An Introduction to the Modeling of Neural Networks - (Malestrom)

"An Introduction to the Modeling of Neural Networks" by Pierre Peretto Collection Alea-Saclay: Monographs and Texts in Statistical Physics Cambridge University Press | 1994 dig.print 2004| ISBN: 0521424879, 0521414512 | 492 pages | PDF 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. Contents Preface Acknowledgments 1 Introduction 1.1 Mind as an emergent property of nervous systems 1.2 Neuronal nets as automata networks: a brief historical overview 1.3 Organization of the book 2 The biology of neural networks: a few features for the sake of non-biologists 2.1 Three approaches to the study of the functioning of central nervous systems 2.2 The anatomy of central nervous systems 2.3 A brief survey of neurophysiology 2.4 Learning and memory: a summary of experimental observations 3 The dynamics of neural networks: a stochastic approach 3.1 Introducing the problem 3.2 Noiseless neural networks 3.3 Taking synaptic noise into account 4 Hebbian models of associative memory 4.1 Noiseless Hebbian models 4.2 Stochastic Hebbian neural networks in the limit of finite numbers of memorized patterns 4.3 Storing an infinite number of patterns in stochastic Hebbian networks: the technique of field distributions 4.4 The replica method approach 4.5 General dynamics of neural networks 5 Temporal sequences of patterns 5.1 Parallel dynamics 52 tochastic dynamics 5.3 An example of conditioned behavior 6 The problem of learning in neural networks 6.1 Introducing the problem 6.2 Linear separability 6.3 Computing the volume of solutions 7 Learning dynamics in 'visible* neural networks 7.1 A classification of learning dynamics 7.2 Constraining the synaptic efficacies 7.3 Projection algorithms 7.4 The perceptron learning rules 7.5 Correlated patterns 8 Solving the problem of credit assignment 8.1 The back-propagation algorithm 8.2 Handling internal representations 8.3 Learning in Boolean networks 9 Self-organization 9.1 Self-organization in simple networks 9.2 Ontogenesis 9.3 Three questions about learning 10 Neurocomputat ion 10.1 Domains of applications of neural networks 10.2 Optimization 10.3 Low-level signal processing 10.4 Pattern matching 10-5 Some speculations on biological systems 10.6 Higher associative functions 11 Neurocomputers 11.1 General principles of neurocomputation 11.2 Semi-paxallel neurocomputers 12 A critical view of the modeling of neural networks 12.1 Information structures the biological system 12.2 The neural code 12.3 The synfire chains 12.4 Computing with attractors versus computing with flows of information 12.5 The issue of low neuronal activities 12.6 Learning and cortical plasticity 12.7 Taking the modular organization of the cortex into account 12.8 Higher-order processing: the problem of artificial intelligence 12.9 Concluding remarks References Index

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Antareth (2011-11-14)

Excellent text book - the subject of neural networks is quite heuristic and in this way difficult to apply. Thanks again malestrom, you are very much appreciated!