Topological Characteristics of Neural Manifolds
Keywords
Loading...
Authors
Issue Date
2020-10-28
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
In recent years, neural population activity has been analysed by treat-
ing it as a point cloud supported on a manifold whose structure gives
information for the the type of computation that the network can per-
form and the features it can represent. Simultaneously a data focused ap-
proach to topology, which is a fundamental property of manifolds, known
as topological data analysis (TDA), has also emerged. We use a method
from that toolbox called persistent homology, it essentially nds the holes
of di erent dimensions and sizes in point clouds and helps us understand
the underlying manifold. We study the topology of neural populations by
creating theoretical models capable of recreating a particular manifold's
topology in their activity and also analysing the topological structure of
neural activity during spontaneous and stimulus induced states in mouse
cortex. We nd signi cant di erences between the topological structure
of neural manifolds for di erent stimulus conditions across the brain.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen