Ubiquitous proximity to a critical state for collective neural activity in the CA1 region of freely moving mice

TL;DR

This study demonstrates that neural activity in the hippocampal CA1 region of freely moving mice consistently hovers near a critical state, with network structure and coupling balance playing key roles in maintaining this criticality.

Contribution

The paper introduces a data-driven statistical modeling approach linking neural activity to spin-glass models, revealing the robustness and structural factors of criticality in neural networks.

Findings

Neural activity is near a critical state across experiments.

Network structure influences proximity to criticality.

Coupling balance supports maintenance of critical state.

Abstract

Using miniscope recordings of calcium fluorescence signals in the CA1 region of the hippocampus of mice, we monitor the neural activity of hippocampal regions while the animals are freely moving in an open chamber. Using a data-driven statistical modeling approach, the statistical properties of the recorded data are mapped to spin-glass models with pairwise interactions. Considering the parameter space of the model, the observed system is generally near a critical state between two distinct phases. The close proximity to the criticality is found to be robust against different ways of sampling and segmentation of the measured data. By independently altering the coupling distribution and the network structure of the statistical model, the network structures are found to be vital to maintain the proximity to the critical state. We further find the observed assignment of the coupling strengths makes the net coupling at each site more balanced with slight variation, which likely helps the maintenance of the critical state. Network analysis on the connectivity obtained by thresholding the coupling strengths find the connectivity of the networks to be well described by a random network model. These results are consistent across different experiments, sampling and segmentation choices in our analysis.