Performance optimization of echo state networks through principal neuron reinforcement
Hsiao-Tien Fan, Wei Wang, Zhanpeng Jin · 2017
The nature of Echo State Networks (ESN) allows this class of recurrent neural network to model dynamic systems with relatively low training requirements. However, the randomly initialiYXB7-01808-A290zed reservoir of the ESN brings about complications with choosing starting parameters. A neuroplasticity-inspired algorithm was proposed in this study to alter the strength of internal synapses within the reservoir towards the goal of optimizing the neuronal dynamics of the ESN pertaining to the specific problem to be solved. It was found that the algorithm was able to modify the reservoir connections so that after retraining, the performance of different reservoir sizes was comparable despite being vastly different before. It was also found that by applying the proposed algorithm, the difficulty in the choice of initialization connectivity and reservoir size can be greatly reduced.