生物医学工程研究2026，Vol.45Issue(2)：98-103,6.DOI:10.19529/j.cnki.1672-6278.2026.02.04
基于小批量K-means优化的Kilosort4锋电位聚类算法研究
Research on Kilosort4 spike clustering algorithm based on mini batch K-means optimization
摘要
Abstract
To address the time complexity bottleneck caused by the traditional K-means algorithm in the template deconvolution stage,when Kilosort4 processes spike data.We proposed an optimization method based on mini batch K-means clustering algorithm.Firstly,the K-means++algorithm was used to initialize cluster centers.Then,during the iterative process,a dynamic data subset was extracted,local clustering was performed and the size of the subset was adaptively adjusted according to the size of the data set.Final-ly,the cluster centers were updated incrementally until convergence.Experimental results demonstrated that the optimized Kilosort4 al-gorithm achieved approximately 8%improvement in processing speed compared to the original algorithm.This algorithm can significant-ly reduce computational complexity while maintaining clustering quality.This research can provide more efficient tools for neuroscience studies.关键词
锋电位/小批量K-means算法/Kilosort4/速度优化/神经科学/电生理信号Key words
Spike/Mini batch K-means algorithm/Kilosort4/Speed optimization/Neuroscience/Electrophysiological signal分类
医药卫生引用本文复制引用
周富豪,李赵春,王玉成..基于小批量K-means优化的Kilosort4锋电位聚类算法研究[J].生物医学工程研究,2026,45(2):98-103,6.基金项目
国家重点研发计划(2023YFB4704600). （2023YFB4704600）
