Functional possibility of chaotic behaviors in a single chaotic neuron model for dynamical signal processing elements
Functional possibility of chaotic behaviors in a single chaotic neuron model for dynamical signal processing elements
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动态信号处理元件的单个混沌神经元模型中混沌行为的功能可能性
DOI：
10.1109/icsmc.1999.814105
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
K. Aihara
中科院分区：
文献类型：
--
作者：
J. Kuroiwa;S. Nara;K. Aihara
在动态信号处理的背景下，采用数值方法研究了单个混沌神经元模型的动态行为。作为外部信号，采用6种平均频率相同但间隔相关性不同的时间尖峰输入。神经元内部状态的衰减效应和相对耐火度是导致这三类输出复杂动态的重要因素；(i) 1或0响应，（ii）弱复杂动力响应，（iii）高度发达的复杂动力响应。在（ii）和（iii）类别的响应中，我们发现，通常来说，输入的尖峰间隔的时间结构反映在输出的动态特性上，即使输出的平均尖峰间隔几乎等于所有的输入。我们发现，通过在二维空间中嵌入输出，可以通过放大输入尖峰区间的特征差来提取输入尖峰区间的高阶统计特征。我们的结果表明：(i)单个混沌神经元可以作为输入的动态采样元素，对输入具有敏感的响应和噪声鲁棒性；（ii）它可以提取输入的动态结构，例如尖峰序列中包含的二阶或更高阶统计特征。
Dynamical behaviors of a single chaotic neuron model are studied by means of numerical methods in the context of dynamical signal processing. As external signals, six kinds of temporal spiking inputs with the same mean rate but different correlations of spiking intervals are employed. A decay effect of internal state of the neuron and a relative refractoriness play important roles in leading to complex dynamics of outputs categorized in the three types; (i) 1 or 0 responses, (ii) weak complex dynamical responses, and (iii) highly developed complex dynamical responses. In the responses of the categories (ii) and (iii), it is found that, typically, speaking, time structures of interspike intervals of inputs are reflected on dynamical properties of outputs even though mean interspike intervals of outputs are almost equal to all the inputs. We find that, by embedding of outputs in two dimensional space, higher order statistical features of spiking intervals of inputs are extracted with amplification of feature difference between them. Our results show that (i) the single chaotic neuron can work as a dynamical sampling element for inputs with sensitive responses to input and with noise robustness, and (ii) it can extract dynamical structures of inputs, for instance a second or higher order statistical features included in spike trains.