An Introduction to the Modeling of Neural Networks

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Cambridge University Press, Oct 29, 1992 - Computers - 473 pages
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This text is a graduate-level introduction to neural networks, focusing on current theoretical models, examining what these models can reveal about how the brain functions, and discussing the ramifications for psychology, artificial intelligence, and the construction of a new generation of intelligent computers. The book is divided into four parts. The first part gives an account of the anatomy of the central nervous system, followed by a brief introduction to neurophysiology. The second part is devoted to the dynamics of neuronal states, and demonstrates how very simple models may stimulate associative memory. The third part of the book discusses models of learning, including detailed discussions on the limits of memory storage, methods of learning and their associated models, associativity, and error correction. The final section of the book reviews possible applications of neural networks in artificial intelligence, expert systems, optimization problems, and the construction of actual neuronal supercomputers, with the potential for one-hundred fold increase in speed over contemporary serial machines.
 

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Contents

Introduction
1
a brief historical overview
6
13 Organization of the book
11
The biology of neural networks a few features for the sake of nonbiologists
13
22 The anatomy of central nervous systems
15
23 A brief survey of neurophysiology
29
a summary of experimental observations
41
The dynamics of neural networks a stochastic approach
57
Solving the problem of credit assignment
269
82 Handling internal representations
278
83 Learning in Boolean networks
292
Selforganization
299
92 Ontogenesis
307
93 Three questions about learning
319
Neurocomputation
325
102 Optimization
326

32 Noiseless neural networks
64
33 Taking synaptic noise into account
78
Hebbian models of associative memory
99
42 Stochastic Hebbian neural networks in the limit of finite numbers of memorized patterns
112
the technique of field distributions
130
44 The replica method approach
141
45 General dynamics of neural networks
149
Temporal sequences of patterns
153
52 Stochastic dynamics
155
53 An example of conditioned behavior
168
The problem of learning in neural networks
173
62 Linear separability
183
63 Computing the volume of solutions
196
Learning dynamics in visible neural networks
209
72 Constraining the synaptic efficacies
212
73 Projection algorithms
218
74 The perceptron learning rules
230
75 Correlated patterns
248
103 Lowlevel signal processing
350
104 Pattern matching
357
105 Some speculations on biological systems
366
106 Higher associative functions
371
Neurocomputers
379
112 Semiparallel neurocomputers
391
A critical view of the modeling of neural networks
403
122 The neural code
404
123 The synfire chains
405
124 Computing with attractors versus computing with flows of information
406
125 The issue of low neuronal activities
408
126 Learning and cortical plasticity
413
127 Taking the modular organization of the cortex into account
414
the problem of artificial intelligence
416
129 Concluding remarks
418
References
421
Index
467
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Page 447 - Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements.
Page 444 - 'Neural' Computation of Decisions in Optimization Problems".
Page 462 - Feigel'man MV 1988 The enhanced storage capacity in neural networks with low activity level Europhys.
Page 432 - Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element.
Page 449 - Nonlinear signal processing using neural networks: Prediction and system modeling.
Page 448 - W. Krauth, J.-P. Nadal, and M.Mezard (1988) The Roles of Stability and Symmetry in the Dynamics of Neural Networks. J. Phys. A: Math. Gen. 21 pp2995-3011.
Page 451 - Llinas, R. , Sugimori, M. and Simon, SM , Transmission by presynaptic spike-like depolarization in the squid giant synapse, Proc. Natl. Acad. Sci. (USA) 79:2415-2419 (1982).
Page 437 - Del Castillo J, Katz B: Statistical factors involved in neuromuscular facilitation and depression. J. Physiol.
Page 447 - Associative neural network model for the generation of temporal patterns: Theory and application to central pattern generators. Biophysical J. 54, 1039-1051. 11. Kleinfeld, D. and Sompolinsky, H. (1989). Associative network models for central pattern generators. In "Methods in Neuronal Modeling: From Synapses to Networks

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