用稀疏编码实现低功耗实时神经元放电分拣，适合边缘设备部署。
Unsupervised Sparse Coding-based Spiking Neural Network for Real-time Spike Sorting
- 基于局部竞争算法的脉冲网络，无监督在线学习波形特征。
- 2比特脉冲在Loihi 2上达77%准确率，比传统方法提升10%。
- 延迟仅0.25毫秒，功耗8.6毫瓦，适合脑机接口边缘计算。
脉冲分拣是解码多通道胞外神经信号的关键步骤，对脑机接口实现实时、低功耗边缘处理提出了挑战。本文提出神经形态稀疏分拣器（Neuromorphic Sparse Sorter, NSS），一种专为高效脉冲分拣设计的紧凑两层脉冲神经网络。NSS采用局部竞争算法（LCA）进行稀疏编码，在降低计算开销的同时从噪声事件中提取关键特征。该模型支持在线无监督学习，能自动分拣检测到的脉冲波形。为充分利用英特尔Loihi 2等神经形态平台的多比特脉冲能力，自定义了可调脉冲位宽的神经元模型，实现灵活的能效权衡。在模拟和真实四电极阵列信号（含生物漂移）上的测试表明，相比WaveClus3和PCA+KMeans等主流方法，NSS性能更优。当使用2比特分级脉冲时，其在Loihi 2上的F1分数达77%（较传统脉冲模型提升10%），功耗仅为8.6毫瓦（增加1.65毫瓦），单次推理处理时间仅0.25毫秒（减少60微秒）。
原文摘要 · Abstract (English)
Spike sorting is a crucial step in decoding multichannel extracellular neural signals, enabling the identification of individual neuronal activity. A key challenge in brain-machine interfaces (BMIs) is achieving real-time, low-power spike sorting at the edge while keeping high neural decoding performance. This study introduces the Neuromorphic Sparse Sorter (NSS), a compact two-layer spiking neural network optimized for efficient spike sorting. NSS leverages the Locally Competitive Algorithm (LCA) for sparse coding to extract relevant features from noisy events with reduced computational demands. NSS learns to sort detected spike waveforms in an online fashion and operates entirely unsupervised. To exploit multi-bit spike coding capabilities of neuromorphic platforms like Intel's Loihi 2, a custom neuron model was implemented, enabling flexible power-performance trade-offs via adjustable spike bit-widths. Evaluations on simulated and real-world tetrode signals with biological drift showed NSS outperformed established pipelines such as WaveClus3 and PCA+KMeans. With 2-bit graded spikes, NSS on Loihi 2 outperformed NSS implemented with leaky integrate-and-fire neuron and achieved an F1-score of 77% (+10% improvement) while consuming 8.6mW (+1.65mW) when tested on a drifting recording, with a computational processing time of 0.25ms (+60 us) per inference.
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