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Extracting structure from feature values represented over point clouds
Point clouds are commonly used in many scientific fields. Each point of a cloud is typically associated with additional values or features. Thus, having the ability to identify if and how a given feature is structured along the cloud can provide insights for many applications. Here, we present the Structure Index, a graph-based method to quantify the distribution of features over data in arbitrary dimensional spaces. Spaces can be defined by neurons, time stamps, pixels, genes, etc... The image illustrates how the Structure Index eases the interpretation of neural representations from the brain head-direction system. Sebastian, Esparza and de la Prida 2024.
Image Credit: Julio Esparza
Citation: (2024) PLoS Computational Biology Issue Image | Vol. 20(1) February 2024. PLoS Comput Biol 20(1): ev20.i01. https://doi.org/10.1371/image.pcbi.v20.i01
Published: February 2, 2024
Copyright: © 2024 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Point clouds are commonly used in many scientific fields. Each point of a cloud is typically associated with additional values or features. Thus, having the ability to identify if and how a given feature is structured along the cloud can provide insights for many applications. Here, we present the Structure Index, a graph-based method to quantify the distribution of features over data in arbitrary dimensional spaces. Spaces can be defined by neurons, time stamps, pixels, genes, etc... The image illustrates how the Structure Index eases the interpretation of neural representations from the brain head-direction system. Sebastian, Esparza and de la Prida 2024.
Image Credit: Julio Esparza