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| 001 | 909578 | ||
| 005 | 20240313094856.0 | ||
| 037 | _ | _ | |a FZJ-2022-03260 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Linssen, Charl |0 P:(DE-Juel1)176305 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 31st Annual Computational Neuroscience Meeting, CNS*2022 |c Melbourne |d 2022-07-16 - 2022-07-20 |w Australia |
| 245 | _ | _ | |a From single-cell modeling to large-scale network dynamics with NEST Simulator |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1662624932_13999 |2 PUB:(DE-HGF) |x Invited |
| 520 | _ | _ | |a NEST 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. |
| 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 0 |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |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 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |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 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 5 |
| 700 | 1 | _ | |a Korcsak-Gorzo, Agnes |0 P:(DE-Juel1)176282 |b 1 |u fzj |
| 700 | 1 | _ | |a Albers, Jasper |0 P:(DE-Juel1)180539 |b 2 |u fzj |
| 700 | 1 | _ | |a Babu, Pooja |0 P:(DE-Juel1)186954 |b 3 |u fzj |
| 700 | 1 | _ | |a Böttcher, Joshua |0 P:(DE-Juel1)188317 |b 4 |u fzj |
| 700 | 1 | _ | |a Mitchell, Jessica |0 P:(DE-Juel1)172945 |b 5 |u fzj |
| 700 | 1 | _ | |a Wybo, Willem |0 P:(DE-Juel1)186881 |b 6 |u fzj |
| 700 | 1 | _ | |a Bruchertseifer, Jens |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Spreizer, Sebastian |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Terhorst, Dennis |0 P:(DE-Juel1)169778 |b 9 |u fzj |
| 856 | 4 | _ | |u https://www.cnsorg.org/cns-2022-tutorials#T1 |
| 909 | C | O | |o oai:juser.fz-juelich.de:909578 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)176305 |
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| 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 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)POF4-5231 |x 1 |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 3 |
| 914 | 1 | _ | |y 2022 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 2 |
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| 980 | _ | _ | |a conf |
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| 980 | _ | _ | |a UNRESTRICTED |
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