Spiking neural P systems (SNP systems) are a type of neuromorphic membrane computing models abstracted based on neuron spiking mechanisms, with nonlinear spiking neural P systems (NSNP systems) being a nonlinear variant of them. Based on the theoretical framework of NSNP systems, this paper proposes a novel spiking neuron model called TC-SNP neurons. The computational model constructed from TC-SNP neurons is referred to as Spiking Neural P Systems with temporal coding. The model can theoretically be regarded as an extension of NSNP systems, while at the implementation level, it constitutes a practical variant of Spiking Neural Networks (SNNs). Due to the introduction of temporal coding, the information transmitted between neurons consists of precisely timed spike sequences. This TC-SNP model achieves dynamic reset operations of membrane voltage during nonlinear regulation processes by introducing a dynamic membrane potential reset mechanism. Unlike the traditional fixed reset methods in spiking neural networks, this model effectively enhances the temporal encoding capability of neurons by dynamically adjusting the post-reset membrane potential reference value, thereby achieving a balance between high accuracy and low latency. In the systematic validation of image classification tasks, experiments conducted on deep learning architectures built upon benchmark datasets such as CIFAR-10, CIFAR-100, and TinyImageNet demonstrate that the proposed model achieves a significant improvement in classification accuracy compared to state-of-the-art spiking neuron models.
Yan et al. (Sat,) studied this question.