def run_sim(ncell):
print "Cells: ", ncell
setup0 = time.time()
sim.setup(timestep=0.1)
hh_cell_type = sim.HH_cond_exp()
hh = sim.Population(ncell, hh_cell_type)
pulse = sim.DCSource(amplitude=0.5, start=20.0, stop=80.0)
pulse.inject_into(hh)
hh.record('v')
setup1 = time.time()
t0 = time.time()
sim.run(100.0)
v = hh.get_data()
sim.end()
t1 = time.time()
setup_total = setup1 - setup0
run_total = t1 - t0
print "Setup: ", setup_total
print "Run: ", run_total
print "Total sim time: ", setup_total + run_total
return run_total
def run_simulation(parameters, plot_figure=False):
"""
"""
import pyNN.neuron as sim
timestamp = datetime.now()
model = build_model(**parameters["network"])
if "full_filename" in parameters["experiment"]:
xml_file = os.path.splitext(parameters["experiment"]["full_filename"])[0] + ".xml"
else:
xml_file = "{}.xml".format(parameters["experiment"]["base_filename"])
model.write(xml_file)
print("Exported model to file {}".format(xml_file))
sim.setup(timestep=parameters["experiment"]["timestep"])
print("Building network")
net = Network(sim, xml_file)
if plot_figure:
stim = net.populations["Ext"]
stim[:100].record('spikes')
exc = net.populations["Exc"]
exc.sample(50).record("spikes")
exc.sample(1).record(["nrn_v", "syn_a"])
inh = net.populations["Inh"]
inh.sample(50).record("spikes")
inh.sample(1).record(["nrn_v", "syn_a"])
else:
##all = net.assemblies["All"]
##all.sample(parameters["experiment"]["n_record"]).record("spikes")
net.populations["Ext"].sample(parameters["experiment"]["n_record"]).record("spikes") ## debugging
print("Running simulation")
t_stop = parameters["experiment"]["duration"]
pb = SimulationProgressBar(t_stop/80, t_stop)
sim.run(t_stop, callbacks=[pb])
print("Handling data")
data = {}
if plot_figure:
data["stim"] = stim.get_data().segments[0]
data["exc"] = exc.get_data().segments[0]
data["inh"] = inh.get_data().segments[0]
else:
if "full_filename" in parameters["experiment"]:
filename = parameters["experiment"]["full_filename"]
else:
filename = "{}_nineml_{:%Y%m%d%H%M%S}.h5".format(parameters["experiment"]["base_filename"],
timestamp)
print("Writing data to {}".format(filename))
##all.write_data(filename) ## debugging
net.populations["Ext"].write_data(filename)
sim.end()
return data
def model_network(param_dict):
"""
This model network consists of a spike source and a neuron (IF_curr_alpha).
The spike rate of the source and the weight can be specified in the
param_dict. Returns the number of spikes fired during 1000 ms of simulation.
Parameters:
param_dict - dictionary with keys
rate - the rate of the spike source (spikes/second)
weight - weight of the connection source -> neuron
Returns:
dictionary with keys:
source_rate - the rate of the spike source
weight - weight of the connection source -> neuron
neuron_rate - spike rate of the neuron
"""
#set up the network
import pyNN.neuron as sim
sim.setup(dt=0.01,
min_delay=1.,
max_delay=1.,
debug=False,
quit_on_end=False)
weight = param_dict['weight']
import NeuroTools.stgen as stgen
stgen = stgen.StGen()
spiketrain = stgen.poisson_generator(param_dict['rate'], t_stop=1000.)
source = sim.Population(1, sim.SpikeSourceArray,
{'spike_times': spiketrain.spike_times})
neuron = sim.Population(1, sim.IF_cond_alpha)
sim.Projection(source,
neuron,
method=sim.OneToOneConnector(weights=param_dict['weight'],
delays=1.))
#set recorder
neuron.record()
neuron.record_v()
#run the simulation
sim.run(1001.)
