La Jolla / Cambridge / Birmingham / New York / Thompson / /
Company
Searle Funds / Princeton University Press / John Wiley & Sons Inc. / MIT Press / Creative Commons / Sincich LC / Computational Neurobiology Laboratory / /
Country
United States / / /
Facility
The Salk Institute / University of Alabama / Indiana University / University of California / /
IndustryTerm
binary response systems / minimal model algorithm / cortical visual processing / data mining / conjugate gradient ascent algorithm / gradient ascent algorithm / neural systems / energy models / energy model / /
MarketIndex
set 20 / /
Organization
National Institute of Health / Salk Institute for Biological Studies / Center for Theoretical Biological Physics / National Science Foundation / MIT / Department of Vision Sciences / Princeton University / Department of Physics / UCSF Institutional Animal Care and Use Committee / Federal Bureau of Investigation / University of Alabama at Birmingham / Receptive / Indiana University / University of California / San Diego / /
Person
Jonathan C. Horton / Ruyter van Steveninck / /
Position
Author / e1002249 Second Order Dimensionality Reduction Author / original author / Editor / /
ProgrammingLanguage
R / /
ProvinceOrState
Alabama / North Carolina / California / /
PublishedMedium
PLoS Computational Biology / the PLoS Computational Biology / /
Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models Jeffrey D. Fitzgerald1,2, Ryan J. Rowekamp1,2, Lawrence C. Sincich3, Tatyana O. Sharpee1,2* 1 Computational Neurobiology Laborator