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| Home > Publications database > NestMC: A morphologically detailed neural network simulator for modern high performance computer architectures |
| Home > Publications database > NestMC: A morphologically detailed neural network simulator for modern high performance computer architectures |
| Poster (Other) | FZJ-2017-08484 |
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2017
Please use a persistent id in citations: http://hdl.handle.net/2128/16273
Abstract: NestMC 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 under the aegis of the NEST Initiative. NestMC will enable new scales and classes of morphologically detailed network simulations on current and future supercomputing architectures.A number of "many-core" architectures such as GPU and Intel Xeon Phi based systems are currently available, to optimally use these emerging architecture new approaches in software development and algorithm design are needed. NestMC is being written specifically with this in mind; it aims to be a flexible platform for neural network simulation, while keeping interoperability with models and workflows of NEST and NEURON.The improvements in performance and flexibility in themselves will enable a variety of novel experiments, but the design is not finalised, and is driven by the requirements of the neuroscientific community. The prototype is open source (1) and we invite you to have a look. We are 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.What directions do you want us to go in?• Simulate large morphological detailed networks for longer time scales: Study of slow 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 data structures for models with time-varying number of neurons, synapses and compartments: simulate neuronal development, healing after injury and age related neuronal degeneration.Do you have other great ideas? Let us know!
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