Sparse ESN with a leaky integrator for matching decision-making problems

A new sparse echo state network (ESN) with a leaky integrator, which is expected to has more neurophysiology characteristics, was proposed and trained using the online supervised learning method so as to make the modeling and prediction of the matching decision-making problem. To evaluate the matching decision-making performance of the network, three kinds of test datasets were set up and an estimation method based on the maximum correlation coefficient for the actual output and the desired one was present. Simulation experimental results show that the proposed model can achieve a better decision-making performance with a less training time. Meanwhile the model has a better robustness on spiking interval change, shifting, and network noise.