Studying a Chaotic Spiking Neural Model
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.
Code (0)
등록된 구현이 없습니다.
Tasks
modelSimilar Papers 제목 키워드 기반
Average activity of excitatory and inhibitory neural populations
We develop an extension of the Ott-Antonsen method that allows obtaining the mean activity (spiking rate) of a population of excitable units. By means of the Ott-Antonsen method, equations for the dynamics of the order p…
Multi-scale variability in neuronal competition
We examine whether a single biophysical cortical circuit model can explain both spiking and perceptual variability. We consider perceptual rivalry, which provides a window into intrinsic neural processing since neural ac…
Open-Ended Question AnsweringDiscrete hybrid Izhikevich neuron model: nodal and network behaviours considering electromagnetic flux coupling
We analyse the dynamics of the improved discretised version of the well known Izhikevich neuronmodel under the action of external electromagnetic field. It is found that the three-dimensional IZHmap shows rich dynamics. …
Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination
Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences for how such networks encode streams of temporal st…
DecoderInvestigation of a chaotic spiking neuron model
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 be…
model