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

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

[img]
Preview
Text
1508706_a2025_Smith.pdf

Download (9MB) | Preview

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 - Journals
Date: 11 January 2022
ISSN: 2472-1751
Identifiers:
NumberType
10.1162/netn_a_00228DOI
1508706Other
Divisions: Schools > School of Science and Technology
Record created by: Jeremy Silvester
Date Added: 14 Jan 2022 10:58
Last Modified: 21 Jan 2022 10:49
URI: http://irep.ntu.ac.uk/id/eprint/45331

Actions (login required)

Edit View Edit View

Views

Views per month over past year

Downloads

Downloads per month over past year