Recently, spiking neural networks (SNNs), deployed on neuromorphic chips, provide highly efficient solutions on edge devices in different scenarios. However, their ability to adapt to distribution shifts after deployment has become a crucial challenge. Online test-time adaptation (OTTA) offers a promising solution by enabling models to dynamically adjust to new data distributions without requiring source data or labeled target samples. Nevertheless, existing OTTA methods are largely designed for traditional artificial neural networks and are not well-suited for SNNs. To address this gap, we propose a low-power, neuromorphic chip-friendly online test-time adaptation framework, aiming to enhance model generalization under distribution shifts. The proposed approach is called Threshold Modulation (TM), which dynamically adjusts the firing threshold through neuronal dynamics-inspired normalization, being more compatible with neuromorphic hardware. Experimental results on benchmark datasets demonstrate the effectiveness of this method in improving the robustness of SNNs against distribution shifts while maintaining low computational cost. The proposed method offers a practical solution for online test-time adaptation of SNNs, providing inspiration for the design of future neuromorphic chips. The demo code is available at github.com/NneurotransmitterR/TM-OTTA-SNN.

翻译：近年来，部署在神经形态芯片上的脉冲神经网络（SNNs）在不同场景的边缘设备中提供了高效解决方案。然而，部署后适应分布偏移的能力成为关键挑战。在线测试时自适应（OTTA）通过使模型无需源数据或带标签目标样本即可动态适应新数据分布，提供了有前景的解决方案。但现有OTTA方法主要针对传统人工神经网络设计，并不适用于SNNs。为填补这一空白，我们提出了一种低功耗、适配神经形态芯片的在线测试时自适应框架，旨在增强模型在分布偏移下的泛化能力。该方法称为阈值调制（TM），通过神经元动力学启发的归一化动态调节放电阈值，与神经形态硬件更具兼容性。在基准数据集上的实验结果表明，该方法能在保持低计算成本的同时有效提升SNNs对分布偏移的鲁棒性。所提方法为SNNs的在线测试时自适应提供了实用方案，为未来神经形态芯片设计提供了启示。演示代码见github.com/NneurotransmitterR/TM-OTTA-SNN。