---
_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  for useful discussions, and R. Friedrich and S. Wiechert for sharing unpublished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. <i>Nature Computational Science</i>.
    2021;1(12):830-842. doi:<a href="https://doi.org/10.1038/s43588-021-00157-1">10.1038/s43588-021-00157-1</a>
  apa: Guzmán, J., Schlögl, A., Espinoza Martinez, C., Zhang, X., Suter, B., &#38;
    Jonas, P. M. (2021). How connectivity rules and synaptic properties shape the
    efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network.
    <i>Nature Computational Science</i>. Springer Nature. <a href="https://doi.org/10.1038/s43588-021-00157-1">https://doi.org/10.1038/s43588-021-00157-1</a>
  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.” <i>Nature Computational Science</i>. Springer Nature, 2021. <a href="https://doi.org/10.1038/s43588-021-00157-1">https://doi.org/10.1038/s43588-021-00157-1</a>.
  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,” <i>Nature Computational
    Science</i>, 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.” <i>Nature Computational Science</i>, vol. 1, no. 12, Springer Nature,
    2021, pp. 830–42, doi:<a href="https://doi.org/10.1038/s43588-021-00157-1">10.1038/s43588-021-00157-1</a>.
  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
file:
- access_level: open_access
  checksum: 9fec5b667909ef52be96d502e4f8c2ae
  content_type: application/pdf
  creator: patrickd
  date_created: 2022-06-02T12:51:07Z
  date_updated: 2022-06-18T22:30:03Z
  embargo: 2022-06-17
  file_id: '11430'
  file_name: Guzmanetal2021.pdf
  file_size: 1699466
  relation: main_file
- access_level: open_access
  checksum: 52a005b13a114e3c3a28fa6bbe8b1a8d
  content_type: application/pdf
  creator: patrickd
  date_created: 2022-06-02T12:53:47Z
  date_updated: 2022-06-18T22:30:03Z
  embargo: 2022-06-17
  file_id: '11431'
  file_name: Guzmanetal2021Suppl.pdf
  file_size: 3005651
  relation: supplementary_material
  title: Supplementary Material
file_date_updated: 2022-06-18T22:30:03Z
has_accepted_license: '1'
intvolume: '         1'
issue: '12'
keyword:
- general medicine
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.biorxiv.org/content/10.1101/647800
month: '12'
oa: 1
oa_version: Submitted Version
page: 830-842
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
publication: Nature Computational Science
publication_identifier:
  issn:
  - 2662-8457
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
  link:
  - relation: press_release
    url: https://ista.ac.at/en/news/spot-the-difference/
  record:
  - id: '10110'
    relation: software
    status: public
scopus_import: '1'
status: public
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
volume: 1
year: '2021'
...