From neuronal spikes to avalanches: Effects and circumvention of time binning

Abstract

Branching with immigration is one of the most common models for the stochastic processes observed in neuronal circuits. However, it is not observed directly and, in order to create branching-like processes, the observed spike time series is processed by attaching time bins to spikes. It has been shown that results such as criticality and size distributions depend on the chosen time bin. A different methodology whose results do not depend on the choice of time bin might therefore be useful and is proposed in this article. This methodology circumvents using time bins altogether by replacing the previously used discrete-time models by continuous-time models. First, the article introduces and characterizes a continuous-time version of the branching process with immigration, which will be called the pumped branching process, and second, it presents an analytical derivation of the corresponding spike statistics, which can be directly compared to observed spike time series. The presented approach allows determining the degree of criticality, the average number of overlapping avalanches, and other observables without using a time bin. Furthermore, the effects caused by using time bins are analyzed and the influence of temporal and spatial subsampling discussed, all of which is compared to experimental data and supported by Monte Carlo simulations.