Spiking neural networks (SNNs) aim to simulate human neural networks with biologically plausible neurons. However, conventional SNNs based on point neurons ignore the inherent dendritic computation of biological neurons. Additionally, these point neurons usually employ single membrane potential decay and a fixed firing threshold, which is in contrast to the heterogeneity of real neural networks and limits the neuronal dynamic diversity needed when dealing with multi-scale sequential tasks. In this work, we propose a dendritic spiking neuron model with combined membrane potential decay and a dynamic firing threshold. Then, we extend the neuron model to the feedforward network level, termed dendritic spiking neural network with combined membrane potential decay and dynamic threshold (CD-DT-DSNN). By learning the heterogeneous neuronal decay factors, which combine two different membrane potential decay mechanisms, and learning adaptive factors, our networks can rapidly respond to input signals and dynamically regulate neuronal firing rates, which help the extraction of multi-scale spatio-temporal features. Experiments on four spike-based audio and image sequential datasets demonstrate that our CD-DT-DSNN outperformed state-of-the-art heterogeneous SNNs and dendritic compartment SNNs with higher classification accuracy and fewer parameters. This work suggests that heterogeneity in neuronal membrane potential decay and neural firing thresholds is a critical component in learning multi-timescale temporal dynamics and maintaining long-term memory, providing a novel perspective for constructing high biologically plausible neuromorphic computing models. It provides a solution for multi-timescale temporal sequential tasks, such as speech recognition, EEG signal recognition, and robot place recognition.
Zhou et al. (Sun,) studied this question.