def create_object(name, color, x=0, y=0, z=0):
obj = Cell()
obj.name = name
obj.id = name
nml_doc.cells.append(obj)
morphology = Morphology(id='mm')
obj.morphology = morphology
pop = Population(id="Pop_%s" % name,
component=obj.id,
type="populationList",
size=0)
net.populations.append(pop)
populations[name] = pop
pop.properties.append(Property(tag="color", value=color))
add_instance(name, x, y, z)
sg = SegmentGroup(id='all')
obj.morphology.segment_groups.append(sg)
return obj
def generate(net_id,
params,
cells=None,
cells_to_plot=None,
cells_to_stimulate=None,
include_muscles=False,
conn_number_override=None,
conn_number_scaling=None,
duration=500,
dt=0.01,
vmin=None,
vmax=None,
seed=1234,
validate=True,
test=False,
verbose=True,
target_directory='./'):
root_dir = os.path.dirname(os.path.abspath(__file__))
params.create_models()
if vmin == None:
if params.level == 'A':
vmin = -72
elif params.level == 'B':
vmin = -52
elif params.level == 'C':
vmin = -60
else:
vmin = -52
if vmax == None:
if params.level == 'A':
vmax = -48
elif params.level == 'B':
vmax = -28
elif params.level == 'C':
vmax = 25
else:
vmax = -28
random.seed(seed)
info = "\n\nParameters and setting used to generate this network:\n\n"+\
" Cells: %s\n" % (cells if cells is not None else "All cells")+\
" Cell stimulated: %s\n" % (cells_to_stimulate if cells_to_stimulate is not None else "All cells")+\
" Connection numbers overridden: %s\n" % (conn_number_override if conn_number_override is not None else "None")+\
" Connection numbers scaled: %s\n" % (conn_number_scaling if conn_number_scaling is not None else "None")+\
" Include muscles: %s\n" % include_muscles
print_(info)
info += "\n%s\n" % (params.bioparameter_info(" "))
nml_doc = NeuroMLDocument(id=net_id, notes=info)
if params.level == "A" or params.level == "B" or params.level == "BC1":
nml_doc.iaf_cells.append(params.generic_muscle_cell)
nml_doc.iaf_cells.append(params.generic_neuron_cell)
elif params.level == "C":
nml_doc.cells.append(params.generic_muscle_cell)
nml_doc.cells.append(params.generic_neuron_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
if params.level == "C" or params.level == "C1":
nml_doc.fixed_factor_concentration_models.append(
params.concentration_model)
cell_names, conns = get_cell_names_and_connection()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {
"comment": info,
"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_neuron_cell.id
}
lems_info["plots"] = []
lems_info["activity_plots"] = []
lems_info["muscle_plots"] = []
lems_info["muscle_activity_plots"] = []
lems_info["to_save"] = []
lems_info["activity_to_save"] = []
lems_info["muscles_to_save"] = []
lems_info["muscles_activity_to_save"] = []
lems_info["cells"] = []
lems_info["muscles"] = []
lems_info["includes"] = []
if params.custom_component_types_definitions:
lems_info["includes"].append(params.custom_component_types_definitions)
if target_directory != './':
def_file = "%s/%s" % (os.path.dirname(os.path.abspath(__file__)),
params.custom_component_types_definitions)
shutil.copy(def_file, target_directory)
nml_doc.includes.append(
IncludeType(href=params.custom_component_types_definitions))
backers_dir = root_dir + "/../../../../OpenWormBackers/" if test else root_dir + "/../../../OpenWormBackers/"
sys.path.append(backers_dir)
import backers
cells_vs_name = backers.get_adopted_cell_names(backers_dir)
populations_without_location = False # isinstance(params.elec_syn, GapJunction)
count = 0
for cell in cell_names:
if cells is None or cell in cells:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_neuron_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_neuron_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(cell):
p = Property(tag="OpenWormBackerAssignedName",
value=cells_vs_name[cell])
pop0.properties.append(p)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file_path = root_dir + "/../../../" if test else root_dir + "/../../" #if running test
cell_file = cell_file_path + 'generatedNeuroML2/%s.cell.nml' % cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
location = doc.cells[0].morphology.segments[0].proximal
if verbose:
print_(
"Loaded morphology: %s; id: %s; location: (%s, %s, %s)" %
(os.path.realpath(cell_file), all_cells[cell].id,
location.x, location.y, location.z))
inst.location = Location(float(location.x), float(location.y),
float(location.z))
target = "../%s/0/%s" % (pop0.id, params.generic_neuron_cell.id)
if populations_without_location:
target = "../%s[0]" % (cell)
if cells_to_stimulate is None or cell in cells_to_stimulate:
input_list = InputList(id="Input_%s_%s" %
(cell, params.offset_current.id),
component=params.offset_current.id,
populations='%s' % cell)
input_list.input.append(
Input(id=0, target=target, destination="synapses"))
net.input_lists.append(input_list)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (
cell, params.generic_neuron_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (cell)
lems_info["plots"].append(plot)
if params.generic_neuron_cell.__class__.__name__ == 'IafActivityCell':
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (
cell, params.generic_neuron_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_plots"].append(plot)
if params.generic_neuron_cell.__class__.__name__ == 'Cell':
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/caConc" % (
cell, params.generic_neuron_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/caConc" % (cell)
lems_info["activity_plots"].append(plot)
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/v" % (cell,
params.generic_neuron_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (cell)
lems_info["to_save"].append(save)
if params.generic_neuron_cell.__class__.__name__ == 'IafActivityCell':
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/activity" % (
cell, params.generic_neuron_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_to_save"].append(save)
if params.generic_neuron_cell.__class__.__name__ == 'Cell':
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/caConc" % (
cell, params.generic_neuron_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/caConc" % (cell)
lems_info["activity_to_save"].append(save)
lems_info["cells"].append(cell)
count += 1
if verbose:
print_("Finished loading %i cells" % count)
mneurons, all_muscles, muscle_conns = get_cell_muscle_names_and_connection(
)
muscles = get_muscle_names()
if include_muscles:
muscle_count = 0
for muscle in muscles:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_muscle_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_muscle_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(muscle):
