Predicting bursting in a complete graph of mixed population through reservoir computing

We report our investigation and success story, to an extent, on the prediction of spiking and bursting dynamics in globally coupled networks, using echo state network/reservoir computing-based learning procedure. Two exemplary dynamical models, Josephson junctions and Hindmarsh-Rose neurons, are used to construct two separate networks and thereby illustrate the efficacy of our strategy. In the absence of coupling, the networks consist of mixed populations in which few nodes are oscillatory (self-sustained spiking) and the rest of the nodes maintain a quiescent state. When single-input data from one oscillatory node of a network (under stronger interactions between the nodes) is used for learning, the ESN is able to predict the key dynamical features (spiking and bursting) of the other nodes. In comparison, the machine performs with improved predictions if it is fed with two inputs: one from the oscillatory population and another from an excitable population. The machine's leaking parameter plays a crucial role, which can be tuned appropriately to enhance prediction. Furthermore, a cluster synchronization in the mixed population is confirmed from the machine-generated output signals. Our work is expected to be useful as a burst predictor.