  Chaos provides many interesting properties that can be used to achieve
computational tasks. Such properties are sensitivity to initial conditions,
space filling, control and synchronization. Chaotic neural models have been
devised to exploit such properties. In this paper, a chaotic spiking neuron
model is investigated experimentally. This investigation is performed to
understand the dynamic behaviours of the model.
  The aim of this research is to investigate the dynamics of the nonlinear
dynamic state neuron (NDS) experimentally. The experimental approach has
revealed some quantitative and qualitative properties of the NDS model such as
the control mechanism, the reset mechanism, and the way the model may exhibit
dynamic behaviours in phase space. It is shown experimentally in this paper
that both the reset mechanism and the self-feed back control mechanism are
important for the NDS model to work and to stabilise to one of the large number
of available unstable periodic orbits (UPOs) that are embedded in its
attractor. The experimental investigation suggests that the internal dynamics
of the NDS neuron provide a rich set of dynamic behaviours that can be
controlled and stabilised. These wide range of dynamic behaviours may be
exploited to carry out information processing tasks.
