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Order reduction for a signaling pathway model of neuronal synaptic plasticity

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


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 → …


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.

ASJC Scopus subject areas


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

Publication forum classification

Field of science, Statistics Finland