RT Journal Article
SR Electronic
T1 State space discovery in spatial representation circuits with persistent cohomology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.06.328773
DO 10.1101/2020.10.06.328773
A1 Louis Kang
A1 Boyan Xu
A1 Dmitriy Morozov
YR 2020
UL http://biorxiv.org/content/early/2020/10/08/2020.10.06.328773.abstract
AB Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We explore the application of persistent cohomology to the brain’s spatial representation system. We simulate populations of grid cells, head direction cells, and conjunctive cells, each of which span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures and demonstrate its robustness to various forms of noise. We identify regimes under which mixtures of populations form product topologies can be detected. Our results suggest guidelines for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.Competing Interest StatementThe authors have declared no competing interest.