  Further analysis and experimentation is carried out in this paper for a
chaotic dynamic model, viz. the Nonlinear Dynamic State neuron (NDS). The
analysis and experimentations are performed to further understand the
underlying dynamics of the model and enhance it as well. Chaos provides many
interesting properties that can be exploited to achieve computational tasks.
Such properties are sensitivity to initial conditions, space filling, control
and synchronization.Chaos might play an important role in information
processing tasks in human brain as suggested by biologists. If artificial
neural networks (ANNs) is equipped with chaos then it will enrich the dynamic
behaviours of such networks. The NDS model has some limitations and can be
overcome in different ways. In this paper different approaches are followed to
push the boundaries of the NDS model in order to enhance it. One way is to
study the effects of scaling the parameters of the chaotic equations of the NDS
model and study the resulted dynamics. Another way is to study the method that
is used in discretization of the original R\"{o}ssler that the NDS model is
based on. These approaches have revealed some facts about the NDS attractor and
suggest why such a model can be stabilized to large number of unstable periodic
orbits (UPOs) which might correspond to memories in phase space.
