Assessment of synchrony in multiple neural spike trains using loglinear point process models

DOI10.1214/10-AOAS429zbMath1223.62159arXiv1107.5872OpenAlexW1979227515WikidataQ35153160 ScholiaQ35153160MaRDI QIDQ641077

Wei-Liem Loh, Robert E. Kass, Ryan C. Kelly

Publication date: 21 October 2011

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1107.5872

zbMATH Keywords

discrete-time approximationloglinear modelmarked processnonstationary point processsimultaneous eventsspike trainsynchrony detection

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Neural biology (92C20) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (8)

Point-Process Principal Components Analysis via Geometric Optimization ⋮ Likelihood Methods for Point Processes with Refractoriness ⋮ Spatiotemporal Conditional Inference and Hypothesis Tests for Neural Ensemble Spiking Precision ⋮ Surrogate Data Methods Based on a Shuffling of the Trials for Synchrony Detection: The Centering Issue ⋮ Towards a mathematical definition of functional connectivity ⋮ Methods for identification of spike patterns in massively parallel spike trains ⋮ Unnamed Item ⋮ A conversation with Robert E. Kass

Cites Work

• Unnamed Item
• The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
• Maximum likelihood analysis of spike trains of interacting nerve cells
• An introduction to the theory of point processes
• Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance
• Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data
• Sharp estimates for finite element approximations to elliptic problems with Neumann boundary data of low regularity

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