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

French

Les couplages de surfaces de données mesurent les liens entre le spiking neuronal et le potentiel de champ local (LFP) et fournissent un outil pour étudier comment les populations neuronales coordonnent l'activité sur des échelles spatiales et temporelles. Alors qu'une électrophysiologie à haute densité moderne permet l'enregistrement simultané de nombreux canaux de LFP et d'unités neuronales, cette échelle massive pose des défis statistiques et informatiques importants pour les analyses de couplage par paires conventionnelles. Le nombre de comparaisons entre canaux et unités augmente rapidement, et une structure de niveau de population cohérente peut être obscurcie par des matrices de couplage haute dimension. L'analyse généralisée de verrouill

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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