sim.end()
# count the number of spikes
spikes = neuron.getSpikes()
numspikes = len(spikes)
# return everything, including the input parameters
return {
'source_rate': param_dict['rate'],
'weight': param_dict['weight'],
'neuron_rate': numspikes
}
def run_simulation(parameters, plot_figure=False):
"""
"""
timestamp = datetime.now()
model = build_model(**parameters["network"])
if "full_filename" in parameters["experiment"]:
xml_file = parameters["experiment"]["full_filename"].replace(
".h5", ".xml")
else:
xml_file = "{}.xml".format(parameters["experiment"]["base_filename"])
model.write(xml_file)
print("Exported model to file {}".format(xml_file))
sim.setup()
print("Building network")
net = Network(sim, xml_file)
if plot_figure:
stim = net.populations["Ext"]
stim[:100].record('spikes')
exc = net.populations["Exc"]
exc.sample(50).record("spikes")
exc.sample(3).record(["nrn_v", "syn_a"])
inh = net.populations["Inh"]
inh.sample(50).record("spikes")
inh.sample(3).record(["nrn_v", "syn_a"])
else:
all = net.assemblies["All"]
all.sample(parameters["experiment"]["n_record"]).record("spikes")
print("Running simulation")
t_stop = parameters["experiment"]["duration"]
pb = SimulationProgressBar(t_stop / 80, t_stop)
sim.run(t_stop, callbacks=[pb])
print("Handling data")
data = {}
if plot_figure:
data["stim"] = stim.get_data().segments[0]
data["exc"] = exc.get_data().segments[0]
data["inh"] = inh.get_data().segments[0]
else:
if "full_filename" in parameters["experiment"]:
filename = parameters["experiment"]["full_filename"]
else:
filename = "{}_nineml_{:%Y%m%d%H%M%S}.h5".format(
parameters["experiment"]["base_filename"], timestamp)
print("Writing data to {}".format(filename))
all.write_data(filename)
sim.end()
return data
def model_network(param_dict):
"""
This model network consists of a spike source and a neuron (IF_curr_alpha).
The spike rate of the source and the weight can be specified in the
param_dict. Returns the number of spikes fired during 1000 ms of simulation.
Parameters:
param_dict - dictionary with keys
rate - the rate of the spike source (spikes/second)
weight - weight of the connection source -> neuron
Returns:
dictionary with keys:
source_rate - the rate of the spike source
weight - weight of the connection source -> neuron
neuron_rate - spike rate of the neuron
"""
#set up the network
import pyNN.neuron as sim
sim.setup(dt = 0.01, min_delay = 1., max_delay = 1., debug = False,
quit_on_end = False)
weight = param_dict['weight']
import NeuroTools.stgen as stgen
stgen = stgen.StGen()
spiketrain = stgen.poisson_generator(param_dict['rate'], t_stop = 1000.)
source = sim.Population(1, sim.SpikeSourceArray,
{'spike_times':spiketrain.spike_times})
neuron = sim.Population(1, sim.IF_cond_alpha)
sim.Projection(source, neuron,
method = sim.OneToOneConnector(weights = param_dict['weight'],
delays = 1.))
#set recorder
neuron.record()
neuron.record_v()
#run the simulation
sim.run(1001.)
sim.end()
# count the number of spikes
spikes = neuron.getSpikes()
numspikes = len(spikes)
# return everything, including the input parameters
return {'source_rate':param_dict['rate'],
'weight':param_dict['weight'],
'neuron_rate':numspikes }
def run_simulation(parameters, plot_figure=False):
"""
"""
import pyNN.neuron as sim
timestamp = datetime.now()
model = build_model(**parameters["network"])
if "full_filename" in parameters["experiment"]:
xml_file = parameters["experiment"]["full_filename"].replace(
".h5", ".xml")
else:
xml_file = "{}.xml".format(parameters["experiment"]["base_filename"])
model.write(xml_file)
print("Exported model to file {}".format(xml_file))
sim.setup()
print("Building network")
net = Network(sim, xml_file)
stim = net.populations["Ext"]
stim.record('spikes')
exc = net.populations["Exc"]
exc.record("spikes")
exc[:3].record(["nrn_v", "syn_a"])
print("Running simulation")
t_stop = parameters["experiment"]["duration"]
pb = SimulationProgressBar(t_stop / 80, t_stop)
sim.run(t_stop, callbacks=[pb])
print("Handling data")
data = {}
if plot_figure:
data["stim"] = stim.get_data().segments[0]
data["exc"] = exc.get_data().segments[0]
data["exc"].annotate(simulator="lib9ML with pyNN.neuron")
else:
if "full_filename" in parameters["experiment"]:
filename = parameters["experiment"]["full_filename"]
else:
filename = "{}_nineml_{:%Y%m%d%H%M%S}.pkl".format(
