Creating Presence by Bridging Between the Past and the Future: the Role of Learning and Memory for the Organization of Life

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].

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