Convenient simulation of spiking neural networks with NEST 2

Abstract

Event Abstract Back to Event Convenient simulation of spiking neural networks with NEST 2 Jochen M. Eppler1, 2*, Moritz Helias1, Eilif Muller3, Markus Diesmann4 and Marc-Oliver Gewaltig1, 2 1 Bernstein Center for Computational Neuroscience, Germany 2 Honda Research Institute Europe GmbH, Germany 3 Ecole Polytechnique Federale de Lausanne, Laboratory for Computational Neuroscience, Switzerland 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.