From Neuronal Spikes to Avalanches -- Effects and Circumvention of Time Binning

TL;DR

This paper introduces a continuous-time model for neuronal spike analysis that avoids the biases introduced by time binning, enabling more accurate assessment of criticality and avalanche dynamics in neural circuits.

Contribution

It proposes a novel continuous-time pumped branching process model and analytical spike statistics, circumventing the need for time binning in neuronal data analysis.

Findings

The new model accurately characterizes spike statistics without time binning.

Time bin effects significantly influence criticality assessments.

The approach aligns well with experimental data and simulations.

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. The new methodology circumvents using time bins altogether by replacing the previously used discrete-time models by continuous-time models. First, the article introduces and characterises a continuous-time version of the branching process with immigration, which will be called 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.