Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study

Abstract One of the central goals of today’s neuroscience is to achieve the conceivably most accurate classification of neuron types in the mammalian brain. As part of this research effort, electrophysiologists commonly utilize current clamp techniques to gain a detailed characterization of the neur...

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Autores principales:	Ferenc Hernáth, Katalin Schlett, Attila Szücs
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f4e7df4bc4414fd9a2a0f24893266d1c
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spelling	oai:doaj.org-article:f4e7df4bc4414fd9a2a0f24893266d1c2021-12-02T15:09:15ZAlternative classifications of neurons based on physiological properties and synaptic responses, a computational study10.1038/s41598-019-49197-82045-2322https://doaj.org/article/f4e7df4bc4414fd9a2a0f24893266d1c2019-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-49197-8https://doaj.org/toc/2045-2322Abstract One of the central goals of today’s neuroscience is to achieve the conceivably most accurate classification of neuron types in the mammalian brain. As part of this research effort, electrophysiologists commonly utilize current clamp techniques to gain a detailed characterization of the neurons’ physiological properties. While this approach has been useful, it is not well understood whether neurons that share physiological properties of a particular phenotype would also operate consistently under the action of natural synaptic inputs. We approached this problem by simulating a biophysically diverse population of model neurons based on 3 generic phenotypes. We exposed the model neurons to two types of stimulation to investigate their voltage responses under conventional current step protocols and under simulated synaptic bombardment. We extracted standard physiological parameters from the voltage responses elicited by current step stimulation and spike arrival times descriptive of the model’s firing behavior under synaptic inputs. The biophysical phenotypes could be reliably identified using classification based on the ‘static’ physiological properties, but not the interspike interval-based parameters. However, the model neurons associated with the biophysically different phenotypes retained cell type specific features in the fine structure of their spike responses that allowed their accurate classification.Ferenc HernáthKatalin SchlettAttila SzücsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-16 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ferenc Hernáth Katalin Schlett Attila Szücs Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study
description	Abstract One of the central goals of today’s neuroscience is to achieve the conceivably most accurate classification of neuron types in the mammalian brain. As part of this research effort, electrophysiologists commonly utilize current clamp techniques to gain a detailed characterization of the neurons’ physiological properties. While this approach has been useful, it is not well understood whether neurons that share physiological properties of a particular phenotype would also operate consistently under the action of natural synaptic inputs. We approached this problem by simulating a biophysically diverse population of model neurons based on 3 generic phenotypes. We exposed the model neurons to two types of stimulation to investigate their voltage responses under conventional current step protocols and under simulated synaptic bombardment. We extracted standard physiological parameters from the voltage responses elicited by current step stimulation and spike arrival times descriptive of the model’s firing behavior under synaptic inputs. The biophysical phenotypes could be reliably identified using classification based on the ‘static’ physiological properties, but not the interspike interval-based parameters. However, the model neurons associated with the biophysically different phenotypes retained cell type specific features in the fine structure of their spike responses that allowed their accurate classification.
format	article
author	Ferenc Hernáth Katalin Schlett Attila Szücs
author_facet	Ferenc Hernáth Katalin Schlett Attila Szücs
author_sort	Ferenc Hernáth
title	Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study
title_short	Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study
title_full	Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study
title_fullStr	Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study
title_full_unstemmed	Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study
title_sort	alternative classifications of neurons based on physiological properties and synaptic responses, a computational study
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/f4e7df4bc4414fd9a2a0f24893266d1c
work_keys_str_mv	AT ferenchernath alternativeclassificationsofneuronsbasedonphysiologicalpropertiesandsynapticresponsesacomputationalstudy AT katalinschlett alternativeclassificationsofneuronsbasedonphysiologicalpropertiesandsynapticresponsesacomputationalstudy AT attilaszucs alternativeclassificationsofneuronsbasedonphysiologicalpropertiesandsynapticresponsesacomputationalstudy
_version_	1718387913354379264

Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study

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