Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models

DOI10.1214/11-AOAS476zbMath1454.62413arXiv1104.2805OpenAlexW2154551474WikidataQ41773612 ScholiaQ41773612MaRDI QIDQ641063

Vincent Q. Vu, Pradeep Ravikumar, Jack L. Gallant, Bin Yu, Thomas Naselaris, Kendrick N. Kay

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/1104.2805

zbMATH Keywords

predictionnonparametricneurosciencefMRIvision

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Minimum distance Lasso for robust high-dimensional regression, A primer on encoding models in sensory neuroscience, Discussion about ``Grouping strategies and thresholding for high dimensional linear models, Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models, The shuffle estimator for explainable variance in fMRI experiments, Superheat: An R Package for Creating Beautiful and Extendable Heatmaps for Visualizing Complex Data

Cites Work

• Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models
• Visualizing ``typical and ``exotic internet traffic data
• Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
• Unnamed Item
• Unnamed Item
• Unnamed Item