title: Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells.
abstract: In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (Gi-max) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of Gi-maxvalues, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of Gi-maxvalues to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental Gi-maxvalues. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.
authors.affiliation: Department of Brain and Behavioral Sciences, University of Pavia Pavia, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Masoli
authors.affiliation: Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-BicoccaMilan, Italy.
authors.forename: Martina F
authors.initials: MF
authors.lastname: Rizza
authors.affiliation: Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Memory and Brain Research Center, Department of Neuroscience, Baylor College of MedicineHouston, TX, USA.
authors.forename: Martina
authors.initials: M
authors.lastname: Sgritta
authors.affiliation: Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland.
authors.forename: Werner
authors.initials: W
authors.lastname: Van Geit
authors.affiliation: Blue Brain Project, École Polytechnique Fédérale de Lausanne Geneva, Switzerland.
authors.forename: Felix
authors.initials: F
authors.lastname: Schürmann
authors.affiliation: Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy; Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy.
authors.forename: Egidio
authors.initials: E
authors.lastname: D'Angelo
keywords: cerebellum; granule cell; intrinsic electroresponsiveness; modeling; optimization techniques