title: Model reduction of strong-weak neurons.
abstract: We consider neurons with large dendritic trees that are weakly excitable in the sense that back propagating action potentials are severly attenuated as they travel from the small, strongly excitable, spike initiation zone. In previous work we have shown that the computational size of weakly excitable cell models may be reduced by two or more orders of magnitude, and that the size of strongly excitable models may be reduced by at least one order of magnitude, without sacrificing the spatio-temporal nature of its inputs (in the sense we reproduce the cell's precise mapping of inputs to outputs). We combine the best of these two strategies via a predictor-corrector decomposition scheme and achieve a drastically reduced highly accurate model of a caricature of the neuron responsible for collision detection in the locust.
authors.affiliation: Department of Computational and Applied Mathematics, Rice University Houston, TX, USA.
authors.forename: Bosen
authors.initials: B
authors.lastname: Du
authors.affiliation: Department of Computational and Applied Mathematics, Rice University Houston, TX, USA.
authors.forename: Danny
authors.initials: D
authors.lastname: Sorensen
authors.affiliation: Department of Computational and Applied Mathematics, Rice University Houston, TX, USA.
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Cox
keywords: LGMD; discrete empirical interpolation; predictor-corrector; proper orthogonal decomposition; quasi-active