MetaSort统一解决神经脉冲波形压缩与少样本分类问题。
MetaSort: An Accelerated Approach for Non-uniform Compression and Few-shot Classification of Neural Spike Waveforms
- 用自适应过零点算法高保真压缩脉冲波形
- 融合隐空间特征与几何信息，提升分类性能
- 适合低功耗芯片部署的神经信号处理场景
以往的尖峰排序研究通常将尖峰分类与压缩分别处理。本文提出一种新算法 MetaSort，同时解决这两个问题。为实现压缩，提出一种新型自适应过零点算法，能高保真地近似尖峰形状；同时利用隐空间特征表示解决分类问题。此外，通过元迁移学习挖掘数据的几何信息，增强模型的鲁棒性与区分能力。基于在体尖峰数据的实验表明，MetaSort表现优异，展现出巨大潜力，推动其向超低功耗、片上实现方向持续发展。
原文摘要 · Abstract (English)
Many previous works in spike sorting study spike classification and compression independently. In this paper, a novel algorithm is proposed called MetaSort to address these two problems. To deal with compression, a novel adaptive level crossing algorithm is proposed to approximate spike shapes with high fidelity. Meanwhile, the latent feature representation is used to handle the classification problem. Besides, to guarantee MetaSort is robust and discriminative, the geometric information of data is exploited simultaneously in the proposed framework by meta-transfer learning. Empirical experiments with in-vivo spike data demonstrate that MetaSort delivers promising performance, highlighting its potential and motivating continued development toward an ultra-low-power, on-chip implementation.
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