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