In this work, we employ reservoir computing, a recently developed machine learning technique, 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 (tonic and bursting) 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. Error analyses of the reservoir's performance are consistent with standard results previously obtained.
CITATION STYLE
Follmann, R., & Rosa, E. (2019). Predicting slow and fast neuronal dynamics with machine learning. Chaos, 29(11). https://doi.org/10.1063/1.5119723
Mendeley helps you to discover research relevant for your work.