# No muscles adopted yet, but just in case they are in future...
p = Property(tag="OpenWormBackerAssignedName",
value=cells_vs_name[muscle])
pop0.properties.append(p)
x = 80 * (-1 if muscle[1] == 'V' else 1)
z = 80 * (-1 if muscle[2] == 'L' else 1)
y = -300 + 30 * int(muscle[3:5])
print_('Positioning muscle: %s at (%s,%s,%s)' % (muscle, x, y, z))
inst.location = Location(x, y, z)
target = "%s/0/%s" % (pop0.id, params.generic_muscle_cell.id)
if populations_without_location:
target = "%s[0]" % (muscle)
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (muscle,
params.generic_muscle_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscle_plots"].append(plot)
if params.generic_muscle_cell.__class__.__name__ == 'IafActivityCell':
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (
muscle, params.generic_muscle_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscle_activity_plots"].append(plot)
if params.generic_muscle_cell.__class__.__name__ == 'Cell':
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/caConc" % (
muscle, params.generic_muscle_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/caConc" % (muscle)
lems_info["muscle_activity_plots"].append(plot)
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/v" % (muscle,
params.generic_muscle_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscles_to_save"].append(save)
if params.generic_muscle_cell.__class__.__name__ == 'IafActivityCell':
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/activity" % (
muscle, params.generic_muscle_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscles_activity_to_save"].append(save)
if params.generic_muscle_cell.__class__.__name__ == 'Cell':
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/caConc" % (
muscle, params.generic_muscle_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/caConc" % (muscle)
lems_info["muscles_activity_to_save"].append(save)
lems_info["muscles"].append(muscle)
muscle_count += 1
if verbose:
print_("Finished creating %i muscles" % muscle_count)
existing_synapses = {}
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info[
"cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell,
conn.synclass, conn.syntype)
elect_conn = False
analog_conn = False
syn0 = params.neuron_to_neuron_exc_syn
if 'GABA' in conn.synclass:
syn0 = params.neuron_to_neuron_inh_syn
if '_GJ' in conn.synclass:
syn0 = params.neuron_to_neuron_elec_syn
elect_conn = isinstance(params.neuron_to_neuron_elec_syn,
GapJunction)
if isinstance(syn0, GradedSynapse):
analog_conn = True
if len(nml_doc.silent_synapses) == 0:
silent = SilentSynapse(id="silent")
nml_doc.silent_synapses.append(silent)
number_syns = conn.number
conn_shorthand = "%s-%s" % (conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (
conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (
conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number * conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = bioparameters.split_neuroml_quantity(
syn0.gbase)
cond0 = "%s%s" % (magnitude * conn.number, unit)
cond1 = "%s%s" % (magnitude * number_syns, unit)
if verbose:
print_(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc,
existing_synapses)
if elect_conn:
if populations_without_location:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
pre_cell_id = "../%s/0/%s" % (
conn.pre_cell, params.generic_neuron_cell.id)
post_cell_id = "../%s/0/%s" % (
conn.post_cell, params.generic_neuron_cell.id)
#print_("Conn %s -> %s"%(pre_cell_id,post_cell_id))
# Add a Connection with the closest locations
conn0 = ElectricalConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
synapse=syn_new.id)
proj0.electrical_connection_instances.append(conn0)
elif analog_conn:
proj0 = ContinuousProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.continuous_projections.append(proj0)
pre_cell_id = "../%s/0/%s" % (conn.pre_cell,
params.generic_neuron_cell.id)
post_cell_id = "../%s/0/%s" % (conn.post_cell,
params.generic_neuron_cell.id)
conn0 = ContinuousConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
pre_component="silent",
post_component=syn_new.id)
proj0.continuous_connection_instances.append(conn0)
else:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
pre_cell_id = "../%s/0/%s" % (
conn.pre_cell, params.generic_neuron_cell.id)
post_cell_id = "../%s/0/%s" % (
conn.post_cell, params.generic_neuron_cell.id)
conn0 = ConnectionWD(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id,
weight = number_syns,
delay = '0ms')
proj0.connection_wds.append(conn0)
if populations_without_location:
raise NotImplementedError
'''
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id="%s[0]"%(conn.pre_cell)
post_cell_id="%s[0]"%(conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)'''
if include_muscles:
for conn in muscle_conns:
if conn.pre_cell in lems_info[
"cells"] and conn.post_cell in muscles:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell,
conn.synclass, conn.syntype)
elect_conn = False
analog_conn = False
syn0 = params.neuron_to_muscle_exc_syn
if 'GABA' in conn.synclass:
syn0 = params.neuron_to_muscle_inh_syn
if '_GJ' in conn.synclass:
syn0 = params.neuron_to_muscle_elec_syn
elect_conn = isinstance(params.neuron_to_muscle_elec_syn,
GapJunction)
if isinstance(syn0, GradedSynapse):
analog_conn = True
if len(nml_doc.silent_synapses) == 0:
silent = SilentSynapse(id="silent")
nml_doc.silent_synapses.append(silent)
number_syns = conn.number
conn_shorthand = "%s-%s" % (conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (
conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (
conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number * conn_number_scaling[
conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = bioparameters.split_neuroml_quantity(
syn0.gbase)
cond0 = "%s%s" % (magnitude * conn.number, unit)
cond1 = "%s%s" % (magnitude * number_syns, unit)
if verbose:
print_(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns,
nml_doc,
existing_synapses)
if elect_conn:
if populations_without_location:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
pre_cell_id = "../%s/0/%s" % (
conn.pre_cell, params.generic_neuron_cell.id)
post_cell_id = "../%s/0/%s" % (
conn.post_cell, params.generic_muscle_cell.id)
#print_("Conn %s -> %s"%(pre_cell_id,post_cell_id))
# Add a Connection with the closest locations
conn0 = ElectricalConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
synapse=syn_new.id)
proj0.electrical_connection_instances.append(conn0)
elif analog_conn:
proj0 = ContinuousProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.continuous_projections.append(proj0)
pre_cell_id = "../%s/0/%s" % (
conn.pre_cell, params.generic_neuron_cell.id)
post_cell_id = "../%s/0/%s" % (
conn.post_cell, params.generic_muscle_cell.id)
conn0 = ContinuousConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
pre_component="silent",
post_component=syn_new.id)
proj0.continuous_connection_instances.append(conn0)
else:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id = "../%s/0/%s" % (
conn.pre_cell, params.generic_neuron_cell.id)
post_cell_id = "../%s/0/%s" % (
conn.post_cell, params.generic_muscle_cell.id)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
if populations_without_location:
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id = "%s[0]" % (conn.pre_cell)
post_cell_id = "%s[0]" % (conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)
# import pprint
# pprint.pprint(lems_info)
template_path = root_dir + '/../' if test else root_dir + '/' # if running test
write_to_file(nml_doc,
lems_info,
net_id,
template_path,
validate=validate,
verbose=verbose,
target_directory=target_directory)
return nml_doc
amplitude=args.ie0,
period='25ms')
nml_doc.sine_generator_dls.append(pulse)
else:
pulse = SineGenerator(id='mod_%s' % pop,
phase='0',
delay='0ms',
duration='%sms' % duration,
amplitude='%snA' % args.ie0,
period='25ms')
nml_doc.sine_generators.append(pulse)
# Create the network
net = Network(id='net1')
net.properties.append(Property('recommended_dt_ms', dt))
net.properties.append(Property('recommended_duration_ms', duration))
nml_doc.networks.append(net)
nml_doc.includes.append(IncludeType('WC_Parameters%s.xml' % dl_str))
colours = ['1 0 0', '0 0 1']
for pop_idx, pop in enumerate(pops):
population = Population(id='%sPop' % pop,
component=(pops[pop_idx]),
size=n_pops[pop_idx],
type='populationList')
net.populations.append(population)
population.properties.append(Property(tag='color', value=colours[pop_idx]))
population.properties.append(Property(tag='radius', value='10'))
def generate(net_id,
params,
cells = None,
cells_to_plot = None,
cells_to_stimulate = None,
include_muscles=False,
conn_number_override = None,
conn_number_scaling = None,
duration = 500,
dt = 0.01,
vmin = -75,
vmax = 20,
seed = 1234,
validate=True, test=False):
random.seed(seed)
info = "\n\nParameters and setting used to generate this network:\n\n"+\
" Cells: %s\n" % (cells if cells is not None else "All cells")+\
" Cell stimulated: %s\n" % (cells_to_stimulate if cells_to_stimulate is not None else "All cells")+\
" Connection numbers overridden: %s\n" % (conn_number_override if conn_number_override is not None else "None")+\
" Connection numbers scaled: %s\n" % (conn_number_scaling if conn_number_scaling is not None else "None")+\
" Include muscles: %s\n" % include_muscles
info += "\n%s\n"%(bioparameter_info(" "))
nml_doc = NeuroMLDocument(id=net_id, notes=info)
nml_doc.iaf_cells.append(params.generic_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
