用脉冲神经网络实时解码脑电信号，助力植入式脑机接口
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
- 用循环脉冲神经网络从皮层放电信号中解码手指速度
- 小型化模型在低功耗下仍保持优于现有方法的精度
- 适合开发可植入、低延迟、省电的脑机接口系统
侵入式皮层脑机接口（BMI）可显著改善运动障碍患者的生活质量。然而，外部支架会带来感染风险，因此亟需全植入式系统。这类系统必须在严格的时间延迟和能耗限制下保持可靠解码性能。虽然循环脉冲神经网络（RSNN）理想适用于类脑硬件上的超低功耗、低延迟处理，但其是否满足上述要求尚不明确。为此，我们训练了RSNN模型，从两只猕猴的皮层放电信号（CSTs）中解码手指速度。首先发现，大型RSNN模型在解码精度上优于现有前馈脉冲神经网络（SNN）和人工神经网络（ANN）。随后，我们设计了一个微型RSNN，具有更小内存占用、低发放率和稀疏连接。尽管计算需求大幅降低，该模型性能仍显著优于现有的SNN与ANN解码器。结果表明，RSNN可在严苛资源约束下实现竞争力强的CST解码性能，是全植入式超低功耗脑机接口的有力候选方案，有望革新患者护理。
原文摘要 · Abstract (English)
Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection risk, which calls for fully implanted systems. Such systems, however, must meet strict latency and energy constraints while providing reliable decoding performance. While recurrent spiking neural networks (RSNNs) are ideally suited for ultra-low-power, low-latency processing on neuromorphic hardware, it is unclear whether they meet the above requirements. To address this question, we trained RSNNs to decode finger velocity from cortical spike trains (CSTs) of two macaque monkeys. First, we found that a large RSNN model outperformed existing feedforward spiking neural networks (SNNs) and artificial neural networks (ANNs) in terms of their decoding accuracy. We next developed a tiny RSNN with a smaller memory footprint, low firing rates, and sparse connectivity. Despite its reduced computational requirements, the resulting model performed substantially better than existing SNN and ANN decoders. Our results thus demonstrate that RSNNs offer competitive CST decoding performance under tight resource constraints and are promising candidates for fully implanted ultra-low-power BMIs with the potential to revolutionize patient care.
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