@misc{10481/63497,
year = {2020},
month = {7},
url = {http://hdl.handle.net/10481/63497},
abstract = {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
and thus prevent these models from being part of brain-area models including thousands
or even millions of neurons. The cerebellar input layer represents a canonical example
of large scale networks. In particular, the cerebellar granule cells, the most numerous
cells in the whole mammalian brain, have been proposed as playing a pivotal role in
the creation of somato-sensorial information representations. Enhanced burst frequency
(spiking resonance) in the granule cells has been proposed as facilitating the input signal
transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell
feature in the operation of the granular layer remains largely unclear. This study aims to
develop a methodological pipeline for creating neuron models that maintain biological
realism and computational efficiency whilst capturing essential aspects of single-neuron
processing. Therefore, we selected a light computational neuron model template (the
adaptive-exponential integrate-and-fire model), whose parameters were progressively
refined using an automatic parameter tuning with evolutionary algorithms (EAs). The
resulting point-neuron models are suitable for reproducing the main firing properties
of a realistic granule cell from electrophysiological measurements, including the spiking
resonance at the theta-frequency band, repetitive firing according to a specified intensityfrequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly,
the proposed model also reproduced some other emergent properties (namely, silent at
rest, rheobase and negligible adaptation under depolarizing currents) even though these
properties were not set in the EA as a target in the fitness function (FF), proving that
these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are
appropriate for future research of the functional implication of bursting resonance at
the theta band in large-scale granular layer network models.},
organization = {FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento under the EmbBrain project 	
A-TIC-276-UGR18},
organization = {University of Granada under the Young Researchers Fellowship},
organization = {Ministerio de Economia y Competitividad (MINECO)-FEDER 	
TIN2016-81041-R},
organization = {European Human Brain Project SGA2 ( H2020-RIA) 	
785907},
organization = {European Human Brain Project SGA3 (European Commission) ( H2020-RIA) 	
945539},
organization = {CEREBIO 	
P18-FR-2378},
publisher = {Frontiers Media},
keywords = {Neuron model},
keywords = {Granule cell},
keywords = {Cerebellum},
keywords = {Model simplification},
keywords = {Spiking resonance},
keywords = {Point neuron},
keywords = {Adaptive exponential integrate-and-fire},
title = {Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell},
doi = {10.3389/fncel.2020.00161},
author = {Marín, Milagros and Sáez Lara, María José and Ros Vidal, Eduardo and Garrido, Jesús A.},
}