Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1783499565346848768 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7032930 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT braganzaoliver quantitativepropertiesofafeedbackcircuitpredictfrequencydependentpatternseparation AT muellerkomorowskadaniel quantitativepropertiesofafeedbackcircuitpredictfrequencydependentpatternseparation AT kellytony quantitativepropertiesofafeedbackcircuitpredictfrequencydependentpatternseparation AT beckheinz quantitativepropertiesofafeedbackcircuitpredictfrequencydependentpatternseparation |