A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.

A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that succes...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Anton V Chizhov, Lyle J Graham
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6de62923867745a2bb58d2c5763d70b6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6de62923867745a2bb58d2c5763d70b6
record_format	dspace
spelling	oai:doaj.org-article:6de62923867745a2bb58d2c5763d70b62021-12-02T19:58:04ZA strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.1553-734X1553-735810.1371/journal.pcbi.1009007https://doaj.org/article/6de62923867745a2bb58d2c5763d70b62021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009007https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications.Anton V ChizhovLyle J GrahamPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009007 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Anton V Chizhov Lyle J Graham A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.
description	A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications.
format	article
author	Anton V Chizhov Lyle J Graham
author_facet	Anton V Chizhov Lyle J Graham
author_sort	Anton V Chizhov
title	A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.
title_short	A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.
title_full	A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.
title_fullStr	A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.
title_full_unstemmed	A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.
title_sort	strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6de62923867745a2bb58d2c5763d70b6
work_keys_str_mv	AT antonvchizhov astrategyformappingbiophysicaltoabstractneuronalnetworkmodelsappliedtoprimaryvisualcortex AT lylejgraham astrategyformappingbiophysicaltoabstractneuronalnetworkmodelsappliedtoprimaryvisualcortex AT antonvchizhov strategyformappingbiophysicaltoabstractneuronalnetworkmodelsappliedtoprimaryvisualcortex AT lylejgraham strategyformappingbiophysicaltoabstractneuronalnetworkmodelsappliedtoprimaryvisualcortex
_version_	1718375782529630208

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.

Ejemplares similares