How to "optimize" a cerebellar granule cell model Stefano Masoli1, Martina Rizza1,3, Michele Migliore4, Egidio D’Angelo1,2 1 Department of Neuroscience, University of Pavia, Via Mondino 2, Pavia, Italy 2 Brain Connectivity Center, Istituto Neurologico IRCCS C. Mondino, Via Mondino 2, Pavia, Italy 3 DISCo - Dipartimento di Informatica Sistemistica e Comunicazione, University of Bicocca, Viale Sarca, 336, Milan, Italy 4 Istituto di Biofisica, CNR, Via Ugo La Malfa 153, Palermo, Italy  Background or Purpose The construction of a realistic computational model of a neuron is a time consuming procedure which normally requires a large data set and lot of effort to find the right combination of ionic conductance yielding a physiologically plausible solution. In the last decade, a series of tools has been developed to improve the modeling workflow but only recently, with the Human Brain Project (HBP), a complete framework has been built, which is potentially applicable to any kind of neurons. A wealth of data is available on granule cell electroresponsiveness and ionic mechanisms, and accurate model are available (D’Angelo and Naldi, 2001; Diwakar and D’Angelo, 2009). This provides an ideal case to test the HBP optimization procedures and to suggest improvements. The purpose of this work was thus to do a field-test of the HBP framework to generate a new population of cerebellar granule cell models starting from an unified automatic optimization principle.  Methods We used either the original optimizer code implemented in NEURON (Druckmann and Segev, 2007) or its HBP version (through a PYTHON interface). The granule cell equivalent morphology and the membrane mechanisms (MOD files) were taken from a previous model without modifications (D'Angelo et al., 2001). The features were chooses among those available in the optimizer frameworks. The experimental constrains were obtained from 3 granule cells recorded in whole-cell patch-clamp configuration (courtesy of Dr. Martina Sgritta). The objectives were defined to optimize simultaneously spike and firing properties. Simulations yielded a final population composed by 100 individuals after 50 generations. The simulations were performed on HBP and on Fermi/Cineca supercomputers.  Results The granule cell models produced by the optimizer have properties very similar to those seen in the original 2001 model. The spike shape, ISI and I/O relationship could be nicely reproduced, although some mismatches occurred in some individuals.  Conclusions 1) The optimizer, with a sufficient number of features, objectives, and physiological data, can generate an arbitrary neuron with appropriate electroresponsive properties in a relatively short time. 2) Constraints on ionic channel ranges need to be properly set starting from VC data 3) Features may be extended in certain cases to account for specific (e.g. just-threshold) behaviors. 4) While neuronal models usually generate "canonical" solutions, the optimizer shows a set of different possible solutions encountered experimentally 5) It is useful to make a final selection among last-generation optimizer solutions in order to determine which model (or models) are best suited to represent the canonical case.  References - D’Angelo et al. (2001). “Theta-Frequency Bursting and Resonance in Cerebellar Granule Cells: Experimental Evidence and Modeling of a Slow K+-Dependent Mechanism.” J Neurosci 21: 759–70. - Diwakar et al. (2009) Axonal Na+ channels ensure fast spike activation and back-propagation in cerebellar granule cells J Neurophysiol 101:519-32 - Druckmann et al. (2007). A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data. Front Neurosci. 1: 7–18