A Modeling Environment for Dynamic and Adaptive Network Models Implemented in Matlab

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Abstract

In this paper a software environment to support Network-Oriented Modeling is presented. The environment has been implemented in Matlab. This code covers the principles of modeling by temporal-causal networks. The software environment has built-in options for network adaptation principles such as the Hebbian Learning princi-ple from Neuroscience and the adaptation principle for bonding based on homophily from Social Science. The implementation is illustrated for an adaptive temporal-causal network model for decision making under acute stress.
Original languageEnglish
Title of host publicationFourth International Congress on Information and Communication Technology
Subtitle of host publicationICICT 2019, London, Volume 1
EditorsX.S. Yang, S. Sherratt, N. Dey, A. Joshi
PublisherSpringer
Pages91-111
Number of pages21
Volume1
ISBN (Electronic)9789811506376
ISBN (Print)9789811506369
DOIs
Publication statusPublished - 2020

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer, Singapore
Volume1041

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Mohammadi Ziabari, S. S., & Treur, J. (2020). A Modeling Environment for Dynamic and Adaptive Network Models Implemented in Matlab. In X. S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 1 (Vol. 1, pp. 91-111). (Advances in Intelligent Systems and Computing; Vol. 1041). Springer. https://doi.org/10.1007/978-981-15-0637-6_8