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      PyNN: A Common Interface for Neuronal Network Simulators


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          Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.

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          Most cited references15

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          Simple model of spiking neurons.

          A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
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            Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

            We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiological protocols (current-clamp injection of pulses and ramps) and apply it to a detailed conductance-based model of a regular spiking neuron. Our simple model predicts correctly the timing of 96% of the spikes (+/-2 ms) of the detailed model in response to injection of noisy synaptic conductances. The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.
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              Simulation of networks of spiking neurons: a review of tools and strategies.

              We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

                Author and article information

                Front Neuroinformatics
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Research Foundation
                21 October 2008
                27 January 2009
                : 2
                [1] 1Unité de Neurosciences Intégratives et Computationelles, CNRS Gif sur Yvette, France
                [2] 2Kirchhoff Institute for Physics, University of Heidelberg Heidelberg, Germany
                [3] 3Honda Research Institute Europe GmbH, Offenbach Germany
                [4] 4Berstein Center for Computational Neuroscience, Albert-Ludwigs-University Freiburg, Germany
                [5] 5Neurobiology and Biophysics, Institute of Biology III, Albert-Ludwigs-University Freiburg, Germany
                [6] 6Institut de Neurosciences Cognitives de la Méditerranée, CNRS Marseille, France
                [7] 7Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
                [8] 8Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria
                Author notes

                Edited by: Rolf Kötter, Radboud University Nijmegen, The Netherlands

                Reviewed by: Graham Cummins, Montana State University, USA; Fred Howell, Textensor Limited, UK

                *Correspondence: Andrew Davison, UNIC, Bât. 32/33, CNRS, 1 Avenue de la Terrasse, 91198 Gif sur Yvette, France. e-mail: andrew.davison@ 123456unic.cnrs-gif.fr
                Copyright © 2009 Davison, Brüderle, Eppler, Kremkow, Muller, Pecevski, Perrinet and Yger.

                This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 19, Pages: 10, Words: 7917
                Original Research

                parallel computing,reproducibility,large-scale models,python,interoperability,translation,simulation,computational neuroscience


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