Statistical complexity is maximized close to criticality in cortical dynamics

TL;DR

This study demonstrates that statistical complexity in cortical dynamics peaks near criticality, using symbolic information measures on neural data and models, highlighting the brain's optimal computational state.

Contribution

It introduces a symbolic information approach to identify maximum complexity near criticality in cortical states, combining empirical data and network models.

Findings

Statistical complexity peaks near criticality in cortical spiking data.

Complexity is maximized at an intermediate state between order and disorder.

The results are consistent across biological data and theoretical models.

Abstract

Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity (with higher spiking variability) to desynchronized and disordered regimes (with lower spiking variability). It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here, we use a symbolic information approach to show that, despite the monotonical increase of the Shannon entropy between ordered and disordered regimes, we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. More specifically, we show that statistical complexity is maximized close to criticality for cortical spiking data of urethane-anesthetized rats, as well as for a network model of excitable elements that presents a critical point of a non-equilibrium phase transition.