大规模神经记录的峰电位聚类算法(特邀)

doi:10.19678/j.issn.1000-3428.0069014

摘要/Abstract

摘要： 峰电位聚类是指在进行细胞外神经记录时,从神经电极记录中检测、聚类并确认出不同峰电位信号,并以一定的可靠度与假定的不同神经元对应。它是对细胞外神经记录进行预处理分析的基本步骤,也是神经科学中神经解码的首要步骤,更是当前高带宽脑机接口(BCI)的重要研究方向之一。传统峰电位聚类包括峰电位检测、峰电位对齐、特征提取、特征聚类等步骤。当前,随着神经电极数量和密度不断增加,神经记录的规模呈爆炸式增长,这对峰电位聚类算法的效率和精度提出重大挑战。此外,针对现有峰电位聚类算法特征提取和表征能力不强、信噪比低、信息混叠等问题,各种算法增强方案乃至人工智能和大数据峰电位聚类方案应运而生,极大促进了对大脑复杂原理和工作机制的理解。研究首先概述侵入式BCI、神经编解码与峰电位聚类的相关性,接着阐述了各类峰电位聚类算法的原理和一般过程,并探讨了大脑神经信号与具体行为的映射关系与应用,最后展望了未来神经编解码所面临的挑战和发展趋势。

关键词: 峰电位聚类, 侵入式脑机接口, 神经编解码, 机器学习, 深度学习

Abstract: Spike sorting refers to the detection, clustering, and identification of distinct extracellular neuronal signals from different recording sites with the aim of reliably assigning them to different putative neurons. This crucial preprocessing is fundamental for neural decoding in neuroscience and represents a prominent research direction in high-bandwidth Brain-Computer Interface (BCI) studies. Conventional spike sorting algorithms involve various steps such as spike detection, spike alignment, feature extraction, and feature clustering. Currently, explosive growth in the number and density of neural electrodes presents significant challenges in terms of the efficiency and accuracy of spike sorting. To address issues such as limited feature extraction capabilities, low Signal-to-Noise Ratio (SNR), and signal superposition, algorithmic advancements, such as artificial intelligence approaches and big data spike sorting solutions have emerged as strategies for enhancing the comprehension of the intricate principles and functions underlying brain activity. This paper provides an overview linking invasive BCIs with neural encoding/decoding and spike sorting methods. Subsequently, it outlines the principles underlying various spike sorting algorithms, while discussing how brain neural signals map onto specific behaviors. Finally, the paper concludes by anticipating future challenges and trends in the development of high-bandwidth neural encoding and decoding.

Key words: spike sorting, invasive Brain-Computer Interface(BCI), neural encoding and decoding, machine learning, deep learning

中图分类号:

TP39

徐明亮, 李芳媛, 马浩然, 何飞. 大规模神经记录的峰电位聚类算法(特邀)[J]. 计算机工程, 2024, 50(6): 1-34.

XU Mingliang, LI Fangyuan, MA Haoran, HE Fei. Spike Sorting Algorithms for Large-Scale Neural Recording[J]. Computer Engineering, 2024, 50(6): 1-34.

https://www.ecice06.com/CN/Y2024/V50/I6/1

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