Hopf bifurcation and chaos in a single delayed neuron equation with non-monotonic activation function

A simple neural network model with discrete time delay is investigated. The linear stability of this model is discussed by analyzing the associated characteristic transcendental equation. For the case with inhibitory influence from the past state, it is found that Hopf bifurcation occurs when this influence varies and passes through a sequence of critical values. The stability of bifurcating periodic solutions and the direction of Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. Chaotic behavior of a single delayed neuron equation with non-monotonously increasing transfer function has been observed in computer simulation. Some waveform diagrams, phase portraits, power spectra and plots of the largest Lyapunov exponent will also be given.