def create_brain():
"""
Initializes PyNN with the neuronal network that has to be simulated
"""
network = import_from_sonata('scaffold_sonata/circuit_config.json', sim)
return network
def test():
if not HAVE_H5PY and HAVE_NEST:
raise SkipTest
sim.setup()
p1 = sim.Population(10,
sim.IF_cond_exp(v_rest=-65,
tau_m=lambda i: 10 + 0.1 * i,
cm=RD('normal', (0.5, 0.05))),
label="population_one")
p2 = sim.Population(20,
sim.IF_curr_alpha(v_rest=-64,
tau_m=lambda i: 11 + 0.1 * i),
label="population_two")
prj = sim.Projection(p1,
p2,
sim.FixedProbabilityConnector(p_connect=0.5),
synapse_type=sim.StaticSynapse(weight=RD(
'uniform', [0.0, 0.1]),
delay=0.5),
receptor_type='excitatory')
net = Network(p1, p2, prj)
export_to_sonata(net, "tmp_serialization_test", overwrite=True)
net2 = import_from_sonata("tmp_serialization_test/circuit_config.json",
sim)
for orig_population in net.populations:
imp_population = net2.get_component(orig_population.label)
assert orig_population.size == imp_population.size
for name in orig_population.celltype.default_parameters:
assert_array_almost_equal(orig_population.get(name),
imp_population.get(name), 12)
w1 = prj.get('weight', format='array')
prj2 = net2.get_component(asciify(prj.label).decode('utf-8') + "-0")
w2 = prj2.get('weight', format='array')
assert_array_almost_equal(w1, w2, 12)
def test():
sim.setup()
p1 = sim.Population(10,
sim.IF_cond_exp(
v_rest=-65,
tau_m=lambda i: 10 + 0.1*i,
cm=RD('normal', (0.5, 0.05))),
label="population_one")
p2 = sim.Population(20,
sim.IF_curr_alpha(
v_rest=-64,
tau_m=lambda i: 11 + 0.1*i),
label="population_two")
prj = sim.Projection(p1, p2,
sim.FixedProbabilityConnector(p_connect=0.5),
synapse_type=sim.StaticSynapse(weight=RD('uniform', [0.0, 0.1]),
delay=0.5),
receptor_type='excitatory')
net = Network(p1, p2, prj)
export_to_sonata(net, "tmp_serialization_test", overwrite=True)
net2 = import_from_sonata("tmp_serialization_test/circuit_config.json", sim)
for orig_population in net.populations:
imp_population = net2.get_component(orig_population.label)
assert orig_population.size == imp_population.size
for name in orig_population.celltype.default_parameters:
assert_array_almost_equal(orig_population.get(name), imp_population.get(name), 12)
w1 = prj.get('weight', format='array')
prj2 = net2.get_component(asciify(prj.label).decode('utf-8') + "-0")
w2 = prj2.get('weight', format='array')
assert_array_almost_equal(w1, w2, 12)
from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan
import pyNN.nest as sim
simulation_plan = load_sonata_simulation_plan(
"../input/simulation_config.json")
simulation_plan.setup(sim)
net = import_from_sonata("../input/circuit_config.json", sim)
simulation_plan.execute(net)
from pyNN.serialization import import_from_sonata, load_sonata_simulation_plan
import pyNN.nest 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)