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Quantitative properties of a feedback circuit predict frequency-dependent pattern separation

Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of n...

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Detalles Bibliográficos
Autores principales: Braganza, Oliver, Mueller-Komorowska, Daniel, Kelly, Tony, Beck, Heinz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7032930/
https://www.ncbi.nlm.nih.gov/pubmed/32077850
http://dx.doi.org/10.7554/eLife.53148
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author Braganza, Oliver
Mueller-Komorowska, Daniel
Kelly, Tony
Beck, Heinz
author_facet Braganza, Oliver
Mueller-Komorowska, Daniel
Kelly, Tony
Beck, Heinz
author_sort Braganza, Oliver
collection PubMed
description Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of netfeedback inhibition in the mouse dentate gyrus, a region critically involved in pattern separation. Feedback inhibition is recruited steeply with a low dynamic range (0% to 4% of active GCs), and with a non-uniform spatial profile. Additionally, net feedback inhibition shows frequency-dependent facilitation, driven by strongly facilitating mossy fiber inputs. Computational analyses show a significant contribution of the feedback circuit to pattern separation of theta modulated inputs, even within individual theta cycles. Moreover, pattern separation was selectively boosted at gamma frequencies, in particular for highly similar inputs. This effect was highly robust, suggesting that frequency-dependent pattern separation is a key feature of the feedback inhibitory microcircuit.
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spelling pubmed-70329302020-02-24 Quantitative properties of a feedback circuit predict frequency-dependent pattern separation Braganza, Oliver Mueller-Komorowska, Daniel Kelly, Tony Beck, Heinz eLife Neuroscience Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of netfeedback inhibition in the mouse dentate gyrus, a region critically involved in pattern separation. Feedback inhibition is recruited steeply with a low dynamic range (0% to 4% of active GCs), and with a non-uniform spatial profile. Additionally, net feedback inhibition shows frequency-dependent facilitation, driven by strongly facilitating mossy fiber inputs. Computational analyses show a significant contribution of the feedback circuit to pattern separation of theta modulated inputs, even within individual theta cycles. Moreover, pattern separation was selectively boosted at gamma frequencies, in particular for highly similar inputs. This effect was highly robust, suggesting that frequency-dependent pattern separation is a key feature of the feedback inhibitory microcircuit. eLife Sciences Publications, Ltd 2020-02-20 /pmc/articles/PMC7032930/ /pubmed/32077850 http://dx.doi.org/10.7554/eLife.53148 Text en © 2020, Braganza et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Braganza, Oliver
Mueller-Komorowska, Daniel
Kelly, Tony
Beck, Heinz
Quantitative properties of a feedback circuit predict frequency-dependent pattern separation
title Quantitative properties of a feedback circuit predict frequency-dependent pattern separation
title_full Quantitative properties of a feedback circuit predict frequency-dependent pattern separation
title_fullStr Quantitative properties of a feedback circuit predict frequency-dependent pattern separation
title_full_unstemmed Quantitative properties of a feedback circuit predict frequency-dependent pattern separation
title_short Quantitative properties of a feedback circuit predict frequency-dependent pattern separation
title_sort quantitative properties of a feedback circuit predict frequency-dependent pattern separation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7032930/
https://www.ncbi.nlm.nih.gov/pubmed/32077850
http://dx.doi.org/10.7554/eLife.53148
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