Since 1982, starting with the work of Hopfield, theoretical physics is contributing to the theory of neural networks. In his pioneering work, Hopfield pointed out a relation between models of disordered magnets (spin glasses) and models of neurons interacting by competing synaptic couplings. This work started an extensive research effort: using models, methods and principles of statistical physics one has described the cooperative behavior of a large system of interacting neurons. Now, almost two decades later, much has been achieved in this field: associative memory, learning from examples, generalization from examples to an unknown rule, time series prediction, optimizing architectures and learning rules, all this has been expressed in a mathematical language which allows to calculate the cooperative properties of infinitely large systems being trained on infinitely many patterns [1, 2].
KeywordsHide Unit Generalization Error Chaotic Sequence Time Series Prediction Multilayer Network
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