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An application of n...
An application of neighbourhoods in digraphs to the classification of binary dynamics
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- Conceicao, Pedro (författare)
- Univ Aberdeen, Inst Math, Aberdeen, Scotland.
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- Govc, Dejan (författare)
- Univ Ljubljana, Fac Math & Phys, Ljubljana, Slovenia.
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- Lazovskis, Janis (författare)
- Riga Tech Univ, Riga Business Sch, Riga, Latvia.
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- Levi, Ran (författare)
- Univ Aberdeen, Inst Math, Aberdeen, Scotland.
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- Riihimaki, Henri (författare)
- KTH,Matematik (Inst.)
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- Smith, Jason P. (författare)
- Nottingham Trent Univ, Dept Math & Phys, Nottingham, England.
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Univ Aberdeen, Inst Math, Aberdeen, Scotland Univ Ljubljana, Fac Math & Phys, Ljubljana, Slovenia. (creator_code:org_t)
- 2022-05-03
- 2022
- Engelska.
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Ingår i: NETWORK NEUROSCIENCE. - : MIT PRESS. - 2472-1751. ; 6:2, s. 528-551
- Relaterad länk:
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https://doi.org/10.1...
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https://direct.mit.e...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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. Author Summary We explore the mathematical concept of a closed neighbourhood in a digraph in relation to classifying binary dynamics on a digraph, with particular emphasis on dynamics on a neuronal network. Using methodology based on selecting neighbourhoods and vectorising them by combinatorial and topological parameters, we experimented with a dataset implemented on the Blue Brain Project reconstruction of a neocortical column, and on an artificial neural network with random underlying graph implemented on the NEST simulator. In both cases the outcome was run through a support vector machine algorithm reaching classification accuracy of up to 88% for the Blue Brain Project data and up to 81% for the NEST data. This work is open to generalisation to other types of networks and the dynamics on them.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- HUMANIORA -- Filosofi, etik och religion -- Filosofi (hsv//swe)
- HUMANITIES -- Philosophy, Ethics and Religion -- Philosophy (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Infrastrukturteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Infrastructure Engineering (hsv//eng)
Nyckelord
- Binary dynamics
- Directed graphs
- Graph and topological parameters
- Neural networks
- Signal classification
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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