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Logic and learning in network cascades

Published online by Cambridge University Press:  14 April 2021

Galen J. Wilkerson Open the ORCID record for Galen J. Wilkerson [Opens in a new window]  and
Sotiris Moschoyiannis Open the ORCID record for Sotiris Moschoyiannis [Opens in a new window]
Galen J. Wilkerson*
Affiliation:
University of Surrey, Guildford, UK GU2 7XH, UK (e-mail: s.moschoyiannis@surrey.ac.uk)
Sotiris Moschoyiannis
Affiliation:
University of Surrey, Guildford, UK GU2 7XH, UK (e-mail: s.moschoyiannis@surrey.ac.uk)
*
*Corresponding author. Email: g.wilkerson@surrey.ac.uk
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Abstract

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Critical cascades are found in many self-organizing systems. Here, we examine critical cascades as a design paradigm for logic and learning under the linear threshold model (LTM), and simple biologically inspired variants of it as sources of computational power, learning efficiency, and robustness. First, we show that the LTM can compute logic, and with a small modification, universal Boolean logic, examining its stability and cascade frequency. We then frame it formally as a binary classifier and remark on implications for accuracy. Second, we examine the LTM as a statistical learning model, studying benefits of spatial constraints and criticality to efficiency. We also discuss implications for robustness in information encoding. Our experiments show that spatial constraints can greatly increase efficiency. Theoretical investigation and initial experimental results also indicate that criticality can result in a sudden increase in accuracy.

Keywords

percolationcritical cascadesinformation cascadeinformation diffusioninfluence maximizationnetwork rewiringneuronal avalanchesstatistical learninglinear threshold modeluniversal Boolean logic
Type
Research Article
Information
Network Science , Volume 9 , Special Issue S1: Complex Networks 2019 , October 2021 , pp. S157 - S174
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

Action Editor: Hocine Cherifi

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