Importing from and exporting to other formats

Other formats for representing spiking network models are also available.

PyNN currently supports NeuroML, NineML and SONATA.


See section on NeuroML.


See section on PyNN and NineML.


SONATA is a data format for representing/storing data-driven spiking neuronal network models, experimental protocols (injecting spikes, currents) and simulation outputs.

In the network representation, all connections are represented explicity, as in PyNN’s FromFileConnector and FromListConnector.

A PyNN model/simulation script can be exported in SONATA format using:

from import Network
from pyNN.serialization import export_to_sonata

# create populations, projections, etc.

# add populations and projections to a Network
net = Network(pop1, pop2, ...., prj1, prj2, ...)

export_to_sonata(net, "sonata_output_dir")

A SONATA model/simulation can be read and executed through PyNN provided the cell types used in the model are compatible with PyNN, i.e. they must be point neurons. (SONATA also supports biophysically/morphologically detailed neuron models).

from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan
import pyNN.neuron as sim

simulation_plan = load_sonata_simulation_plan("simulation_config.json")
net = import_from_sonata("circuit_config.json", sim)

Simulation results from such a simulation are stored in the SONATA outputs format. Support for this format will soon be added to Neo, but for the time being you can read the results as follows:

from pyNN.serialization.sonata import SonataIO

data = SonataIO("sonata_output_dir").read()