parameters["experiment"]["base_filename"], timestamp)
print("Writing data to {}".format(filename))
exc.write_data(filename)
sim.end()
return data
def run_simulation(parameters, plot_figure=False):
"""
"""
import pyNN.neuron as sim
timestamp = datetime.now()
model = build_model(**parameters["network"])
if "full_filename" in parameters["experiment"]:
xml_file = parameters["experiment"]["full_filename"].replace(".h5", ".xml")
else:
xml_file = "{}.xml".format(parameters["experiment"]["base_filename"])
model.write(xml_file)
print("Exported model to file {}".format(xml_file))
sim.setup()
print("Building network")
net = Network(sim, xml_file)
stim = net.populations["Ext"]
stim.record('spikes')
exc = net.populations["Exc"]
exc.record("spikes")
n_record = int(parameters["experiment"]["n_record"])
exc[:n_record].record(["nrn_v", "syn_a"])
print("Running simulation")
t_stop = parameters["experiment"]["duration"]
pb = SimulationProgressBar(t_stop/80, t_stop)
sim.run(t_stop, callbacks=[pb])
print("Handling data")
data = {}
if plot_figure:
data["stim"] = stim.get_data().segments[0]
data["exc"] = exc.get_data().segments[0]
data["exc"].annotate(simulator="lib9ML with pyNN.neuron")
else:
if "full_filename" in parameters["experiment"]:
filename = parameters["experiment"]["full_filename"]
else:
filename = "{}_nineml_{:%Y%m%d%H%M%S}.pkl".format(parameters["experiment"]["base_filename"],
timestamp)
print("Writing data to {}".format(filename))
exc.write_data(filename)
sim.end()
return data
def run_simulation(parameters, plot_figure=False):
"""
"""
timestamp = datetime.now()
dt = 0.1
seed = parameters["experiment"]["seed"]
sim.setup(timestep=dt)
print("Building network")
stim, exc, inh = build_network(sim, seed=seed, **parameters["network"])
if plot_figure:
stim[:100].record('spikes')
exc.sample(50).record("spikes")
exc.sample(3).record("nrn_v")
inh.sample(50).record("spikes")
inh.sample(3).record("nrn_v")
else:
all = exc + inh
all.sample(parameters["experiment"]["n_record"]).record("spikes")
print("Running simulation")
t_stop = parameters["experiment"]["duration"]
pb = SimulationProgressBar(t_stop/80, t_stop)
sim.run(t_stop, callbacks=[pb])
print("Handling data")
data = {}
if plot_figure:
data["stim"] = stim.get_data().segments[0]
data["exc"] = exc.get_data().segments[0]
data["inh"] = inh.get_data().segments[0]
else:
if "full_filename" in parameters["experiment"]:
filename = parameters["experiment"]["full_filename"]
else:
filename = "{}_ninemlpartial_{:%Y%m%d%H%M%S}.h5".format(parameters["experiment"]["base_filename"],
timestamp)
print("Writing data to {}".format(filename))
all.write_data(filename)
sim.end()
return data
import pyNN.neuron as sim # can of course replace `nest` with `neuron`, `brian`, etc.
import matplotlib.pyplot as plt
from quantities import nA
sim.setup()
cell = sim.Population(1, sim.HH_cond_exp())
step_current = sim.DCSource(start=20.0, stop=80.0)
step_current.inject_into(cell)
cell.record('v')
for amp in (-0.2, -0.1, 0.0, 0.1, 0.2):
step_current.amplitude = amp
sim.run(100.0)
sim.reset(annotations={"amplitude": amp * nA})
data = cell.get_data()
sim.end()
for segment in data.segments:
vm = segment.analogsignals[0]
plt.plot(vm.times, vm,
label=str(segment.annotations["amplitude"]))
plt.legend(loc="upper left")
plt.xlabel("Time (%s)" % vm.times.units._dimensionality)
plt.ylabel("Membrane potential (%s)" % vm.units._dimensionality)
plt.show()
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
cells.recorders['nmda_g'].write("Results/nineml_neuron.g_nmda", filter=[cells[0]])
cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_cobaExcit", filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:, 1]
v = cells.recorders['iaf_V'].get()[:, 2]
gNMDA = cells.recorders['nmda_g'].get()[:, 2]
gExcit = cells.recorders['cobaExcit_g'].get()[:, 2]
import pylab
pylab.subplot(211)
pylab.plot(t, v)
pylab.ylabel('voltage [mV]')
pylab.subplot(212)
pylab.plot(t, gNMDA, label='g-NMDA')
pylab.plot(t, gExcit, label='g-Excitatory Synapse')
pylab.ylabel('conductance [nS]')
pylab.xlabel('t [ms]')
pylab.legend()
pylab.suptitle("From Tree-Model Pathway")
pylab.show()
sim.end()
def run(plot_and_show=True):
import sys
from os.path import abspath, realpath, join
import nineml
root = abspath(join(realpath(nineml.__path__[0]), "../../.."))
