Amir Khani, Christian Beste, Shervin Safavi · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26304 · cited by 0 works (OpenAlex) · open-access PDF
스파이크 필드 커플링은 신경 커플링과 로컬 필드 잠재력 (LFP) 사이의 연결을 측정하고 신경 인구가 공간 및 시간 규모에 걸쳐 활동을 조정하는 방법을 연구하는 도구를 제공합니다. 현대 고밀도 전기 생물학은 수많은 LFP 채널과 신경 단위의 동시에 기록을 허용하는 반면,이 거대한 규모는 전통적인 쌍으로 커플링 분석을위한 중요한 통계 및 계산 문제를 제기합니다. 채널 단위 비교의 수는 빠르게 증가하고 있으며, 일관된 인구 수준 구조는 고차원 커플링 매트릭스에 의해 어두워질 수 있습니다. 일반화된 단계 잠금 분석 (GPLA)은 높은 단계를 기반으로하는 커플링 분석을위한 채널 및 단위
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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