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@INPROCEEDINGS{Plesser:137506,
author = {Plesser, Hans Ekkehard and Eppler, Jochen Martin and
Gewaltig, Marc-Oliver},
title = {20 {Y}ears of {NEST}: {A} {M}ature {B}rain {S}imulator},
address = {Lausanne},
publisher = {Frontiers Research Foundation},
reportid = {FZJ-2013-03943},
pages = {0},
year = {2013},
abstract = {Efficient and reliable simulation tools are essential for
progress in brain research. Since the early days of neuronal
computing (Farley $\&$ Clark, 1954), a wide range of
simulators have been developed, each specialized on one or
few spatial and temporal scales (Brette et al., 2007). But
the reliable 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 simulation tools in recent
years. Neuronal network simulation is thus coming of age:
Just as our colleagues in electrophysiology, we begin to
base our work increasingly on the use of standard tools,
with modifications and adaptations for our particular
research, instead of building home-brew solutions from
scratch. This concentration was not least the result of a
series of large-scale EU funded projects, 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 (Gewaltig $\&$
Diesmann, 2007) saw its first incarnation as the SYNOD
simulator in 1995 (Diesmann et al., 1995), leading to
exciting results on synfire chains early on (Diesmann et
al., 1999). By tightly coupling software development with
computational neuroscience research (Kunkel et al., 2010),
simulator technology evolved steadily, facilitating new
scientific insight at (nearly) every step. Some key examples
were parallelization (Morrison et al., 2005; Plesser et al.,
2007), exact integration of model equations (Rotter $\&$
Diesmann, 1999), precise spike times in a time-driven
simulator (Morrison et al., 2007; Hanuschkin et al., 2010),
spike- time-dependent (Morrison et al., 2007) and
neuro-modulated plasticity (Potjans et al., 2010), and a
Topology module for spatially structured networks (Plesser
$\&$ Enger, 2013). Streamlined data-structures (Kunkel et
al., 2011) allow NEST to efficiently exploit the
capabilities of some of the largest computers on Earth for
simula- tions on the brain scale (Helias et al., 2012).
Systematic quality assurance through testsuites (Eppler et
al., 2009) and continuous integration technology (Zaytsev
$\&$ Morrison, 2013) ensure simulator reliability (within
limits). With a user-friendly Python-based interface (Eppler
et al., 2008; Gewaltig et al., 2012), integration with PyNN
(Davison et al., 2008) for simulator-independent scripting
and MUSIC support (Djurfeldt et al., 2010) for integrated
multi-scale simulation, NEST is a powerful simulation tool
for brain-scale simulations today.NEST has been publicly
available since 2004 and has been taught at summer schools
and graduate courses since, training a generation of
computational scientists. This has lead to a steady increase
in computational neuroscience publications based on NEST
simulations in recent years (see
http://www.nest-initiative.org for a list), indicating that
NEST is indeed establishing itself as a widely used tool for
the simulation of large networks of (comparatively) simple
model neurons.As of the NEST 2.0 release in 2012, NEST is
available under the GNU Public License to ensure wide
dissemination. The further development of NEST is chaperoned
by the NEST Initiative, a non-for-profit organization
incorporated in Ecublens, Switzerland, which is open for
interested scientists. We are currently preparing to move
NEST source code to a distributed version control system,
allowing all NEST users ”real time” access to bug fixes
and improvements, and to facilitate contributions by the
NEST Community.In our demonstration, we will illustrate the
capabilities and versatility of NEST. We will in particular
focus on three complementary approaches to simulating
large-scale cortical networks: A data-driven approach based
on detailed connectivity information (based on data from the
Blue Brain Project), constructive network generation, based
on connectivity patterns (Potjans $\&$ Diesmann, 2012), and
simulation of advanced 3D topological
networks.AcknowledgementsWe present this work on behalf of
the NEST Initiative. Many institutions have supported NEST
development including: Weizmann Institute, U Bochum, U $\&$
BCCN Freiburg, Honda Research Institute Europe, MPI for
Fluid Dynamics, Norwegian U of Life Sciences, RIKEN Brain
Science Institute, Helmholtz Gesellschaft and
Forschungszentrum Jülich, EPFL and BlueBrainProject, EU
grants FACETS (FP6-15879) and BrainScales (FP7-269921) and
Research Council of Norway grant eNeuro
(178892/V30).ReferencesBrette, R., Rudolph, M., Carnevale,
T., Hines, M., Beeman, D., Bower, J. M., Diesmann, M.,
Morrison, A., Goodman, P. H., Jr., F. C. H., Zirpe, M.,
Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt,
M., Lansner, A., Rochel, O., Vieville, T., Muller, E.,
Davison, A. P., Boustani, S. E., $\&$ Destexhe, A. (2007).
