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Projection-based order reduction of a nonlinear biophysical neuronal network model

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


Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control (CDC)
Number of pages6
ISBN (Electronic)978-1-7281-1398-2
ISBN (Print)978-1-7281-1399-9
Publication statusPublished - 2019
Publication typeA4 Article in a conference publication
EventIEEE Conference on Decision and Control -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE Conference on Decision & Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


ConferenceIEEE Conference on Decision and Control
Period1/01/00 → …


In this study mathematical model order reduction is applied to a nonlinear model of a network of biophysically realistic heterogeneous neurons. The neuron model describes a pyramidal cell in the hippocampal CA3 area of the brain and includes a state-triggered jump condition. The network displays synchronized firing of action potentials (spikes), a fundamental phenomenon of sensory information processing in the brain. Simulation of the system is computationally expensive, which limits network size and hence biological realism. We reduce the network using advanced variations of Proper Orthogonal Decomposition and Discrete Empirical Interpolation Method. The reduced models should recreate the original spiking activity. We show that reduction methods with online adaptivity achieve the most accurate reduction results. Some of the reduced models consume less computational resources than the original, at the cost of changes in population activity of the tested network model.


  • Neuroscience, Control theory, Model reduction

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