Update mapping NeuroML2 to & from PyNN

[pgleeson]

Examples of integration between PyNN and NeuroML2 are being added to: https://github.com/OpenSourceBrain/PyNNShowcase

Scenario: Model in PyNN; convert it equivalent in NeuroML2

In progress here: https://github.com/pgleeson/PyNN/tree/master/pyNN/neuroml (recent version has been merged into PyNN master: NeuralEnsemble/PyNN#509). There is example of a PyNN file converted to NeuroML2 on https://github.com/OpenSourceBrain/PyNNShowcase (latest export is here)

• add basic example to https://github.com/OpenSourceBrain/PyNNShowcase
• test validity of generated NML2
• add examples of PyNN connection types, input types, etc.
• open PR to main NeuroEnsemble repo
• add documentation in PyNN docs
• test with more PyNN core examples

Longer term:

• Export network connectivity to binary format (e.g. HDF5) from PyNN & have that supported also by NeuroML pipeline (see HDF5 in NeuroML2 #93)

Scenario: Network model in NeuroML 2; need to convert it to equivalent in PyNN using jNeuroML

Started here: https://github.com/NeuroML/org.neuroml.export/blob/development/src/main/java/org/neuroml/export/pynn/PyNNWriter.java.

Initially only worked for models only using NML2 equivalents of PyNN cells, e.g. https://github.com/NeuroML/NeuroML2/blob/development/examples/NML2_PyNNCells.nml. Now using tips here and here to make sure this jNeuroML generated code is compatible with PyNN.

• test for export of abstract cells, e.g. IaF, Izhikevich; add to .travis.yml
• support plotting/saving of non v variables
• test for export of connections; add to .travis.yml
• test for export of connectionWDs; add to .travis.yml
• test for single cond based cell; add to .travis.yml
• test for multicomp cells; add to .travis.yml
• test/document running generated examples on parallel Neuron

Scenario: Cell model in NeuroML 2/LEMS; need to use it in PyNN script as a cell for Population

Similar to above, but use statement like:

cell_type, cell_instance = import_neuroml2_cell(file="MyNeuroML.nml",cell_id = "mycell") 

inside existing PyNN script. Note something similar to this is possible in NetPyNE already, see here.