  We discuss, in this paper, the dynamical properties of extremely diluted,
non-monotonic neural networks. Assuming parallel updating and the Hebb
prescription for the synaptic connections, a flow equation for the macroscopic
overlap is derived. A rich dynamical phase diagram was obtained, showing a
stable retrieval phase, as well as a cycle two and chaotic behavior. Numerical
simulations were performed, showing good agreement with analytical results.
Furthermore, the simulations give an additional insight into the microscopic
dynamical behavior during the chaotic phase. It is shown that the freezing of
individual neuron states is related to the structure of chaotic attractors.
