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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 13455-13460 |
| Number of pages | 6 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 112 |
| Issue number | 44 |
| DOIs | |
| State | Published - Nov 3 2015 |
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All Science Journal Classification (ASJC) codes
- General
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Clique topology reveals intrinsic geometric structure in neural correlations. / Giusti, Chad; Pastalkova, Eva; Curto, Carina; Itskov, Vladimir.
In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 112, No. 44, 03.11.2015, p. 13455-13460.Research output: Contribution to journal › Article
TY - JOUR
T1 - Clique topology reveals intrinsic geometric structure in neural correlations
AU - Giusti, Chad
AU - Pastalkova, Eva
AU - Curto, Carina
AU - Itskov, Vladimir
PY - 2015/11/3
Y1 - 2015/11/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84946594591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946594591&partnerID=8YFLogxK
U2 - 10.1073/pnas.1506407112
DO - 10.1073/pnas.1506407112
M3 - Article
C2 - 26487684
AN - SCOPUS:84946594591
VL - 112
SP - 13455
EP - 13460
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
SN - 0027-8424
IS - 44
ER -