Neuronal Avalanches: Where Temporal Complexity and Criticality Meet

TL;DR

This paper demonstrates that a continuous leaky integrate-and-fire model with Gaussian noise exhibits neural avalanches and temporal complexity at criticality, resolving previous inconsistencies caused by discretization artifacts.

Contribution

It shows that replacing stochastic noise with Gaussian noise in a continuous model aligns neural avalanches with criticality, clarifying prior discrepancies.

Findings

Power-law distribution of neural avalanches occurs at criticality.

Continuous models with Gaussian noise match temporal complexity and avalanche patterns.

Discretization artifacts can cause false indications of supercriticality.

Abstract

The model of the current paper is an extension of a previous publication, wherein we used the leaky integrate-and-fire model on a regular lattice with periodic boundary conditions, and introduced the temporal complexity as a genuine signature of criticality. In that work, the power-law distribution of neural avalanches was manifestation of supercriticality rather than criticality. Here, however, we show that continuous solution of the model and replacing the stochastic noise with a Gaussian zero-mean noise leads to the coincidence of power-law display of temporal complexity and spatiotemporal patterns of neural avalanches at the critical point. We conclude that the source of inconsistency may in fact be a numerical artifact originated by the discrete description of the model, which may imply slow numerical convergence of avalanche distribution compared to temporal complexity.