def build_network(sim, order=1000, epsilon=0.1, delay=1.5, J=0.1, theta=20.0,
tau=20.0, tau_syn=0.1, tau_refrac=2.0, v_reset=10.0,
R=1.5, g=5, eta=2, seed=None):
NE = 4 * order
NI = 1 * order
CE = int(epsilon * NE) # number of excitatory synapses per neuron
CI = int(epsilon * NI) # number of inhibitory synapses per neuron
CMem = tau/R
J_unit = psp_height(tau, R, tau_syn)
J_ex = J / J_unit
J_in = -g * J_ex
nu_th = theta / (J_ex * CE * R * tau_syn)
nu_ex = eta * nu_th
p_rate = 1000.0 * nu_ex * CE
assert seed is not None
rng = NumpyRNG(seed)
neuron_params = {
"nrn_tau": tau,
"nrn_v_threshold": theta,
"nrn_refractory_period": tau_refrac,
"nrn_v_reset": v_reset,
"nrn_R": R,
"syn_tau": tau_syn
}
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
exc = sim.Population(NE, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**neuron_params))
inh = sim.Population(NI, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**neuron_params))
all = exc + inh
all.initialize(v=RandomDistribution('uniform', (0.0, theta), rng=rng))
stim = sim.Population(NE + NI, nineml_cell_type('Poisson', read("sources/Poisson.xml")['Poisson'], {})(rate=p_rate))
print("Connecting network")
exc_synapse = sim.StaticSynapse(weight=J_ex, delay=delay)
inh_synapse = sim.StaticSynapse(weight=J_in, delay=delay)
input_connections = sim.Projection(stim, all, sim.OneToOneConnector(), exc_synapse, receptor_type="syn")
exc_connections = sim.Projection(exc, all, sim.FixedNumberPreConnector(n=CE), exc_synapse, receptor_type="syn") # check is Pre not Post
inh_connections = sim.Projection(inh, all, sim.FixedNumberPreConnector(n=CI), inh_synapse, receptor_type="syn")
return stim, exc, inh
def test_write_read_roundtrip(self):
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
try:
doc = document.clone()
except:
document.clone()
raise
url = os.path.join(
self._tmp_dir, 'test{}v{}{}'.format(i, version, ext))
nineml.write(url, doc, format=format, version=version,
indent=2)
if self.print_serialized and format in self.printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True)
self.assertTrue(doc.equals(reread_doc),
doc.find_mismatch(reread_doc))
def function():
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
doc = document.clone()
url = os.path.join(_tmp_dir,
'test{}v{}{}'.format(i, version, ext))
nineml.write(url,
doc,
format=format,
version=version,
indent=2)
if print_serialized and format in printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True) # @UnusedVariable
shutil.rmtree(_tmp_dir)
def _ref_network(self, simulator, external_input=None, **kwargs):
if simulator == 'nest':
NetworkClass = NetworkNEST
Simulation = NESTSimulation
elif simulator == 'neuron':
NetworkClass = NetworkNEURON
Simulation = NeuronSimulation
else:
assert False
model = nineml.read(self.reduced_brunel_path).as_network(
'ReducedBrunel')
with Simulation(dt=self.dt * un.ms, seed=self.seed,
**model.delay_limits()) as sim:
network = NetworkClass(model, **kwargs)
if external_input is not None:
network.component_array('Ext').play('spike_input__cell',
external_input)
for pop_name in self.recorded_pops:
network.component_array(pop_name).record('spike_output')
sim.run(self.t_stop * un.ms)
recordings = {}
for pop_name in self.recorded_pops:
recordings[pop_name] = network.component_array(pop_name).recording(
'spike_output')
return recordings
def test_load_and_validate_all(self):
for p in self.iterate_xml_paths(os.path.join(self.repo_root, 'xml')):
# Just check to see whether all elements of the document load
# without error
all_elems = list(nineml.read(p).elements)
def read(self, filename):
document = nineml.read(filename)
return Network(
name='root',
populations=dict((p.name, p) for p in document.populations),
projections=dict((p.name, p) for p in document.projections),
selections=dict((s.name, s) for s in document.selections))
def test_write_read_roundtrip(self):
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
try:
doc = document.clone()
except:
document.clone()
raise
url = os.path.join(self._tmp_dir,
'test{}v{}{}'.format(i, version, ext))
nineml.write(url,
doc,
format=format,
version=version,
indent=2)
if self.print_serialized and format in self.printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True)
self.assertTrue(doc.equals(reread_doc),
doc.find_mismatch(reread_doc))
def write_nmodl(nineml_file, weight_variables={}, hierarchical_mode=False):
components = nineml.read(nineml_file)
output_dir = os.path.dirname(nineml_file)
basename = os.path.basename(nineml_file)
if len(components) == 0:
print 'No components found in file!'
