Max-Planck-Institute for the Physics of Complex Systems, Dresden, Germany.
PLoS Comput Biol. 2013;9(8):e1003170. doi: 10.1371/journal.pcbi.1003170. Epub 2013 Aug 15.
Stochastic signals with pronounced oscillatory components are frequently encountered in neural systems. Input currents to a neuron in the form of stochastic oscillations could be of exogenous origin, e.g. sensory input or synaptic input from a network rhythm. They shape spike firing statistics in a characteristic way, which we explore theoretically in this report. We consider a perfect integrate-and-fire neuron that is stimulated by a constant base current (to drive regular spontaneous firing), along with Gaussian narrow-band noise (a simple example of stochastic oscillations), and a broadband noise. We derive expressions for the nth-order interval distribution, its variance, and the serial correlation coefficients of the interspike intervals (ISIs) and confirm these analytical results by computer simulations. The theory is then applied to experimental data from electroreceptors of paddlefish, which have two distinct types of internal noisy oscillators, one forcing the other. The theory provides an analytical description of their afferent spiking statistics during spontaneous firing, and replicates a pronounced dependence of ISI serial correlation coefficients on the relative frequency of the driving oscillations, and furthermore allows extraction of certain parameters of the intrinsic oscillators embedded in these electroreceptors.
在神经系统中，经常会遇到具有明显振荡成分的随机信号。以随机振荡形式输入到神经元的电流可能来自外部，例如感觉输入或来自网络节律的突触输入。它们以特征的方式塑造尖峰发射的统计数据，我们在本报告中从理论上进行了探讨。我们考虑一个完美的积分-点火神经元，它受到恒定的基本电流（驱动规则自发发射）、高斯窄带噪声（随机振荡的一个简单例子）和宽带噪声的刺激。我们推导出第 n 阶区间分布、其方差以及尖峰间隔（ISI）的序列相关系数的表达式，并通过计算机模拟确认这些分析结果。然后，该理论被应用于来自匙吻鲟电感受器的实验数据，匙吻鲟电感受器有两种不同类型的内部噪声振荡器，一种强迫另一种。该理论提供了对其自发发射期间传入尖峰统计数据的分析描述，并复制了 ISI 序列相关系数对驱动振荡相对频率的显著依赖性，并且还允许提取嵌入这些电感受器中的固有振荡器的某些参数。
