Morphologically Accurate Reduced Order Modeling of Spiking Neurons

dc.contributor.authorKellems, Anthony R.en_US
dc.contributor.authorChaturantabut, Saifonen_US
dc.contributor.authorSorensen, Danny C.en_US
dc.contributor.authorCox, Steven J.en_US
dc.date.accessioned2018-06-19T17:45:06Zen_US
dc.date.available2018-06-19T17:45:06Zen_US
dc.date.issued2009-04en_US
dc.date.noteApril 2009en_US
dc.description.abstractAccurately simulating neurons with realistic morphological structure and synaptic inputs requires the solution of large systems of nonlinear ordinary differential equations. We apply model reduction techniques to recover the complete nonlinear voltage dynamics of a neuron using a system of much lower dimension. Using a proper orthogonal decomposition, we build a reduced-order system from salient snapshots of the full system output, thus reducing the number of state variables. A discrete empirical interpolation method is then used to reduce the complexity of the nonlinear term to be proportional to the number of reduced variables. Together these two techniques allow for up to two orders of magnitude dimension reduction without sacrificing the spatially-distributed input structure, with an associated order of magnitude speed-up in simulation time. We demonstrate that both nonlinear spiking behavior and subthreshold response of realistic cells are accurately captured by these low-dimensional models.en_US
dc.format.extent27 ppen_US
dc.identifier.citationKellems, Anthony R., Chaturantabut, Saifon, Sorensen, Danny C., et al.. "Morphologically Accurate Reduced Order Modeling of Spiking Neurons." (2009) <a href="https://hdl.handle.net/1911/102117">https://hdl.handle.net/1911/102117</a>.en_US
dc.identifier.digitalTR09-12en_US
dc.identifier.urihttps://hdl.handle.net/1911/102117en_US
dc.language.isoengen_US
dc.titleMorphologically Accurate Reduced Order Modeling of Spiking Neuronsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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