Modeling Multi-Order Adaptive Processes by Self-Modeling Networks

Jan Treur*

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


This paper covers the contents of the Keynote Speech with the same title. The paper addresses the use of self-modeling networks to model adaptive biological, mental, and social processes of any order of adaptation. A self-modeling network for some base network is a network extension that represents part of the base network structure by a self-model in terms of added network nodes and connections for them. A network structure, in general, involves network characteristics for connectivity (connections between nodes), aggregation (combining multiple incoming impacts on a node), and timing (node state dynamics speed). By representing some of these network characteristics by a self-model using dynamic node states, these characteristics become adaptive. By iterating this construction, multi-order network adaptation is easily obtained. A dedicated software environment for self-modeling networks that has been developed supports the modeling and simulation processes. This will be illustrated for a number of adaptation principles from a number of application domains, for example, for Cognitive Neuroscience by a second-order adaptive network model to model plasticity of connections and node excitability, and metaplasticity to control such plasticity.
Original languageEnglish
Title of host publicationMachine Learning and Intelligent Systems
Subtitle of host publicationProceedings of MLIS 2020
EditorsAntonio J. Tallon-Ballesteros, Chi-Hua Chen
PublisherIOS Press BV
Number of pages12
ISBN (Electronic)9781643681375
ISBN (Print)9781643681368
Publication statusPublished - 2020
Event2020 International Conference on Machine Learning and Intelligent Systems, MLIS 2020 - Virtual, Online, Korea, Republic of
Duration: 25 Oct 202028 Oct 2020

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


Conference2020 International Conference on Machine Learning and Intelligent Systems, MLIS 2020
CountryKorea, Republic of
CityVirtual, Online


  • Adaptive network
  • Multi-order adaptive
  • Self-modeling network


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