Pulse-type hardware chaotic neuron model and its bifurcation phenomena
Pulse-type hardware chaotic neuron model and its bifurcation phenomena
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脉冲型硬件混沌神经元模型及其分岔现象
DOI：
10.1016/s0893-6080(98)00099-9
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Y. Sekine
中科院分区：
文献类型：
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作者：
K. Someya;Hidekazu Shinozaki;Y. Sekine
最近，对用于神经网络的信息处理功能的生物神经元的硬件进行了大量研究。具有混沌动力学的单个神经元的模型（被称为相原提出的“混沌神经元模型”）具有生物神经元的以下性质：分级响应、相对不应性和输入的时空总和。该模型不仅表现出周期响应，但也混沌响应周期刺激。提出了利用混沌神经元模型，在神经网络中实现新的信息处理结构。因此，单神经元模型应该被设计成具有生物神经元的特性。但脉冲型硬件混沌神经元模型尚未找到，我们之前提出了一种由负阻电路、电阻和电容组成的脉冲型硬件神经元模型。我们还报道了脉冲型硬件神经元模型在恒定的刺激电流下表现出连续放电。但神经元模型的混沌特性尚未得到研究。本文首先证明了脉冲型硬件神经元模型具有混沌神经元模型的特征，并证明了它对脉冲型硬件混沌神经元模型是有用的。其次，我们证明了脉冲型硬件混沌神经元模型在周期脉冲串激励下的分岔图中存在三个混沌区域，并阐明了每个混沌区域的分岔路径和返回映射。
A number of studies has recently been made on hardware for a biological neuron for application to information processing functions of neural networks. A model of a single neuron with chaotic dynamics (called “the chaotic neuron model” proposed by Aihara) has the following properties of biological neurons: graded responses, relative refractoriness, and spatio-temporal summation of inputs. The model exhibits not only periodic responses but also chaotic responses for periodic stimulation. It is suggested that, with the chaotic neuron model, new information processing structures can be realized in neural networks. Accordingly, a single-neuron model should be designed so as to have the properties of biological neurons. But a pulse-type hardware chaotic neuron model has not been found. We previously proposed a pulse-type hardware neuron model composed of a negative resistance circuit, resistors, and capacitors. We reported also that the pulse-type hardware neuron model exhibited continuous firing for a constant stimulus current. But chaotic features of the neuron model have not been investigated. In this paper, we show, firstly, that the pulse-type hardware neuron model has the features of the chaotic neuron model, and we show that it is useful for the pulse-type hardware chaotic neuron model. Next, we show that the pulse-type hardware chaotic neuron model has three chaotic regions in the bifurcation diagram to periodic pulse train stimulation, and we clarify the bifurcation route and the return map in each chaotic region.