# Use the spreadsheet reader to give a list of all cells and a list of all connections
# This could be replaced with a call to "DatabaseReader" or "OpenWormNeuroLexReader" in future...
# If called from unittest folder ammend path to "../../../../"
spreadsheet_location = "../../../../" if test else "../../../"
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet(spreadsheet_location, include_nonconnected_cells=True)
cell_names.sort()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {"comment": info,
"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_cell.id}
lems_info["plots"] = []
lems_info["activity_plots"] = []
lems_info["muscle_plots"] = []
lems_info["muscle_activity_plots"] = []
lems_info["to_save"] = []
lems_info["activity_to_save"] = []
lems_info["muscles_to_save"] = []
lems_info["muscles_activity_to_save"] = []
lems_info["cells"] = []
lems_info["muscles"] = []
lems_info["includes"] = []
if hasattr(params.generic_cell, 'custom_component_type_definition'):
lems_info["includes"].append(params.generic_cell.custom_component_type_definition)
backers_dir = "../../../../OpenWormBackers/" if test else "../../../OpenWormBackers/"
sys.path.append(backers_dir)
import backers
cells_vs_name = backers.get_adopted_cell_names(backers_dir)
populations_without_location = isinstance(params.elec_syn, GapJunction)
count = 0
for cell in cell_names:
if cells is None or cell in cells:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(cell):
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[cell])
pop0.properties.append(p)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file_path = "../../../" if test else "../../" #if running test
cell_file = cell_file_path+'generatedNeuroML2/%s.nml'%cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
location = doc.cells[0].morphology.segments[0].proximal
print("Loaded morphology file from: %s, with id: %s, location: (%s, %s, %s)"%(cell_file, all_cells[cell].id, location.x, location.y, location.z))
inst.location = Location(float(location.x), float(location.y), float(location.z))
target = "%s/0/%s"%(pop0.id, params.generic_cell.id)
if populations_without_location:
target = "%s[0]" % (cell)
exp_input = ExplicitInput(target=target, input=params.offset_current.id)
if cells_to_stimulate is None or cell in cells_to_stimulate:
net.explicit_inputs.append(exp_input)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (cell, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (cell)
lems_info["plots"].append(plot)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (cell, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_plots"].append(plot)
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/v" % (cell, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (cell)
lems_info["to_save"].append(save)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/activity" % (cell, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (cell)
lems_info["activity_to_save"].append(save)
lems_info["cells"].append(cell)
count+=1
print("Finished loading %i cells"%count)
mneurons, all_muscles, muscle_conns = SpreadsheetDataReader.readMuscleDataFromSpreadsheet(spreadsheet_location)
muscles = get_muscle_names()
if include_muscles:
muscle_count = 0
for muscle in muscles:
inst = Instance(id="0")
if not populations_without_location:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_cell.id,
type="populationList")
pop0.instances.append(inst)
else:
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_cell.id,
size="1")
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(muscle):
# No muscles adopted yet, but just in case they are in future...
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[muscle])
pop0.properties.append(p)
inst.location = Location(100, 10*muscle_count, 100)
target = "%s/0/%s"%(pop0.id, params.generic_cell.id)
if populations_without_location:
target = "%s[0]" % (muscle)
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (muscle, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscle_plots"].append(plot)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_cell.id)
if populations_without_location:
plot["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscle_activity_plots"].append(plot)
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/v" % (muscle, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/v" % (muscle)
lems_info["muscles_to_save"].append(save)
if hasattr(params.generic_cell, 'custom_component_type_definition'):
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_cell.id)
if populations_without_location:
save["quantity"] = "%s[0]/activity" % (muscle)
lems_info["muscles_activity_to_save"].append(save)
lems_info["muscles"].append(muscle)
muscle_count+=1
print("Finished creating %i muscles"%muscle_count)
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info["cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
elect_conn = False
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
if '_GJ' in conn.synclass:
syn0 = params.elec_syn
elect_conn = isinstance(params.elec_syn, GapJunction)
number_syns = conn.number
conn_shorthand = "%s-%s"%(conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s"%(magnitude*number_syns, unit)
print(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc)
if not elect_conn:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id)
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
if populations_without_location:
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id="%s[0]"%(conn.pre_cell)
post_cell_id="%s[0]"%(conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
if include_muscles:
for conn in muscle_conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in muscles:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
elect_conn = False
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
if '_GJ' in conn.synclass:
syn0 = params.elec_syn
elect_conn = isinstance(params.elec_syn, GapJunction)
number_syns = conn.number
conn_shorthand = "%s-%s"%(conn.pre_cell, conn.post_cell)
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
if number_syns != conn.number:
magnitude, unit = split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s"%(magnitude*number_syns, unit)
