We are currently soliciting community feedback for the next 60 days on PyNN, a simulator-independent language for building neuronal network models, which was submitted to INCF for endorsement as a standard. If you are interested in providing feedback, please go to: https://f1000research.com/documents/8-74 to review the INCF standards and best practices committee’s review of PyNN, and the criteria in which it was evaluated (open, FAIR, testing and implementation, governance, adoption and use, stability and support, and comparison to similar standards).
About PyNN: 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.