elif len(components) == 1:
output_filename = basename.replace(".xml", ".mod").replace("-", "_")
print "Converting %s to %s" % (nineml_file, output_filename)
write_nmodldirect(component=components[0],
mod_filename=os.path.join(output_dir,
output_filename),
weight_variables=weight_variables,
hierarchical_mode=hierarchical_mode)
else:
for c in components.itervalues():
if isinstance(c, al.DynamicsClass):
output_filename = basename.replace(".xml",
"_%s.mod" % c.name).replace("-",
"_")
print "Converting %s to %s" % (nineml_file, output_filename)
write_nmodldirect(component=c,
mod_filename=os.path.join(output_dir,
output_filename),
weight_variables=weight_variables,
hierarchical_mode=hierarchical_mode)
def test_convert_format(self):
in_path = './' + os.path.join(os.path.relpath(ninemlcatalog.root),
'neuron', 'Izhikevich.xml')
out_path = os.path.join(self.tmpdir, 'Izhikevich.yml')
print(out_path)
args = '{} {}'.format(in_path, out_path)
convert.run(args.split())
# Check the output file is yaml
with open(out_path) as f:
contents = yaml.load(f)
self.assertEqual(list(contents.keys()), [b'NineML'])
# Check the converted document is equivalent
in_doc = read(in_path)
out_doc = read(out_path)
in_doc._url = None
out_doc._url = None
self.assertEqual(in_doc, out_doc)
def test_convert_version(self):
in_path = './' + os.path.join(os.path.relpath(ninemlcatalog.root),
'neuron', 'Izhikevich.xml')
out_path = os.path.join(self.tmpdir, 'Izhikevich.xml')
args = '--nineml_version 2 {} {}'.format(in_path, out_path)
convert.run(args.split())
# Check the document has been written in version 2 format
with open(out_path) as f:
xml = etree.parse(f)
root = xml.getroot()
self.assertEqual(root.tag, '{http://nineml.net/9ML/2.0}NineML')
# Check the converted document is equivalent
in_doc = read(in_path)
out_doc = read(out_path)
in_doc._url = None
out_doc._url = None
self.assertEqual(in_doc, out_doc)
def test_convert_format(self):
in_path = './' + os.path.join(os.path.relpath(ninemlcatalog.root),
'neuron', 'Izhikevich.xml')
out_path = os.path.join(self.tmpdir, 'Izhikevich.yml')
print(out_path)
args = '{} {}'.format(in_path, out_path)
convert.run(args.split())
# Check the output file is yaml
with open(out_path) as f:
contents = yaml.load(f)
self.assertEqual(list(contents.keys()), [b'NineML'])
# Check the converted document is equivalent
in_doc = read(in_path)
out_doc = read(out_path)
in_doc._url = None
out_doc._url = None
self.assertEqual(in_doc, out_doc)
def test_convert_version(self):
in_path = './' + os.path.join(os.path.relpath(ninemlcatalog.root),
'neuron', 'Izhikevich.xml')
out_path = os.path.join(self.tmpdir, 'Izhikevich.xml')
args = '--nineml_version 2 {} {}'.format(in_path, out_path)
convert.run(args.split())
# Check the document has been written in version 2 format
with open(out_path) as f:
xml = etree.parse(f)
root = xml.getroot()
self.assertEqual(root.tag, '{http://nineml.net/9ML/2.0}NineML')
# Check the converted document is equivalent
in_doc = read(in_path)
out_doc = read(out_path)
in_doc._url = None
out_doc._url = None
self.assertEqual(in_doc, out_doc)
def test_to_xml(self):
context = read(os.path.join(examples_dir, 'normal.xml'))
comp_class = context['NormalDistribution']
xml = comp_class.to_xml()
self.assertEquals(_Element, type(xml))
with tempfile.TemporaryFile() as f:
ElementTree(xml).write(f, encoding="UTF-8", pretty_print=True,
xml_declaration=True)
def test_to_xml(self):
document = read(os.path.join(examples_dir, 'AllToAll.xml'))
