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Sparse sign-consistent Johnson–Lindenstrauss matrices: Compression with neuroscience-based constraints: Fig. 1

2014
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Proceedings of the National Academy of Sciences of the United States of America
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Johnson-Lindenstrauss (JL) matrices implemented by sparse random synaptic connections are thought to be a prime candidate for how convergent pathways in the brain compress information. However, to date, there is no complete mathematical support for such implementations given the constraints of real neural tissue. The fact that neurons are either excitatory or inhibitory implies that every so implementable JL matrix must be sign-consistent (i.e., all entries in a single column must be either all

doi:10.1073/pnas.1419100111
pmid:25385619
pmcid:PMC4250157
fatcat:aoulhcj4vzdo3nf5sqmg6ya6xy