sys.path.append(join(root, "lib9ml/python/examples/AL"))
sys.path.append(join(root, "code_generation/nmodl"))
from nineml.abstraction_layer.example_models import get_hierachical_iaf_2coba
from nineml.abstraction_layer.flattening import ComponentFlattener
import pyNN.neuron as sim
import pyNN.neuron.nineml as pyNNml
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.1, min_delay=0.1)
testModel = get_hierachical_iaf_2coba()
celltype_cls = pyNNml.nineml_celltype_from_model(
name="iaf_2coba",
nineml_model=testModel,
synapse_components=[
pyNNml.CoBaSyn(
namespace='cobaExcit', weight_connector='q'),
pyNNml.CoBaSyn(
namespace='cobaInhib', weight_connector='q'),
]
)
parameters = {
'iaf.cm': 1.0,
'iaf.gl': 50.0,
'iaf.taurefrac': 5.0,
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'cobaExcit.tau': 2.0,
'cobaInhib.tau': 5.0,
'cobaExcit.vrev': 0.0,
'cobaInhib.vrev': -70.0,
}
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(2, sim.SpikeSourcePoisson, {'rate': 100})
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
# connector = sim.OneToOneConnector(weights=20.0, delays=0.5)
conn = [sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
sim.Projection(input[1:2], cells, connector, target='cobaInhib')]
cells._record('iaf_V')
cells._record('cobaExcit_g')
cells._record('cobaInhib_g')
cells._record('regime')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
cells.recorders['regime'].write("Results/nineml_neuron.regime", filter=[cells[0]])
cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
cells.recorders['cobaInhib_g'].write("Results/nineml_neuron.g_inh", filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:, 1]
v = cells.recorders['iaf_V'].get()[:, 2]
regime = cells.recorders['regime'].get()[:, 2]
gInh = cells.recorders['cobaInhib_g'].get()[:, 2]
gExc = cells.recorders['cobaExcit_g'].get()[:, 2]
if plot_and_show:
import pylab
pylab.subplot(311)
pylab.plot(t, v)
pylab.subplot(312)
pylab.plot(t, gInh)
pylab.plot(t, gExc)
pylab.subplot(313)
pylab.plot(t, regime)
pylab.ylim((999, 1005))
pylab.suptitle("From Tree-Model Pathway")
pylab.show()
sim.end()
def run(plot_and_show=True):
import sys
from os.path import abspath, realpath, join
import numpy
import nineml
root = abspath(join(realpath(nineml.__path__[0]), "../../.."))
sys.path.append(join(root, "lib9ml/python/examples/AL"))
sys.path.append(join(root, "code_generation/nmodl"))
sys.path.append(join(root, "code_generation/nest2"))
#from nineml.abstraction_layer.example_models import get_hierachical_iaf_3coba
from nineml.abstraction_layer.testing_utils import TestableComponent
from nineml.abstraction_layer.flattening import ComponentFlattener
import pyNN.neuron as sim
import pyNN.neuron.nineml as pyNNml
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.1, min_delay=0.1)
#test_component = get_hierachical_iaf_3coba()
test_component = TestableComponent('hierachical_iaf_3coba')()
from nineml.abstraction_layer.writers import DotWriter
DotWriter.write(test_component, 'test1.dot')
from nineml.abstraction_layer.writers import XMLWriter
XMLWriter.write(test_component, 'iaf_3coba.xml')
celltype_cls = pyNNml.nineml_celltype_from_model(
name = "iaf_3coba",
nineml_model = test_component,
synapse_components = [
pyNNml.CoBaSyn( namespace='AMPA', weight_connector='q' ),
pyNNml.CoBaSyn( namespace='GABAa', weight_connector='q' ),
pyNNml.CoBaSyn( namespace='GABAb', weight_connector='q' ),
]
)
parameters = {
'iaf.cm': 1.0,
'iaf.gl': 50.0,
'iaf.taurefrac': 5.0,
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'AMPA.tau': 2.0,
'GABAa.tau': 5.0,
'GABAb.tau': 50.0,
'AMPA.vrev': 0.0,
'GABAa.vrev': -70.0,
