Amir Khani, Christian Beste, Shervin Safavi · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26304 · cited by 0 works (OpenAlex) · open-access PDF
Spike-field 数据连接测量神经元连接和本地领域潜力(LFP)之间的联系,并提供一种工具来研究神经群体如何在空间和时间范围内协调活动。当现代高密度电气物理学允许数个LFP频道和神经元单元的同时记录时,这个大规模的规模为常规双重连接分析提出了显著的统计和计算挑战。频道单元比较的数量正在迅速增长,而一致的人口水平的结构可以被高尺寸连接矩阵掩盖。 通用阶段锁分析(GPLA)解决了这一挑战,通过在复杂的连接矩阵中组织激发的LFP阶段信息并提取主导的低级模式。 这些模式解释了是否一个人口被连接到该领域,这些渠道和单元用于基于对接
Machine translation (Meta M2M100); the English original follows.
Spike-field couplings measure the links between neuronal spiking and local field potential (LFP) and provide a tool to study how neural populations coordinate activity across spatial and temporal scales. While modern high-density electrophysiology enables the simultaneous recording of numerous LFP channels and neuronal units, this massive scale poses significant statistical and computational challenges for conventional pairwise coupling analyses. The number of channel-unit comparisons grows rapidly, and a coherent population-level structure can be obscured by high-dimensional coupling matrices. Generalized Phase Locking Analysis (GPLA) addresses this challenge by organizing spike-triggered LFP phase information into a complex coupling matrix and extracting dominant low-rank modes. These modes summarize whether a population is coupled to the field, which channels and units contribute to the coupling, and how their relative phases are organized. Here, we present PyGPLA, an open-source Python package implementing GPLA for high-density electrophysiological recordings. Designed for straightforward integration into existing neural data analysis pipelines, PyGPLA supports multivariate spike-LFP coupling analysis, from coupling estimation and interpretation to significance assessment using analytical and surrogate-based tests.
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Topics: Python (programming language), Computer science, Local field potential, Coupling (piping), Pairwise comparison, Population
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