Duccio Fanelli (University of Florence, Italy)

Title: Noise-driven neuromorphic tuned amplifier

Abstract: Living systems execute an extraordinary plethora of complex functions, that result from the intertwined interactions among key microscopic actors. Positive and negative feedbacks appear to orchestrate the necessary degree of macroscopic coordination, by propagating information to distant sites while supporting the processing steps that underly categorization and decision making. Excitatory and inhibitory circuits play, in this respect, a role of paramount importance. As an example, networks of excitatory and inhibitory neurons constitute the primary computational units in the brain cortex and can adjust to different computational modalities, as triggered by distinct external stimuli. The genetic regulation also relies on sophisticated inhibitory and excitatory loops. Working in this context, we shall here discuss a minimal model for a discrete collection of agents in mutual interaction via excitatory and inhibitory loops, bearing universality traits in light of its inherent simplicity. Endogenous-noise stemming from finite size corrections induces quasi-cyclic dynamics that display unusual long-range correlations, persisting over arbitrary large network structures. When the excitatory and inhibitory species are distributed on a directed network, the internal noise seeds giant quasi-cycles, with tunable frequency and amplitude. The system spontaneously behaves as an effective, stochastic driven pacemaker, a non-trivial self-organized dynamics that hold general interest, for its fundamental to applied implications. The phenomenon is characterized analytically. The theoretical prediction is corroborated by direct stochastic simulations.