PyNEST: A convenient interface to the NEST simulator
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Open Access
- 1 January 2008
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Neuroscience
Abstract
The neural simulation tool NEST (http://www.nest-initiative.org) is a simulator for heterogeneous networks of point neurons or neurons with a small number of compartments. It aims at simulations of large neural systems with more than 10^4 neurons and 10^7 to 10^9 synapses. NEST is implemented in C++ and can be used on a large range of architectures from single-core laptops over multi-core desktop computers to super-computers with thousands of processor cores. Python (http://www.python.org) is a modern programming language that has recently received considerable attention in Computational Neuroscience. Python is easy to learn and has many extension modules for scientific computing (e.g. http://www.scipy.org). In this contribution we describe PyNEST, the new user interface to NEST. PyNEST combines NEST’s efficient simulation kernel with the simplicity and flexibility of Python. Compared to NEST’s native simulation language SLI, PyNEST makes it easier to set up simulations, generate stimuli, and analyze simulation results. We describe how PyNEST connects NEST and Python and how it is implemented. With a number of examples, we illustrate how it is used.Keywords
This publication has 7 references indexed in Scilit:
- Towards Reproducible Descriptions of Neuronal Network ModelsPLoS Computational Biology, 2009
- Phenomenological models of synaptic plasticity based on spike timingBiological Cybernetics, 2008
- PyNN: a common interface for neuronal network simulatorsFrontiers in Neuroscience, 2008
- Brian: a simulator for spiking neural networks in PythonFrontiers in Neuroscience, 2008
- Advancing the Boundaries of High-Connectivity Network Simulation with Distributed ComputingNeural Computation, 2005
- The systems biology markup language (SBML): a medium for representation and exchange of biochemical network modelsBioinformatics, 2003
- Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking NeuronsJournal of Computational Neuroscience, 2000