Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.

翻译：目的。尖峰分类作为神经数据处理中的关键步骤，旨在根据不同的波形对单电极记录中的尖峰事件进行分类。本研究旨在开发一种名为NeuSort的新型在线尖峰分类器，该分类器利用神经形态模型，能够自适应地调整以应对神经信号的变化，包括波形变形和新神经元的出现。方法。NeuSort采用神经形态模型来模拟模板匹配过程。该模型融入了受生物神经系统启发的可塑性学习机制，从而促进了对在线参数的实时调整。结果。实验结果表明，NeuSort能够在波形变形过程中追踪神经元活动，并实时识别新神经元。NeuSort在处理非平稳神经信号方面表现出色，显著增强了其在长期尖峰分类任务中的适用性。此外，其在神经形态芯片上的实现保证了计算过程中的超低能耗。意义。NeuSort通过神经形态方法满足了脑机接口中实时尖峰分类的需求。其无监督、自动化的尖峰分类过程使其成为在线尖峰分类的即插即用解决方案。