'GABAb.vrev': -95.0,
}
parameters = ComponentFlattener.flatten_namespace_dict( parameters )
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(3, sim.SpikeSourceArray)
numpy.random.seed(12345)
input[0].spike_times = numpy.add.accumulate(numpy.random.exponential(1000.0/100.0, size=1000))
input[1].spike_times = numpy.add.accumulate(numpy.random.exponential(1000.0/20.0, size=1000))
input[2].spike_times = numpy.add.accumulate(numpy.random.exponential(1000.0/50.0, size=1000))
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [sim.Projection(input[0:1], cells, connector, target='AMPA'),
sim.Projection(input[1:2], cells, connector, target='GABAa'),
sim.Projection(input[2:3], cells, connector, target='GABAb')]
cells._record('iaf_V')
cells._record('AMPA_g')
cells._record('GABAa_g')
cells._record('GABAb_g')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
cells.recorders['AMPA_g'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
cells.recorders['GABAa_g'].write("Results/nineml_neuron.g_gabaA", filter=[cells[0]])
cells.recorders['GABAb_g'].write("Results/nineml_neuron.g_gagaB", filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:,1]
v = cells.recorders['iaf_V'].get()[:,2]
gInhA = cells.recorders['GABAa_g'].get()[:,2]
gInhB = cells.recorders['GABAb_g'].get()[:,2]
gExc = cells.recorders['AMPA_g'].get()[:,2]
if plot_and_show:
import pylab
pylab.subplot(211)
pylab.plot(t,v)
pylab.ylabel('voltage [mV]')
pylab.suptitle("AMPA, GABA_A, GABA_B")
pylab.subplot(212)
pylab.plot(t,gInhA,label='GABA_A')
pylab.plot(t,gInhB, label='GABA_B')
pylab.plot(t,gExc, label='AMPA')
pylab.ylabel('conductance [nS]')
pylab.xlabel('t [ms]')
pylab.legend()
pylab.show()
sim.end()
def t4():
print 'Loading Forth XML File (iaf-2coba-Model)'
print '----------------------------------------'
component = readers.XMLReader.read_component(Join(tenml_dir,
'iaf_2coba.10ml'),
component_name='iaf')
writers.XMLWriter.write(
component,
'/tmp/nineml_toxml4.xml',
)
model = readers.XMLReader.read_component(Join(tenml_dir, 'iaf_2coba.10ml'))
from nineml.abstraction_layer.flattening import flatten
from nineml.abstraction_layer.dynamics.utils.modifiers import (
DynamicsModifier)
flatcomponent = flatten(model, componentname='iaf_2coba')
DynamicsModifier.close_analog_port(component=flatcomponent,
port_name='iaf_iSyn',
value='0')
writers.XMLWriter.write(flatcomponent, '/tmp/nineml_out_iaf_2coba.9ml')
import pyNN.neuron as sim
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.1, min_delay=0.1)
print 'Attempting to simulate From Model:'
print '----------------------------------'
celltype_cls = pyNNml.nineml_celltype_from_model(
name="iaf_2coba",
nineml_model=flatcomponent,
synapse_components=[
pyNNml.CoBaSyn(namespace='cobaExcit', weight_connector='q'),
pyNNml.CoBaSyn(namespace='cobaInhib', weight_connector='q'),
])
parameters = {
'iaf.cm': 1.0,
'iaf.gl': 50.0,
'iaf.taurefrac': 5.0,
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'cobaExcit.tau': 2.0,
'cobaInhib.tau': 5.0,
'cobaExcit.vrev': 0.0,
'cobaInhib.vrev': -70.0,
}
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(2, sim.SpikeSourcePoisson, {'rate': 100})
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [
sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
sim.Projection(input[1:2], cells, connector, target='cobaInhib')
]
cells._record('iaf_V')
cells._record('cobaExcit_g')
cells._record('cobaInhib_g')
cells._record('cobaExcit_I')
cells._record('cobaInhib_I')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V",
filter=[cells[0]])
cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
