E-Sort用迁移学习和并行后处理，提升多通道神经信号分拣的准确率与速度。
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing
- 基于端到端神经网络，结合迁移学习减少标注数据需求44%。
- 在合成Neuropixels数据上，50秒数据仅需1.32秒完成分拣，准确率超现有方法25.68%。
- 兼容深度学习框架，适用于不同探针结构、噪声和漂移场景。
解码细胞外记录是电生理学和脑机接口中的关键任务。随着现代神经探针通道数增加，尖峰分拣从细胞外记录中区分尖峰及其潜在神经元变得计算密集。为应对高负载和复杂神经元交互问题，我们提出E-Sort，一种基于端到端神经网络的尖峰分拣器，融合迁移学习与可并行化后处理。相比从零训练，其训练所需标注尖峰减少44%，准确率最高提升25.68%。此外，新提出的后处理算法兼容深度学习框架，使E-Sort显著快于当前最优尖峰分拣器。在合成Neuropixels记录上，E-Sort性能与Kilosort4相当，但处理50秒数据仅耗时1.32秒。该方法在多种探针几何、噪声水平和漂移条件下均表现出鲁棒性，相较现有分拣器在准确率与运行效率上均有显著提升。
原文摘要 · Abstract (English)
Decoding extracellular recordings is a crucial task in electrophysiology and brain-computer interfaces. Spike sorting, which distinguishes spikes and their putative neurons from extracellular recordings, becomes computationally demanding with the increasing number of channels in modern neural probes. To address the intensive workload and complex neuron interactions, we propose E-Sort, an end-to-end neural network-based spike sorter with transfer learning and parallelizable post-processing. Our framework reduces the required number of annotated spikes for training by 44% compared to training from scratch, achieving up to 25.68% higher accuracy. Additionally, our novel post-processing algorithm is compatible with deep learning frameworks, making E-Sort significantly faster than state-of-the-art spike sorters. On synthesized Neuropixels recordings, E-Sort achieves comparable accuracy with Kilosort4 while sorting 50 seconds of data in only 1.32 seconds. Our method demonstrates robustness across various probe geometries, noise levels, and drift conditions, offering a substantial improvement in both accuracy and runtime efficiency compared to existing spike sorters.
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