Importing from and exporting to other formats¶
Other formats for representing spiking network models are also available.
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
A PyNN model/simulation script can be exported in SONATA format using:
from pyNN.network import Network from pyNN.serialization import export_to_sonata sim.setup() ... # 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") simulation_plan.setup(sim) net = import_from_sonata("circuit_config.json", sim) simulation_plan.execute(net)
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()