001     1050703
005     20260115203949.0
024 7 _ |a 10.1007/s10827-025-00911-8
|2 doi
024 7 _ |a 10.34734/FZJ-2026-00448
|2 datacite_doi
037 _ _ |a FZJ-2026-00448
082 _ _ |a 610
100 1 _ |a Skaar, JE. W.
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a A simplified model of NMDA-receptor-mediated dynamics in leaky integrate-and-fire neurons
260 _ _ |a Dordrecht [u.a.]
|c 2025
|b Springer Science + Business Media B.V
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1768473494_18321
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a A model for NMDA-receptor-mediated synaptic currents in leaky integrate-and-fire neurons, first proposed by Wang (J Neurosci, 1999), has been widely studied in computational neuroscience. The model features a fast rise in the NMDA conductance upon spikes in a pre-synaptic neuron followed by a slow decay. In a general implementation of this model which allows for arbitrary network connectivity and delay distributions, the summed NMDA current from all neurons in a pre-synaptic population cannot be simulated in aggregated form. Simulating each synapse separately is prohibitively slow for all but small networks, which has largely limited the use of the model to fully connected networks with identical delays, for which an efficient simulation scheme exists. We propose an approximation to the original model that can be efficiently simulated for arbitrary network connectivity and delay distributions. Our results demonstrate that the approximation incurs minimal error and preserves network dynamics. We further use the approximate model to explore binary decision making in sparsely coupled networks.
536 _ _ |a 5231 - Neuroscientific Foundations (POF4-523)
|0 G:(DE-HGF)POF4-5231
|c POF4-523
|f POF IV
|x 0
536 _ _ |a 5232 - Computational Principles (POF4-523)
|0 G:(DE-HGF)POF4-5232
|c POF4-523
|f POF IV
|x 1
536 _ _ |a 5234 - Emerging NC Architectures (POF4-523)
|0 G:(DE-HGF)POF4-5234
|c POF4-523
|f POF IV
|x 2
536 _ _ |a 5235 - Digitization of Neuroscience and User-Community Building (POF4-523)
|0 G:(DE-HGF)POF4-5235
|c POF4-523
|f POF IV
|x 3
536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
|0 G:(EU-Grant)945539
|c 945539
|f H2020-SGA-FETFLAG-HBP-2019
|x 4
700 1 _ |a Haug, N.
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Plesser, Hans Ekkehard
|0 P:(DE-Juel1)169781
|b 2
773 _ _ |a 10.1007/s10827-025-00911-8
|0 PERI:(DE-600)1473055-8
|p 475-487
|t Journal of computational neuroscience
|v 53
|y 2025
|x 0929-5313
856 4 _ |u https://juser.fz-juelich.de/record/1050703/files/Article.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1050703
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)169781
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-523
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5231
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-523
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5232
|x 1
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-523
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5234
|x 2
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-523
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5235
|x 3
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2024-12-10
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2024-12-10
915 _ _ |a DEAL Springer
|0 StatID:(DE-HGF)3002
|2 StatID
|d 2024-12-10
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-10
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2024-12-10
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b J COMPUT NEUROSCI : 2022
|d 2024-12-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-10
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2024-12-10
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-10
920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
|k IAS-6
|l Computational and Systems Neuroscience
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IAS-6-20130828


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21