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-max 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 Gi-max 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 Gi-max 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.

Parameter tuning differentiates granule cell subtypes enriching the repertoire of retransmission properties at the cerebellum input stage

“…The model derived from previous ones (D'Angelo et al, 2001;Nieus et al, 2006;Diwakar et al, 2009) and was upgraded to account for advanced mechanisms of spike generation and conduction in the axon (Dover et al, 2016). The ionic channels were distributed among dendrites, soma, hillock, axonal initial segment (AIS), ascending axon (AA) and parallel fibers (PF) (Masoli et al, 2017). The maximum ionic conductances (Gmax) were optimized using routines based on genetic algorithms (BluePyOpt) (Deb et al, 2002;Zitzler and Künzli, 2004;Van Geit et al, 2016;Masoli et al, 2017).…”

“…The ionic channels were distributed among dendrites, soma, hillock, axonal initial segment (AIS), ascending axon (AA) and parallel fibers (PF) (Masoli et al, 2017). The maximum ionic conductances (Gmax) were optimized using routines based on genetic algorithms (BluePyOpt) (Deb et al, 2002;Zitzler and Künzli, 2004;Van Geit et al, 2016;Masoli et al, 2017). The optimization was run iteratively to improve models fitness to an experimental "template" through the automatic evaluation of "feature" values parameterizing the spike properties of the template.…”

“…The data were taken at three current injection steps (10, 16 and 22 pA), using the "Electrophys Feature Extraction Library" (EFel) (https://github.com/BlueBrain/eFEL) (Van Geit, 2015). Accordingly to (Masoli et al, 2017), the features comprised resting membrane potential, spike width and height, fast and slow AHP depth, mean spike frequency, time-to-first spike, adaptation and coefficient of variation of the interspike interval (ISI-CV) ( Table 3 in Supplemental Material).…”

“…After each optimization, the best individuals of the last generation were used as a guidance to reshape the ionic conductance ranges and to improve the fitness values. The parameter range for maximum ionic conductances was limited by physiological measurements as in (Masoli et al, 2017). The results of optimization were validated by evaluating spike generation and conduction.…”

“…In a recent work (Masoli et al, 2017), automatic optimization procedures (Druckmann et al, 2007;Druckmann et al, 2011;Van Geit et al, 2016;Migliore et al, 2018) were applied to GrC models yielding a family of solutions with maximum ionic conductance values falling within the range of physiological variability. During short current injections (500-800 ms, as used in previous experiments), all GrC models conformed to the canonical firing pattern but, unexpectedly, prolonged current injections (2 sec) revealed a rich repertoire of adaptation properties.…”