The Computational Brain, 25th Anniversary Edition

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MIT Press, 28 жовт. 2016 р. - 568 стор.
An anniversary edition of the classic work that influenced a generation of neuroscientists and cognitive neuroscientists.

Before The Computational Brain was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. In The Computational Brain, Patricia Churchland and Terrence Sejnowski developed a different conceptual framework, based on large populations of neurons. They did this by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, The Computational Brain is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research.

This approach influenced a generation of researchers. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of The Computational Brain is still relevant.

 

Зміст

1 Introduction
1
2 Neuroscience Overview
17
Levels in Nervous Systems
18
Structure at Various Levels of Organization
27
A Short List of Brain Facts
48
3 Computational Overview
61
Looking Up The Answer
69
Linear Associators
77
Cells Circuits Brains and Behavior
239
Learning and the Hippocampus
243
Donald Hebb and Synaptic Plasticity
250
Mechanisms of Neuronal Plasticity
254
Cells and Circuits
281
Decreasing Synaptic Strength
289
Back to Systems and Behavior
295
Being and Timing
305

Hopfield Networks and Boltzmann Machines
82
Learning in Neural Nets
96
Competitive Learning
102
Curve Fitting
105
Two Examples
107
Recurrent Nets
115
From Toy World to Real World
125
What Good Are Optimization Procedures to Neuroscience?
130
Realistic and Abstract
136
Concluding Remarks
137
4 Representing the World
141
Constructing a Visual World
142
Thumbnail Sketch of the Mammalian Visual System
148
What Can We Learn From the Visual System?
157
What Is So Special About Distribution?
163
World Enough and Time
174
A Neurocomputational Study
183
Stereo Vision
188
Computational Models of Stereo Vision
199
From Mystery to Mechanism
221
Vector Averaging
233
Concluding Remarks
237
Development of Nervous Systems
307
Modules and Networks
316
6 Sensorimotor Integration
331
LeechNet
341
Computation and the VestibuloOcular Reflex
353
Time and Time Again
379
The Segmental Swimming Oscillator
388
Modeling the Neuron
399
Concluding Remarks
411
7 Concluding and Beyond
413
Afterword
425
Anatomical and Physiological Techniques
427
Reversible Lesions and Microlesions
430
Imagine Techniques
432
Gross Electrical and Magnetic Recording
437
SingleUnit Recording
440
Anatomical Tract Tracing
442
Notes
445
Glossary
457
References
479
Index
525
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Про автора (2016)

Patricia S. Churchland is President's Professor of Philosophy Emerita at the University of California, San Diego, and Adjunct Professor at the Salk Institute for Biological Sciences. She is the author of many books, including Neurophilosophy and Brain-Wise (both published by the MIT Press).

Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Diego. He was a member of the advisory committee for the Obama administration's BRAIN initiative and is President of the Neural Information Processing (NIPS) Foundation. He is the author of The Deep Learning Revolution (MIT Press) and other books.

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