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Bifurcation phenomena of simple pulse-coupled spiking neuron models with filtered base signal

机译:具有滤波基本信号的简单脉冲耦合尖峰神经元模型的分岔现象

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This paper studies nonlinear phenomena of pulse-coupled bifurcating neurons. Repeating integrate-and-fire dynamics between a constant threshold and periodic base signal, the bifurcating neuron outputs a spike-train. Applying a low-pass filter to the periodic square wave, we obtain the base signal. As parameters of the filter vary, the shape of the base signal varies and the neurons can exhibit a variety of periodic/chaotic spike-trains and related bifurcation phenomena. We consider typical phenomena. First, the single bifurcating neuron can exhibit the period doubling bifurcation where both period and the number of spike-trains are doubling. Second, the pulse-coupled two bifurcating neurons can exhibit the tangent bifurcation that causes “chaos + chaos = order”: chaotic spiketrains of two single neurons are changed into periodic spiketrain by the pulse-coupling. Such phenomena are filter-induced bifurcations because they are caused by the filtering. The bifurcation sets are calculated precisely based on the state equation of the filter and the one-dimensional spike-phase map. Presenting a simple test circuit, typical phenomena are confirmed experimentally.
机译:本文研究了脉冲偶联分叉神经元的非线性现象。在恒定阈值和周期性基础信号之间重复集成和射击动态,分叉神经元输出尖峰列车。将低通滤波器应用于周期性方波,我们获得基本信号。随着滤波器的参数变化,基本信号的形状变化,神经元可以表现出各种周期性/混沌钉列表和相关分叉现象。我们考虑典型的现象。首先,单个分叉神经元可以表现出周期和尖峰列车的数量加倍的时期倍增分叉。其次,脉冲偶联的两个分叉神经元可以表现出导致“混沌+混沌=顺序”的切线分岔:两个单个神经元的混沌穗通过脉冲耦合被改变为周期性尖峰。这种现象是过滤诱导的分叉,因为它们是由过滤引起的。基于过滤器的状态方程和一维峰值相位图,精确地计算分叉组。提出简单的测试电路,实验证实了典型的现象。

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