Learning limit cycles via Hebbian synaptic plasticity

Abstract

We investigate high-dimensional, non-linear dynamical systems when exposed to incoherent periodic inputs and Hebbian-like synaptic plasticity. Our findings reveal a striking phenomenon: depending on the interplay between the strength of the periodic drive and synaptic plasticity, the system's phase diagram can give rise to a region where, once both inputs are removed, the collective dynamics spontaneously settles into a limit cycle. This suggests that periodic drives can imprint lasting rhythmic patterns into the network through plasticity, effectively teaching it to oscillate on its own. Numerical simulations on finite size systems show that the limit cycle phase can be easily detected on single-sample trajectories, while averaged curves are affected by strong finite size effects due to sample-to-sample fluctuations of the period of the limit cycles.