LAUSR

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. Compared with the traditional neural network, the spiking neural network (SNN) has better biogenesis and stronger computing power. In this paper, an SNN learning model based on an evolutionary membrane algorithm is proposed to solve the problem of supervised classification. The proposed algorithm uses the P system’s object, reaction rules, and membrane structure to solve these problems. Specifically, the proposed algorithm can automatically adjust the learning parameters of the network by adjusting the synaptic weight in the learning stage of the spiking neural model according to different application data, providing a better solution model for balance exploration and exploitation. In the simulation experiment, effectiveness verification research is carried out. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving twelve supervised classification benchmark problems through learning curves and quantified classification results.