print(">> Changing number of effective synapses connection %s -> %s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc)
if not elect_conn:
if not populations_without_location:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id="../%s/0/%s"%(conn.pre_cell, params.generic_cell.id)
post_cell_id="../%s/0/%s"%(conn.post_cell, params.generic_cell.id)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
if populations_without_location:
# <synapticConnection from="hh1pop[0]" to="hh2pop[0]" synapse="syn1exp" destination="synapses"/>
pre_cell_id="%s[0]"%(conn.pre_cell)
post_cell_id="%s[0]"%(conn.post_cell)
conn0 = SynapticConnection(from_=pre_cell_id,
to=post_cell_id,
synapse=syn_new.id,
destination="synapses")
net.synaptic_connections.append(conn0)
else:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
# Add a Connection with the closest locations
conn0 = ElectricalConnection(id="0", \
pre_cell="0",
post_cell="0",
synapse=syn_new.id)
proj0.electrical_connections.append(conn0)
# import pprint
# pprint.pprint(lems_info)
template_path = '../' if test else '' # if running test
write_to_file(nml_doc, lems_info, net_id, template_path, validate=validate)
return nml_doc
pop1 = Population(id="IafPop1", component=IafCell1.id, size=size1)
net.populations.append(pop1)
size2 = int(5 * scale)
pop2 = Population(id="IzhPop", component=iz0.id, size=size2)
net.populations.append(pop2)
cell_num = int(4 * scale)
pop = Population(id="Pop_x",
component=IafCell0.id,
type="populationList",
size=cell_num)
net.populations.append(pop)
pop.properties.append(Property(tag="color", value="1 0 0"))
x_size = 500
y_size = 500
z_size = 500
for i in range(cell_num):
inst = Instance(id=i)
pop.instances.append(inst)
inst.location = Location(x=str(x_size * random.random()),
y=str(y_size * random.random()),
z=str(z_size * random.random()))
prob_connection = 0.5
proj_count = 0
def run():
cell_num = 10
x_size = 500
y_size = 500
z_size = 500
nml_doc = NeuroMLDocument(id="Net3DExample")
syn0 = ExpOneSynapse(id="syn0", gbase="65nS", erev="0mV", tau_decay="3ms")
nml_doc.exp_one_synapses.append(syn0)
net = Network(id="Net3D")
nml_doc.networks.append(net)
proj_count = 0
#conn_count = 0
for cell_id in range(0, cell_num):
cell = Cell(id="Cell_%i" % cell_id)
cell.morphology = generateRandomMorphology()
nml_doc.cells.append(cell)
pop = Population(id="Pop_%i" % cell_id,
component=cell.id,
type="populationList")
net.populations.append(pop)
pop.properties.append(Property(tag="color", value="1 0 0"))
inst = Instance(id="0")
pop.instances.append(inst)
inst.location = Location(x=str(x_size * random()),
y=str(y_size * random()),
z=str(z_size * random()))
prob_connection = 0.5
for post in range(0, cell_num):
if post is not cell_id and random() <= prob_connection:
from_pop = "Pop_%i" % cell_id
to_pop = "Pop_%i" % post
pre_seg_id = 0
post_seg_id = 1
projection = Projection(id="Proj_%i" % proj_count,
presynaptic_population=from_pop,
postsynaptic_population=to_pop,
synapse=syn0.id)
net.projections.append(projection)
connection = Connection(id=proj_count, \
pre_cell_id="%s[%i]"%(from_pop,0), \
pre_segment_id=pre_seg_id, \
pre_fraction_along=random(),
post_cell_id="%s[%i]"%(to_pop,0), \
post_segment_id=post_seg_id,
post_fraction_along=random())
projection.connections.append(connection)
proj_count += 1
#net.synaptic_connections.append(SynapticConnection(from_="%s[%i]"%(from_pop,0), to="%s[%i]"%(to_pop,0)))
####### Write to file ######
nml_file = 'tmp/net3d.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Written network file to: " + nml_file)
###### Validate the NeuroML ######
from neuroml.utils import validate_neuroml2
validate_neuroml2(nml_file)
def generate(net_id,
params,
data_reader = "SpreadsheetDataReader",
cells = None,
cells_to_plot = None,
cells_to_stimulate = None,
muscles_to_include=[],
conns_to_include=[],
conn_number_override = None,
conn_number_scaling = None,
conn_polarity_override = None,
duration = 500,
dt = 0.01,
vmin = None,
vmax = None,
seed = 1234,
test=False,
verbose=True,
param_overrides={},
target_directory='./'):
validate = not (params.is_level_B() or params.is_level_C0())
root_dir = os.path.dirname(os.path.abspath(__file__))
for k in param_overrides.keys():
v = param_overrides[k]
print_("Setting parameter %s = %s"%(k,v))
params.set_bioparameter(k, v, "Set with param_overrides", 0)
params.create_models()
if vmin==None:
if params.is_level_A():
vmin=-72
elif params.is_level_B():
vmin=-52
elif params.is_level_C():
vmin=-60
elif params.is_level_D():
vmin=-60
else:
vmin=-52
if vmax==None:
if params.is_level_A():
vmax=-48
elif params.is_level_B():
vmax=-28
elif params.is_level_C():
vmax=25
elif params.is_level_D():
vmax=25
else:
vmax=-28
random.seed(seed)
info = "\n\nParameters and setting used to generate this network:\n\n"+\
" Data reader: %s\n" % data_reader+\
" Cells: %s\n" % (cells if cells is not None else "All cells")+\
" Cell stimulated: %s\n" % (cells_to_stimulate if cells_to_stimulate is not None else "All neurons")+\
" Connection: %s\n" % (conns_to_include if conns_to_include is not None else "All connections") + \
" Connection numbers overridden: %s\n" % (conn_number_override if conn_number_override is not None else "None")+\
" Connection numbers scaled: %s\n" % (conn_number_scaling if conn_number_scaling is not None else "None")+ \
" Connection polarities override: %s\n" % conn_polarity_override + \
" Muscles: %s\n" % (muscles_to_include if muscles_to_include is not None else "All muscles")
if verbose: print_(info)
info += "\n%s\n"%(params.bioparameter_info(" "))
nml_doc = NeuroMLDocument(id=net_id, notes=info)
if params.is_level_A() or params.is_level_B() or params.level == "BC1":
nml_doc.iaf_cells.append(params.generic_muscle_cell)
nml_doc.iaf_cells.append(params.generic_neuron_cell)
elif params.is_level_C():
nml_doc.cells.append(params.generic_muscle_cell)
nml_doc.cells.append(params.generic_neuron_cell)
elif params.is_level_D():
nml_doc.cells.append(params.generic_muscle_cell)
net = Network(id=net_id)
nml_doc.networks.append(net)
nml_doc.pulse_generators.append(params.offset_current)
if is_cond_based_cell(params):
