001050703 001__ 1050703
001050703 005__ 20260115203949.0
001050703 0247_ $$2doi$$a10.1007/s10827-025-00911-8
001050703 0247_ $$2datacite_doi$$a10.34734/FZJ-2026-00448
001050703 037__ $$aFZJ-2026-00448
001050703 082__ $$a610
001050703 1001_ $$0P:(DE-HGF)0$$aSkaar, JE. W.$$b0$$eCorresponding author
001050703 245__ $$aA simplified model of NMDA-receptor-mediated dynamics in leaky integrate-and-fire neurons
001050703 260__ $$aDordrecht [u.a.]$$bSpringer Science + Business Media B.V$$c2025
001050703 3367_ $$2DRIVER$$aarticle
001050703 3367_ $$2DataCite$$aOutput Types/Journal article
001050703 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1768473494_18321
001050703 3367_ $$2BibTeX$$aARTICLE
001050703 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001050703 3367_ $$00$$2EndNote$$aJournal Article
001050703 520__ $$aA 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.
001050703 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001050703 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001050703 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x2
001050703 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x3
001050703 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
001050703 7001_ $$0P:(DE-HGF)0$$aHaug, N.$$b1
001050703 7001_ $$0P:(DE-Juel1)169781$$aPlesser, Hans Ekkehard$$b2
001050703 773__ $$0PERI:(DE-600)1473055-8$$a10.1007/s10827-025-00911-8$$p475-487$$tJournal of computational neuroscience$$v53$$x0929-5313$$y2025
001050703 8564_ $$uhttps://juser.fz-juelich.de/record/1050703/files/Article.pdf$$yOpenAccess
001050703 909CO $$ooai:juser.fz-juelich.de:1050703$$popenaire$$popen_access$$pdriver$$pVDB$$pec_fundedresources$$pdnbdelivery
001050703 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169781$$aForschungszentrum Jülich$$b2$$kFZJ
001050703 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001050703 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
001050703 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x2
001050703 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x3
001050703 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2024-12-10
001050703 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001050703 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2024-12-10$$wger
001050703 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001050703 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ COMPUT NEUROSCI : 2022$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-10
001050703 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2024-12-10$$wger
001050703 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-10
001050703 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001050703 9801_ $$aFullTexts
001050703 980__ $$ajournal
001050703 980__ $$aVDB
001050703 980__ $$aUNRESTRICTED
001050703 980__ $$aI:(DE-Juel1)IAS-6-20130828