Induced Coherence Resonance in an Electrochemical System

Applying noise to an excitable electrochemical systems near homoclinic orbits may lead to the emergence of coherent dynamics. A noise driver-control was developed for this purpose, using a neural network reference model assisted by a Kalman filter. The model is constructed to predict the occurrence of spiking behavior and its long-term prediction describes the statistics of the spike train. The strategy involving the use of the Kalman filter allows for the on-line correction of model inaccuracies. This adaptive protocol is coupled with a stochastic search algorithm, allowing the pinpointing of the optimal noise amplitude that induces maximum coherence resonance in the system. The protocol was tested in an experimental Fe-potassium sulfate system, exhibiting a robust response even in the presence of electrochemical drift.