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Nexa: A scalable neural simulator with integrated analysis
Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Royal Institute of Technology, Sweden.
2012 (English)In: Network: Computation in Neural Systems, ISSN 0954-898X, Vol. 23, no 4, 254-271 p.Article in journal (Refereed) Published
Abstract [en]

Large-scale neural simulations encompass challenges in simulator design, data handling and understanding of simulation output. As the computational power of supercomputers and the size of network models increase, these challenges become even more pronounced. Here we introduce the experimental scalable neural simulator Nexa, for parallel simulation of large-scale neural network models at a high level of biological abstraction and for exploration of the simulation methods involved. It includes firing-rate models and capabilities to build networks using machine learning inspired methods for e.g. self-organization of network architecture and for structural plasticity. We show scalability up to the size of the largest machines currently available for a number of model scenarios. We further demonstrate simulator integration with online analysis and real-time visualization as scalable solutions for the data handling challenges.

Place, publisher, year, edition, pages
2012. Vol. 23, no 4, 254-271 p.
Keyword [en]
network models, simulation technology
National Category
Computer Systems
Research subject
Computer Science
URN: urn:nbn:se:su:diva-97264ISI: 000311837300009OAI: diva2:676343
Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2016-03-17Bibliographically approved

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Lansner, Anders
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