PyNN is a simulator-independent language for building neuronal network models. 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.
Learn more: http://neuralensemble.org/PyNN
NeuroML, SpineML, NineML
- PyNN Python package implements the PyNN API for the NEURON, NEST and Brian simulators https://pypi.org/project/PyNN/
Neuron model based on a standard point neuron model as supported by PyNN (e.g. Integrate and Fire, Izhikevich, adaptive exponential, simple Hodgkin Huxley)
Models that consists of interacting populations of cells where overall network activity is more important than individual cell activity
NeuroML and PyNN are both standards endorsed by INCF, and the only two recommended and supported formats for sharing models on the Open Source Brain repository. This document describes some of the similarities and differences between them, and gives examples of instances when one might prefer to use one vs. the other.