquents

Fréquemment cités

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À propos de l'auteur (2000)

James L. McClelland is Professor of Psychology and Director of the Center for Mind, Brain, and Computation at Stanford University. He is the coauthor of Parallel Distributed Processing (1986) and Semantic Cognition (2004), both published by the MIT Press. With David E. Rumelhart, he was awarded the 2002 University of Louisville Grawemeyer Award for Psychology for his work in the field of cognitive neuroscience on a cognitive framework called parallel distributed processing and the concept of connectionism.

Informations bibliographiques