Call for community review of PyNN — A simulator-independent language for building neuronal network models

Abstract

The purpose of this document is to solicit community feedback on&nbsp;PyNN, a&nbsp;simulator-independent language for building neuronal network models, which was submitted to INCF for endorsement as a standard for imaging data. The document contains 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).&nbsp;For the next 60 days, we are seeking community feedback on PyNN.<br /><br />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.