---
_id: '9329'
abstract:
- lang: eng
  text: "Background: To understand information coding in single neurons, it is necessary
    to analyze subthreshold synaptic events, action potentials (APs), and their interrelation
    in different behavioral states. However, detecting excitatory postsynaptic potentials
    (EPSPs) or currents (EPSCs) in behaving animals remains challenging, because of
    unfavorable signal-to-noise ratio, high frequency, fluctuating amplitude, and
    variable time course of synaptic events.\r\nNew method: We developed a method
    for synaptic event detection, termed MOD (Machine-learning Optimal-filtering Detection-procedure),
    which combines concepts of supervised machine learning and optimal Wiener filtering.
    Experts were asked to manually score short epochs of data. The algorithm was trained
    to obtain the optimal filter coefficients of a Wiener filter and the optimal detection
    threshold. Scored and unscored data were then processed with the optimal filter,
    and events were detected as peaks above threshold.\r\nResults: We challenged MOD
    with EPSP traces in vivo in mice during spatial navigation and EPSC traces in
    vitro in slices under conditions of enhanced transmitter release. The area under
    the curve (AUC) of the receiver operating characteristics (ROC) curve was, on
    average, 0.894 for in vivo and 0.969 for in vitro data sets, indicating high detection
    accuracy and efficiency.\r\nComparison with existing methods: When benchmarked
    using a (1 − AUC)−1 metric, MOD outperformed previous methods (template-fit, deconvolution,
    and Bayesian methods) by an average factor of 3.13 for in vivo data sets, but
    showed comparable (template-fit, deconvolution) or higher (Bayesian) computational
    efficacy.\r\nConclusions: MOD may become an important new tool for large-scale,
    real-time analysis of synaptic activity."
acknowledged_ssus:
- _id: SSU
acknowledgement: This project has received funding from the European Research Council
  (ERC) under the European Union’s Horizon 2020 research and innovation programme
  (grant agreement number 692692 to P.J.) and the Fond zur Förderung der Wissenschaftlichen
  Forschung (Z 312-B27, Wittgenstein award to P.J.). We thank Drs. Jozsef Csicsvari,
  Christoph Lampert, and Federico Stella for critically reading previous manuscript
  versions. We are also grateful to Drs. Josh Merel and Ben Shababo for their help
  with applying the Bayesian detection method to our data. We also thank Florian Marr
  for technical assistance, Eleftheria Kralli-Beller for manuscript editing, and the
  Scientific Service Units of IST Austria for efficient support.
article_number: '109125'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Xiaomin
  full_name: Zhang, Xiaomin
  id: 423EC9C2-F248-11E8-B48F-1D18A9856A87
  last_name: Zhang
- first_name: Alois
  full_name: Schlögl, Alois
  id: 45BF87EE-F248-11E8-B48F-1D18A9856A87
  last_name: Schlögl
  orcid: 0000-0002-5621-8100
- first_name: David H
  full_name: Vandael, David H
  id: 3AE48E0A-F248-11E8-B48F-1D18A9856A87
  last_name: Vandael
  orcid: 0000-0001-7577-1676
- 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: 'Zhang X, Schlögl A, Vandael DH, Jonas PM. MOD: A novel machine-learning optimal-filtering
    method for accurate and efficient detection of subthreshold synaptic events in
    vivo. <i>Journal of Neuroscience Methods</i>. 2021;357(6). doi:<a href="https://doi.org/10.1016/j.jneumeth.2021.109125">10.1016/j.jneumeth.2021.109125</a>'
  apa: 'Zhang, X., Schlögl, A., Vandael, D. H., &#38; Jonas, P. M. (2021). MOD: A
    novel machine-learning optimal-filtering method for accurate and efficient detection
    of subthreshold synaptic events in vivo. <i>Journal of Neuroscience Methods</i>.
    Elsevier. <a href="https://doi.org/10.1016/j.jneumeth.2021.109125">https://doi.org/10.1016/j.jneumeth.2021.109125</a>'
  chicago: 'Zhang, Xiaomin, Alois Schlögl, David H Vandael, and Peter M Jonas. “MOD:
    A Novel Machine-Learning Optimal-Filtering Method for Accurate and Efficient Detection
    of Subthreshold Synaptic Events in Vivo.” <i>Journal of Neuroscience Methods</i>.
    Elsevier, 2021. <a href="https://doi.org/10.1016/j.jneumeth.2021.109125">https://doi.org/10.1016/j.jneumeth.2021.109125</a>.'
  ieee: 'X. Zhang, A. Schlögl, D. H. Vandael, and P. M. Jonas, “MOD: A novel machine-learning
    optimal-filtering method for accurate and efficient detection of subthreshold
    synaptic events in vivo,” <i>Journal of Neuroscience Methods</i>, vol. 357, no.
    6. Elsevier, 2021.'
  ista: 'Zhang X, Schlögl A, Vandael DH, Jonas PM. 2021. MOD: A novel machine-learning
    optimal-filtering method for accurate and efficient detection of subthreshold
    synaptic events in vivo. Journal of Neuroscience Methods. 357(6), 109125.'
  mla: 'Zhang, Xiaomin, et al. “MOD: A Novel Machine-Learning Optimal-Filtering Method
    for Accurate and Efficient Detection of Subthreshold Synaptic Events in Vivo.”
    <i>Journal of Neuroscience Methods</i>, vol. 357, no. 6, 109125, Elsevier, 2021,
    doi:<a href="https://doi.org/10.1016/j.jneumeth.2021.109125">10.1016/j.jneumeth.2021.109125</a>.'
  short: X. Zhang, A. Schlögl, D.H. Vandael, P.M. Jonas, Journal of Neuroscience Methods
    357 (2021).
date_created: 2021-04-18T22:01:39Z
date_published: 2021-03-09T00:00:00Z
date_updated: 2023-08-07T14:36:14Z
day: '09'
ddc:
- '570'
department:
- _id: PeJo
- _id: ScienComp
doi: 10.1016/j.jneumeth.2021.109125
ec_funded: 1
external_id:
  isi:
  - '000661088500005'
file:
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has_accepted_license: '1'
intvolume: '       357'
isi: 1
issue: '6'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
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: Journal of Neuroscience Methods
publication_identifier:
  eissn:
  - 1872-678X
  issn:
  - 0165-0270
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'MOD: A novel machine-learning optimal-filtering method for accurate and efficient
  detection of subthreshold synaptic events in vivo'
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volume: 357
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