Visual Structures and Integrated Functions (Research Notes in Neural Computing) (Research Notes in Neural Computing (3)).

Arbib, Michael A.:

ISBN 10: 3540542418 / ISBN 13: 9783540542414

Published by Springer, 1991

About this Item

456 p. In very good condition. ISBN: 9783540542414 Sprache: Englisch Gewicht in Gramm: 770 17,0 x 2,6 x 24,2 cm, softcover.

Seller Inventory # 59714

Bibliographic Details

• Title: Visual Structures and Integrated Functions (Research Notes in Neural Computing) (Research Notes in Neural Computing (3)).
• Publisher: Springer
• Publication Date: 1991
• Language: English
• ISBN 10: 3540542418
• ISBN 13: 9783540542414
• Binding: Soft cover
• Condition: Sehr gut
• Weight: 770 grams
• Seller catalogs:
  • Informatik, Hardware, Software

About this title

Synopsis

The book integrates data on visuomotor coordination in frog and toad - and comparative sudies of salamander, bird, rep- tile, turtle, rat, gerbil, rabbit, and monkey - with the ex- position of a set of models, Rana computatrix, with broad implications for neuroethology and the design of neural net- works. The study provides insight int organizational prin- ciples for the brain which are to be sought not solely in terms ofcellular mechanisms but also in terms of neural layers and modules functional constructs called schemas, and computational strategies.

Synopsis

This volume is a collection of papers on brain mechanisms which integrates theory and experiment to place visual information within the context of the action systems which use it. The book stresses both the intrinsic interest of frog and toad as animals in which to study the neural mechanisms of visuomotor co-ordination and the importance of comparative studies with other organisms. The book demonstrates the interaction between theory and experiment in neuroscience, complementing current data with a rich array of models which together constitute Rana computatrix, the "frog that computes". High-level schema models show the basic functional interactions underlying visuomotor co-ordination, testable by lesion experiments. Neural network models address data from neurophysiology and neuroanatomy. Although the stress in the modeling is on Computational Neuroscience to understand neurobiological phenomena, the results have implications for Neural Engineering, the use of ideas inspired by the study of the brain to design highly parallel, often adaptive, machines.