title: Fast and accurate low-dimensional reduction of biophysically detailed neuron models.
abstract: Realistic modeling of neurons are quite successful in complementing traditional experimental techniques. However, their networks require a computational power beyond the capabilities of current supercomputers, and the methods used so far to reduce their complexity do not take into account the key features of the cells nor critical physiological properties. Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to > 40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions.
authors.affiliation: Department of Mathematics and Applications R. Caccioppoli, University of Naples Federico II, Via Cintia, 80126 Naples, Italy.
authors.forename: Addolorata
authors.initials: A
authors.lastname: Marasco
authors.forename: Alessandro
authors.initials: A
authors.lastname: Limongiello
authors.forename: Michele
authors.initials: M
authors.lastname: Migliore
mesh_terms: D000200:Action Potentials; D001703:Biophysics; D006801:Humans; D008954:Models, Biological; D009474:Neurons / Q000502:physiology*