Order reduction for a signaling pathway model of neuronal synaptic plasticity

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

    3 Citations (Scopus)


    In this study a nonlinear mathematical model of plasticity in the brain is reduced using the Proper Orthogonal Decomposition and Discrete Empirical Interpolation Method. Such methods are remarkably useful for connecting reduced small scale models via the inputs and outputs to form optimally performing large scale models. Novel results were obtained as mathematical model order reduction has not been applied in neuroscience without linearization of the mathematical model and never to the model presented here. The reduced order model consumes considerably less computational resources than the original while maintaining a low root mean square error between the original and reduced model.

    Original languageEnglish
    Title of host publication20th IFAC World Congress
    Number of pages6
    Publication statusPublished - 1 Jul 2017
    Publication typeA4 Article in a conference publication
    EventIFAC World Congress -
    Duration: 1 Jan 2000 → …

    Publication series

    ISSN (Electronic)2405-8963


    ConferenceIFAC World Congress
    Period1/01/00 → …


    • cell signaling
    • Discrete Empirical Interpolation Method
    • model reduction
    • nonlinear models
    • Proper Orthogonal Decomposition
    • synaptic plasticity

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Control and Systems Engineering


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