cells.recorders['cobaInhib_g'].write("Results/nineml_neuron.g_inh",
filter=[cells[0]])
cells.recorders['cobaExcit_I'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
cells.recorders['cobaInhib_I'].write("Results/nineml_neuron.g_inh",
filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:, 1]
v = cells.recorders['iaf_V'].get()[:, 2]
gInh = cells.recorders['cobaInhib_g'].get()[:, 2]
gExc = cells.recorders['cobaExcit_g'].get()[:, 2]
IInh = cells.recorders['cobaInhib_I'].get()[:, 2]
IExc = cells.recorders['cobaExcit_I'].get()[:, 2]
import pylab
pylab.subplot(311)
pylab.ylabel('Voltage')
pylab.plot(t, v)
pylab.subplot(312)
pylab.ylabel('Conductance')
pylab.plot(t, gInh)
pylab.plot(t, gExc)
pylab.subplot(313)
pylab.ylabel('Current')
pylab.plot(t, IInh)
pylab.plot(t, IExc)
pylab.suptitle("From Tree-Model Pathway")
pylab.show()
sim.end()
def t4():
print 'Loading Forth XML File (iaf-2coba-Model)'
print '----------------------------------------'
component = readers.XMLReader.read_component(
Join(tenml_dir, 'iaf_2coba.10ml'), component_name='iaf')
writers.XMLWriter.write(component, '/tmp/nineml_toxml4.xml', )
model = readers.XMLReader.read_component(Join(tenml_dir, 'iaf_2coba.10ml'))
from nineml.abstraction_layer.flattening import flatten
from nineml.abstraction_layer.component_modifiers import ComponentModifier
flatcomponent = flatten(model, componentname='iaf_2coba')
ComponentModifier.close_analog_port(component=flatcomponent, port_name='iaf_iSyn', value='0')
writers.XMLWriter.write(flatcomponent, '/tmp/nineml_out_iaf_2coba.9ml')
import pyNN.neuron as sim
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.1, min_delay=0.1)
print 'Attempting to simulate From Model:'
print '----------------------------------'
celltype_cls = pyNNml.nineml_celltype_from_model(
name="iaf_2coba",
nineml_model=flatcomponent,
synapse_components=[
pyNNml.CoBaSyn(namespace='cobaExcit', weight_connector='q'),
pyNNml.CoBaSyn(namespace='cobaInhib', weight_connector='q'),
]
)
parameters = {
'iaf.cm': 1.0,
'iaf.gl': 50.0,
'iaf.taurefrac': 5.0,
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'cobaExcit.tau': 2.0,
'cobaInhib.tau': 5.0,
'cobaExcit.vrev': 0.0,
'cobaInhib.vrev': -70.0,
}
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(2, sim.SpikeSourcePoisson, {'rate': 100})
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
sim.Projection(input[1:2], cells, connector, target='cobaInhib')]
cells._record('iaf_V')
cells._record('cobaExcit_g')
cells._record('cobaInhib_g')
cells._record('cobaExcit_I')
cells._record('cobaInhib_I')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
cells.recorders['cobaInhib_g'].write("Results/nineml_neuron.g_inh", filter=[cells[0]])
cells.recorders['cobaExcit_I'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
cells.recorders['cobaInhib_I'].write("Results/nineml_neuron.g_inh", filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:, 1]
v = cells.recorders['iaf_V'].get()[:, 2]
gInh = cells.recorders['cobaInhib_g'].get()[:, 2]
gExc = cells.recorders['cobaExcit_g'].get()[:, 2]
IInh = cells.recorders['cobaInhib_I'].get()[:, 2]
IExc = cells.recorders['cobaExcit_I'].get()[:, 2]
import pylab
pylab.subplot(311)
pylab.ylabel('Voltage')
pylab.plot(t, v)
pylab.subplot(312)
pylab.ylabel('Conductance')
pylab.plot(t, gInh)
pylab.plot(t, gExc)
pylab.subplot(313)
pylab.ylabel('Current')
pylab.plot(t, IInh)
pylab.plot(t, IExc)
pylab.suptitle("From Tree-Model Pathway")
pylab.show()
sim.end()
def run(plot_and_show=True):
import sys
from os.path import abspath, realpath, join
import numpy
import nineml
root = abspath(join(realpath(nineml.__path__[0]), "../../.."))
