Solutions to Improve the Performance of the Algorithm with the Adaptive Decay Time for the Spiking Neural Nets

This paper introduces an adaptation of the learning rate and momentum for the backpropagation algorithm during the training spiking neural network with the decay time of the spike response model. The efficiency of the algorithm with the proposed solutions is compared with the original algorithm through the XOR classification problem and the aerodynamic coefficients identification of an aircraft from the data sets recorded from flights. The results show that the algorithm with the proposed solutions has a higher successful classification rate on the XOR problem as well as higher accuracy in the aerodynamic coefficients identification problem compared to the original algorithm, whereas the number of epochs is much smaller.