---
_id: '10816'
abstract:
- lang: eng
text: Pattern separation is a fundamental brain computation that converts small
differences in input patterns into large differences in output patterns. Several
synaptic mechanisms of pattern separation have been proposed, including code expansion,
inhibition and plasticity; however, which of these mechanisms play a role in the
entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation
circuit, remains unclear. Here we show that a biologically realistic, full-scale
EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive
inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator.
Both external gamma-modulated inhibition and internal lateral inhibition mediated
by PV+-INs substantially contributed to pattern separation. Both local connectivity
and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness.
Similarly, mossy fiber synapses with conditional detonator properties contributed
to pattern separation. By contrast, perforant path synapses with Hebbian synaptic
plasticity and direct EC–CA3 connection shifted the network towards pattern completion.
Our results demonstrate that the specific properties of cells and synapses optimize
higher-order computations in biological networks and might be useful to improve
the deep learning capabilities of technical networks.
acknowledged_ssus:
- _id: SSU
acknowledgement: We thank A. Aertsen, N. Kopell, W. Maass, A. Roth, F. Stella and
T. Vogels for critically reading earlier versions of the manuscript. We are grateful
to F. Marr and C. Altmutter for excellent technical assistance, E. Kralli-Beller
for manuscript editing, and the Scientific Service Units of IST Austria for efficient
support. Finally, we thank T. Carnevale, L. Erdös, M. Hines, D. Nykamp and D. Schröder
for useful discussions, and R. Friedrich and S. Wiechert for sharing unpublished
data. This project received funding from the European Research Council (ERC) under
the European Union’s Horizon 2020 research and innovation programme (grant agreement
no. 692692, P.J.) and the Fond zur Förderung der Wissenschaftlichen Forschung (Z
312-B27, Wittgenstein award to P.J. and P 31815 to S.J.G.).
article_processing_charge: No
article_type: original
author:
- first_name: José
full_name: Guzmán, José
id: 30CC5506-F248-11E8-B48F-1D18A9856A87
last_name: Guzmán
orcid: 0000-0003-2209-5242
- first_name: Alois
full_name: Schlögl, Alois
id: 45BF87EE-F248-11E8-B48F-1D18A9856A87
last_name: Schlögl
orcid: 0000-0002-5621-8100
- first_name: 'Claudia '
full_name: 'Espinoza Martinez, Claudia '
id: 31FFEE2E-F248-11E8-B48F-1D18A9856A87
last_name: Espinoza Martinez
orcid: 0000-0003-4710-2082
- first_name: Xiaomin
full_name: Zhang, Xiaomin
id: 423EC9C2-F248-11E8-B48F-1D18A9856A87
last_name: Zhang
- first_name: Benjamin
full_name: Suter, Benjamin
id: 4952F31E-F248-11E8-B48F-1D18A9856A87
last_name: Suter
orcid: 0000-0002-9885-6936
- first_name: Peter M
full_name: Jonas, Peter M
id: 353C1B58-F248-11E8-B48F-1D18A9856A87
last_name: Jonas
orcid: 0000-0001-5001-4804
citation:
ama: Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. How connectivity
rules and synaptic properties shape the efficacy of pattern separation in the
entorhinal cortex–dentate gyrus–CA3 network. Nature Computational Science.
2021;1(12):830-842. doi:10.1038/s43588-021-00157-1
apa: Guzmán, J., Schlögl, A., Espinoza Martinez, C., Zhang, X., Suter, B., &
Jonas, P. M. (2021). How connectivity rules and synaptic properties shape the
efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network.
Nature Computational Science. Springer Nature. https://doi.org/10.1038/s43588-021-00157-1
chicago: Guzmán, José, Alois Schlögl, Claudia Espinoza Martinez, Xiaomin Zhang,
Benjamin Suter, and Peter M Jonas. “How Connectivity Rules and Synaptic Properties
Shape the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3
Network.” Nature Computational Science. Springer Nature, 2021. https://doi.org/10.1038/s43588-021-00157-1.
ieee: J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, and P. M.
Jonas, “How connectivity rules and synaptic properties shape the efficacy of pattern
separation in the entorhinal cortex–dentate gyrus–CA3 network,” Nature Computational
Science, vol. 1, no. 12. Springer Nature, pp. 830–842, 2021.
ista: Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. 2021.
How connectivity rules and synaptic properties shape the efficacy of pattern separation
in the entorhinal cortex–dentate gyrus–CA3 network. Nature Computational Science.
1(12), 830–842.
mla: Guzmán, José, et al. “How Connectivity Rules and Synaptic Properties Shape
the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3
Network.” Nature Computational Science, vol. 1, no. 12, Springer Nature,
2021, pp. 830–42, doi:10.1038/s43588-021-00157-1.
short: J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, P.M. Jonas,
Nature Computational Science 1 (2021) 830–842.
date_created: 2022-03-04T08:32:36Z
date_published: 2021-12-16T00:00:00Z
date_updated: 2023-08-10T22:30:10Z
day: '16'
ddc:
- '610'
department:
- _id: PeJo
doi: 10.1038/s43588-021-00157-1
ec_funded: 1
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intvolume: ' 1'
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keyword:
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language:
- iso: eng
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url: https://www.biorxiv.org/content/10.1101/647800
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project:
- _id: 25B7EB9E-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '692692'
name: Biophysics and circuit function of a giant cortical glumatergic synapse
- _id: 25C5A090-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00312
name: The Wittgenstein Prize
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- 2662-8457
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publisher: Springer Nature
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related_material:
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url: https://ista.ac.at/en/news/spot-the-difference/
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title: How connectivity rules and synaptic properties shape the efficacy of pattern
separation in the entorhinal cortex–dentate gyrus–CA3 network
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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...