Newest papers

PyGPLA: A Python Package for Generalized Phase Locking Analysis

Amir Khani, Christian Beste, Shervin Safavi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26304 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

スパイクフィールドコンパイリングは、神経コンパイリングとローカルフィールド潜在力(LFP)の間のリンクを測定し、神経人口が空間および時間のスケールで活動を調整する方法を研究するためのツールを提供します。現代の高密度電気物理学は、多数のLFPチャンネルとニューラルユニットの同時記録を可能にしますが、この大規模なスケールは、従来のスパイクコンパイリング分析のための重要な統計的および計算的課題を引き起こします。チャンネルユニット比較の数は急速に増加し、一貫した人口レベルの構造は、高次元コンパイリングマトリックスによって遮蔽することができます。一般化フェーズロックアナ

Machine translation (Meta M2M100); the English original follows.

Abstract

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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