Ermentrout Bard, Saunders David
Department of Mathematics, University of Pittsburgh, Pittsburgh, PA 15260, USA.
J Comput Neurosci. 2006 Apr;20(2):179-90. doi: 10.1007/s10827-005-5427-0. Epub 2006 Apr 6.
A number of experimental groups have recently computed Phase Response Curves (PRCs) for neurons. There is a great deal of noise in the data. We apply methods from stochastic nonlinear dynamics to coupled noisy phase-resetting maps and obtain the invariant density of phase distributions. By exploiting the special structure of PRCs, we obtain some approximations for the invariant distributions. Comparisons to Monte-Carlo simulations are made. We show how phase-dependence of the noise can move the peak of the invariant density away from the peak expected from the analysis of the deterministic system and thus lead to noise-induced bifurcations.
最近，一些实验小组计算了神经元的相位响应曲线（PRC）。数据中存在大量噪声。我们将随机非线性动力学方法应用于耦合噪声相位重置映射，并获得相位分布的不变密度。通过利用PRC的特殊结构，我们得到了不变分布的一些近似值。与蒙特卡罗模拟进行了比较。我们展示了噪声的相位依赖性如何使不变密度的峰值偏离确定性系统分析所预期的峰值，从而导致噪声诱导的分岔。
