Modeling Brain Function: The World of Attractor Neural Networks. Amit, D. J. Cambridge University Press, 1992. Paper abstract bibtex Exploring one of the most exciting and potentially rewarding areas of scientific research, the study of the principles and mechanisms underlying brain function, this book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. Substantial progress in understanding memory, the learning process, and self-organization by studying the properties of models of neural networks have resulted in discoveries of important parallels between the properties of statistical, nonlinear cooperative systems in physics and neural networks. The author presents a coherent and clear, nontechnical view of all the basic ideas and results. More technical aspects are restricted to special sections and appendices in each chapter.
@book{Amit1992,
title = {Modeling {Brain} {Function}: {The} {World} of {Attractor} {Neural} {Networks}},
isbn = {0-521-42124-1},
url = {http://books.google.com/books?id=fvLYch1yQncC&pgis=1},
abstract = {Exploring one of the most exciting and potentially rewarding areas of scientific research, the study of the principles and mechanisms underlying brain function, this book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. Substantial progress in understanding memory, the learning process, and self-organization by studying the properties of models of neural networks have resulted in discoveries of important parallels between the properties of statistical, nonlinear cooperative systems in physics and neural networks. The author presents a coherent and clear, nontechnical view of all the basic ideas and results. More technical aspects are restricted to special sections and appendices in each chapter.},
urldate = {2014-04-15},
publisher = {Cambridge University Press},
author = {Amit, Daniel J.},
year = {1992},
keywords = {\#nosource, attractors, theory},
}
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