标题： A电流和I型/II型转换决定了来自共同输入的集体放电 显示英文标题

标题： The A-current and Type I / Type II transition determine collective spiking from common input

摘要： 相关神经元对和群体之间的同步放电机制及其影响正在整个神经系统中受到深入研究。 一种普遍的电路特征可以导致这种相关性，即对神经元对和细胞群体的重叠或共同输入，导致共同的尖峰序列响应。 在这里，我们使用计算工具来研究接收细胞的生理特性如何调节共同输入电流转化为共同尖峰输出的过程。 我们关注一种关键的膜 conductance - gA，即 A 型钾电流 - 它使神经元在“II 型”兴奋性（低 gA）和“I 型”兴奋性（高 gA）之间转换。 无论 gA 的值如何，细胞都会将共同输入波动转化为几乎同时放电的趋势。 然而，这一过程在低 gA 值时更为明显，这与之前由简化“相位”模型预测的一致。 因此，对于给定水平的共同输入，II 型神经元产生的尖峰在短时间尺度上相对更加相关。 在长时间尺度上，趋势会反转，II 型神经元产生的尖峰序列相对较少相关。 这是因为这些细胞在同时放电方面的增强倾向被较大时间延迟下的相反倾向所平衡。 我们展示了神经信号处理的一个新含义：具有长时间常数的下游细胞会被上游的 I 型细胞群体选择性驱动，而具有短时间常数的细胞则被 II 型细胞群体驱动。 我们的结果通过高通量数值模拟得出，并通过细胞的滤波特性和非线性动力学进行解释。 显示英文摘要

摘要： The mechanisms and impact of correlated, or synchronous, firing among pairs and groups of neurons is under intense investigation throughout the nervous system. A ubiquitous circuit feature that can give rise to such correlations consists of overlapping, or common, inputs to pairs and populations of cells, leading to common spike train responses. Here, we use computational tools to study how the transfer of common input currents into common spike outputs is modulated by the physiology of the recipient cells. We focus on a key conductance - gA, for the A-type potassium current - which drives neurons between "Type II" excitability (low gA), and "Type I" excitability (high gA). Regardless of gA, cells transform common input fluctuations into a ten- dency to spike nearly simultaneously. However, this process is more pronounced at low gA values, as previously predicted by reduced "phase" models. Thus, for a given level of common input, Type II neurons produce spikes that are relatively more correlated over short time scales. Over long time scales, the trend reverses, with Type II neurons producing relatively less correlated spike trains. This is because these cells' increased tendency for simultaneous spiking is balanced by opposing tendencies at larger time lags. We demonstrate a novel implication for neural signal processing: downstream cells with long time constants are selectively driven by Type I cell populations upstream, and those with short time constants by Type II cell populations. Our results are established via high-throughput numerical simulations, and explained via the cells' filtering properties and nonlinear dynamics.