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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 publicationProceedings of the IEEE Conference on Decision and Control
PublisherIEEE Xplore
Publication statusAccepted/In press - 19 Jul 2019
Publication typeA4 Article in a conference publication
EventIEEE Conference on Decision and Control -
Duration: 1 Jan 2014 → …


ConferenceIEEE Conference on Decision and Control
Period1/01/14 → …


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 De-
composition 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


  • Neuroscience, Control theory, Model reduction

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