nml_doc.fixed_factor_concentration_models.append(params.concentration_model)
cell_names, conns = get_cell_names_and_connection(data_reader)
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {"comment": info,
"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax}
lems_info["plots"] = []
lems_info["activity_plots"] = []
lems_info["muscle_plots"] = []
lems_info["muscle_activity_plots"] = []
lems_info["to_save"] = []
lems_info["activity_to_save"] = []
lems_info["muscles_to_save"] = []
lems_info["muscles_activity_to_save"] = []
lems_info["cells"] = []
lems_info["muscles"] = []
lems_info["includes"] = []
if params.custom_component_types_definitions:
if isinstance(params.custom_component_types_definitions, str):
params.custom_component_types_definitions = [params.custom_component_types_definitions]
for ctd in params.custom_component_types_definitions:
lems_info["includes"].append(ctd)
if target_directory != './':
def_file = "%s/%s"%(os.path.dirname(os.path.abspath(__file__)), ctd)
shutil.copy(def_file, target_directory)
nml_doc.includes.append(IncludeType(href=ctd))
backers_dir = root_dir+"/../../../../OpenWormBackers/" if test else root_dir+"/../../../OpenWormBackers/"
sys.path.append(backers_dir)
import backers
cells_vs_name = backers.get_adopted_cell_names(backers_dir)
count = 0
for cell in cell_names:
if cells is None or cell in cells:
inst = Instance(id="0")
if not params.is_level_D():
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_neuron_cell.id,
type="populationList")
cell_id = params.generic_neuron_cell.id
else:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=cell,
type="populationList")
cell_id = cell
pop0.instances.append(inst)
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(cell):
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[cell])
pop0.properties.append(p)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file_path = root_dir+"/../../../" if test else root_dir+"/../../" #if running test
cell_file = cell_file_path+'generatedNeuroML2/%s.cell.nml'%cell
doc = loaders.NeuroMLLoader.load(cell_file)
all_cells[cell] = doc.cells[0]
if params.is_level_D():
new_cell = params.create_neuron_cell(cell, doc.cells[0].morphology)
nml_cell_doc = NeuroMLDocument(id=cell)
nml_cell_doc.cells.append(new_cell)
new_cell_file = 'cells/'+cell+'_D.cell.nml'
nml_file = target_directory+'/'+new_cell_file
print_("Writing new cell to: %s"%os.path.realpath(nml_file))
writers.NeuroMLWriter.write(nml_cell_doc, nml_file)
nml_doc.includes.append(IncludeType(href=new_cell_file))
lems_info["includes"].append(new_cell_file)
inst.location = Location(0,0,0)
else:
location = doc.cells[0].morphology.segments[0].proximal
inst.location = Location(float(location.x), float(location.y), float(location.z))
if verbose:
print_("Loaded morphology: %s; id: %s; placing at location: (%s, %s, %s)"%(os.path.realpath(cell_file), all_cells[cell].id, inst.location.x, inst.location.y, inst.location.z))
if cells_to_stimulate is None or cell in cells_to_stimulate:
target = "../%s/0/%s"%(pop0.id, cell_id)
if params.is_level_D():
target+="/0"
input_list = InputList(id="Input_%s_%s"%(cell,params.offset_current.id),
component=params.offset_current.id,
populations='%s'%cell)
input_list.input.append(Input(id=0,
target=target,
destination="synapses"))
net.input_lists.append(input_list)
if cells_to_plot is None or cell in cells_to_plot:
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (cell, cell_id)
lems_info["plots"].append(plot)
if params.is_level_B():
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (cell, cell_id)
lems_info["activity_plots"].append(plot)
if is_cond_based_cell(params):
plot = {}
plot["cell"] = cell
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/caConc" % (cell, cell_id)
lems_info["activity_plots"].append(plot)
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/v" % (cell, cell_id)
lems_info["to_save"].append(save)
if params.is_level_B():
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/activity" % (cell, cell_id)
lems_info["activity_to_save"].append(save)
if is_cond_based_cell(params):
save = {}
save["cell"] = cell
save["quantity"] = "%s/0/%s/caConc" % (cell, cell_id)
lems_info["activity_to_save"].append(save)
lems_info["cells"].append(cell)
count+=1
if verbose:
print_("Finished loading %i cells"%count)
mneurons, all_muscles, muscle_conns = get_cell_muscle_names_and_connection(data_reader)
#if data_reader == "SpreadsheetDataReader":
# all_muscles = get_muscle_names()
if muscles_to_include == None or muscles_to_include == True:
muscles_to_include = all_muscles
elif muscles_to_include == False:
muscles_to_include = []
for m in muscles_to_include:
assert m in all_muscles
if len(muscles_to_include)>0:
muscle_count = 0
for muscle in muscles_to_include:
inst = Instance(id="0")
# build a Population data structure out of the cell name
pop0 = Population(id=muscle,
component=params.generic_muscle_cell.id,
type="populationList")
pop0.instances.append(inst)
# put that Population into the Network data structure from above
net.populations.append(pop0)
if cells_vs_name.has_key(muscle):
# No muscles adopted yet, but just in case they are in future...
p = Property(tag="OpenWormBackerAssignedName", value=cells_vs_name[muscle])
pop0.properties.append(p)
x, y, z = get_muscle_position(muscle, data_reader)
print_('Positioning muscle: %s at (%s,%s,%s)'%(muscle,x,y,z))
inst.location = Location(x,y,z)
#target = "%s/0/%s"%(pop0.id, params.generic_muscle_cell.id) # unused
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/v" % (muscle, params.generic_muscle_cell.id)
lems_info["muscle_plots"].append(plot)
if params.generic_muscle_cell.__class__.__name__ == 'IafActivityCell':
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_muscle_cell.id)
lems_info["muscle_activity_plots"].append(plot)
if params.generic_muscle_cell.__class__.__name__ == 'Cell':
plot = {}
plot["cell"] = muscle
plot["colour"] = get_random_colour_hex()
plot["quantity"] = "%s/0/%s/caConc" % (muscle, params.generic_muscle_cell.id)
lems_info["muscle_activity_plots"].append(plot)
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/v" % (muscle, params.generic_muscle_cell.id)
lems_info["muscles_to_save"].append(save)
