A fast learning algorithm of self-learning spiking neural network

The paper introduces a Newton-type modification of temporal Hebbian rule-based learning algorithm of a self-learning spiking neural network. Similar to conventional artificial neural networks domain, the learning algorithm modification based on second-order optimization procedures allows of improving performance of the third generation neural networks. The experimental research results are presented to confirm the proposed improvement of self-learning spiking neural network learning algorithm.