standards > model/format > DOI:10.25504/FAIRsharing.3CCrPF


ready PyNN


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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.



How to cite this record FAIRsharing.org: PyNN; PyNN; DOI: https://doi.org/10.25504/FAIRsharing.3CCrPF; Last edited: Sept. 20, 2019, 11:52 a.m.; Last accessed: Dec 09 2019 10:02 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;


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Record added: April 26, 2019, 10:37 a.m.
Record updated: June 25, 2019, 11 a.m. by The FAIRsharing Team.

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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

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