  Dynamics of a chaotic spiking neuron model are being studied mathematically
and experimentally. The Nonlinear Dynamic State neuron (NDS) is analysed to
further understand the model and improve it. Chaos has many interesting
properties such as sensitivity to initial conditions, space filling, control
and synchronization. As suggested by biologists, these properties may be
exploited and play vital role in carrying out computational tasks in human
brain. The NDS model has some limitations; in thus paper the model is
investigated to overcome some of these limitations in order to enhance the
model. Therefore, the models parameters are tuned and the resulted dynamics are
studied. Also, the discretization method of the model is considered. Moreover,
a mathematical analysis is carried out to reveal the underlying dynamics of the
model after tuning of its parameters. The results of the aforementioned methods
revealed some facts regarding the NDS attractor and suggest the stabilization
of a large number of unstable periodic orbits (UPOs) which might correspond to
memories in phase space.
