## Research Summary

I'm an invited researcher at l'Institut Denis Poisson (Tours, France) developing theoretical tools based on statistical physics to understand the brain.

Presently, my efforts are focused around

This line of research poses many interesting questions about nonequilibrium phenomena in brain networks, information processing, computation, and neurological disorders. Using the cortical branching model, I am now formally characterizing the so-called

Presently, my efforts are focused around

**quasicriticality**, the idea that the brain adjusts its dynamics and network properties depending on external stimuli in order to reach a relative computational optimality near a continuous phase transition. Alongside my collaborators, I've published a number of articles (arXiv, Google Scholar) describing the introduction of the quasicriticality hypothesis, development of its formal theory, and introduction of**causal webs**–a novel method which separates causal and non-causal events in neuronal avalanches. Our recent validation of the quasicriticality hypothesis using empirical and simulated data has been published and selected as an Editors' Suggestion: Phys. Rev. Lett.**126**, 098101 (2021).This line of research poses many interesting questions about nonequilibrium phenomena in brain networks, information processing, computation, and neurological disorders. Using the cortical branching model, I am now formally characterizing the so-called

**quasiperiodic phase**, which exhibits chaos and soliton dynamics. Whereas the quasicritical region (which displays relative computational optimality) may represent healthy brain function, the quasiperiodic phase may represent epileptiform discharges or other disorders. The observed soliton dynamics carry the potential for establishing intertheoretic relations with simpler models based on partial differential equations (such as the KdV equation), which could further deepen our understanding of brain function and neurological disorders.