Tomar Rimjhim, Kostal Lubomir
Department of Computational Neuroscience, Institute of Physiology, Czech Academy of Sciences, Prague, Czechia.
Second Medical Faculty, Charles University, Prague, Czechia.
Front Comput Neurosci. 2021 Jun 7;15:620410. doi: 10.3389/fncom.2021.620410. eCollection 2021.
The apparent stochastic nature of neuronal activity significantly affects the reliability of neuronal coding. To quantify the encountered fluctuations, both in neural data and simulations, the notions of variability and randomness of inter-spike intervals have been proposed and studied. In this article we focus on the concept of the instantaneous firing rate, which is also based on the spike timing. We use several classical statistical models of neuronal activity and we study the corresponding probability distributions of the instantaneous firing rate. To characterize the firing rate variability and randomness under different spiking regimes, we use different indices of statistical dispersion. We find that the relationship between the variability of interspike intervals and the instantaneous firing rate is not straightforward in general. Counter-intuitively, an increase in the randomness (based on entropy) of spike times may either decrease or increase the randomness of instantaneous firing rate, in dependence on the neuronal firing model. Finally, we apply our methods to experimental data, establishing that instantaneous rate analysis can indeed provide additional information about the spiking activity.
神经元活动明显的随机性显著影响神经元编码的可靠性。为了量化神经数据和模拟中遇到的波动，人们提出并研究了峰峰间隔的变异性和随机性概念。在本文中，我们关注同样基于脉冲时间的瞬时发放率概念。我们使用了几种经典的神经元活动统计模型，并研究了瞬时发放率的相应概率分布。为了表征不同发放模式下发放率的变异性和随机性，我们使用了不同的统计离散指数。我们发现，一般来说，峰峰间隔的变异性与瞬时发放率之间的关系并不直接。与直觉相反，脉冲时间的随机性（基于熵）增加可能会降低或增加瞬时发放率的随机性，这取决于神经元发放模型。最后，我们将我们的方法应用于实验数据，证实瞬时发放率分析确实可以提供有关发放活动的额外信息。
