Convenient simulation of spiking neural networks with NEST 2
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1
Bernstein Center for Computational Neuroscience, Germany
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2
Honda Research Institute Europe GmbH, Germany
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3
Ecole Polytechnique Federale de Lausanne, Laboratory for Computational Neuroscience, Switzerland
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4
RIKEN Brain Science Institute, Japan
NEST is a simulation environment for large heterogeneous networks of point-neurons or neurons with a small number of compartments [1].
We present NEST 2 with its new user interface PyNEST [2], which is based on the Python programming language (http://www.python.org). Python is free and provides a large number of libraries for scientific computing (http://www.scipy.org), which make it a powerful alternative to Matlab. PyNEST makes it easy to learn and use NEST. Users can simulate, analyze, and visualize networks and simulation data in a single interactive Python session. Other features of NEST 2 include support for synaptic plasticity, a wide range of neuron models, and parallel simulation on multi-core computers as well as computer clusters [3]. To customize NEST to their own purposes, users can add new neuron and synapse models, as well as new connection and analysis functions. Pre-releases of NEST 2 have already been used with great success and appreciation at Advanced Course in Computational Neuroscience in Arcachon (2005-2007) and Freiburg (2008).
NEST is released under an open source license for non-commercial use. For details and to download it, visit the NEST Initiative at http://www.nest-initiative.org.
References
1. Gewaltig M-O, Diesmann M; NEST (Neural Simulation Tool), Scholarpedia 2(4):1430, 2007
2. Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig M-O; PyNEST: A convenient interface to the NEST simulator, Front. Neuroinform. 2:12, 2008
3. Plesser HE, Eppler JM, Morrison A, Diesmann M, Gewaltig M-O; Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers, Springer-Verlag LNCS 4641:672-681, 2007
Conference:
Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.
Presentation Type:
Poster Presentation
Topic:
Learning and plasticity
Citation:
Eppler
JM,
Helias
M,
Muller
E,
Diesmann
M and
Gewaltig
M
(2009). Convenient simulation of spiking neural networks with NEST 2.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.neuro.10.2009.14.103
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Received:
27 Aug 2009;
Published Online:
27 Aug 2009.
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Correspondence:
Jochen M Eppler, Bernstein Center for Computational Neuroscience, Freiburg, Germany, frontiers@mindzoo.de