ORIGINAL RESEARCH published: 14 July 2020 doi: 10.3389/fncel.2020.00161 Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell Milagros Marín 1,2*, María José Sáez-Lara 2, Eduardo Ros 1 and Jesús A. Garrido 1* 1Department of Computer Architecture and Technology—CITIC, University of Granada, Granada, Spain, 2Department of Biochemistry and Molecular Biology I, University of Granada, Granada, Spain Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy Edited by: and thus prevent these models from being part of brain-area models including thousands Egidio D’Angelo, or even millions of neurons. The cerebellar input layer represents a canonical example University of Pavia, Italy of large scale networks. In particular, the cerebellar granule cells, the most numerous Reviewed by: cells in the whole mammalian brain, have been proposed as playing a pivotal role in Laurens Bosman, Erasmus Medical Center, the creation of somato-sensorial information representations. Enhanced burst frequency Netherlands (spiking resonance) in the granule cells has been proposed as facilitating the input signal William Martin Connelly, transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell University of Tasmania, Australia feature in the operation of the granular layer remains largely unclear. This study aims to *Correspondence: Milagros Marín develop a methodological pipeline for creating neuron models that maintain biological
[email protected] realism and computational efficiency whilst capturing essential aspects of single-neuron Jesús A.