Studying a Chaotic Spiking Neural Model
This work addresses model improvements for researchers in computational neuroscience, but it appears incremental as it builds on an existing model.
The paper tackled the limitations of the Nonlinear Dynamic State (NDS) chaotic spiking neuron model by tuning its parameters and analyzing the dynamics, resulting in the stabilization of many unstable periodic orbits that may correspond to memories.
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.