Pikovsky Arkady, Rosenblum Michael
Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476, Potsdam-Golm, Germany.
J Comput Neurosci. 2025 Mar 22. doi: 10.1007/s10827-025-00900-x.
We tackle a quantification of synchrony in a large ensemble of interacting neurons from the observation of spiking events. In a simulation study, we efficiently infer the synchrony level in a neuronal population from a point process reflecting spiking of a small number of units and even from a single neuron. We introduce a synchrony measure (order parameter) based on the Bartlett covariance density; this quantity can be easily computed from the recorded point process. This measure is robust concerning missed spikes and, if computed from observing several neurons, does not require spike sorting. We illustrate the approach by modeling populations of spiking or bursting neurons, including the case of sparse synchrony.
我们从尖峰事件的观测出发，对大量相互作用神经元的同步性进行量化。在一项模拟研究中，我们能够从反映少量神经元尖峰活动的点过程甚至单个神经元中，有效地推断出神经元群体的同步水平。我们引入了一种基于巴特利特协方差密度的同步性度量（序参量）；该量可从记录的点过程中轻松计算得出。此度量对于漏记的尖峰具有鲁棒性，并且如果是从多个神经元的观测中计算得到，不需要进行尖峰分类。我们通过对尖峰发放或爆发性神经元群体进行建模（包括稀疏同步的情况）来说明该方法。
