  In this study, we use a reservoir computing based echo state network (ESN) to
predict the collective burst synchronization of neurons. Specifically, we
investigate the ability of ESN in predicting the burst synchronization of an
ensemble of Rulkov neurons placed on a scale-free network. We have shown that a
limited number of nodal dynamics used as input in the machine can capture the
real trend of burst synchronization in this network. Further, we investigate on
the proper selection of nodal inputs of degree-degree (positive and negative)
correlated networks. We show that for a disassortative network, selection of
different input nodes based on degree has no significant role in machine's
prediction. However, in the case of assortative network, training the machine
with the information (i.e time series) of low-degree nodes gives better results
in predicting the burst synchronization. Finally, we explain the underlying
mechanism responsible for observing this differences in prediction in a degree
correlated network.
