The Spiking Neural Network (SNN), due to its unique spiking-driven nature, is a more energy-efficient and effective neural network compared to Artificial Neural Networks (ANNs). The encoding method directly influences the overall performance of the network, and currently, direct encoding is primarily used for directly trained SNNs. When working with static image datasets, direct encoding inputs the same feature map at every time step, failing to fully exploit the spatiotemporal properties of SNNs. While temporal encoding converts input data into spike trains with spatiotemporal characteristics, traditional SNNs utilize the same neurons when processing input data across different time steps, limiting their ability to integrate and utilize spatiotemporal information effectively.To address this, this paper employs temporal encoding and proposes the Adaptive Spiking Neural Network (ASNN), enhancing the utilization of temporal encoding in conventional SNNs. Additionally, temporal encoding is less frequently used because short time steps can lead to significant loss of input data information, often necessitating a higher number of time steps in practical applications. However, training large SNNs with long time steps is challenging due to hardware constraints. To overcome this, this paper introduces a hybrid encoding approach that not only reduces the required time steps for training but also continues to improve the overall network performance.Notably, significant improvements in classification performance are observed on both Spikformer and Spiking ResNet architectures.our code is available at https://github.com/hhx0320/ASNN

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