Machine Learning library comparisons for testing brain architectures

There are already nice ML software comparisons out there:

1. https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

Here, I compare free ML/deep-learning and bio-realistic libraries/frameworks for suitability of use in archibrain.
Particularly, we want
0. python / julia support

1. arbitrary even spiking neurons
2. auto-differentiation of arbitrary functions
3. ease of implementing different learning algos (not just backprop) - typically bio-realistic simulators only allow synaptically-local and online learning rules, while ML/deep-learning frameworks do backprop over multiple layers.
4. can use GPUs (and CPUs) across multiple nodes (openmp/cuda/opencl/multi-node?).
5. ease of implementing both RL and supervised learning tasks.

library	py/ju?	arb nrns?	auto-diff?	arb learn algo?	multi-node GPU/CPU	tasks
tensorflow	py	?	yes	?	cuda,multi-node	?
mxnet	py/ju	?	yes	?	openmp,cuda,multi-node	?
scikit-learn
theano	py	?	yes	?	openmp,cuda,(dev-opencl),multi-node	?
keras	py	?	yes	?	theano-backend,multi-node	?
torch
cntk
brian	py	yes	no	only local,online	?	no
nengo	py	lif,lifrate, hack others	no	PES,etc, hack other local,online	opencl,not multi-node	no
numenta/nupic