How to cite this record
FAIRsharing.org: PyNN; PyNN; DOI: https://doi.org/10.25504/FAIRsharing.3CCrPF;
Last edited: April 16, 2021, 3:01 p.m.; Last accessed: Apr 17 2021 4:13 p.m.
Publication for citation
PyNN: A Common Interface for Neuronal Network Simulators.
Davison AP,Bruderle D,Eppler J,Kremkow J,Muller E,Pecevski D,Perrinet L,Yger P; Front Neuroinform ;
2009;
10.3389/neuro.11.011.2008;
Edits to 'https://fairsharing.org/FAIRsharing.3CCrPF' by 'The FAIRsharing Team' at 14:58, 16 Apr 2021 (approved):
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Edits to 'https://fairsharing.org/FAIRsharing.3CCrPF' by 'The FAIRsharing Team' at 10:28, 23 Feb 2021 (approved):
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Edits to 'https://fairsharing.org/FAIRsharing.3CCrPF' by 'The FAIRsharing Team' at 11:00, 25 Jun 2019 (approved):
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NeuralEnsemble|https://neuralensemble.org/|Maintains
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forum|https://groups.google.com/forum/#!forum/neuralensemble
forum|https://github.com/NeuralEnsemble/PyNN/issues
online documentation|http://neuralensemble.org/docs/PyNN/
online documentation|https://github.com/NeuralEnsemble/PyNN/
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forum|https://groups.google.com/forum/#!forum/neuralensemble
forum|https://github.com/NeuralEnsemble/PyNN/issues
online documentation|http://neuralensemble.org/docs/PyNN/
online documentation|https://github.com/NeuralEnsemble/PyNN/
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Before: The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way, either using the Connection Set Algebra or by writing your own Python code.
After: The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way, either using the Connection Set Algebra or by writing your own Python code. PyNN has been developed as a procedural description in Python which can be used to instantiate a network across multiple simulators.
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Edits to 'https://fairsharing.org/FAIRsharing.3CCrPF' by 'The FAIRsharing Team' at 09:50, 29 Apr 2019 (approved):
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