Chaotic Neural Model Research Articles

Bounded noise-induced coherence resonance in a single Rulkov neuron

Based on the chaotic Rulkov model, we investigate the impacts of bounded noise on firing activity of the neural system. First, the chaotic neural model and bounded noise are introduced and analyzed. Then, different firing patterns of the neural model driven by bounded noise are present. Finally, taking the coherence parameter R to measure the regularity of firing behavior, it is demonstrated that the regularity of neural membrane potential could achieve optimal by adjusting either the intensity of bounded noise or its frequency to an appropriate value, which means that the phenomenon of coherence resonance occurs. Moreover, it is found that coherence resonance can also be induced by another kind of bounded noise, called sine-Wiener noise. The results of this paper are beneficial to explore the functional role of noise on neural system.

Investigation 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 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.

Implementations of Modified Chaotic Neural Models with Analog Reconfigurable Hardware

This paper focuses on implementations of two modified Aihara's chaotic neuron models and a simple chaotic neural network constructed with two chaotic neurons in a programmable and reconfigurable manner with an analog programmable device, FPAA (Field Programmable Analog Array). After testing the chaotic behaviors of two chaotic neuron models and a simple chaotic neural network through numerical analyses that consist of time domain responses, phase portrait illustrations and bifurcation diagrams, the experimental setup is constructed with a FPAA device. The parametric adjustments of chaotic neural structures are possible with the proposed flexible design methodology and different chaotic neuron models are constructed on the same reconfigurable device without any hardware changes. Experimental results verify the dynamic behaviors of these chaotic neural structures and demonstrate the efficiency of programmable implementations.

A novel delayed chaotic neural model and its circuitry implementation

In this paper, we investigate a novel delayed chaotic neural model, in which a non-monotonously increasing transfer function is employed as activation function. Local stability and existence of Hopf bifurcation are analyzed in details. Chaos behavior of the neuron model is observed in computer simulations. An electronic implementation of the neuron is also considered. The dynamical behavior of the designed circuits is closely similar to the results simulated by numerical experiments.