Supervised Learning Strategy for Spiking Neurons Based on Their Segmental Running Characteristics

Supervised learning of spiking neurons is an effective simulation method to explore the learning mechanism of real neurons. Desired output spike trains are often used as supervised signals to control the synaptic strength adjustment of neurons for precise emission. The goal of supervised learning is also to allow spiking neurons to enter the desired running and firing state. The running process of a spiking neuron is a continuous process, but because of absolute refractory periods, it is regarded as several running segments. Based on the segmental characteristic, a new supervised learning strategy for spiking neurons is proposed to expand the action mode of supervised signals in supervised learning. Desired output spikes are used to actively regulate the running segments and make them more efficient in achieving the desired running and firing state. Supervised signals actively regulate the running process of neurons and are more comprehensively involved in the learning process than simply participating in adjusting synaptic weights. Based on two weight adjustment mechanisms of spiking neurons, two new specific supervised learning methods are proposed. The experimental results obtained using various settings indicate that the two learning methods have higher learning performance, which indicates the effectiveness of the new learning strategy.