Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

TL;DR

This paper models large neural populations with latent dynamical variables, reproducing observed criticality signatures and suggesting that coupling to latent stimuli underlies scaling behaviors in neural activity.

Contribution

It introduces a model of neural activity coupled to latent variables that explains observed criticality signatures in large biological systems.

Findings

Reproduces experimental scalings using the model

Coupling to latent stimuli is essential for scaling emergence

Predicts place cells respond to non-place stimuli

Abstract

Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse-graining had been previously applied to experimental neural recordings, which showed over two decades of scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model the experiment by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally-observed scalings, suggesting that they may arise from coupling the neural population activity to latent dynamic stimuli. Further, parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to non-place stimuli.