A Supervised Multi-Spike Learning Algorithm for Spiking Neural Networks

The formulation of efficient supervised learning algorithms for Spiking Neural Network (SNN) is difficult and remains challenging. This paper presents a supervised multi spike learning algorithm, which is used to train neurons to output spike train with a target firing rate. The proposed algorithm simplifies the expression of the membrane potential by assuming a special condition of the threshold, thus allows the application of a gradient descent to optimize the synaptic weights. Additionally, in the presented experimental results, the proposed algorithm is evaluated regarding its initial setups, its classification performance for rate-based and timing-based patterns and its capability to sound recognition. The results also demonstrate that the proposed algorithm can achieve a competitive accuracy in temporal pattern classification and sound recognition.