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| Home > Publications database > Arbor: A morphologically detailed neural network simulator for modern high performance computer architectures |
| Home > Publications database > Arbor: A morphologically detailed neural network simulator for modern high performance computer architectures |
| Poster (Other) | FZJ-2017-07937 |
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2017
Please use a persistent id in citations: http://hdl.handle.net/2128/16128
Abstract: Arbor is a new multicompartment neural network simulator currently under development as a collaboration between the Neuroscience SimLab at the Forschungszentrum Jülich, Barcelona Supercomputing Center and the Swiss National Supercomputing Center. Arbor will enable new scales and classes of morphologically detailed neuronal network simulations on current and future supercomputing architectures such as the Human Brain Project SCs. A number of many-core architectures such as GPU and Intel Xeon Phi based systems are available. To optimally use these emerging architectures, new approaches in software development are needed. Arbor is being written specifically with performance for this hardware in mind 1; it aims to be a flexible platform for neural network simulation while keeping interoperability with models and workflows developed for NEST and NEURON. Improvements in performance and flexibility will enable a variety of novel experiments. The design is not yet finalized and is driven by the requirements of the neuroscientific community. The prototype is open source (https://github.com/eth-cscs/nestmc-proto). Build this next generation neurosimulator together with us! • Simulate large morphological detailed networks for longer time scales: Study slowly developing phenomena.• Reduce the time to solution: Perform more repeat experiments for increased statistical power. • Create high performance interfaces with other software: Perform online statistical analysis and visualization of your running models, study the brain at multiple scales with specialized tools or embed detailed networks in physically modelled animals. • Optimize dynamic structures for models with time-varying number of neurons, synapses and compartments: simulate neuronal development, healing after injury and age related neuronal degeneration.
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