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dc.contributor.authorMarín, Milagros
dc.contributor.authorSáez Lara, María José 
dc.contributor.authorRos Vidal, Eduardo 
dc.contributor.authorGarrido, Jesús A.
dc.date.accessioned2020-09-21T12:41:30Z
dc.date.available2020-09-21T12:41:30Z
dc.date.issued2020-07-14
dc.identifier.citationMarín M, Sáez-Lara MJ, Ros E and Garrido JA (2020) Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case o0f the Cerebellar Granule Cell. Front. Cell. Neurosci. 14:161. [doi: 10.3389/fncel.2020.00161]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/63497
dc.description.abstractBiologically 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.es_ES
dc.description.sponsorshipFEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento under the EmbBrain project A-TIC-276-UGR18es_ES
dc.description.sponsorshipUniversity of Granada under the Young Researchers Fellowshipes_ES
dc.description.sponsorshipMinisterio de Economia y Competitividad (MINECO)-FEDER TIN2016-81041-Res_ES
dc.description.sponsorshipEuropean Human Brain Project SGA2 ( H2020-RIA) 785907es_ES
dc.description.sponsorshipEuropean Human Brain Project SGA3 (European Commission) ( H2020-RIA) 945539es_ES
dc.description.sponsorshipCEREBIO P18-FR-2378es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Mediaes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectNeuron modeles_ES
dc.subjectGranule celles_ES
dc.subjectCerebellumes_ES
dc.subjectModel simplificationes_ES
dc.subjectSpiking resonancees_ES
dc.subjectPoint neurones_ES
dc.subjectAdaptive exponential integrate-and-firees_ES
dc.titleOptimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Celles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3389/fncel.2020.00161


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