if params.generic_muscle_cell.__class__.__name__ == 'IafActivityCell':
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/activity" % (muscle, params.generic_muscle_cell.id)
lems_info["muscles_activity_to_save"].append(save)
if params.generic_muscle_cell.__class__.__name__ == 'Cell':
save = {}
save["cell"] = muscle
save["quantity"] = "%s/0/%s/caConc" % (muscle, params.generic_muscle_cell.id)
lems_info["muscles_activity_to_save"].append(save)
lems_info["muscles"].append(muscle)
muscle_count+=1
if muscle in cells_to_stimulate:
target = "../%s/0/%s"%(pop0.id, params.generic_muscle_cell.id)
if params.is_level_D():
target+="/0"
input_list = InputList(id="Input_%s_%s"%(muscle,params.offset_current.id),
component=params.offset_current.id,
populations='%s'%pop0.id)
input_list.input.append(Input(id=0,
target=target,
destination="synapses"))
net.input_lists.append(input_list)
if verbose:
print_("Finished creating %i muscles"%muscle_count)
existing_synapses = {}
for conn in conns:
if conn.pre_cell in lems_info["cells"] and conn.post_cell in lems_info["cells"]:
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
conn_shorthand = "%s-%s" % (conn.pre_cell, conn.post_cell)
elect_conn = False
analog_conn = False
syn0 = params.neuron_to_neuron_exc_syn
orig_pol = "exc"
if 'GABA' in conn.synclass:
syn0 = params.neuron_to_neuron_inh_syn
orig_pol = "inh"
if '_GJ' in conn.synclass:
syn0 = params.neuron_to_neuron_elec_syn
elect_conn = isinstance(params.neuron_to_neuron_elec_syn, GapJunction)
conn_shorthand = "%s-%s_GJ" % (conn.pre_cell, conn.post_cell)
if conns_to_include and conn_shorthand not in conns_to_include:
continue
print conn_shorthand + " " + str(conn.number) + " " + orig_pol + " " + conn.synclass
polarity = None
if conn_polarity_override and conn_polarity_override.has_key(conn_shorthand):
polarity = conn_polarity_override[conn_shorthand]
if polarity and not elect_conn:
if polarity == 'inh':
syn0 = params.neuron_to_neuron_inh_syn
else:
syn0 = params.neuron_to_neuron_exc_syn
if verbose and polarity != orig_pol:
print_(">> Changing polarity of connection %s -> %s: was: %s, becomes %s " % \
(conn.pre_cell, conn.post_cell, orig_pol, polarity))
if isinstance(syn0, GradedSynapse) or isinstance(syn0, GradedSynapse2):
analog_conn = True
if len(nml_doc.silent_synapses)==0:
silent = SilentSynapse(id="silent")
nml_doc.silent_synapses.append(silent)
number_syns = conn.number
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
"""if polarity:
print "%s %s num:%s" % (conn_shorthand, polarity, number_syns)
elif elect_conn:
print "%s num:%s" % (conn_shorthand, number_syns)
else:
print "%s %s num:%s" % (conn_shorthand, orig_pol, number_syns)"""
if number_syns != conn.number:
if analog_conn or elect_conn:
magnitude, unit = bioparameters.split_neuroml_quantity(syn0.conductance)
else:
magnitude, unit = bioparameters.split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s" % (get_str_from_expnotation(magnitude * number_syns), unit)
gj = "" if not elect_conn else " GapJunction"
if verbose:
print_(">> Changing number of effective synapses connection %s -> %s%s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, gj, conn.number, cond0, number_syns, cond1))
#print "######## %s-%s %s %s" % (conn.pre_cell, conn.post_cell, conn.synclass, number_syns)
#known_motor_prefixes = ["VA"]
#if conn.pre_cell.startswith(tuple(known_motor_prefixes)) or conn.post_cell.startswith(tuple(known_motor_prefixes)):
# print "######### %s-%s %s %s" % (conn.pre_cell, conn.post_cell, number_syns, conn.synclass)
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc, existing_synapses)
if elect_conn:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
pre_cell_id=get_cell_id_string(conn.pre_cell, params)
post_cell_id= get_cell_id_string(conn.post_cell, params)
#print_("Conn %s -> %s"%(pre_cell_id,post_cell_id))
# Add a Connection with the closest locations
conn0 = ElectricalConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
synapse=syn_new.id)
proj0.electrical_connection_instances.append(conn0)
elif analog_conn:
proj0 = ContinuousProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.continuous_projections.append(proj0)
pre_cell_id= get_cell_id_string(conn.pre_cell, params)
post_cell_id= get_cell_id_string(conn.post_cell, params)
conn0 = ContinuousConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
pre_component="silent",
post_component=syn_new.id)
proj0.continuous_connection_instances.append(conn0)
else:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
pre_cell_id= get_cell_id_string(conn.pre_cell, params)
post_cell_id= get_cell_id_string(conn.post_cell, params)
conn0 = ConnectionWD(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id,
weight = number_syns,
delay = '0ms')
proj0.connection_wds.append(conn0)
if len(muscles_to_include)>0:
for conn in muscle_conns:
if not conn.post_cell in muscles_to_include:
continue
if not conn.pre_cell in lems_info["cells"] and not conn.pre_cell in muscles_to_include:
continue
# take information about each connection and package it into a
# NeuroML Projection data structure
proj_id = get_projection_id(conn.pre_cell, conn.post_cell, conn.synclass, conn.syntype)
conn_shorthand = "%s-%s" % (conn.pre_cell, conn.post_cell)
elect_conn = False
analog_conn = False
syn0 = params.neuron_to_muscle_exc_syn
orig_pol = "exc"
if 'GABA' in conn.synclass:
syn0 = params.neuron_to_muscle_inh_syn
orig_pol = "inh"
if '_GJ' in conn.synclass :
elect_conn = isinstance(params.neuron_to_muscle_elec_syn, GapJunction)
conn_shorthand = "%s-%s_GJ" % (conn.pre_cell, conn.post_cell)
if conn.pre_cell in lems_info["cells"]:
syn0 = params.neuron_to_muscle_elec_syn
elif conn.pre_cell in muscles_to_include:
try:
syn0 = params.muscle_to_muscle_elec_syn
except:
syn0 = params.neuron_to_muscle_elec_syn
if conns_to_include and conn_shorthand not in conns_to_include:
continue
print conn_shorthand + " " + str(conn.number) + " " + orig_pol + " " + conn.synclass
polarity = None
if conn_polarity_override and conn_polarity_override.has_key(conn_shorthand):
polarity = conn_polarity_override[conn_shorthand]
if polarity and not elect_conn:
if polarity == 'inh':
syn0 = params.neuron_to_neuron_inh_syn
