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首页> 外文期刊>Journal of Computational Neuroscience >Emergent spike patterns in neuronal populations
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Emergent spike patterns in neuronal populations

机译：神经元群体中出现的尖峰模式

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This numerical study documents and analyzes emergent spiking behavior in local neuronal populations. Emphasis is given to a phenomenon we call clustering, by which we refer to a tendency of random groups of neurons large and small to spontaneously coordinate their spiking activity in some fashion. Using a sparsely connected network of integrate-and-fire neurons, we demonstrate that spike clustering occurs ubiquitously in both high firing and low firing regimes. As a practical tool for quantifying such spike patterns, we propose a simple scheme with two parameters, one setting the temporal scale and the other the amount of deviation from the mean to be regarded as significant. Viewing population activity as a sequence of events, meaning relatively brief durations of elevated spiking, separated by inter-event times, we observe that background activity tends to give rise to extremely broad distributions of event sizes and inter-event times, while driving a system imposes a certain regularity on its inter-event times, producing a rhythm consistent with broad-band gamma oscillations. We note also that event sizes and inter-event times decorrelate very quickly. Dynamical analyses supported by numerical evidence are offered.
机译：这项数值研究记录和分析了局部神经元种群中的突峰行为。强调了一种称为聚类的现象，通过这种现象，我们指的是大大小小的神经元随机组以某种方式自发地协调其尖峰活动的趋势。使用集成和发射神经元的稀疏连接网络，我们证明了在高发射和低发射两种情况下都普遍存在尖峰聚类。作为量化此类尖峰模式的实用工具，我们提出了一种简单的方案，其中包含两个参数，一个参数设置时间标度，另一个参数与平均值的偏离量被认为是重要的。将人口活动视为一系列事件，即尖峰持续时间相对较短，由事件间时间分隔，我们观察到背景活动往往会在驱动系统时引起事件大小和事件间时间的极其广泛的分布在事件间隔时间上施加一定的规律性，产生与宽带伽马振荡一致的节奏。我们还注意到，事件大小和事件间时间之间的关联非常迅速。提供了数值证据支持的动力学分析。

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