comp_class = document['AllToAll']
xml = comp_class.to_xml()
self.assertEquals(_Element, type(xml))
with tempfile.TemporaryFile() as f:
ElementTree(xml).write(f, encoding="UTF-8", pretty_print=True,
xml_declaration=True)
def __init__(self, nineml_model, build_mode='lazy', **kwargs):
if isinstance(nineml_model, basestring):
nineml_model = nineml.read(nineml_model).as_network(
name=os.path.splitext(os.path.basename(nineml_model))[0])
elif isinstance(nineml_model, Document):
if nineml_model.url is not None:
name = os.path.splitext(os.path.basename(nineml_model.url))[0]
else:
name = "Anonymous"
nineml_model = nineml_model.as_network(name=name)
self._nineml = nineml_model.clone()
# Get RNG for random distribution values and connectivity
rng = self.Simulation.active().properties_rng
if build_mode != 'build_only':
self.nineml.resample_connectivity(
connectivity_class=self.ConnectivityClass, rng=rng)
(flat_comp_arrays, flat_conn_groups,
flat_selections) = self._flatten_to_arrays_and_conns(self._nineml)
self._component_arrays = {}
# Build the PyNN populations
# Add build args to distinguish models built for this network as
# opposed to other networks
build_url = kwargs.pop('build_url', nineml_model.url)
build_version = nineml_model.name + kwargs.pop('build_version', '')
for name, comp_array in flat_comp_arrays.items():
self._component_arrays[name] = self.ComponentArrayClass(
comp_array, build_mode=build_mode,
build_url=build_url, build_version=build_version, **kwargs)
self._selections = {}
# Build the PyNN Selections
for selection in flat_selections.values():
# TODO: Assumes that selections are only concatenations (which is
# true for 9MLv1.0 but not v2.0)
self._selections[selection.name] = self.SelectionClass(
selection, *[self.component_array(p.name)
for p in selection.populations])
if build_mode != 'build_only':
# Set the connectivity objects of the projections to the
# PyNNConnectivity class
if self.nineml.connectivity_has_been_sampled():
raise Pype9RuntimeError(
"Connections have already been sampled, please reset them"
" using 'resample_connectivity' before constructing "
"network")
self._connection_groups = {}
for name, conn_group in flat_conn_groups.items():
try:
source = self._component_arrays[conn_group.source.name]
except KeyError:
source = self._selections[conn_group.source.name]
try:
destination = self._component_arrays[
conn_group.destination.name]
except KeyError:
destination = self._selections[conn_group.destination.name]
self._connection_groups[name] = self.ConnectionGroupClass(
conn_group, source=source, destination=destination)
self._finalise_construction()
def test_serialization(self):
for ext in ext_to_format:
fname = mkstemp(suffix=ext)[1]
try:
self.d.write(fname, register=False, preserve_order=True)
except NineMLSerializerNotImportedError:
continue
reread_d = nineml.read(fname + '#d')
self._test_indices(reread_d)
def nineml_document(doc_path):
if doc_path.startswith(CATALOG_PREFIX):
model = ninemlcatalog.load(doc_path[len(CATALOG_PREFIX):])
else:
if (not doc_path.startswith('/') and not doc_path.startswith('./')
and not doc_path.startswith('../')):
doc_path = './' + doc_path
model = nineml.read(doc_path, relative_to=os.getcwd())
return model
def read(self, filename):
document = nineml.read(filename)
return Network(name='root',
populations=dict(
(p.name, p) for p in document.populations),
projections=dict(
(p.name, p) for p in document.projections),
selections=dict(