else:
syn0 = params.neuron_to_neuron_exc_syn
if verbose and polarity != orig_pol:
print_(">> Changing polarity of connection %s -> %s: was: %s, becomes %s " % \
(conn.pre_cell, conn.post_cell, orig_pol, polarity))
if isinstance(syn0, GradedSynapse) or isinstance(syn0, GradedSynapse2):
analog_conn = True
if len(nml_doc.silent_synapses)==0:
silent = SilentSynapse(id="silent")
nml_doc.silent_synapses.append(silent)
number_syns = conn.number
if conn_number_override is not None and (conn_number_override.has_key(conn_shorthand)):
number_syns = conn_number_override[conn_shorthand]
elif conn_number_scaling is not None and (conn_number_scaling.has_key(conn_shorthand)):
number_syns = conn.number*conn_number_scaling[conn_shorthand]
'''
else:
print conn_shorthand
print conn_number_override
print conn_number_scaling'''
"""if polarity:
print "%s %s num:%s" % (conn_shorthand, polarity, number_syns)
elif elect_conn:
print "%s num:%s" % (conn_shorthand, number_syns)
else:
print "%s %s num:%s" % (conn_shorthand, orig_pol, number_syns)"""
if number_syns != conn.number:
if analog_conn or elect_conn:
magnitude, unit = bioparameters.split_neuroml_quantity(syn0.conductance)
else:
magnitude, unit = bioparameters.split_neuroml_quantity(syn0.gbase)
cond0 = "%s%s"%(magnitude*conn.number, unit)
cond1 = "%s%s" % (get_str_from_expnotation(magnitude * number_syns), unit)
gj = "" if not elect_conn else " GapJunction"
if verbose:
print_(">> Changing number of effective synapses connection %s -> %s%s: was: %s (total cond: %s), becomes %s (total cond: %s)" % \
(conn.pre_cell, conn.post_cell, gj, conn.number, cond0, number_syns, cond1))
syn_new = create_n_connection_synapse(syn0, number_syns, nml_doc, existing_synapses)
if elect_conn:
proj0 = ElectricalProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.electrical_projections.append(proj0)
pre_cell_id= get_cell_id_string(conn.pre_cell, params)
post_cell_id= get_cell_id_string(conn.post_cell, params, muscle=True)
#print_("Conn %s -> %s"%(pre_cell_id,post_cell_id))
# Add a Connection with the closest locations
conn0 = ElectricalConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
synapse=syn_new.id)
proj0.electrical_connection_instances.append(conn0)
elif analog_conn:
proj0 = ContinuousProjection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell)
net.continuous_projections.append(proj0)
pre_cell_id= get_cell_id_string(conn.pre_cell, params)
post_cell_id= get_cell_id_string(conn.post_cell, params, muscle=True)
conn0 = ContinuousConnectionInstance(id="0", \
pre_cell=pre_cell_id,
post_cell=post_cell_id,
pre_component="silent",
post_component=syn_new.id)
proj0.continuous_connection_instances.append(conn0)
else:
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
net.projections.append(proj0)
# Add a Connection with the closest locations
pre_cell_id= get_cell_id_string(conn.pre_cell, params)
post_cell_id= get_cell_id_string(conn.post_cell, params, muscle=True)
conn0 = Connection(id="0", \
pre_cell_id=pre_cell_id,
post_cell_id=post_cell_id)
proj0.connections.append(conn0)
# import pprint
# pprint.pprint(lems_info)
template_path = root_dir+'/../' if test else root_dir+'/' # if running test
write_to_file(nml_doc, lems_info, net_id, template_path, validate=validate, verbose=verbose, target_directory=target_directory)
return nml_doc
a="0.03per_ms",
b="-2nS",
c="-50.0mV",
d="100pA")
nml_doc.izhikevich2007_cells.append(iz0)
syn0 = ExpOneSynapse(id="syn0", gbase="65nS", erev="0mV", tau_decay="3ms")
nml_doc.exp_one_synapses.append(syn0)
net = Network(id="IzNet")
nml_doc.networks.append(net)
size0 = 5
pop0 = Population(id="IzPop0", component=iz0.id, size=size0)
# Set optional color property. Note: used later when generating graphs etc.
pop0.properties.append(Property(tag='color', value='0 0 .8'))
net.populations.append(pop0)
size1 = 5
pop1 = Population(id="IzPop1", component=iz0.id, size=size1)
pop1.properties.append(Property(tag='color', value='.8 0 0'))
net.populations.append(pop1)
proj = Projection(id='proj',
presynaptic_population=pop0.id,
postsynaptic_population=pop1.id,
synapse=syn0.id)
net.projections.append(proj)
random.seed(123)
prob_connection = 0.8
def generatePopulationLEMS(pops, n_pops, amplitudes, baseline, sim_length,
delay):
def generatePopulationProjection(from_pop, to_pop, n_from_pop, n_to_pop,
w_to_from_pop, p_to_from_pop, net):
connection_count = 0
projection = ContinuousProjection(
id='%s_%s' % (from_pop, to_pop),
presynaptic_population='%sPop' % from_pop,
postsynaptic_population='%sPop' % to_pop)
for idx_from_pop in range(n_from_pop):
for idx_to_pop in range(n_to_pop):
if random.random() <= p_to_from_pop:
pre_comp = from_pop.upper()
to_comp = to_pop.upper()
connection = ContinuousConnectionInstanceW(
id=connection_count,
pre_cell='../%sPop/%i/%s' %
(from_pop, idx_from_pop, pre_comp),
post_cell='../%sPop/%i/%s' %
(to_pop, idx_to_pop, to_comp),
pre_component='silent1',
post_component='rs',
weight=w_to_from_pop / (p_to_from_pop * n_from_pop))
projection.continuous_connection_instance_ws.append(
connection)
connection_count += 1
if connection_count > 0:
net.continuous_projections.append(projection)
# Connection probabilities for each pop in the population
w_to_from_pops = np.array([[2.42, -.33, -0.80, 0], [2.97, -3.45, -2.13, 0],
[4.64, 0, 0, -2.79], [0.71, 0, -0.16, 0]])
# p_to_from_pop = np.array([[1, 1, 1, 0],
# [1, 1, 1, 0],
# [1, 0, 0, 1],
# [1, 0, 1, 0]])
p_to_from_pop = np.array([[0.02, 1, 1, 0], [0.01, 1, 0.85, 0],
[0.01, 0, 0, 0.55], [0.01, 0, 0.5, 0]])
nml_doc = NeuroMLDocument(id='RandomPopulation')
# Add silent synapsis
silent_syn = SilentSynapse(id='silent1')
nml_doc.silent_synapses.append(silent_syn)
for pop_idx, pop in enumerate(pops):
pulse = PulseGenerator(id='baseline_%s' % pop,
delay='0ms',
duration=str(sim_length) + 'ms',
amplitude=amplitudes[pop_idx])
nml_doc.pulse_generators.append(pulse)
if pop == 'vip':
# time point when additional current is induced
pulse_mod = PulseGenerator(id='modVIP',
delay=str(delay) + 'ms',
duration=str(sim_length - delay) + 'ms',
amplitude='10 pA')
nml_doc.pulse_generators.append(pulse_mod)
# Create the network and add the 4 different populations
net = Network(id='net2')
