Statistical Mechanics of Neural Networks: Proceedings of the XIth Sitges Conference Sitges, Barcelona, Spain, 3-7 June 1990

by Luis Garrido (Editor)

Overview

Combined for researchers and graduate students the articles from the Sitges Summer School together form an excellent survey of the applications of neural-network theory to statistical mechanics and computer-science biophysics. Various mathematical models are presented together with their interpretation, especially those to do with collective behaviour, learning and storage capacity, and dynamical stability.

Product Details

ISBN-13:	9783662137857
Publisher:	Springer Berlin Heidelberg
Publication date:	08/23/2014
Series:	Lecture Notes in Physics , #368
Edition description:	Softcover reprint of the original 1st ed. 1990
Pages:	477
Product dimensions:	6.69(w) x 9.61(h) x 0.04(d)

Table of Contents

On the statistical-mechanical formulation of neural networks.- Model neurons: From Hodgkin-Huxley to hopfield.- Statistical mechanics for networks of analog neurons.- Properties of neural networks with multi-state neurons.- Adaptive recurrent neural networks and dynamic stability.- Neuronal oscillators: Experiments and models.- Neuronal networks in the hippocampus involved in memory.- Basins of attraction and spurious states in neural networks.- Tailoring the performance of attractor neural networks.- Learning and optimization.- Statistical dynamics of learning.- Learning and retrieving marked patterns.- Learning algorithm for binary synapses.- Statistical mechanics of the perceptron with maximal stability.- Simulation and hardware implementation of competitive learning neural networks.- Learning in multilayer networks: A geometric computational approach.- Storage capacity of diluted neural networks.- Dynamics and storage capacity of neural networks with sign-constrained weights.- The neural basis of the locomotion of nematodes.- Reversibility in neural processing systems.- Lyapunov functional for neural networks with delayed interactions and statistical mechanics of temporal associations.- Semi-local signal processing in the visual system.- Statistical mechanics and error-correcting codes.- Synergetic computers — An alternative to neurocomputers.- Dynamics of the Kohonen map.- Equivalence between connectionist classifiers and logical classifiers.- On Potts-glass neural networks with biased patterns.- Ising-spin neural networks with spatial structure.- Kinetically disordered lattice systems.- A programming system for implementing neural nets.- An auto-augmenting neural network architecture for diagnostic reasoning.- Formal integrators and neural networks.- Disorderedmodels of acquired dyslexia.- Higher order memories in optimally structured neural networks.- Random Boolean networks for autoassociative memory: Optimization and sequential learning.

Editorial Reviews

An introduction (on the statistical-mechanical formulation of neural networks) and 18 papers on topics including model neurons, neuronal oscillators, adaptive recurrent neural networks, attractor neural networks, learning and optimization, learning and retrieving marked patterns, competitive learning neural networks, learning in multilayer networks, and the neural basis of the locomotion of nematodes. No index. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Booknews