Scaling of causal neural avalanches in a neutral model

TL;DR

This paper investigates the scaling behavior of neural avalanches in a neutral model, revealing critical phenomena and universal scaling laws that align with experimental observations of brain activity.

Contribution

It introduces a neutral theory-based model for neural avalanches and demonstrates critical scaling and data collapse, providing a unified framework for understanding brain criticality.

Findings

Critical exponents match scaling hypothesis predictions.

Data collapse observed for avalanche profiles and distributions.

Evidence of critical slowing-down near the critical point.

Abstract

Neural avalanches are collective firings of neurons that exhibit emergent scale-free behavior. Understanding the nature and distribution of these avalanches is an important element in understanding how the brain functions. We study a model of neural avalanches for which the dynamics are governed by neutral theory. The neural avalanches are defined using causal connections between the firing neurons. We analyze the scaling of causal neural avalanches as the critical point is approached from the absorbing phase. By using cluster analysis tools from percolation theory, we characterize the critical properties of the neural avalanches. We identify the tuning parameters consistent with experiments. The scaling hypothesis provides a unified explanation of the power laws which characterize the critical point. The critical exponents characterizing the avalanche distributions and divergence of the response functions are consistent with the predictions of the scaling hypothesis. We use a universal scaling function for the avalanche profile to find that the firing rates for avalanches of different durations show data collapse after appropriate rescaling. We also find data collapse for the avalanche distribution functions, which is stronger evidence of criticality than just the existence of power laws. Critical slowing-down and power law relaxation of avalanches is observed as the system is tuned to its critical point. We discuss how our results motivate future empirical studies of criticality in the brain.