@misc{10481/69540,
year = {2021},
month = {6},
url = {http://hdl.handle.net/10481/69540},
abstract = {This article extends a recent methodological workflow for creating realistic and
computationally efficient neuron models whilst capturing essential aspects of singleneuron
dynamics. We overcome the intrinsic limitations of the extant optimization
methods by proposing an alternative optimization component based on multimodal
algorithms. This approach can natively explore a diverse population of neuron model
configurations. In contrast to methods that focus on a single global optimum, the
multimodal method allows directly obtaining a set of promising solutions for a single
but complex multi-feature objective function. The final sparse population of candidate
solutions has to be analyzed and evaluated according to the biological plausibility and
their objective to the target features by the expert. In order to illustrate the value of
this approach, we base our proposal on the optimization of cerebellar granule cell
(GrC) models that replicate the essential properties of the biological cell. Our results
show the emerging variability of plausible sets of values that this type of neuron can
adopt underlying complex spiking characteristics. Also, the set of selected cerebellar
GrC models captured spiking dynamics closer to the reference model than the single
model obtained with off-the-shelf parameter optimization algorithms used in our previous
article. The method hereby proposed represents a valuable strategy for adjusting a varied
population of realistic and simplified neuron models. It can be applied to other kinds of
neuron models and biological contexts.},
organization = {EU Grant HBP 

H2020 SGA3. 945539},
organization = {Spanish Ministry of Economy and Competitiveness 

RTI2018-095993-B-I00},
organization = {national grant INTSENSO 

MICINN-FEDER-PID2019-109991GB-I00},
organization = {Junta de Andalucia

CEREBIO: JA FEDER P18-FR-2378
P18-RT-1193
A-TIC-276-UGR18},
organization = {University of Almeria 

UAL18-TIC-A020-B},
publisher = {Frontiers Research Foundation},
keywords = {Granule cell},
keywords = {Cerebellum},
keywords = {Neuron model},
keywords = {Optimization},
keywords = {Adaptive exponential integrate-and-fire},
keywords = {Multimodal evolutionary algorithm},
title = {On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell},
doi = {10.3389/fninf.2021.663797},
author = {Marín Alejo, Milagros and Cruz, Nicolás C. and Martínez Ortigosa, Eva and Sáez Lara, María José and Garrido Alcázar, Jesús Alberto and Carrillo Sánchez, Richard Rafael},
}