Quantifying the distribution of feature values over data represented in arbitrary dimensional spaces.

ABSTRACT: Identifying the structured distribution (or lack thereof) of a given feature over a point cloud is a general research question. In the neuroscience field, this problem arises while investigating representations over neural manifolds (e.g., spatial coding), in the analysis of neurophysiological signals (e.g., sensory coding) or in anatomical image segmentation. We introduce the Structure Index (SI) as a directed graph-based metric to quantify the distribution of feature values projected over data in arbitrary D-dimensional spaces (defined from neurons, time stamps, pixels, genes, etc). The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood of the cloud. Using arbitrary data clouds, we show how the SI provides quantification

SUBMITTER: Sebastian ER 

PROVIDER: S-EPMC10793935 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature