TY - JOUR

T1 - An application of neighbourhoods in digraphs to the classification of binary dynamics

AU - Rodrigues Da Conceicao, Pedro Vitor

AU - Govc, Dejan

AU - Lazovskis, Janis

AU - Levi, Ran

AU - Riihimaki, Henri

AU - Smith, Jason P

N1 - Ran Levi, Engineering and Physical Sciences Research Council (https://dx.doi.org/10.13039/501100000266), Award ID: EP/P025072/1. Dejan Govc, Javna Agencija za Raziskovalno Dejavnost RS (https://dx.doi.org/10.13039/501100004329), Award ID: P1-0292-0083.

PY - 2022/6/1

Y1 - 2022/6/1

N2 - 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 nonbiological random graph with similar density.

AB - 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 nonbiological random graph with similar density.

KW - binary dynamics

KW - directed graphs

KW - graph and topological parameters

KW - neural networks

KW - signal classification

KW - Graph and topological parameters

KW - Neural networks

KW - Directed graphs

KW - Binary dynamics

KW - Signal classification

UR - http://www.scopus.com/inward/record.url?scp=85131254066&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/834eff80-7026-350a-a8c4-05467702c0ab/

U2 - 10.1162/netn_a_00228

DO - 10.1162/netn_a_00228

M3 - Article

C2 - 35733429

VL - 6

SP - 528

EP - 551

JO - Network Neuroscience

JF - Network Neuroscience

SN - 2472-1751

IS - 2

ER -