TY - JOUR

T1 - Sparse sign-consistent Johnson-Lindenstrauss matrices

T2 - Compression with neuroscience-based constraints

AU - Allen-Zhu, Zeyuan

AU - Gelashvili, Rati

AU - Micali, Silvio

AU - Shavit, Nir

PY - 2014/11/25

Y1 - 2014/11/25

N2 - 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 nonnegative or all nonpositive), and the fact that any given neuron connects to a relatively small subset of other neurons implies that the JL matrix should be sparse. We construct sparse JL matrices that are sign consistent and prove that our construction is essentially optimal. Our work answers a mathematical question that was triggered by earlier work and is necessary to justify the existence of JL compression in the brain and emphasizes that inhibition is crucial if neurons are to perform efficient, correlation-preserving compression.

AB - 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 nonnegative or all nonpositive), and the fact that any given neuron connects to a relatively small subset of other neurons implies that the JL matrix should be sparse. We construct sparse JL matrices that are sign consistent and prove that our construction is essentially optimal. Our work answers a mathematical question that was triggered by earlier work and is necessary to justify the existence of JL compression in the brain and emphasizes that inhibition is crucial if neurons are to perform efficient, correlation-preserving compression.

KW - Johnson-Lindenstrauss compression

KW - Sign-consistent matrices

KW - Synaptic-connectivity matrices

UR - http://www.scopus.com/inward/record.url?scp=84912123029&partnerID=8YFLogxK

U2 - 10.1073/pnas.1419100111

DO - 10.1073/pnas.1419100111

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

C2 - 25385619

AN - SCOPUS:84912123029

SN - 0027-8424

VL - 111

SP - 16872

EP - 16876

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

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

IS - 47

ER -