Reservoir Computing: Structure analysis and dynamics predictability

Reservoir computing in machine learning is promoting better and faster predictability at lower computational cost. In this work we investigate the effects of reservoir network topology structures on temporal predictability. We employ reservoir computing to predict the time evolution of neuronal activity produced by the Hindmarsh-Rose neuronal model. Our results show accurate short and long-term predictions for periodic neuronal behaviors, but only short-term accurate predictions for chaotic neuronal states. However, after the accuracy of the short-term predictability deteriorates in the chaotic regime, the predicted output continues to display similarities with the actual neuronal behavior. This is reinforced by a striking resemblance between the bifurcation diagrams of the actual and of the predicted outputs. Distinct network topologies are tested, and error analyses of the reservoir's performance are consistent with standard results previously obtained. Given the relevance of early detection of troubling neuronal activity, particularly in the case of individuals with neurological disorders, we conceive the possibility for development of devices capable of anticipating trends toward undesirable neuronal states, early enough for effective preventive intervention.