(s.name, s) for s in document.selections))
def test_serialization(self):
for ext in ext_to_format:
fname = mkstemp(suffix=ext)[1]
try:
self.d.write(fname, register=False, preserve_order=True)
except NineMLSerializerNotImportedError:
continue
reread_d = nineml.read(fname + '#d')
self._test_indices(reread_d)
def load(path, name=None):
"""
Retrieves a model from the catalog from the given path
"""
doc = nineml.read(get_full_path(path))
if name is not None:
elem = doc[name]
else:
elem = doc
return elem
def test_to_xml(self):
document = read(os.path.join(examples_dir, 'AllToAll.xml'))
comp_class = document['AllToAll']
xml = comp_class.to_xml()
self.assertEquals(_Element, type(xml))
with tempfile.TemporaryFile() as f:
ElementTree(xml).write(f,
encoding="UTF-8",
pretty_print=True,
xml_declaration=True)
def load(path, name=None):
"""
Retrieves a model from the catalog from the given path
"""
doc = nineml.read(get_full_path(path))
if name is not None:
elem = doc[name]
else:
elem = doc
return elem
def nineml_document(doc_path):
if doc_path.startswith(CATALOG_PREFIX):
model = ninemlcatalog.load(doc_path[len(CATALOG_PREFIX):])
else:
if (not doc_path.startswith('/') and
not doc_path.startswith('./') and
not doc_path.startswith('../')):
doc_path = './' + doc_path
model = nineml.read(doc_path, relative_to=os.getcwd())
return model
def test_url_resolution(self):
tmp_dir = tempfile.mkdtemp()
os.chdir(tmp_dir)
write(self.tmp_path, dynA, dynB)
reread_dynA = read('{}#dynA'.format(self.tmp_path))
self.assertEqual(dynA, reread_dynA)
# Read again using document cache via Dynamics Properties
dynBProps = DynamicsProperties(
name='dynBProps',
definition='{}#dynB'.format(os.path.join(tmp_dir, self.tmp_path)),
properties={'P1': 1, 'P2': 2, 'P3': 3})
self.assertEqual(dynB, dynBProps.component_class)
def test_url_resolution(self):
tmp_dir = tempfile.mkdtemp()
os.chdir(tmp_dir)
write(self.tmp_path, dynA, dynB)
reread_dynA = read('{}#dynA'.format(self.tmp_path))
self.assertEqual(dynA, reread_dynA)
# Read again using document cache via Dynamics Properties
dynBProps = DynamicsProperties(
name='dynBProps',
definition='{}#dynB'.format(os.path.join(tmp_dir, self.tmp_path)),
properties={
'P1': 1,
'P2': 2,
'P3': 3
})
self.assertEqual(dynB, dynBProps.component_class)
def main():
h('{nrn_load_dll("'+LIBNRNMECHPATH+'")}')
dcn = nineml.read(os.path.join(
os.environ['HOME'], 'git', 'CerebellarNuclei', '9ml',
'dcn.xml'))['DCN']
mc = MultiCompartmentSplit(dcn)
#dcn_cell = CellMetaClass(dcn)
mc.check_complexity_file()
mc.setup_sections()
mc.setup_mechanisms()
mc.multisplit()
mc.set_vec_t()
mc.set_vec_v('DCN[100]')
#mc.show_all_sections()
mc.run_simulation()
mc.show_info()
mc.show_plot()
def function():
for version in (1.0, 2.0):
if version == 1.0:
docs = v1_safe_docs
else:
docs = list(instances_of_all_types['NineML'].values())
for format in format_to_serializer: # @ReservedAssignment
try:
ext = format_to_ext[format]
except KeyError:
continue # ones that can't be written to file (e.g. dict)
for i, document in enumerate(docs):
doc = document.clone()
url = os.path.join(
_tmp_dir, 'test{}v{}{}'.format(i, version, ext))
nineml.write(url, doc, format=format, version=version,
indent=2)
if print_serialized and format in printable:
with open(url) as f:
print(f.read())
reread_doc = nineml.read(url, reload=True) # @UnusedVariable
shutil.rmtree(_tmp_dir)
def _ref_network(self, simulator, external_input=None, **kwargs):
nest.ResetKernel()
if simulator == 'nest':
