Self-Evolutionary Reservoir Computer Based on Kuramoto Model

The human brain's synapses have remarkable activity-dependent plasticity, where the connectivity patterns of neurons change dramatically, relying on neuronal activities. As a biologically inspired neural network, reservoir computing (RC) has unique advantages in processing spatiotemporal information. However, typical reservoir architectures only take static random networks into account or consider the dynamics of neurons and connectivity separately. In this paper, we propose a structural autonomous development reservoir computing model (sad-RC), which structure can adapt to the specific problem at hand without any human expert knowledge. Specifically, we implement the reservoir by adaptive networks of phase oscillators, a commonly used model for synaptic plasticity in biological neural networks. In this co-evolving dynamic system, the dynamics of nodes and coupling weights in the reservoir constantly interact and evolve together when disturbed by external inputs.