Abstract: In order to improve the training ability of Spiking neural network,this paper takes multi-label classification problem as the research breakthrough point and adopts bee colony algorithm to optimize the model.There are many neural network models based on the concept of Spiking.Probabilistic Spiking neural network(PSNN) is selected for multi-label classification.Firstly,a probabilistic Spiking neural network classification model is established.The ignition time sequence is coded,and the pulse res-ponse is triggered to realize data transmission.Then,the weight,dynamic threshold and forgetting parameters of Spiking neural network are used to construct bee colony,and the accuracy of multi-label classification is used as the fitness function of artificial bee colony(ABC) algorithm,so that the optimal individual can be obtained by constantly updating the fitness value of individual bee colony.Finally,the multi-label classification of probabilistic Spiking neural network is completed with the optimal parameters.Experimental results show that ABC-PSNN algorithm can achieve high multi-label classification accuracy by reasonably setting the individual size of bee colony and honey source search range.Compared with other Spiking neural network models and commonly used multi-label classification algorithms,ABC-PSNN algorithm has higher classification accuracy and stability.

Key words: Spiking neural network, Probabilistic Spiking neural network, Bee colony algorithm, Multi label classification;Impulse response