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

Edits to 'https://fairsharing.org/FAIRsharing.3CCrPF' by 'The FAIRsharing Team' at 11:00, 25 Jun 2019 (approved):

	'organizations' has been modified:
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			NeuralEnsemble|https://neuralensemble.org/|Maintains
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			NeuralEnsemble|https://neuralensemble.org/|Maintains
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	'supportLinks' has been modified:
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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/
		Added:
			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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	'description' has been modified:
		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.  

	'licences' has been modified:
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			CeCILL|http://www.cecill.info/licences.en.html|Data
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			CeCILL|http://www.cecill.info/licences.en.html|Data
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	'onto_domains' has been modified:
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			Network simulation
			None
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			Network simulation
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	'miriam_id' has been modified:
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	'dataProcesses' has been modified:
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			Download
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			Download
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	'taxonomies' has been modified:
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			Not applicable
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			All
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			All
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			Not applicable

            

Edits to 'https://fairsharing.org/FAIRsharing.3CCrPF' by 'The FAIRsharing Team' at 09:50, 29 Apr 2019 (approved):

	'doi' has been modified:
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	'miriam_id' has been modified:
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		After: None