PRJC: Optimal control of excitable systems near criticality

The Physical Review Journal Club hosted authors Daniel B. Larremore and Juan G. Restrepo (University of Colorado, Boulder) to discuss their recently published Physical Review Research paper: Optimal control of excitable systems near criticality.

To better understand basic aspects of controlling neural activity fluctuations, the researchers numerically and analytically studied a network of binary neurons. Larremore, Restrepo, and colleagues determined how the efficacy of controlling the population firing rate depends on proximity to criticality as well as different structural properties of the network. The team found that control is most effective—errors are minimal for the widest range of target firing rates—near criticality. Optimal control is slightly away from criticality for networks with heterogeneous degree distributions. Thus, while criticality is the noisiest dynamical regime, it is also the regime that is easiest to control, which may offer a way to mitigate the noise.

Larremore and Restrepo presented their results, and then answered attendee questions during a live question-and-answer session moderated by Raissa D’Souza, University of California, Davis, and co-Lead Editor of PRResearch.