An application of neighbourhoods in digraphs to the classification of binary dynamics

Conceição, P, Govc, D, Lazovskis, J, Levi, R, Riihimäki, H and Smith, JP ORCID: https://orcid.org/0000-0002-4209-1604, 2022. An application of neighbourhoods in digraphs to the classification of binary dynamics. Network Neuroscience. ISSN 2472-1751

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Abstract

A binary state on a graph means an assignment of binary values to its vertices. A time dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a non-biological random graph with similar density.

Item Type:	Journal article
Publication Title:	Network Neuroscience
Creators:	Conceição, P., Govc, D., Lazovskis, J., Levi, R., Riihimäki, H. and Smith, J.P.
Publisher:	MIT Press
Date:	11 January 2022
ISSN:	2472-1751
Identifiers:	Number Type 10.1162/netn_a_00228 DOI 1508706 Other
Rights:	© 2022 Massachusetts Institute of Technology. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	14 Jan 2022 10:58
Last Modified:	25 Jan 2022 14:16
URI:	https://irep.ntu.ac.uk/id/eprint/45331

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