Package: neuromllite
NeuroMLlite: a common framework for reading/writing/generating network specifications based on NeuroMLNeuroMLlite is in active development. This will evolve into a framework for more portable, concise network specifications which will form an important part ofNeuroML v3.For some more background to this package see here:https://github.com/NeuroML/NetworkShorthand.ExamplesThe best way to see the currently proposed structure is to look at the examplesEx. 1: Simple network, 2 populations & projectionJSON|Python scriptCan be exported to:NeuroML 2(XML or HDF5 format)Graph(see above)Ex. 2: Simple network, 2 populations, projection & inputsJSON|Python script|Generated NeuroML2Can be exported to:NeuroML 2(XML or HDF5 format)Graph(see above)Ex. 3: As above, with simulation specificationJSON for network|JSON for simulation|Python script|Generated NeuroML2|Generated LEMSCan be exported to:NeuroML 2(XML or HDF5 format)Graph(see Ex2)Can be simulated using:NetPyNEjNeuroMLNEURONgenerated fromjNeuroMLNetPyNEgenerated fromjNeuroMLEx. 4: A network with PyNN cells & inputsJSON|Python script|Generated NeuroML2Can be exported to:NeuroML 2(XML or HDF5 format)Graph(see above)Can be simulated using:NESTviaPyNNNEURONviaPyNNBrianviaPyNNjNeuroMLNEURONgenerated fromjNeuroMLNetPyNEgenerated fromjNeuroMLEx. 5: A network with the Blue Brain Project connectivity dataJSON|Python scriptCan be exported to:NeuroML 2(XML or HDF5 format)Graph(see above)Matrix(see above)Can be simulated using:NetPyNEEx. 6: A network based on Potjans and Diesmann 2014 (work in progress)JSON|Python script|Generated NeuroML2Can be exported to:NeuroML 2(XML or HDF5 format)Graph(see above)Matrix(see above)Ex. 7: A network based on Brunel 2000 (work in progress)JSON|Python script|Generated NeuroML2Can be exported to:NeuroML 2(XML or HDF5 format)Graph(see above)Can be simulated using:jNeuroMLInstallation & usageInstallation of the basic framework should be fairly straightforward:git clone https://github.com/NeuroML/NeuroMLlite.git
cd NeuroMLlite
sudo python setup.py installThen simple examples can be run:cd examples
python Example1.py  #  Generates the JSON representation of the network (console & save to file)To generate the NeuroML 2 version of the network, first install pyNeuroML, then use the -nml flag:sudo pip install pyNeuroML
python Example2.py -nml       # Saves the network structure to a *net.nml XML fileOther options (which will requireNeuron,NetPyNE,PyNN,NEST,Brainetc. to be installed) include:python Example4.py -jnml       # Generate NeuroML2 & LEMS simulation & run using jNeuroML
python Example4.py -jnmlnrn    # Generate NeuroML2 & LEMS simulation, use jNeuroML to generate Neuron code (py/hoc/mod), then run in Neuron
python Example4.py -jnmlnrn    # Generate NeuroML2 & LEMS simulation, use jNeuroML to generate NetPyNE code (py/hoc/mod), then run in NetPyNE
python Example4.py -netpyne    # Generate network in NetPyNE directly & run simulation
python Example4.py -pynnnrn    # Generate network in PyNN, run using simulator Neuron
python Example4.py -pynnnest   # Generate network in PyNN, run using simulator NEST
python Example4.py -pynnbrian  # Generate network in PyNN, run using simulator BrianGraphs of the network structure can be generated at many levels of detail (1-6) and
laid out usingGraphVizengines (d - dot (default); c - circo;
n - neato; f - fdp). See above images for generated examples.python Example6.py -graph3d
python Example6.py -graph2f
python Example6.py -graph1nOther examplesNeuroMLlite is being tested/used in the following repositories on OSB:Wilson & CowanMejias et al. 2016Pospischil et al. 2008Bezaire et al. 2016PING networksPsyNeuLink testsOpenWorm - c302See also:ModECI MDF