Response of a pacemaker neuron model to stochastic pulse trains
Response of a pacemaker neuron model to stochastic pulse trains
复制标题
起搏器神经元模型对随机脉冲序列的响应
DOI：
10.1007/s00422-001-0287-9
复制
发表时间：
2002
影响因子：
1.9
通讯作者：
K. Pakdaman
中科院分区：
文献类型：
--
作者：
T. Yamanobe;K. Pakdaman
摘要：我们研究了起搏神经元模型对抑制性随机脉冲扰动的响应。 该模型捕捉到了起搏神经元动力学的基本方面。特别是，该模型再现线性随机脉冲序列，即消失的矛盾段，其中的起搏神经元的输出放电率与抑制率增加，作为输入脉冲序列的变异系数的增加。为了研究模型对随机脉冲序列的响应，我们使用了马尔可夫算子来控制相变。我们展示了如何线性化发生的基础上的马尔可夫算子的谱分析。此外，使用李雅普诺夫指数，我们表明，可变的输入引起可靠的发射，即使在情况下，周期性刺激相同的平均速率不。
Abstract. We investigated the response of a pacemaker neuron model to trains of inhibitory stochastic impulsive perturbations. The model captures the essential aspect of the dynamics of pacemaker neurons. Especially, the model reproduces linearization by stochastic pulse trains, that is, the disappearance of the paradoxical segments in which the output firing rate of pacemaker neurons increases with inhibition rate, as the coefficient of variation of the input pulse train increases. To study the response of the model to stochastic pulse trains, we use a Markov operator governing the phase transition. We show how linearization occurs based on the spectral analysis of the Markov operator. Moreover, using Lyapunov exponents, we show that variable inputs evoke reliable firing, even in situations where periodic stimulation with the same mean rate does not.