Between perfectly critical and fully irregular: a reverberating model captures and predicts cortical spike propagation

TL;DR

This paper introduces a subsampling-invariant model that captures cortical spike propagation, revealing a reverberating regime rather than asynchronous or critical states, and predicts properties from short recordings across different circuits.

Contribution

The study presents a novel minimal model for cortical spike dynamics that is invariant to subsampling, bridging the gap between asynchronous and critical states, and enabling predictions from limited data.

Findings

Cortical spike activity resides in a reverberating regime, not purely asynchronous or critical.

The model accurately predicts properties from short recordings across species and circuits.

Reverberating regime informs about coding principles, network timescales, and responses to perturbations.

Abstract

Knowledge about the collective dynamics of cortical spiking is very informative about the underlying coding principles. However, even most basic properties are not known with certainty, because their assessment is hampered by spatial subsampling, i.e. the limitation that only a tiny fraction of all neurons can be recorded simultaneously with millisecond precision. Building on a novel, subsampling-invariant estimator, we fit and carefully validate a minimal model for cortical spike propagation. The model interpolates between two prominent states: asynchronous and critical. We find neither of them in cortical spike recordings across various species, but instead identify a narrow reverberating regime. This approach enables us to predict yet unknown properties from very short recordings and for every circuit individually, including responses to minimal perturbations, intrinsic network timescales, and the strength of external input compared to recurrent activation - thereby informing about the underlying coding principles for each circuit, area, state and task.