Models of Information Processing in the Basal Ganglia (Computational Neuroscience)

Houk, James C.; Davis, Joel L.

ISBN 10: 0262082349 / ISBN 13: 9780262082341

Edité par Bradford Books, 1994

A propos de cet article

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N° de réf. du vendeur ZEV.0262082349.G

Détails bibliographiques

• Titre: Models of Information Processing in the Basal Ganglia (Computational Neuroscience)
• Éditeur: Bradford Books
• Date de parution: 1994
• Langue: anglais
• ISBN à 10 chiffres: 0262082349
• ISBN à 13 chiffres: 9780262082341
• Reliure: Couverture rigide
• État de l'article: good

A propos de ce titre

Synopsis

This book brings together the biology and computational features of the basal ganglia and their related cortical areas along with select examples of how this knowledge can be integrated into neural network models.

Recent years have seen a remarkable expansion of knowledge about the anatomical organization of the part of the brain known as the basal ganglia, the signal processing that occurs in these structures, and the many relations both to molecular mechanisms and to cognitive functions. This book brings together the biology and computational features of the basal ganglia and their related cortical areas along with select examples of how this knowledge can be integrated into neural network models. Organized in four parts - fundamentals, motor functions and working memories, reward mechanisms, and cognitive and memory operations - the chapters present a unique admixture of theory, cognitive psychology, anatomy, and both cellular- and systems- level physiology written by experts in each of these areas. The editors have provided commentaries as a helpful guide to each part. Many new discoveries about the biology of the basal ganglia are summarized, and their impact on the computational role of the forebrain in the planning and control of complex motor behaviors discussed. The various findings point toward an unexpected role for the basal ganglia in the contextual analysis of the environment and in the adaptive use of this information for the planning and execution of intelligent behaviors. Parallels are explored between these findings and new connectionist approaches to difficult control problems in robotics and engineering.

Contributors
James L. Adams, P. Apicella, Michael Arbib, Dana H. Ballard, Andrew G. Barto, J. Brian Burns, Christopher I. Connolly, Peter F. Dominey, Richard P. Dum, John Gabrieli, M. Garcia-Munoz, Patricia S. Goldman-Rakic, Ann M. Graybiel, P. M. Groves, Mary M. Hayhoe, J. R. Hollerman, George Houghton, James C. Houk, Stephen Jackson, Minoru Kimura, A. B. Kirillov, Rolf Kotter, J. C. Linder, T. Ljungberg, M. S. Manley, M. E. Martone, J. Mirenowicz, C. D. Myre, Jeff Pelz, Nathalie Picard, R. Romo, S. F. Sawyer, E Scarnat, Wolfram Schultz, Peter L. Strick, Charles J. Wilson, Jeff Wickens, Donald J. Woodward, S. J. Young

À propos de l?auteur

Joel L. Davis is Program Officer, Cognitive, Neural, and Biomolecular Science and Technology Division, Office of Naval Research.