sys.path.append(join(root, "lib9ml/python/examples/AL"))
sys.path.append(join(root, "code_generation/nmodl"))
sys.path.append(join(root, "code_generation/nest2"))
#from nineml.abstraction_layer.example_models import get_hierachical_iaf_3coba
from nineml.abstraction_layer.testing_utils import TestableComponent
from nineml.abstraction_layer.flattening import ComponentFlattener
import pyNN.neuron as sim
import pyNN.neuron.nineml as pyNNml
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.1, min_delay=0.1)
#test_component = get_hierachical_iaf_3coba()
test_component = TestableComponent('hierachical_iaf_3coba')()
from nineml.abstraction_layer.writers import DotWriter
DotWriter.write(test_component, 'test1.dot')
from nineml.abstraction_layer.writers import XMLWriter
XMLWriter.write(test_component, 'iaf_3coba.xml')
celltype_cls = pyNNml.nineml_celltype_from_model(
name="iaf_3coba",
nineml_model=test_component,
synapse_components=[
pyNNml.CoBaSyn(namespace='AMPA', weight_connector='q'),
pyNNml.CoBaSyn(namespace='GABAa', weight_connector='q'),
pyNNml.CoBaSyn(namespace='GABAb', weight_connector='q'),
])
parameters = {
'iaf.cm': 1.0,
'iaf.gl': 50.0,
'iaf.taurefrac': 5.0,
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'AMPA.tau': 2.0,
'GABAa.tau': 5.0,
'GABAb.tau': 50.0,
'AMPA.vrev': 0.0,
'GABAa.vrev': -70.0,
'GABAb.vrev': -95.0,
}
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(3, sim.SpikeSourceArray)
numpy.random.seed(12345)
input[0].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 100.0, size=1000))
input[1].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 20.0, size=1000))
input[2].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 50.0, size=1000))
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [
sim.Projection(input[0:1], cells, connector, target='AMPA'),
sim.Projection(input[1:2], cells, connector, target='GABAa'),
sim.Projection(input[2:3], cells, connector, target='GABAb')
]
cells._record('iaf_V')
cells._record('AMPA_g')
cells._record('GABAa_g')
cells._record('GABAb_g')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V",
filter=[cells[0]])
cells.recorders['AMPA_g'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
cells.recorders['GABAa_g'].write("Results/nineml_neuron.g_gabaA",
filter=[cells[0]])
cells.recorders['GABAb_g'].write("Results/nineml_neuron.g_gagaB",
filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:, 1]
v = cells.recorders['iaf_V'].get()[:, 2]
gInhA = cells.recorders['GABAa_g'].get()[:, 2]
gInhB = cells.recorders['GABAb_g'].get()[:, 2]
gExc = cells.recorders['AMPA_g'].get()[:, 2]
if plot_and_show:
import pylab
pylab.subplot(211)
pylab.plot(t, v)
pylab.ylabel('voltage [mV]')
pylab.suptitle("AMPA, GABA_A, GABA_B")
pylab.subplot(212)
pylab.plot(t, gInhA, label='GABA_A')
pylab.plot(t, gInhB, label='GABA_B')
pylab.plot(t, gExc, label='AMPA')
pylab.ylabel('conductance [nS]')
pylab.xlabel('t [ms]')
pylab.legend()
pylab.show()
sim.end()
STN_LFP_Block = neo.Block(name='STN_LFP')
STN_LFP_seg = neo.Segment(name='segment_0')
STN_LFP_Block.segments.append(STN_LFP_seg)
STN_LFP_signal = neo.AnalogSignal(STN_LFP, units='mV', t_start=0*pq.ms, sampling_rate=pq.Quantity(simulator.state.dt, '1/ms'))
STN_LFP_seg.analogsignals.append(STN_LFP_signal)
w = neo.io.NeoMatlabIO(filename="/Simulation_Output_Results/"+simulation_label+"/STN_LFP.mat")
w.write_block(STN_LFP_Block)
# Write LFP AMPA and GABAa conmponents to file
STN_LFP_AMPA_Block = neo.Block(name='STN_LFP_AMPA')
STN_LFP_AMPA_seg = neo.Segment(name='segment_0')
STN_LFP_AMPA_Block.segments.append(STN_LFP_AMPA_seg)
STN_LFP_AMPA_signal = neo.AnalogSignal(STN_LFP_AMPA, units='mV', t_start=0*pq.ms, sampling_rate=pq.Quantity(simulator.state.dt, '1/ms'))
STN_LFP_AMPA_seg.analogsignals.append(STN_LFP_AMPA_signal)
w = neo.io.NeoMatlabIO(filename="/Simulation_Output_Results/"+simulation_label+"/STN_LFP_AMPA.mat")
w.write_block(STN_LFP_AMPA_Block)
STN_LFP_GABAa_Block = neo.Block(name='STN_LFP_GABAa')
STN_LFP_GABAa_seg = neo.Segment(name='segment_0')
STN_LFP_GABAa_Block.segments.append(STN_LFP_GABAa_seg)
STN_LFP_GABAa_signal = neo.AnalogSignal(STN_LFP_GABAa, units='mV', t_start=0*pq.ms, sampling_rate=pq.Quantity(simulator.state.dt, '1/ms'))
STN_LFP_GABAa_seg.analogsignals.append(STN_LFP_GABAa_signal)
w = neo.io.NeoMatlabIO(filename="/Simulation_Output_Results/"+simulation_label+"/STN_LFP_GABAa.mat")
w.write_block(STN_LFP_GABAa_Block)
"""
print("Steady State Simulation Done!")
