Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure

DOI10.1214/20-AOAS1383zbMath1475.62256arXiv1911.04387OpenAlexW3137102631MaRDI QIDQ127507

Surya Tokdar, Jeffrey T Mohl, Chris Glynn, Jennifer M Groh, Valeria C Caruso, Azeem Zaman, Shawn M Willett, Valeria C. Caruso, Jennifer M. Groh, Jeff T. Mohl, Shawn M. Willett, Chris Glynn, Surya T. Tokdar, Azeem Zaman

Publication date: 11 November 2019

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

Full work available at URL: https://arxiv.org/abs/1911.04387

zbMATH Keywords

Gaussian processDirichlet processBayesian inferencespike traindynamic admixture of Poisson processesmultiple stimuli

Mathematics Subject Classification ID

Gaussian processes (60G15) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (1)

neuromplex

Cites Work

• Unnamed Item
• Unnamed Item
• A Bayesian analysis of some nonparametric problems
• Default priors for density estimation with mixture models
• A Spike-Train Probability Model
• Spike Count Correlation Increases with Length of Time Interval in the Presence of Trial-to-Trial Variation
• Hierarchical Dirichlet Processes
• A Method for Selecting the Bin Size of a Time Histogram
• Poisson/gamma random field models for spatial statistics
• Bayesian Density Estimation and Inference Using Mixtures
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

This page was built for publication: Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure