A high-throughput screening approach to discovering good forms of biologically inspired visual representation

PLoS Computational Biology

Volumn 5, Issue 11, 2009, Pages

  Pinto, Nicolas ^a,b   Doukhan, David ^a,b   DiCarlo, James J ^a,b   Cox, David D ^a,b,c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MOLECULAR BIOLOGY; NETWORK ARCHITECTURE; OBJECT RECOGNITION; SMART CARDS;

BIOLOGICAL OBJECTS; BIOLOGICALLY-INSPIRED; CHOICE OF PARAMETERS; HIGH THROUGHPUT SCREENING; OBJECTS RECOGNITION; PARAMETER SET; PERFORMANCE; PROPERTY; SCREENING APPROACHES; VISUAL REPRESENTATIONS;

THROUGHPUT;

ARTICLE; COMPUTER MODEL; COMPUTER SYSTEM; HIGH THROUGHPUT SCREENING; MICROPROCESSOR; PARAMETER; TASK PERFORMANCE; VISION; VISUAL MEMORY; VISUAL SYSTEM; ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BIOMIMETICS; COMPUTER SIMULATION; HUMAN; METHODOLOGY; PATTERN RECOGNITION; PHYSIOLOGY; VISUAL CORTEX;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BIOMIMETICS; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; VISUAL CORTEX;

EID: 73449129720     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1000579     Document Type: Article

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