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Article: Active dendrites enable strong but sparse inputs to determine orientation selectivity

TitleActive dendrites enable strong but sparse inputs to determine orientation selectivity
Authors
KeywordsDendrite
Dendritic spike
Pyramidal cell
Synaptic integration
Visual cortex
Issue Date2021
Citation
Proceedings of the National Academy of Sciences of the United States of America, 2021, v. 118, n. 30, article no. e2017339118 How to Cite?
AbstractThe dendrites of neocortical pyramidal neurons are excitable. However, it is unknown how synaptic inputs engage nonlinear dendritic mechanisms during sensory processing in vivo, and how they in turn influence action potential output. Here, we provide a quantitative account of the relationship between synaptic inputs, nonlinear dendritic events, and action potential output. We developed a detailed pyramidal neuron model constrained by in vivo dendritic recordings. We drive this model with realistic input patterns constrained by sensory responses measured in vivo and connectivity measured in vitro. We show mechanistically that under realistic conditions, dendritic Na+ and NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a surprisingly small number of strong synaptic inputs, in some cases even by single synapses. We predict that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output. Active dendrites therefore allow smaller sub-circuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.
Persistent Identifierhttp://hdl.handle.net/10722/342976
ISSN
2023 Impact Factor: 9.4
2023 SCImago Journal Rankings: 3.737

 

DC FieldValueLanguage
dc.contributor.authorGoetz, Lea-
dc.contributor.authorRoth, Arnd-
dc.contributor.authorHäusser, Michael-
dc.date.accessioned2024-05-10T09:04:28Z-
dc.date.available2024-05-10T09:04:28Z-
dc.date.issued2021-
dc.identifier.citationProceedings of the National Academy of Sciences of the United States of America, 2021, v. 118, n. 30, article no. e2017339118-
dc.identifier.issn0027-8424-
dc.identifier.urihttp://hdl.handle.net/10722/342976-
dc.description.abstractThe dendrites of neocortical pyramidal neurons are excitable. However, it is unknown how synaptic inputs engage nonlinear dendritic mechanisms during sensory processing in vivo, and how they in turn influence action potential output. Here, we provide a quantitative account of the relationship between synaptic inputs, nonlinear dendritic events, and action potential output. We developed a detailed pyramidal neuron model constrained by in vivo dendritic recordings. We drive this model with realistic input patterns constrained by sensory responses measured in vivo and connectivity measured in vitro. We show mechanistically that under realistic conditions, dendritic Na+ and NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a surprisingly small number of strong synaptic inputs, in some cases even by single synapses. We predict that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output. Active dendrites therefore allow smaller sub-circuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.-
dc.languageeng-
dc.relation.ispartofProceedings of the National Academy of Sciences of the United States of America-
dc.subjectDendrite-
dc.subjectDendritic spike-
dc.subjectPyramidal cell-
dc.subjectSynaptic integration-
dc.subjectVisual cortex-
dc.titleActive dendrites enable strong but sparse inputs to determine orientation selectivity-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1073/pnas.2017339118-
dc.identifier.pmid34301882-
dc.identifier.scopuseid_2-s2.0-85111132748-
dc.identifier.volume118-
dc.identifier.issue30-
dc.identifier.spagearticle no. e2017339118-
dc.identifier.epagearticle no. e2017339118-
dc.identifier.eissn1091-6490-

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