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.

本研究解决了传统脑机接口在感染风险和能量限制方面的挑战，提出了一种新颖的递归尖峰神经网络 (RSNN) 方法来从猴子的皮层尖峰列中解码手指速度。研究结果表明，即使在严格的资源约束下，RSNN实现了优于现有解码器的性能，这为完全植入的超低功耗脑机接口提供了新的可能性，显著改善了运动障碍患者的护理质量。

通过递归尖峰神经网络解码皮层尖峰列的手指速度