end()
def std_pynn_simulation(test_component, parameters, initial_values,
synapse_components, records, plot=True, sim_time=100.,
synapse_weights=1.0, syn_input_rate=100):
from nineml.abstraction_layer.flattening import ComponentFlattener
import pyNN.neuron as sim
import pyNN.neuron.nineml as pyNNml
from pyNN.neuron.nineml import CoBaSyn
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.01, min_delay=0.1)
synapse_components_ML = [CoBaSyn(namespace=ns, weight_connector=wc)
for (ns, wc) in synapse_components]
celltype_cls = pyNNml.nineml_celltype_from_model(
name=test_component.name,
nineml_model=test_component,
synapse_components=synapse_components_ML,
)
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
initial_values = ComponentFlattener.flatten_namespace_dict(initial_values)
cells = sim.Population(1, celltype_cls, parameters)
# Set Initial Values:
for state, state_initial_value in initial_values.iteritems():
cells.initialize(state, state_initial_value)
# For each synapse type, create a spike source:
if synapse_components:
input = sim.Population(
len(synapse_components), sim.SpikeSourcePoisson,
{'rate': syn_input_rate})
connector = sim.OneToOneConnector(weights=synapse_weights, delays=0.5)
conn = []
for i, (ns, weight_connector) in enumerate(synapse_components):
proj = sim.Projection(input[i:i + 1], cells, connector, target=ns),
conn.append(proj)
# Setup the Records:
for record in records:
cells.record(record.what)
cells.record('spikes')
# Run the simulation:
sim.run(sim_time)
if len(records) == 0:
assert False
# Write the Results to a file:
cells.write_data("Results/nineml.pkl")
# Plot the values:
results = cells.get_data().segments[0]
# Create a list of the tags:
tags = []
for record in records:
if not record.tag in tags:
tags.append(record.tag)
# Plot the graphs:
if plot:
import pylab
nGraphs = len(tags)
# Plot the Records:
for graphIndex, tag in enumerate(tags):
pylab.subplot(nGraphs, 1, graphIndex + 1)
for r in records:
if r.tag != tag:
continue
trace = results.filter(name=r.what)[0]
pylab.plot(trace.times, trace, label=r.label)
pylab.ylabel(tag)
pylab.legend()
# Plot the spikes:
# pylab.subplot(nGraphs,1, len(tags)+1)
# t_spikes = cells[0:1].getSpikes()[:1]
# pylab.plot( [1,3],[1,3],'x' )
# print t_spikes
# if t_spikes:
# pylab.scatter( t_spikes, t_spikes )
# Add the X axis to the last plot:
pylab.xlabel('t [ms]')
# pylab.suptitle("From Tree-Model Pathway")
pylab.show()
sim.end()
return results
self.assertEqual(native_cell.esyn.e, native_cell.e_e)
self.assertEqual(native_cell.esyn.U, U)
self.assertRaises(AttributeError, lambda x: native_cell.isyn.U, None)
native_cell.use_Tsodyks_Markram_synapses(
'inhibitory', U, tau_rec, tau_facil, u0)
self.assertEqual(native_cell.isyn.tau, native_cell.tau_i)
self.assertEqual(native_cell.isyn.e, native_cell.e_i)
self.assertEqual(native_cell.isyn.U, U)
class LoadMechanismsTest(unittest.TestCase):
def test_load_mechanisms(self):
self.assertRaises(Exception, neuron.simulator.load_mechanisms, path="/dev/null")
class SetupTest(unittest.TestCase):
def test_cvode(self):
neuron.setup(use_cvode=True)
if __name__ == "__main__":
if '-python' in sys.argv:
sys.argv.remove('-python')
for arg in sys.argv:
if 'bin/nrniv' in arg:
sys.argv.remove(arg)
neuron.setup()
unittest.main()
neuron.end()
#cell_params = {'a':0.1779222, 'b':-5e-09, 'c':-59.52801, 'd':0.1531787, v_init=-'73.32355','i_offset':0}#, u_init, i_offset}
#pop = p.Population(NETSIZE, p.IF_curr_exp(i_offset=0))
neuron_type = p.Izhikevich() #cell_params)
pop = p.Population(NETSIZE, neuron_type)
#for ind in pop:
print(p.connect)
pop.record("spikes")
p.run(100)
pop.set(i_offset=1.0)
p.run(100)
pop.set(i_offset=0.0)
p.run(100)
spikes = pop.get_data("spikes")
p.end()
#p.setup(1.0)
#pop = sim.Population(100, sim.IZKCurrExp(cell_params))
print(spikes)
# In[ ]:
#nldf['From']
#nldf.index('Granule')
#df = nldf[nldf['Name']==3]
#you'll likely need a user for authentication
#user = '******'