NetworkClass = NetworkNEST
pyNN_simulator = pyNN.nest.simulator.state
elif simulator == 'neuron':
NetworkClass = NetworkNEURON
pyNN_simulator = pyNN.neuron.simulator.state
else:
assert False
model = nineml.read(self.reduced_brunel_path).as_network(
'ReducedBrunel')
network = NetworkClass(model, **kwargs)
if external_input is not None:
network.component_array('Ext').play('spike_input__cell',
external_input)
for pop_name in self.recorded_pops:
network.component_array(pop_name).record('spikes')
pyNN_simulator.run(self.t_stop)
recordings = {}
for pop_name in self.recorded_pops:
recordings[pop_name] = network.component_array(pop_name).recording(
'spikes')
return recordings
def test_xml_540degree_roundtrip(self):
document1 = read(self.test_file)
xml = document1.to_xml()
document2 = load(xml, read_from=self.test_file)
self.assertEquals(document1, document2)
def test_load(self):
document = read(os.path.join(examples_dir, 'Normal.xml'))
self.assertEquals(type(document['NormalDistribution']),
DistributionClass)
def test_xml_540degree_roundtrip(self):
context1 = read(self.test_file)
xml = context1.to_xml()
context2 = load(xml, read_from=self.test_file)
self.assertEquals(context1, context2)
from pyNN.neuron.nineml import nineml_cell_type
from pyNN.utility.plotting import Figure, Panel
t_stop = 100000
dt = 0.1
sim.setup(timestep=dt)
cell_parameters = {'v_reset': 10.0, 'tau_m': 20.0, 'v_rest': 0.0,
'tau_refrac': 2.0, 'v_thresh': 20.0, 'tau_syn_E': 2.0}
p = sim.Population(1, sim.IF_curr_alpha(**cell_parameters))
p.initialize(v=0.0)
rate = 20
stim = sim.Population(1, nineml_cell_type('Poisson', read("../sources/Poisson.xml")['Poisson'], {})(rate=rate))
stim.initialize(t_next=numpy.random.exponential(1000/rate))
weight = 0.1
delay = 0.5
prj = sim.Projection(stim, p,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=weight, delay=delay),
receptor_type='excitatory')
stim.record('spikes')
p.record('v')
sim.run(t_stop)
nrn_data = p.get_data().segments[0]
def test_prototype_xml_540degree_roundtrip(self):
test_file = os.path.join(examples_dir, 'HodgkinHuxleyModified.xml')
document1 = read(test_file)
xml = document1.to_xml()
document2 = load(xml, read_from=test_file)
self.assertEquals(document1, document2)
cell_parameters = {
'v_reset': 10.0,
'tau_m': 20.0,
'v_rest': 0.0,
'tau_refrac': 2.0,
'v_thresh': 20.0,
'tau_syn_E': 2.0
}
p = sim.Population(1, sim.IF_curr_alpha(**cell_parameters))
p.initialize(v=0.0)
rate = 20
stim = sim.Population(
1,
nineml_cell_type('Poisson',
read("../sources/Poisson.xml")['Poisson'], {})(rate=rate))
stim.initialize(t_next=numpy.random.exponential(1000 / rate))
weight = 0.1
delay = 0.5
prj = sim.Projection(stim,
p,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=weight, delay=delay),
receptor_type='excitatory')
stim.record('spikes')
p.record('v')
sim.run(t_stop)
def test_xml_540degree_roundtrip(self):
document1 = read(self.test_file)
xml = document1.to_xml()
document2 = load(xml, read_from=self.test_file)
self.assertEquals(document1.items(), document2.items())
def test_mismatch_dimension(self):
context = read(os.path.join(examples_dir, 'HodgkinHuxleyBadUnits.xml'))
with self.assertRaises(NineMLUnitMismatchError):
context['HodgkinHuxleyBadUnits']
def test_load(self):
context = read(os.path.join(examples_dir, 'normal.xml'))
self.assertEquals(type(context['NormalDistribution']), ComponentClass)
def test_load(self):
document = read(os.path.join(examples_dir, 'Normal.xml'))
