Abstract
Synaptic plasticity and intrinsic plasticity, as two of the most common neural plasticity mechanisms, occur in all neural circuits throughout life. Neurobiological studies indicated that the interplay between synaptic and intrinsic plasticity contributes to the adaptation of the nervous system to different synaptic input signals. However, most existing computational models of neural plasticity consider these two plasticity mechanisms separately, which is biologically implausible. In this paper, a synergistic plasticity learning rule is proposed to adapt the reservoir connections in echo state networks (ESNs), which not only takes into account the regulation of synaptic weights, but also considers the adjustment of neuronal intrinsic excitability. The proposed synergetic plasticity rule is verified on a number of prediction and classification benchmark problems and our empirical results demonstrate that the ESN with synergistic plasticity learning rule performs much better than the state-of-the-art ESN models, and an ESN with a single neural plasticity rule.