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| Home > Publications database > 20 years of NEST: a mature brain simulator |
| Poster (Other) | FZJ-2014-04526 |
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2013
Please use a persistent id in citations: http://hdl.handle.net/2128/9176
Abstract: imulators have been developed, each specialized on one or few spatial and temporal scales [1]. But thereliable and reproducible simulation of such complex systems as the brain is a very demanding challenge.Thus, the Computational Neuroscience community concentrated on a few reliable and widely used simula-tion tools in recent years. This concentration was not least the result of a series of large-scale EU fundedprojects, such as FACETS, BrainScaleS and the recently announced Human Brain Project.From its humble beginnings as a PhD-student project 20 years ago, the Neural Simulation Tool NEST [2]saw its first incarnation as the SYNOD simulator in 1995 [3]. By tightly coupling software development withcomputational neuroscience research [4], simulator technology evolved steadily, facilitating new scientificinsight at almost every step. Some key examples were parallelization [5,6], exact integration of modelequations [7], precise spike times in a time-driven simulator [8,9], spike-timing dependent [10] and neuro-modulated plasticity [11], and a Topology module for spatially structured networks [12]. Streamlined datastructures [13] allow NEST to efficiently exploit the capabilities of some of the largest computers on Earth forsimulations on the brain scale [14]. Systematic quality assurance through testsuites [15] and continuousintegration technology [16] ensure simulator reliability. With a user-friendly Python-based interface [17],integration with PyNN [18] for simulator-independent scripting and MUSIC support [19] for integrated multi-scale simulation, NEST is a powerful simulation tool for brain-scale simulations today. References[1] Brette et al (2007) Simulation of networks of spiking neurons: A review of tools and strategies. J Comput Neurosci.[2] Gewaltig & Diesmann (2007) NEST (NEural Simulation Tool). Scholarpedia.[3] Diesmann et al. (1995) SYNOD: an Environment for Neural Systems Simulations. The Weizmann Institute of Science.[4] Kunkel et al. (2010) NEST: Science-driven development of neuronal network simulation software.[5] Morrison et al. (2005) Advancing the boundaries of high connectivity network simulation with distributed computing.[6] Plesser et al. (2007) Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers.[7] Rotter & Diesmann (1999) Exact digital simulation of time-invariant linear systems with applications to neuronal modeling.[8] Morrison et al. (2007) Exact subthreshold integration with continuous spike times in discrete time neural network simulations.[9] Hanuschkin et al. (2010) A general and efficient method for incorporating exact spike times in globally time-driven simulations.[10] Morrison et al. (2007) Spike-time dependent plasticity in balanced recurrent networks.[11] Potjans et al. (2010) Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity.[12] Plesser & Enger (2013) NEST Topology User Manual.[13] Kunkel (2011) Meeting the memory challenges of brain-scale network simulation Front.[14] Helias et al. (2012 Supercomputers ready for use as discovery machines for neuroscience.[15] Eppler et al. (2009) A testsuite for a neural simulation engine.[16] Zaytsev (2013) Increasing quality and managing complexity in neuroinformatics software development with continuous integration.[17] Eppler et al. (2008) PyNEST: A Convenient Interface to the NEST Simulator.[18] Davison et al. (2008) PyNN: a common interface for neuronal network simulators.[19] Djurfeldt et al. (2010) Run-time interoperability between neuronal network simulators based on the MUSIC framework.
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