Predicting spatio-temporal patterns of cells guided by time-varying guidance cues with reservoir computing

While the ability of reservoir computers to predict the temporal evolution of certain dynamical systems has been demonstrated extensively, it is limited to the portion of the phase space that the reservoir is shown during the learning process. This problem is further exacerbated when the training data is short in time and includes a stochastic component. We therefore propose a reservoir computer architecture with an additional input vector that mimics the temporal dynamics of a parameter of the dynamical system. Through this, the reservoir is able to learn the correspondence between the dynamics and its parameter value. We show that the reservoir is able to predict not only the steady state climate of the parameter regime, but also the climate of the transient behavior of the dynamical system when the parameter value is suddenly changed. Finally, the ability of the reservoir to predict the actual local dynamics is also assessed.