neuromllite 0.1.4

A common JSON based format for compact network specification, closely tied to NeuroML

## NeuroMLlite: a common framework for reading/writing/generating network specifications

Work in progress. For the background to this see here: https://github.com/NeuroML/NetworkShorthand.



## Examples
The best way to see the currently proposed structure is to look at the examples

### Ex. 1: Simple network, 2 populations & projection

[JSON](examples/Example1_TestNetwork.json) | [Python script](examples/Example1.py)

Can be exported to:
- **NeuroML 2** (XML or HDF5 format)
- **Graph** (see above)

### Ex. 2: Simple network, 2 populations, projection & inputs

[JSON](examples/Example2_TestNetwork.json) | [Python script](examples/Example2.py) | [Generated NeuroML2](examples/Example2_TestNetwork.net.nml)

Can be exported to:
- **NeuroML 2** (XML or HDF5 format)
- **Graph** (see above)

### Ex. 3: As above, with simulation specification

[JSON for network](examples/Example3_Network.json) | [JSON for simulation](examples/SimExample3.json) | [Python script](examples/Example3.py) | [Generated NeuroML2](examples/Example3_Network.net.nml) | [Generated LEMS](examples/LEMS_SimExample3.xml)

Can be exported to:
- **NeuroML 2** (XML or HDF5 format)
- **Graph** (see Ex2)

Can be simulated using:
- **NetPyNE**
- **jNeuroML**
- **NEURON** generated from **jNeuroML**
- **NetPyNE** generated from **jNeuroML**


### Ex. 4: A network with PyNN cells & inputs
<img alt="Ex4" src="examples/images/Ex4.png" height="250"/>

[JSON](examples/Example4_PyNN.json) | [Python script](examples/Example4.py) | [Generated NeuroML2](examples/Example4_PyNN.net.nml)

Can be exported to:
- **NeuroML 2** (XML or HDF5 format)
- **Graph** (see above)

Can be simulated using:
- **NEST** via **PyNN**
- **NEURON** via **PyNN**
- **Brian** via **PyNN**
- **jNeuroML**
- **NEURON** generated from **jNeuroML**
- **NetPyNE** generated from **jNeuroML**


### Ex. 5: A network with the Blue Brain Project connectivity data
<img alt="Ex4" src="examples/images/Ex5_BBP_5percent.png" height="150"/>

[JSON](examples/BBP_5percent.json) | [Python script](examples/Example5.py)

Can be exported to:
- **NeuroML 2** (XML or HDF5 format)
- **Graph** (see above)

Can be simulated using:
- **NetPyNE**

### Ex. 6: A network based on Potjans and Diesmann 2014 (work in progress)
<img alt="Ex6d" src="examples/images/Ex6.dot.png" height="100"/> <img alt="Ex6f" src="examples/images/Ex6.fdp.png" height="100"/> <img alt="Ex6c" src="examples/images/Ex6.circo.png" height="100"/>

[JSON](examples/Example6_PyNN.json) | [Python script](examples/Example6.py) | [Generated NeuroML2](examples/Example6_PyNN.net.nml)

Can be exported to:
- **NeuroML 2** (XML or HDF5 format)
- **Graph** (see above)

### Ex. 7: A network based on Brunel 2000 (work in progress)
<img alt="Ex7" src="examples/images/Ex7.png" height="250"/>

[JSON](examples/Example7_Brunel2000.json) | [Python script](examples/Example7.py) | [Generated NeuroML2](examples/Example7_Brunel2000.net.nml)

Can be exported to:
- **NeuroML 2** (XML or HDF5 format)
- **Graph** (see above)

Can be simulated using:
- **jNeuroML**

## Installation & usage

Installation of the basic framework should be fairly straightforward:

```
git clone https://github.com/NeuroML/NeuroMLlite.git
cd NeuroMLlite
sudo python setup.py install
```

Then 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 file
```

Other options (which will require [Neuron](https://neuron.yale.edu/neuron/), [NetPyNE](http://www.netpyne.org/),
[PyNN](http://neuralensemble.org/PyNN/), [NEST](http://www.nest-simulator.org/), [Brain](http://briansimulator.org/) etc. 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 Brian
```

Graphs of the network structure can be generated at many levels of detail (1-6) and
laid out using [GraphViz](http://graphviz.org/) engines (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 -graph1n



[](https://travis-ci.org/NeuroML/NeuroMLlite)