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An Introduction to the Modeling of Neural Networks (Collection Alea-Saclay: Monographs and Texts in Statistical Physics)
 
 
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An Introduction to the Modeling of Neural Networks (Collection Alea-Saclay: Monographs and Texts in Statistical Physics) [Paperback]

Pierre Peretto (Author)

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Book Description

November 27, 1992 Collection Alea-Saclay: Monographs and Texts in Statistical Physics (Book 2)
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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"...a beginning graduate-level text that discusses a wide range of neural network models and algorithms: simulated annealing, Aleksander's model, Boltzmann machine, perceptron, backpropagation, Hopfield's models, self-organization, and others. It may be especially useful for those with no or limited knowledge of the biology of neural networks and their relation to artificial neural networks." George Georgiou, Mathematical Reviews

"...excellent introductions to this exciting new enterprise...this comprehensive summary of research results in neural networks with both practical and biological applications provides an invaluable resource for the graduate student or researcher working in this field...summarizes some of the important questions that remain in our understanding of biological neural networks that may be addressed with greater integration of neural network modeling and biological experimentation." Roderick V. Jensen, American Journal of Physics

Book Description

Focusing on current theoretical models, this graduate-level introduction examines what neural networks can reveal about brain function as well as the implications for psychology, artificial intelligence and computer design.

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Inside This Book (learn more)
First Sentence:
Mind has always been a mystery and it is fair to say that it is still one. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
heterosynaptic junctions, overcrowding catastrophe, hillock zone, neuronal states, synapses impinging, perceptron rule, synaptic dynamics, binary synapse, memorized patterns, neuronal dynamics, memorized states, linearly separable functions, synaptic efficacies, perceptron algorithm, cortical bands, synfire chains, spurious states, tiling algorithm, neuron selectivity, possible input states, formal neurons, memory storage capacity, spin glass phase, stabilization parameters, automata networks
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Edwards Anderson, Maxwell Boltzmann, Von Neumann
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