A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields

Journal of Computational Neuroscience

Volumn 22, Issue 2, 2007, Pages 135-146

  Rehn, Martin ^a   Sommer, Friedrich T ^b  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Biological vision; Receptive field learning; Sparse coding

Indexed keywords

CODES (SYMBOLS); NETWORK CODING;

ACTIVE NEURONS; BIOLOGICAL VISIONS; COMPUTATIONAL MODELLING; LOCALISED; NETWORK MODELS; NEURONAL ACTIVITIES; PRIMARY VISUAL CORTEX; RECEPTIVE FIELD LEARNING; RECEPTIVE FIELDS; SPARSE CODING;

NEURONS;

ARTICLE; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; RETINA IMAGE; RETINA RECEPTIVE FIELD; SENSORIMOTOR FUNCTION; VISUAL CORTEX;

EID: 33847100046     PISSN: 09295313     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10827-006-0003-9     Document Type: Article

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