基于小批量K-means优化的Kilosort4锋电位聚类算法研究OA

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.