Protachevicz P R, Bonin C A, Iarosz K C, Caldas I L, Batista A M
Institute of Physics, University of São Paulo, São Paulo, Brazil.
Department of Mathematics and Statistics, State University of Ponta Grossa, Ponta Grossa, Brazil.
Cogn Neurodyn. 2022 Dec;16(6):1461-1470. doi: 10.1007/s11571-022-09789-z. Epub 2022 Feb 28.
Neuronal spike variability is a statistical property associated with the noise environment. Considering a linearised Hodgkin-Huxley model, we investigate how large spike variability can be induced in a typical stellate cell when submitted to constant and noise current amplitudes. For low noise current, we observe only periodic firing (active) or silence activities. For intermediate noise values, in addition to only active or inactive periods, we also identify a single transition from an initial spike-train (active) to silence dynamics over time, where the spike variability is low. However, for high noise current, we find intermittent active and silence periods with different values. The spike intervals during active and silent states follow the exponential distribution, which is similar to the Poisson process. For non-maximal noise current, we observe the highest values of inter-spike variability. Our results suggest sub-threshold oscillations as a possible mechanism for the appearance of high spike variability in a single neuron due to noise currents.
神经元放电变异性是一种与噪声环境相关的统计特性。考虑一个线性化的霍奇金 - 赫胥黎模型，我们研究在典型的星状细胞中，当施加恒定电流幅度和噪声电流幅度时，能诱导出多大的放电变异性。对于低噪声电流，我们仅观察到周期性放电（活跃）或静息活动。对于中等噪声值，除了仅活跃或不活跃期外，我们还识别出随着时间从初始的放电序列（活跃）到静息动态的单一转变，此时放电变异性较低。然而，对于高噪声电流，我们发现有不同值的间歇性活跃和静息期。活跃和静息状态下的放电间隔遵循指数分布，这与泊松过程相似。对于非最大噪声电流，我们观察到放电间期变异性的最高值。我们的结果表明阈下振荡是由于噪声电流导致单个神经元出现高放电变异性的一种可能机制。
