Main Menu (Mobile)- Block

Main Menu - Block

Clique topology reveals intrinsic geometric structure in neural correlations.

Our Research / Publications
janelia7_blocks-janelia7_biblio_header | block
Proceedings of the National Academy of Sciences of the United States of America. 2015 Nov 3;112(44):13455-60. doi: 10.1073/pnas.1506407112
View Article on
Clique topology reveals intrinsic geometric structure in neural correlations. Pastalkova Lab
Giusti C, Pastalkova E, Curto C, Itskov V
janelia7_blocks-janelia7_biblio_abstract | block

Abstract

Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading. We introduce a novel approach to matrix analysis, called clique topology, that extracts features of the data invariant under nonlinear monotone transformations. These features can be used to detect both random and geometric structure, and depend only on the relative ordering of matrix entries. We then analyzed the activity of pyramidal neurons in rat hippocampus, recorded while the animal was exploring a 2D environment, and confirmed that our method is able to detect geometric organization using only the intrinsic pattern of neural correlations. Remarkably, we found similar results during nonspatial behaviors such as wheel running and rapid eye movement (REM) sleep. This suggests that the geometric structure of correlations is shaped by the underlying hippocampal circuits and is not merely a consequence of position coding. We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.

PMID: 26487684 [PubMed - indexed for MEDLINE]

node:body | entity_field
janelia7_blocks-janelia7_biblio_authors | block

Janelia Authors

janelia7_blocks-janelia7_biblio_tools | block