  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.
