---
_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. Journal of Neuroscience Methods. 2021;357(6). doi:10.1016/j.jneumeth.2021.109125'
apa: 'Zhang, X., Schlögl, A., Vandael, D. H., & Jonas, P. M. (2021). MOD: A
novel machine-learning optimal-filtering method for accurate and efficient detection
of subthreshold synaptic events in vivo. Journal of Neuroscience Methods.
Elsevier. https://doi.org/10.1016/j.jneumeth.2021.109125'
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.” Journal of Neuroscience Methods.
Elsevier, 2021. https://doi.org/10.1016/j.jneumeth.2021.109125.'
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,” Journal of Neuroscience Methods, 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.”
Journal of Neuroscience Methods, vol. 357, no. 6, 109125, Elsevier, 2021,
doi:10.1016/j.jneumeth.2021.109125.'
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:
- access_level: open_access
checksum: 2a5800d91b96d08b525e17319dcd5e44
content_type: application/pdf
creator: dernst
date_created: 2021-04-19T08:30:22Z
date_updated: 2021-04-19T08:30:22Z
file_id: '9339'
file_name: 2021_JourNeuroscienceMeth_Zhang.pdf
file_size: 6924738
relation: main_file
success: 1
file_date_updated: 2021-04-19T08:30:22Z
has_accepted_license: '1'
intvolume: ' 357'
isi: 1
issue: '6'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
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'
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 357
year: '2021'
...