Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that involves simultaneously training synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning~(STL-SNNs) achieve significantly superior performance on various static and neuromorphic datasets than SNNs trained with two degenerated single-learning models. During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework. Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.

翻译：脉冲神经网络（SNNs）在多种智能场景中展现出卓越性能。现有SNN训练方法大多基于突触可塑性原理，但真实大脑的学习过程还利用了神经元的内在非突触机制。生物神经元的脉冲阈值作为关键内在神经元特征，在毫秒时间尺度上表现出丰富动态特性，已被提出作为促进神经信息处理的潜在机制。在本研究中，我们开发了一种新颖的协同学习方法，该方法同时训练SNN中的突触权重和脉冲阈值。通过突触-阈值协同学习（STL-SNNs）训练的SNN，在多种静态和神经形态数据集上取得的性能显著优于两种退化单学习模型训练的SNN。训练过程中，协同学习方法优化神经阈值，通过适当的发放率为网络提供稳定的信号传输。进一步分析表明，STL-SNNs对噪声数据具有鲁棒性，并在深层网络结构中表现出低能耗特性。此外，通过引入广义联合决策框架，STL-SNN的性能可进一步提升。总体而言，我们的研究发现表明，突触机制与内在非突触机制之间的生物合理性协同作用，可能为开发高效SNN学习方法提供有前景的途径。