Neurodynamics - Proceedings Of The 9th Summer Workshop
Will be published approx. on 1. October 1991
Book
Hardback
244 pages
978-981-02-0811-0 (ISBN)
Description
This volume presents applications of mathematical techniques for modelling and performance analysis of neural networks. The collection of articles is motivated by the observation that the theory of neural network dynamics, i.e. Neurodynamics, still has to be given a thorough mathematical foundation. Therefore, the volume comprises research work on different mathematical approaches to neural networks; analytical and numerical techniques of dynamical systems theory, geometrical techniques, and methods of statistical physics. Articles analyse dynamics of neural netwroks in general or concentrate on specific network models of biological or neurocomputing origin. A few of the articles serve as a good introduction to these subjects.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
ISBN-13
978-981-02-0811-0 (9789810208110)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Content
Dynamical systems analysis; network dynamics; principles and problems, M.W. Hirsch; description and training of neural network dynamics, R. Rohwer; neural networks; flexible modelling, mathematical analysis, and applications, U. an der Heiden; equilibria, periodicity, bursting and chaos in neural activity, A.V. Holden, et al; neurodynamics, J.G. Taylor; statistical performance analysis; temporal association in neural networks with transmission delays; dynamics and statistical physics, J.L. van Hemmen, et al; limit points and limit cycles in neural nets with nonlinear synapses, H. Englisch; deterministic neuronal nets; examples, M. Marinaro and A. Esposito; perceptron learning by constrained optimization; the ada tron algorithm, M. Biehl, et al; optimal dilution of a hopfield network, K.E. Kurten; two sufficient conditions for convergence in multilayer perceptrons, P. Rujan.