Since digital spiking signals can carry rich information and propagate with low computational consumption, spiking neural networks (SNNs) have received great attention from neuroscientists and are regarded as the future development object of neural networks. However, generating the appropriate spiking signals remains challenging, which is related to the dynamics property of neurons. Most existing studies imitate the biological neurons based on the correlation of synaptic input and output, but these models have only one time constant, thus ignoring the structural differentiation and versatility in biological neurons. In this article, we propose the reconstruction of adaptive leaky integrate-and-fire (R-ALIF) neuron to perform complex behaviors similar to real neurons. First, a synaptic cleft time constant is introduced into the membrane voltage charging equation to distinguish the leakage degree between the neuron membrane and the synaptic cleft, which can expand the representation space of spiking neurons to facilitate SNNs to obtain better information expression way. Second, R-ALIF constructs a voltage threshold adjustment equation to balance the firing rate of output signals. Third, three time constants are transformed into learnable parameters, enabling the adaptive adjustment of dynamics equation and enhancing the information expression ability of SNNs. Fourth, the computational graph of R-ALIF is optimized to improve the performance of SNNs. Moreover, we adopt a temporal dropout (TemDrop) method to solve the overfitting problem in SNNs and propose a data augmentation method for neuromorphic datasets. Finally, we evaluate our method on CIFAR10-DVS, ASL-DVS, and CIFAR-100, and achieve top1 accuracy of 81.0% , 99.8% , and 67.83% , respectively, with few time steps. We believe that our method will further promote the development of SNNs trained by spatiotemporal backpropagation (STBP).