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

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MIT Press, 28 août 2000 - 532 pages
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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.

 

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Table des matières

Introduction and Overview
1
Basic Neural Computational Mechanisms
2
Individual Neurons
7
Perception and Attention
20
2
23
Networks of Neurons
71
21
96
26
108
Memory
311
Working Memory
321
Language
351
HigherLevel Cognition
379
HigherLevel Cognition
381
Conclusions
411
Simulator Details
425
A Introduction to the PDP++ Simulation Environment
427

Networks of Neurons
113
Hebbian Model Learning
115
ErrorDriven Task Learning
127
7
132
Exploration of SelfOrganizing Learning
138
Deriving the Delta Rule
147
Hebbian Model Learning
153
Combined Model and Task Learning and Other Mechanisms
173
ErrorDriven Task Learning
179
Combined Model and Task Learning and Other Mechanisms
180
LargeScale Brain Area Organization and Cognitive Phenomena
203
LargeScale Brain Area Functional Organization
205
Perception and Attention
227
Memory
275
Language
282
A Introduction to the PDP++ Simulation Environment B Tutorial for Constructing Simulations in PDP++
435
Leabra Implementation Reference
455
References
467
Author Index
485
Subject Index
491
1
493
147
494
205
495
227
496
323
498
427
499
485
500
455
503
Droits d'auteur

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Expressions et termes fréquents

Fréquemment cités

Page 467 - REFERENCES Allport, A. (1989). Visual attention. In MI Posner (ed.), Foundations of cognitive science (pp. 631-682). Cambridge, MA: MIT Press. Allport, A.
Page 479 - Robbins, TW (1993). Contrasting mechanisms of impaired attentional set-shifting in patients with frontal lobe damage or Parkinson's disease. Brain, 116, 1 159-79.
Page 483 - In RP Lippmann, JE Moody, & DS Touretzky (Eds.), Advances in neural information processing systems (Vol. 3, pp.649-655). San Mateo, CA: Morgan Kaufmann. Kruse, JM, Overmier, JB, Konz, WA, & Rokke, E. (1983). Pavlovian conditioned stimulus effects upon instrumental choice behaviour are reinforcer specific. Learning and Motivation, 14, 165-181. Kummer, H., & Goodall, J. (1985). Conditions of innovative...
Page 483 - A neural dissociation within language: Evidence that the mental dictionary is part of declarative memory, and that grammatical rules are part of the procedural system.
Page 482 - Sloman, SA, & Rumelhart, DE (1992). Reducing interference in distributed memories through episodic gating. In AF Healy, SM Kosslyn, & RM Shiffrin (Eds.), From learning theory to connectionist theory: Essays in honor of William K.

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