Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells

• Published in: Frontiers in cellular neuroscience Vol. 11; p. 71
• Main Authors: Masoli, Stefano, Rizza, Martina F, Sgritta, Martina, Van Geit, Werner, Schürmann, Felix, D'Angelo, Egidio
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 15.03.2017; Frontiers Media S.A
• Subjects: Algorithms; Cerebellum; Conductance; Firing pattern; Genetic algorithms; Granule cells; Mutation; Neuroscience; Neurosciences; Optimization; Oscillations; Parameter estimation; Physiology; Values; Italy; modeling; intrinsic electroresponsiveness; cerebellum; granule cell; optimization techniques
• ISSN: 1662-5102; 1662-5102
• DOI: 10.3389/fncel.2017.00071

In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (G ) 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 G values, 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 G values 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 G values. 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.