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| Home > Publications database > Nidus by NEST: A morphologically detailed neural network simulator for many core high performance computer architectures > print |
| Home > Publications database > Nidus by NEST: A morphologically detailed neural network simulator for many core high performance computer architectures > print |
| 001 | 825603 | ||
| 005 | 20210129225358.0 | ||
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| 037 | _ | _ | |a FZJ-2016-08048 |
| 100 | 1 | _ | |a Klijn, Wouter |0 P:(DE-Juel1)168169 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Human Brain Project Summit 2016 |c Florence |d 2016-10-12 - 2016-10-15 |w Italy |
| 245 | _ | _ | |a Nidus by NEST: A morphologically detailed neural network simulator for many core high performance computer architectures |
| 260 | _ | _ | |c 2016 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a The Nidus multicompartment neural network simulator will enable new scales and classes of morphologically detailed network simulations on current and future supercomputing architectures. Nidus is being developed as a collaboration between the Neuroscience SimLab at the Forschungszentrum Juelich and the Swiss National Supercomputing Center (CSCS) under the aegis of the NEST Initiative. The trend towards "many-core" architectures such as GPU and Intel Xeon Phi based systems demands new approaches in software development and algorithm design. Nidus is being written specifically for these architectures; it aims to be a flexible platform for neural network simulation, interoperable with the models and workflows of NEST and NEURON.Improvements in performance and flexibility will enable a variety of novel experiments, but the design isn't finalised, and will be driven by the requirements of the community. This is where you come in! We are very interested in your ideas for features which will make new science possible: we ask you to think outside of the box and build this next generation neurosimulator together with us.Possible features and use cases:o Simulate significantly larger networks over longer time scales - Larger proportion of CNS systems with morphological detail - Longer simulations for slowly developing phenomenon - Improved statistical power by leveraging large data setso A well defined high performance C++ API which allows tight integration with other codes - Multiscale by coupling with simulations at other scales - Real-time visualization on HPC resources - Online statistics to avoid scaling bottlenecks - Networks embedded in physically modeled animalso Dynamic data structures which allow the creation of models with a time-varying number of neurons, synapses and compartments - Neuronal development - Healing after injury - Age related neuronal degeneration.What questions haven't you asked yet?AcknowledgementsWe would like to thank the following organizations for their support: Helmholtz Portfolio Theme "Supercomputing and Modeling for the Human Brain", Human Brain Project SP7 High Performance Analytics and Computing Platform, and the Jülich-Aachen Research Alliance |
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