Dispersive propagation skew effects in iterative neural networks

During communication between neurons in a continuous-time analog neural network, propagation skew typically varies from neuron pair to neuron pair. For no dispersion, S. Oh et al. (Opt. Eng., vol.28, p.526-32, 1989) previously demonstrated that the steady-state performance of an iterative neural network is not affected if the combination of the neural network's weights and neural nonlinearity is contractive. This result is extended to the case of dispersive skew. It is shown that, under nearly the same conditions, the same steady-state result will occur in the neural network in the presence of dispersive skew.<
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