title: Nexa: a scalable neural simulator with integrated analysis.
abstract: 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.
authors.affiliation: Department of Computational Biology, Royal Institute of Technology, 114 21 Stockholm, Sweden. simonbe@kth.se
authors.forename: Simon
authors.initials: S
authors.lastname: Benjaminsson
authors.forename: Anders
authors.initials: A
authors.lastname: Lansner
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D011381:Programming Languages; D012984:Software; D012985:Software Design; D018511:Systems Integration