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Title: Large-scale functional models of visual cortex for remote sensing

Abstract

Neuroscience has revealed many properties of neurons and of the functional organization of visual cortex that are believed to be essential to human vision, but are missing in standard artificial neural networks. Equally important may be the sheer scale of visual cortex requiring {approx}1 petaflop of computation. In a year, the retina delivers {approx}1 petapixel to the brain, leading to massively large opportunities for learning at many levels of the cortical system. We describe work at Los Alamos National Laboratory (LANL) to develop large-scale functional models of visual cortex on LANL's Roadrunner petaflop supercomputer. An initial run of a simple region VI code achieved 1.144 petaflops during trials at the IBM facility in Poughkeepsie, NY (June 2008). Here, we present criteria for assessing when a set of learned local representations is 'complete' along with general criteria for assessing computer vision models based on their projected scaling behavior. Finally, we extend one class of biologically-inspired learning models to problems of remote sensing imagery.

Authors:
 [1];  [1];  [1];  [1];  [1];  [2]
  1. Los Alamos National Laboratory
  2. PORTLAND STATE UNIV.
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
978329
Report Number(s):
LA-UR-09-07673; LA-UR-09-7673
TRN: US201010%%49
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: 2009 38th IEEE Applied Imagery Pattern Recognition Workshop ; October 14, 2009 ; Washington, DC
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; BRAIN; COMPUTERS; FUNCTIONAL MODELS; FUNCTIONALS; LANL; LEARNING; NERVE CELLS; NEURAL NETWORKS; PATTERN RECOGNITION; REMOTE SENSING; RETINA; VISION

Citation Formats

Brumby, Steven P, Kenyon, Garrett, Rasmussen, Craig E, Swaminarayan, Sriram, Bettencourt, Luis, and Landecker, Will. Large-scale functional models of visual cortex for remote sensing. United States: N. p., 2009. Web. doi:10.1109/AIPR.2009.5466323.
Brumby, Steven P, Kenyon, Garrett, Rasmussen, Craig E, Swaminarayan, Sriram, Bettencourt, Luis, & Landecker, Will. Large-scale functional models of visual cortex for remote sensing. United States. https://doi.org/10.1109/AIPR.2009.5466323
Brumby, Steven P, Kenyon, Garrett, Rasmussen, Craig E, Swaminarayan, Sriram, Bettencourt, Luis, and Landecker, Will. 2009. "Large-scale functional models of visual cortex for remote sensing". United States. https://doi.org/10.1109/AIPR.2009.5466323. https://www.osti.gov/servlets/purl/978329.
@article{osti_978329,
title = {Large-scale functional models of visual cortex for remote sensing},
author = {Brumby, Steven P and Kenyon, Garrett and Rasmussen, Craig E and Swaminarayan, Sriram and Bettencourt, Luis and Landecker, Will},
abstractNote = {Neuroscience has revealed many properties of neurons and of the functional organization of visual cortex that are believed to be essential to human vision, but are missing in standard artificial neural networks. Equally important may be the sheer scale of visual cortex requiring {approx}1 petaflop of computation. In a year, the retina delivers {approx}1 petapixel to the brain, leading to massively large opportunities for learning at many levels of the cortical system. We describe work at Los Alamos National Laboratory (LANL) to develop large-scale functional models of visual cortex on LANL's Roadrunner petaflop supercomputer. An initial run of a simple region VI code achieved 1.144 petaflops during trials at the IBM facility in Poughkeepsie, NY (June 2008). Here, we present criteria for assessing when a set of learned local representations is 'complete' along with general criteria for assessing computer vision models based on their projected scaling behavior. Finally, we extend one class of biologically-inspired learning models to problems of remote sensing imagery.},
doi = {10.1109/AIPR.2009.5466323},
url = {https://www.osti.gov/biblio/978329}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2009},
month = {Thu Jan 01 00:00:00 EST 2009}
}

Conference:
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