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| Home > Publications database > Rapid prototyping in spiking neural network modeling with NESTML and NEST Desktop > print |
| Home > Publications database > Rapid prototyping in spiking neural network modeling with NESTML and NEST Desktop > print |
| 001 | 1034642 | ||
| 005 | 20250203103352.0 | ||
| 037 | _ | _ | |a FZJ-2024-07401 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Spreizer, Sebastian |0 P:(DE-Juel1)188319 |b 0 |
| 111 | 2 | _ | |a Bernstein Conference 2024 |c Frankfurt am Main |d 2024-09-29 - 2024-10-02 |w Germany |
| 245 | _ | _ | |a Rapid prototyping in spiking neural network modeling with NESTML and NEST Desktop |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1736512237_6153 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a NEST is a well-established open source simulator providing researchers in computational neuroscience with the ability to perform high-performance simulations of spiking neuronal networks. However, as the simulation kernel is written in C++ for performance reasons, this makes it challenging for researchers without a programming background to customize and extend the built-in neuron and synapse models.In order to satisfy both the need for high-performance simulation codes and a good user experience in terms of easy-to-use modeling of neurons and synapses, NESTML was created as a domain-specific language with an unambiguous syntax. Alongside the language itself, a toolchain was developed that parses the model, analyzes the underlying equations, and performs code generation. The code generated by NESTML can be used in simulations of brain activity on several platforms, in particular the NEST Simulator. However, specification of the network architecture requires using the NEST Python API, which still requires users to be skilled at programming. This poses a problem for beginners, as experience shows that they usually have little to no coding experience.This issue was addressed by the development of NEST Desktop, a graphical user interface that serves as an intuitive, programming-free interface to NEST. The interface is easily installed and accessed via an internet browser on the computers of individual users, or in cloud-based deployments, which have already been proven in the field in student courses at universities across Europe.Here, we demonstrate for the first time the integration of all three components: NEST Desktop, NESTML and NEST. As a result, researchers and students can customize existing models or develop new ones using NESTML, and have them instantly available to create networks using the graphical interface of NEST Desktop, before simulating them efficiently using NEST as a back-end. The combined strength of these components creates a low-barrier environment for rapid prototyping and exploration of neuron, synapse and network models. |
| 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 0 |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 1 |
| 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 2 |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 3 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 4 |
| 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 5 |
| 536 | _ | _ | |a ACA - Advanced Computing Architectures (SO-092) |0 G:(DE-HGF)SO-092 |c SO-092 |x 6 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 7 |
| 536 | _ | _ | |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |c PHD-NO-GRANT-20170405 |x 8 |
| 700 | 1 | _ | |a Linssen, Charl |0 P:(DE-Juel1)176305 |b 1 |
| 700 | 1 | _ | |a Babu, Pooja |0 P:(DE-Juel1)186954 |b 2 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 3 |
| 700 | 1 | _ | |a Morrison, Abigail |0 P:(DE-Juel1)151166 |b 4 |
| 700 | 1 | _ | |a Weyers, Benjamin |0 P:(DE-HGF)0 |b 5 |
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| 910 | 1 | _ | |a University of Trier |0 I:(DE-HGF)0 |b 0 |6 P:(DE-Juel1)188319 |
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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 0 |
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| 914 | 1 | _ | |y 2024 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 1 |
| 980 | _ | _ | |a poster |
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| 980 | _ | _ | |a UNRESTRICTED |
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