让脑深部刺激更省电：通过智能算法降低神经刺激能耗80%
Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation
- 在强化学习中加入刺激器能耗作为奖励，实现能量感知训练
- 相比持续刺激，抑制异常脑波45.2%的同时减少80%刺激电荷量
- 模型压缩后仅0.52毫瓦功耗，比传统硬件低28.1倍，适合植入设备
神经形态与边缘计算研究聚焦于降低神经网络控制器的推理能耗，但在物理闭环系统中，执行器的能耗可能超过高效控制器。因此，仅优化控制器不足以解决问题，当推理能耗不再主导时，执行器能耗成为需重点降低的瓶颈。本文提出能量感知学习方法，将执行器能耗直接纳入强化学习奖励函数，并在帕金森病的闭环深部脑刺激（DBS）中验证。基于生物物理皮层-基底节-丘脑环路模型训练的深度脉冲Q网络，可使病理α-β振荡抑制45.2%，同时相比连续刺激减少80.0%的刺激电荷量。通过稀疏约束知识蒸馏，策略被压缩至SynSense XyloAudio 3神经形态处理器上，推理功耗仅为0.52 mW，相较等效人工神经网络在传统边缘硬件上的能耗降低28.1倍。该框架协同优化刺激能耗与推理效率，解决了可植入神经调控系统的两大主要能耗问题。
原文摘要 · Abstract (English)
Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems the actuator can rival or exceed an efficient controller in energy. An efficient controller is therefore necessary but not sufficient, because the actuator becomes the cost worth reducing once inference no longer dominates it. Here, we introduce energy-aware learning, an approach that incorporates actuator energy directly into the reinforcement learning reward, and demonstrate it in closed-loop deep brain stimulation (DBS) for Parkinson's disease. A deep spiking Q-network, trained in a biophysical cortico-basal ganglia-thalamic circuit model, learns to suppress pathological alpha-beta oscillations by 45.2% while reducing stimulation charge by 80.0% relative to continuous DBS. Sparsity-constrained knowledge distillation compresses the policy onto the SynSense XyloAudio 3 neuromorphic processor at 0.52 mW inference power, yielding 28.1x lower energy per inference than an equivalent artificial neural network on conventional edge hardware. By co-optimizing stimulation energy and inference efficiency, the framework addresses both major power demands in implantable neuromodulation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。