For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs)

three learning rate adaptation methods (heuristic rule

delta-delta rule

and delta-bar-delta rule)

which are used to speed up training in artificial neural networks

are used to develop the training algorithms for feedforward SNN.The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem

Iris dataset

fault diagnosis in the Tennessee Eastman process

and Poisson trains of discrete spikes.The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm.Furthermore

if the adaptive learning rate is used in combination with the momentum term

the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence.In the three learning rate adaptation methods

delta-bar-delta rule performs the best.The delta-bar-delta method with momentum has the fastest convergence rate

the greatest stability of training process

and the maximum accuracy of network learning.The proposed algorithms in this paper are simple and efficient

and consequently valuable for practical applications of SNN.