Intracortical brain-computer interfaces suffer from day-to-day neural signal shifts that degrade pretrained decoders. Existing unsupervised adaptation methods rely on deep recurrent or adversarial architectures that are too computationally expensive for implantable hardware. We propose Membrane Potential Alignment (MPA), a test-time adaptation method for spiking neural networks that realigns a pretrained decoder to shifted recordings by only matching membrane potential distributions via KL divergence. By restricting updates to low-rank (LoRA) weights, MPA adapts fewer than 9% of parameters. On a non-human primate reaching task spanning over one month, MPA achieves performance competitive with the state-of-the-art NoMAD method, while using a simpler architecture and finer temporal resolution (4 ms vs. 20 ms). These results show that efficient SNN-based test-time adaptation is a practical path toward long-term, recalibration-free brain-computer interfaces.

翻译：皮层内脑机接口面临每日神经信号漂移问题，这会降低预训练解码器的性能。现有无监督适配方法依赖深度循环或对抗性架构，其计算开销过大，难以应用于植入式硬件。我们提出膜电位对齐方法（MPA），这是一种适用于脉冲神经网络的测试时适配方法，通过仅基于KL散度匹配膜电位分布，将预训练解码器重新对齐至漂移后的记录数据。通过将更新限制在低秩（LoRA）权重上，MPA仅适配不到9%的参数。在跨越一个多月的非人灵长类动物抓取任务中，MPA达到了与当前最优的NoMAD方法相当的性能，同时采用更简单的架构和更精细的时间分辨率（4毫秒对比20毫秒）。这些结果表明，基于SNN的高效测试时适配是实现长期免校准脑机接口的实用途径。