Abstract
A network of automata is made up of distinct units (or automata) i ; i = 1,…,N. One assumes that an automaton may be in one of a (generally finite) number of internal states σ i . At (discrete) time v the overall state I(v) of the network is defined as the set of states that the units take at this very time : I(v) = {σ i 7,(v)}. The time evolution of the network is driven by a dynamics which depends on a number of parameters {J}. The parameters {J}, which determine the structure of the network, define how the units influence each other and how information proceeds through the system.
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© 1992 Springer Science+Business Media Dordrecht
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Peretto, P., Gordon, M., Rodriguez-Girones, M. (1992). A Brief Account of Statistical Theories of Learning and Generalization in Neural Networks. In: Goles, E., Martínez, S. (eds) Statistical Physics, Automata Networks and Dynamical Systems. Mathematics and Its Applications, vol 75. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-2578-9_5
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DOI: https://doi.org/10.1007/978-94-011-2578-9_5
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