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- W1562807589 abstract "Multilayered feed-forward networks (perceptrons) are special cases of the general McCulloch-Pitts neural network with arbitrarily interconnected neurons. On the other hand, any general “recurrent” neural network can be considered to be represented by a feed-forward perceptron, albeit one with possibly very many layers. The reason for this strange equivalence is that the temporal evolution (3.5) of an arbitrary network constructed from binary neurons $$ {s_i}left( {t,, + ,,1} right),, = ,,{mathop{rm sgn}} left[ {{h_i}left( t right)} right],, equiv ,,{mathop{rm sgn}} left[ {sumlimits_{j, = ,1}^N {{w_{ij}}{s_j}left( t right)} } right] $$(13.1)is necessarily periodic. This statement follows immediately from the observation that the N neurons can only assume 2 N configurations altogether, and hence some state of the network must reoccur after at most 2 N steps. Since only the present state of the network enters on the right-hand side of the evolution law (13.1), the sub se quent evolution proceeds strictly periodically from that moment on. If one considers the neural network at a certain moment t = n as the nth layer of a perceptron (with all layers identical!), the temporal-evolution law can be viewed as the law governing the flow of information from one layer to the next. It is then sufficient to take into account only a finite number of such layers, just as many as there are time steps leading up to the first repetition of a network configuration." @default.
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- W1562807589 date "1995-01-01" @default.
- W1562807589 modified "2023-10-18" @default.
- W1562807589 title "Coupled Neural Networks" @default.
- W1562807589 doi "https://doi.org/10.1007/978-3-642-57760-4_14" @default.
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