self.assertEquals(type(document['NormalDistribution']),
DistributionClass)
def test_load(self):
document = read(os.path.join(examples_dir, 'AllToAll.xml'))
self.assertEquals(type(document['AllToAll']),
ConnectionRuleClass)
def test_mismatch_dimension(self):
document = read(os.path.join(examples_dir, 'HodgkinHuxleyBadUnits.xml'))
with self.assertRaises(NineMLUnitMismatchError):
document['HodgkinHuxleyBadUnits']
def test_component_xml_540degree_roundtrip(self):
test_file = os.path.join(examples_dir, 'HodgkinHuxley.xml')
document1 = read(test_file)
xml = document1.to_xml()
document2 = load(xml, read_from=test_file)
self.assertEquals(document1, document2)
def get_Izh_FS_component():
"""
Load Fast spiking Izhikevich XML definition from file and parse into
Abstraction Layer of Python API.
"""
return nineml.read('NineML_Izh_FS.xml')['IzhikevichClass']
def test_load_and_validate_all(self):
for p in self.iterate_xml_paths(os.path.join(self.repo_root, 'xml')):
# Just check to see whether all elements of the document load
# without error
all_elems = list(nineml.read(p).elements)
def test_prototype_xml_540degree_roundtrip(self):
test_file = os.path.join(examples_dir, 'HodgkinHuxleyModified.xml')
context1 = read(test_file)
xml = context1.to_xml()
context2 = load(xml, read_from=test_file)
self.assertEquals(context1, context2)
evs = cm.getTrajEventTimes('test')
plt.figure(8)
plt.plot(pts['t'], pts['V'], 'k')
plt.title('Combined passive response model')
plt.xlabel('t')
plt.ylabel('V')
# ==========
print("Testing Hodgkin Huxley cell model")
#test_HH()
print("Testing adaptive Integrate and Fire cell model")
#test_aeIF()
#print("Testing compound cell model")
#test_compound()
print("Testing basic Izhikevich cell model")
#test_Izh()
fs = nineml.read('NineML_Izh_FS.xml')
print("Testing Izhikevich fast spiking cell model from XML import")
print(" at three input current levels")
test_Izh_FS([100, 200, 400])
plt.show()
cm = cell_parameters['nrn_tau']/cell_parameters['nrn_R']
nu_thresh = 1000.0 * cell_parameters['nrn_v_threshold'] * cm / (
w_eff * cell_parameters['nrn_tau'] * cell_parameters['syn_tau'])
print("\ntau = {}, R = {}, tau_syn = {}".format(cell_parameters['nrn_tau'],
cell_parameters["nrn_R"],
cell_parameters["syn_tau"]))
print("\nEffective weight = {} nA".format(w_eff))
print("Threshold rate = {} Hz\n".format(nu_thresh))
# PyNN/NineML simulation
sim.setup(timestep=dt)
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("../sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("../sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
p1 = sim.Population(4, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**cell_parameters))
cell_parameters_no_spikes = copy(cell_parameters)
cell_parameters_no_spikes["nrn_v_threshold"] = 1000.0
p2 = sim.Population(4, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**cell_parameters_no_spikes))
stim = sim.Population(4,
nineml_cell_type('Poisson', read("../sources/Poisson.xml")['Poisson'], {})(
rate=[0.5*nu_thresh, nu_thresh, 2*nu_thresh, 0.0]))
prj1 = sim.Projection(stim, p1,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=w_eff, delay=delay),
receptor_type='syn')
def __init__(self, nineml_model, build_mode='lazy', timestep=None,
min_delay=None, max_delay=None, rng=None, **kwargs):
if isinstance(nineml_model, basestring):
nineml_model = nineml.read(nineml_model).as_network(
name=os.path.splitext(os.path.basename(nineml_model))[0])
elif isinstance(nineml_model, Document):
