Titlebook: Neural and Automata Networks; Dynamical Behavior a Eric Goles,Servet Martínez Book 1990 Kluwer Academic Publishers 1990 Approximation.Finit

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Algebraic Invariants on Neural Networks, particular cases the two previous ones. We also study memory iteration where the updating consider a longer history of each site. Finally, we also use algebraic invariantes to study majority networks.

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Algebraic Invariants on Neural Networks,ical models of neural computation. We include different ways of updating the networks: synchronous, sequential and block-sequential, which contains as particular cases the two previous ones. We also study memory iteration where the updating consider a longer history of each site. Finally, we also us

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Lyapunov Functionals Associated to Neural Network, of periods. Unfortunately, the transient behavior does not yield as readily to a study based on such class of invariants. To overcome the difficulty, we introduce here Lyapunov functionals driving the network dynamics. Using this kind of functionals, explicit bounds to the transient length will be

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Potts Automata, where the spins may take several orientations rather than just two, as in the binary case (up and down). The Hamiltonian is: .where . is the finite set of orientations, (., .)∈ . means the sites . and . are neighbours, and δ. is the Kroeneker function: δ.(.,.) = 1 iff . = .. It is not difficult to

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