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.
|Title of host publication||Fourth International Congress on Information and Communication Technology|
|Subtitle of host publication||ICICT 2019, London, Volume 1|
|Editors||X.S. Yang, S. Sherratt, N. Dey, A. Joshi|
|Number of pages||21|
|Publication status||Published - 2020|
|Name||Advances in Intelligent Systems and Computing|
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