Overview

"How do groups of neurons interact to enable the organism to see, decide, and move appropriately? What are the principles whereby networks of neurons represent and compute? These are the central questions probed by ""The Computational Brain"". Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. ""The Computational Brain"" is a unified and broadly accessible book bringing together computational concepts and behavioural data within a neurobiological framework. Computer models constrained by neurobiological data can help reveal how networks of neurons subserve perception and behaviour - how their physical interactions can yield global results in perception and behaviour, and how their physical properties are used to code information and computer solutions. ""The Computational Brain"" focuses mainly on three domains: visual perception, learning and memory, and sensorimotor integration. Examples of recent computer models in these domains are discussed in detail, highlighting strengths and weaknesses, and extracting principles applicable to other domains. Churchland and Sejnowski show how both abstract models and neurobiologically realistic models can have useful roles in computational neuroscience, and they predict the coevolution of models and experiments at many levels of organization, from the neuron to the system. ""The Computational Brain"" addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a precis of neurobiological techniques."

Full Product Details

Author:   Patricia S. Churchland ,  Terrence J. Sejnowski
Publisher:   MIT Press Ltd
Imprint:   MIT Press
Dimensions:   Width: 17.50cm , Height: 3.30cm , Length: 24.90cm
Weight:   1.362kg
ISBN:  

9780262031882

ISBN 10:   0262031884
Pages:   558
Publication Date:   12 June 1992
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   Out of stock  

Table of Contents

Part 1 Neuroscience overview: levels in nervous systems; structure at various levels of organization; a short list of brain facts. Part 2 Computational overview: looking up the answer; linear associators; constraint satisfaction - Hopfield networks and Boltzmann machines; learning in neural nets; competitive learning; curve fitting; feedforward nets - two examples; recurrent nets; from toy world to real world; what good are optimization procedures to neuroscience?; models - realistic and abstract; concluding remarks. Part 3 Representing the world: constructing a visual world; thumbnail sketch of the mammalian visual system; representing in the brain - what can we learn from the visual system?; what is so special about distribution; world enough and time; shape from shading - a neurocomputational study; stereo vision; computational models of stereo vision; hyperacuity - from mystery to mechanism; vector averaging; concluding remarks. Part 4 Plasticity - cells, circuits, brains, and behaviour: learning and the hippocampus; Donald Hebb and synaptic plasticity; memories are made of this - mechanisms of neuronal plasticity; cells and circuits; decreasing synaptic strength; back to systems and behaviour; being and timing; development of nervous systems; modules and networks. Part 5 Sensorimotor integration: LeechNet; computation and the vetibulo-ocular reflex; time and time again; the segmental swimming oscillator; modelling the neuron; concluding remarks. Part 6 Concluding and beyond. Appendix: anatomical and physiological techniques - permanent lesions, reversible lesions and microlesions, imaging techniques, gross electrical and magnetic recording, single-unit recording, anatomical tract tracing.

Reviews

This attractive and well-illustrated volume falls somewherebetween a trade book and a textbook, with a style well suitedfor the Scientific American reader, as well as the activescientist, who may know something of either computer scienceor neuroscience but welcomes a crisp narrative that includesthe necessary background from each discipline... The readerwill be well rewarded who seeks to understand, from well-chosenexamples, how to merge the analysis of neuroscientific datawith the developments of computational principles. Michael A. Arbib, Science

"""This attractive and well-illustrated volume falls somewherebetween a trade book and a textbook, with a style well suitedfor the Scientific American reader, as well as the activescientist, who may know something of either computer scienceor neuroscience but welcomes a crisp narrative that includesthe necessary background from each discipline... The readerwill be well rewarded who seeks to understand, from well-chosenexamples, how to merge the analysis of neuroscientific datawith the developments of computational principles."" Michael A. Arbib, Science"

Author Information

Patricia Smith Churchland is UC President's Professor of Philosophy at the University of California, San Diego, and Adjunct Professor at the Salk Institute. Terrence J. Sejnowski is Francis Crick Professor, Director of the Computational Neurobiology Laboratory, and a Howard Hughes Medical Institute Investigator at the Salk Institute for Biological Studies and Professor of Biology at the University of California, San Diego.

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