Sparse sign-consistent Johnson-Lindenstrauss matrices: Compression with neuroscience-based constraints

Proceedings of the National Academy of Sciences of the United States of America

Volumn 111, Issue 47, 2014, Pages 16872-16876

  Allen Zhu, Zeyuan ^a   Gelashvili, Rati ^a   Micali, Silvio ^a   Shavit, Nir ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Johnson Lindenstrauss compression; Sign consistent matrices; Synaptic connectivity matrices

Indexed keywords

ARTICLE; BRAIN; CENTRAL NERVOUS SYSTEM; COMPRESSION; CONNECTOME; JOHNSON LINDENSTRAUSS COMPRESSION; MATHEMATICAL MODEL; NERVE CELL; NEUROSCIENCE; SYNAPSE; BIOLOGICAL MODEL;

MODELS, BIOLOGICAL; NEUROSCIENCES;

EID: 84912123029     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1419100111     Document Type: Article

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