if nineml_model.url is not None:
name = os.path.splitext(os.path.basename(nineml_model.url))[0]
else:
name = "Anonymous"
nineml_model = nineml_model.as_network(name=name)
self._nineml = deepcopy(nineml_model)
timestep = timestep if timestep is not None else self.time_step
min_delay = min_delay if min_delay is not None else self.min_delay
max_delay = max_delay if max_delay is not None else self.max_delay
self._set_simulation_params(timestep=timestep, min_delay=min_delay,
max_delay=max_delay, **kwargs)
self._rng = rng if rng else NumpyRNG()
if build_mode != 'build_only':
# Convert
self.nineml.resample_connectivity(
connectivity_class=self.ConnectivityClass)
(flat_comp_arrays, flat_conn_groups,
flat_selections) = self._flatten_to_arrays_and_conns(self._nineml)
self._component_arrays = {}
code_gen = self.CellCodeGenerator()
# Build the PyNN populations
for name, comp_array in flat_comp_arrays.iteritems():
self._component_arrays[name] = self.ComponentArrayClass(
comp_array, rng=self._rng, build_mode=build_mode,
build_dir=code_gen.get_build_dir(
self.nineml.url, name, group=self.nineml.name), **kwargs)
self._selections = {}
# Build the PyNN Selections
for selection in flat_selections.itervalues():
# TODO: Assumes that selections are only concatenations (which is
# true for 9MLv1.0 but not v2.0)
self._selections[selection.name] = self.SelectionClass(
selection, *[self.component_array(p.name)
for p in selection.populations])
if build_mode != 'build_only':
# Set the connectivity objects of the projections to the
# PyNNConnectivity class
if self.nineml.connectivity_has_been_sampled():
raise Pype9RuntimeError(
"Connections have already been sampled, please reset them"
" using 'resample_connectivity' before constructing "
"network")
self._connection_groups = {}
for name, conn_group in flat_conn_groups.iteritems():
try:
source = self._component_arrays[conn_group.source.name]
except KeyError:
source = self._selections[conn_group.source.name]
try:
destination = self._component_arrays[
conn_group.destination.name]
except KeyError:
destination = self._selections[conn_group.destination.name]
self._connection_groups[name] = self.ConnectionGroupClass(
conn_group, source=source, destination=destination,
rng=self._rng)
self._finalise_construction()
def test_load(self):
document = read(os.path.join(examples_dir, 'AllToAll.xml'))
self.assertEquals(type(document['AllToAll']), ConnectionRuleClass)
def read(url, class_map=class_map, **kwargs):
return nineml.read(url, class_map=class_map, **kwargs)
weight = 0.1 # EPSP height from a single spike received at resting potential
scale_factor = psp_height(cell_parameters['nrn_tau'],
cell_parameters["nrn_R"],
cell_parameters["syn_tau"])
w_eff = weight/scale_factor
delay = 0.5
print("\nEffective weight = {} nA\n".format(w_eff))
# PyNN/NineML simulation
sim.setup(timestep=dt)
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("../sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("../sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
p = sim.Population(2, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**cell_parameters))
stim = sim.Population(1, sim.SpikeSourceArray(spike_times=spike_times))
prj = sim.Projection(stim, p,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=w_eff, delay=delay),
receptor_type='syn')
p.record(['nrn_v', 'syn_a', 'syn_b'])
sim.run(t_stop)
def read(url, class_map=class_map, **kwargs):
return nineml.read(url, class_map=class_map, **kwargs)
