Asymptotic analysis of cyclic transitions in the discrete-time neural networks with antisymmetric and circular interconnection weights

Evaluation of cyclic transitions in the discrete-time neural networks with antisymmetric and circular interconnection weights has been derived in an asymptotic mathematical form. The type and the number of limit cycles generated by circular networks, in which each neuron is connected only to its nearest neurons, have been investigated through analytical method. The results show that the estimated numbers of state vectors generating n- or 2n-periodic limit cycles are an exponential function of (1.6)ⁿ for a large number of neuron, n. The sufficient conditions for state vectors to generate limit cycles of period n or 2n are also given.