摘要
Significant progress has been made in brain-computer science and technology through applying spiking neural networks(SNNs)[1].More recently,due to its potential of processing complex spatio-temporal information,SNNs have been successfully applied in many fields,such as action recognition[2].There are two effective ways to design network models:converting artificial neural networks(ANNs)into SNNs and directly designing SNNs based on spike mechanisms.In the ANN-SNN method,the integrate-andfire(IF)neurons are used to replace the activation layer to convert ANNs into SNNs,which have some inherent drawbacks,such as inevitable accuracy loss,more delays and energy consumption.Although existing direct training strategies have shown outstanding performance in image classification tasks,SNNs face significant difficulties in handling complex video understanding tasks.In light of the considerable success achieved by the ANNs employed in the field of human action recognition,more researchers have recently focused their attention on using SNNs for action recognition.In order to design more efficient SNNs,some researchers have proposed a series of effective training and feature learning mechanisms in residual network,e.g.,Hu et al.
基金
supported by the National Natural Science Foundation of China(Grant Nos.62376261,U21A20487)
the Natural Science Foundation of Guangdong Province(Grant Nos.2024A1515011754,2023A1515011307,2022A1515140119).