Simulation of networks of spiking neurons: A review of tools
and strategies. J Comput Neurosci 23, 349–398.Davison, A.,
Brüderle, D., Eppler, J., Kremkow, J., Muller, E.,
Pecevski, D., Perrinet, L., $\&$ Yger, P. (2008). PyNN: a
common interface for neuronal network simulators. Front
Neuroinform 2, 11.Diesmann, M., Gewaltig, M.-O., $\&$
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Eppler, J. M., Dudani, N., Helias, M., Potjans, T. C.,
Bhalla, U. S., Diesmann, M., Kotaleski, J. H., $\&$ Ekeberg,
O. (2010). Run-time interoperability between neuronal
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Muller, E., Diesmann, M., $\&$ Gewaltig, M.-O. (2008).
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Plzen. International Neuroinformatics Coordinating
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(2007). Nest (neural simulation tool). Scholarpedia 2(4),
1430.Gewaltig, M.-O., Morrison, A., $\&$ Plesser, H. E.
(2012). NEST by example: An introduction to the neural
simulation tool NEST. In N. Le Novère (Ed.), Computational
Systems Neurobiology, Chapter 18, pp. 533–558. Dordrecht:
Springer Science+Business Media.Hanuschkin, A., Kunkel, S.,
Helias, M., Morrison, A., $\&$ Diesmann, M. (2010). A
general and efficient method for incorporating exact spike
times in globally time-driven simulations. Front
Neuroinformatics 4, 113.Helias, M., Kunkel, S., Masumoto,
G., Igarashi, J., Eppler, J. M., Ishii, S., Fukai, T.,
Morrison, A., $\&$ Diesmann, M. (2012). Supercomputers ready
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Gewaltig, M.-O., Diesmann, M., $\&$ Morrison, A. (2010).
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Potjans, T. C., Eppler, J. M., Plesser, H. E., Morrison, A.,
$\&$ Diesmann, M. (2011). Meeting the memory challenges of
brain-scale network simulation. Front. Neuroinform. 5,
35.Morrison, A., Aertsen, A., $\&$ Diesmann, M. (2007).
Spike-time dependent plasticity in balanced recurrent
networks. Neural Comput 19, 1437–1467.Morrison, A.,
Mehring, C., Geisel, T., Aertsen, A., $\&$ Diesmann, M.
(2005). Advancing the boundaries of high connectivity
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$\&$ Diesmann, M. (2007). Exact subthreshold integration
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simulations. Neural Comput 19, 47–79.Plesser, H. E., $\&$
Enger, H. (2013). Nest topology user manual.Plesser, H. E.,
Eppler, J. M., Morrison, A., Diesmann, M., $\&$ Gewaltig,
M.-O. (2007). Efficient parallel simulation of large-scale
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Springer-Verlag.Potjans, T. C., $\&$ Diesmann, M. (2012).
The cell-type specific cortical microcircuit: Relating
structure and activity in a full-scale spiking network
model. Cereb Cortex.Potjans, W., Morrison, A., $\&$
Diesmann, M. (2010). Enabling functional neural circuit
simulations with distributed computing of neuromodulated
plasticity. Front Comput Neurosci 4, 141.Rotter, S., $\&$
Diesmann, M. (1999). Exact digital simulation of
time-invariant linear systems with applications to neuronal
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continuous integration. Front Neuroinform 6, 31.},
month = {Aug},
date = {2013-08-27},
organization = {Neuroinformatics 2013, Stockholm
(Sweden), 27 Aug 2013 - 29 Aug 2013},
cin = {INM-6 / IAS-6 / JARA-HPC},
ddc = {610},
cid = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
$I:(DE-82)080012_20140620$},
pnm = {331 - Signalling Pathways and Mechanisms in the Nervous
System (POF2-331) / BRAINSCALES - Brain-inspired multiscale
computation in neuromorphic hybrid systems (269921) /
BTN-Peta - The Next-Generation Integrated Simulation of
Living Matter (BTN-Peta-2008-2012) / Brain-Scale Simulations
$(jinb33_20121101)$},
pid = {G:(DE-HGF)POF2-331 / G:(EU-Grant)269921 /
G:(DE-Juel1)BTN-Peta-2008-2012 /
$G:(DE-Juel1)jinb33_20121101$},
typ = {PUB:(DE-HGF)8},
url = {https://juser.fz-juelich.de/record/137506},
}
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