000909578 001__ 909578
000909578 005__ 20240313094856.0
000909578 037__ $$aFZJ-2022-03260
000909578 041__ $$aEnglish
000909578 1001_ $$0P:(DE-Juel1)176305$$aLinssen, Charl$$b0$$eCorresponding author$$ufzj
000909578 1112_ $$a31st Annual Computational Neuroscience Meeting, CNS*2022$$cMelbourne$$d2022-07-16 - 2022-07-20$$wAustralia
000909578 245__ $$aFrom single-cell modeling to large-scale network dynamics with NEST Simulator
000909578 260__ $$c2022
000909578 3367_ $$033$$2EndNote$$aConference Paper
000909578 3367_ $$2DataCite$$aOther
000909578 3367_ $$2BibTeX$$aINPROCEEDINGS
000909578 3367_ $$2DRIVER$$aconferenceObject
000909578 3367_ $$2ORCID$$aLECTURE_SPEECH
000909578 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1662624932_13999$$xInvited
000909578 520__ $$aNEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial provides hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most efficiently. Participants do not have to install software as all tools can be accessed via the cloud.First, we look at NEST Desktop [2], a web-based graphical user interface (GUI), which allows the exploration of essential concepts in computational neuroscience without the need to learn a programming language. This advances both the quality and speed of teaching in computational neuroscience. To get acquainted with the GUI, we will create and analyze abalanced two-population network.The model is then exported to a Jupyter notebook and endowed with a data-driven spatial connectivity profile of the cortex, enabling us to study the propagation of activity. Then, we make the synapses in the network plastic and let the network learn a reinforcement learning task, whereby the learning rule goes beyond pre-synaptic and post-synaptic spikes by addinga dopamine signal as a modulatory third factor. NESTML [3] makes it easy to express this and other advanced synaptic plasticity rules and neuron models, and automatically translates them into fast simulation code.More morphologically detailed models, with a large number of compartments and custom ion channels and receptor currents, can also be defined using NESTML. We first implement a simple dendritic layout and use it to perform a sequence discrimination task. Next, we implement a compartmental layout representing semi-independent subunits and recurrentlyconnect several such neurons to elicit an NMDA-spike driven network state.
000909578 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000909578 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000909578 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x2
000909578 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x3
000909578 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
000909578 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x5
000909578 7001_ $$0P:(DE-Juel1)176282$$aKorcsak-Gorzo, Agnes$$b1$$ufzj
000909578 7001_ $$0P:(DE-Juel1)180539$$aAlbers, Jasper$$b2$$ufzj
000909578 7001_ $$0P:(DE-Juel1)186954$$aBabu, Pooja$$b3$$ufzj
000909578 7001_ $$0P:(DE-Juel1)188317$$aBöttcher, Joshua$$b4$$ufzj
000909578 7001_ $$0P:(DE-Juel1)172945$$aMitchell, Jessica$$b5$$ufzj
000909578 7001_ $$0P:(DE-Juel1)186881$$aWybo, Willem$$b6$$ufzj
000909578 7001_ $$0P:(DE-HGF)0$$aBruchertseifer, Jens$$b7
000909578 7001_ $$0P:(DE-HGF)0$$aSpreizer, Sebastian$$b8
000909578 7001_ $$0P:(DE-Juel1)169778$$aTerhorst, Dennis$$b9$$ufzj
000909578 8564_ $$uhttps://www.cnsorg.org/cns-2022-tutorials#T1
000909578 909CO $$ooai:juser.fz-juelich.de:909578$$pec_fundedresources$$pVDB$$popenaire
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176305$$aForschungszentrum Jülich$$b0$$kFZJ
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176282$$aForschungszentrum Jülich$$b1$$kFZJ
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180539$$aForschungszentrum Jülich$$b2$$kFZJ
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186954$$aForschungszentrum Jülich$$b3$$kFZJ
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188317$$aForschungszentrum Jülich$$b4$$kFZJ
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172945$$aForschungszentrum Jülich$$b5$$kFZJ
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186881$$aForschungszentrum Jülich$$b6$$kFZJ
000909578 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169778$$aForschungszentrum Jülich$$b9$$kFZJ
000909578 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$$x0
000909578 9131_ $$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$$vNeuromorphic Computing and Network Dynamics$$x1
000909578 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$$x2
000909578 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x3
000909578 9141_ $$y2022
000909578 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000909578 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000909578 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000909578 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x3
000909578 980__ $$aconf
000909578 980__ $$aVDB
000909578 980__ $$aI:(DE-Juel1)INM-6-20090406
000909578 980__ $$aI:(DE-Juel1)INM-10-20170113
000909578 980__ $$aI:(DE-Juel1)IAS-6-20130828
000909578 980__ $$aI:(DE-Juel1)JSC-20090406
000909578 980__ $$aUNRESTRICTED
000909578 981__ $$aI:(DE-Juel1)IAS-6-20130828