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猫听觉神经中神经脉冲序列的脉冲数分布。

Pulse-number distribution for the neural spike train in the cat's auditory nerve.

作者信息

Teich M C, Khanna S M

出版信息

J Acoust Soc Am. 1985 Mar;77(3):1110-28. doi: 10.1121/1.392176.

Abstract

Pulse-number distributions (PNDs) were recorded from primary afferent fibers in the auditory nerve of the cat, using standard extracellular microelectrode recording techniques. Pure-tone and broadband-noise stimuli were used. The number of neural spikes (pulses) n was measured in a set of contiguous intervals, each of duration T seconds. The quantity n varies from one interval to another. These data were then used to determine the PND, which is the probability p(n,T) of occurrence of n spikes in the time T, versus the number n. The estimated mean and variance of p(n,T) were obtained. Two different values of T were used. An unexpected observation was that the count mean-to-variance ratio R is relatively constant and independent of the stimulus intensity. Use of the PND as a statistical measure of the underlying neural point process has a number of virtues. For example, the PND readily exhibits the existence of spike clusters (e.g., pairs) for some units. The PND is essentially unaffected by time jitter and time quantization and provides a statistically significant measure for units firing at low rates. A study of the scaled and unscaled pulse-interval distributions (PIDs), under conditions of spontaneous firing, demonstrates that the occurrences of neural events are generally not describable by a renewal process. Our investigation shows that none of the point processes customarily used to model the auditory neural spike train is consistent with all of the data. It appears that the encoding of acoustic information into nerve spikes in the peripheral auditory system takes the form of a cluster point process similar to the Neyman-Scott type. For pure-tone excitation, the PND will be well represented as a multinomial distribution in this case.

摘要

采用标准的细胞外微电极记录技术,记录猫听觉神经初级传入纤维的脉冲数分布(PND)。使用了纯音和宽带噪声刺激。在一组连续的时间间隔内测量神经冲动(脉冲)的数量(n),每个时间间隔的持续时间为(T)秒。数量(n)在不同的时间间隔内会有所变化。然后利用这些数据来确定PND,即时间(T)内出现(n)个脉冲的概率(p(n,T))与脉冲数(n)的关系。得到了(p(n,T))的估计均值和方差。使用了两个不同的(T)值。一个意外的发现是计数均值与方差之比(R)相对恒定,且与刺激强度无关。将PND用作潜在神经点过程的统计量有许多优点。例如,对于某些神经元,PND很容易显示出脉冲簇(如成对脉冲)的存在。PND基本上不受时间抖动和时间量化的影响,并且为低发放率的神经元提供了具有统计学意义的量度。在自发放电条件下对缩放和未缩放的脉冲间隔分布(PID)的研究表明,神经事件的发生通常不能用更新过程来描述。我们的研究表明,通常用于模拟听觉神经脉冲序列的点过程都与所有数据不一致。看来,外周听觉系统中声学信息编码为神经脉冲的形式是一种类似于奈曼 - 斯科特类型的簇点过程。在这种情况下,对于纯音激励,PND将很好地表示为多项分布。

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