nml_doc.networks.append(net)
colours = ['0 0 1', '1 0 0', '.5 0 .5', '0 1 0']
centres = [(0, 0, 0), (-1200, 0, 0), (-800, 800, 0), (0, 1200, 0)]
radii = [800, 200, 200, 200]
# Populate the network with the 4 populations
for pop_idx, pop in enumerate(pops):
pop = Population(id='%sPop' % pop,
component=(pops[pop_idx]).upper(),
size=n_pops[pop_idx],
type='populationList')
net.populations.append(pop)
pop.properties.append(Property(tag='color', value=colours[pop_idx]))
pop.properties.append(Property(tag='radius', value=10))
for n_pop in range(n_pops[pop_idx]):
inst = Instance(id=n_pop)
pop.instances.append(inst)
x, y, z = centres[pop_idx]
r = (random.random() * radii[pop_idx]**3)**(1. / 3)
theta = random.random() * math.pi
phi = random.random() * math.pi * 2
inst.location = Location(
x=str(x + r * math.sin(theta) * math.cos(phi)),
y=str(y + r * math.sin(theta) * math.sin(phi)),
z=str(z + r * math.cos(theta)))
for from_idx, from_pop in enumerate(pops):
for to_idx, to_pop in enumerate(pops):
generatePopulationProjection(pops[from_idx], pops[to_idx],
n_pops[from_idx], n_pops[to_idx],
w_to_from_pops[to_idx, from_idx],
p_to_from_pop[to_idx, from_idx], net)
# Add inputs
for pop_idx, pop in enumerate(pops):
input_list = InputList(id='baseline_%s' % pop,
component='baseline_%s' % pops[pop_idx],
populations='%sPop' % pop)
net.input_lists.append(input_list)
if pop == 'vip':
input_list_mod = InputList(id='modulation_%s' % pop,
component='modVIP',
populations='%sPop' % pop)
net.input_lists.append(input_list_mod)
for n_idx in range(n_pops[pop_idx]):
input = Input(id=n_idx,
target='../%sPop/%i/%s' % (pop, n_idx, pop.upper()),
destination='synapses')
input_list.input.append(input)
# if vip add modulatory input
if pop == 'vip':
mod_input = Input(id=n_idx,
target='../vipPop/%i/VIP' % n_idx,
destination='synapses')
input_list_mod.input.append(mod_input)
nml_file = 'RandomPopulationRate_%s_baseline.nml' % baseline
writers.NeuroMLWriter.write(nml_doc, nml_file)
print('Written network file to: %s' % nml_file)
# Validate the NeuroML
from neuroml.utils import validate_neuroml2
validate_neuroml2(nml_file)
def generate(
scale_populations=1,
percentage_exc_detailed=0,
#exc2_cell = 'SmithEtAl2013/L23_Retuned_477127614',
exc2_cell='SmithEtAl2013/L23_NoHotSpot',
#exc2_cell = 'BBP/cADpyr229_L23_PC_5ecbf9b163_0_0',
#exc2_cell = 'BBP/cNAC187_L23_NBC_9d37c4b1f8_0_0',
#exc2_cell = 'Thalamocortical/L23PyrRS',
percentage_inh_detailed=0,
scalex=1,
scaley=1,
scalez=1,
exc_exc_conn_prob=0.25,
exc_inh_conn_prob=0.25,
inh_exc_conn_prob=0.75,
inh_inh_conn_prob=0.75,
ee2_conn_prob=0,
ie2_conn_prob=0,
Bee=.1,
Bei=.1,
Bie=-.2,
Bii=-.2,
Bee2=1,
Bie2=-2,
Be_bkg=.1,
Be_stim=.1,
r_bkg=0,
r_bkg_ExtExc=0,
r_bkg_ExtInh=0,
r_bkg_ExtExc2=0,
r_stim=0,
fraction_inh_pert=0.75,
Ttrans=500, # transitent time to discard the data (ms)
Tblank=1500, # simulation time before perturbation (ms)
Tstim=1500, # simulation time of perturbation (ms)
Tpost=500, # simulation time after perturbation (ms)
connections=True,
connections2=False,
exc_target_dendrites=False,
inh_target_dendrites=False,
duration=1000,
dt=0.025,
global_delay=.1,
max_in_pop_to_plot_and_save=10,
format='xml',
suffix='',
run_in_simulator=None,
num_processors=1,
target_dir='./temp/',
v_clamp=False,
simulation_seed=11111):
reference = ("ISN_net%s" % (suffix)).replace('.', '_')
ks = open('kernelseed', 'w')
ks.write('%i' % simulation_seed)
ks.close()
info = (' Generating ISN network: %s\n' % reference)
info += (
' Duration: %s; dt: %s; scale: %s; simulator: %s (num proc. %s)\n' %
(duration, dt, scale_populations, run_in_simulator, num_processors))
info += (' Bee: %s; Bei: %s; Bie: %s; Bii: %s\n' % (Bee, Bei, Bie, Bii))
info += (' Bkg exc at %sHz\n' % (r_bkg_ExtExc))
info += (' Bkg inh at %sHz\n' % (r_bkg_ExtInh))
info += (' Be_stim: %s at %sHz (i.e. %sHz for %s perturbed I cells)\n' %
(Be_stim, r_stim, r_bkg_ExtInh + r_stim, fraction_inh_pert))
info += (' Exc detailed: %s%% - Inh detailed %s%%\n' %
(percentage_exc_detailed, percentage_inh_detailed))
info += (' Seed: %s' % (simulation_seed))
print('-------------------------------------------------')
print(info)
print('-------------------------------------------------')
num_exc = scale_pop_size(np.round(100 * exc_inh_fraction),
scale_populations)
num_exc2 = int(math.ceil(num_exc * percentage_exc_detailed / 100.0))
num_exc -= num_exc2
num_inh = scale_pop_size(np.round(100 * (1 - exc_inh_fraction)),
scale_populations)
num_inh2 = int(math.ceil(num_inh * percentage_inh_detailed / 100.0))
num_inh -= num_inh2
nml_doc, network = oc.generate_network(reference,
network_seed=simulation_seed)
nml_doc.notes = info
network.notes = info
#exc_cell_id = 'AllenHH_480351780'
#exc_cell_id = 'AllenHH_477127614'
#exc_cell_id = 'HH_477127614'
exc_cell_id = 'HH2_477127614'
exc_type = exc_cell_id.split('_')[0]
oc.include_neuroml2_cell_and_channels(
nml_doc, 'cells/%s/%s.cell.nml' % (exc_type, exc_cell_id), exc_cell_id)
#inh_cell_id = 'AllenHH_485058595'
#inh_cell_id = 'AllenHH_476686112'
#inh_cell_id = 'AllenHH_477127614'
#inh_cell_id = 'HH_476686112'
inh_cell_id = 'HH2_476686112'
inh_type = exc_cell_id.split('_')[0]
oc.include_neuroml2_cell_and_channels(
nml_doc, 'cells/%s/%s.cell.nml' % (inh_type, inh_cell_id), inh_cell_id)
if percentage_exc_detailed > 0:
exc2_cell_id = exc2_cell.split('/')[1]
exc2_cell_dir = exc2_cell.split('/')[0]
oc.include_neuroml2_cell_and_channels(
nml_doc, 'cells/%s/%s.cell.nml' % (exc2_cell_dir, exc2_cell_id),
exc2_cell_id)
if percentage_inh_detailed > 0:
inh2_cell_id = 'cNAC187_L23_NBC_9d37c4b1f8_0_0'
oc.include_neuroml2_cell_and_channels(
nml_doc, 'cells/BBP/%s.cell.nml' % inh2_cell_id, inh2_cell_id)
xDim = 700 * scalex
yDim = 100 * scaley
yDimExc2 = 50 * scaley
zDim = 700 * scalez
xs = -1 * xDim / 2
ys = -1 * yDim / 2
zs = -1 * zDim / 2
##### Synapses
synAmpaEE = oc.add_exp_one_syn(nml_doc,
id="ampaEE",
gbase="%snS" % Bee,
erev="0mV",
tau_decay="1ms")
synAmpaEI = oc.add_exp_one_syn(nml_doc,
id="ampaEI",
gbase="%snS" % Bei,
erev="0mV",
tau_decay="1ms")
synGabaIE = oc.add_exp_one_syn(nml_doc,
id="gabaIE",
gbase="%snS" % abs(Bie),
erev="-80mV",
tau_decay="2ms")
synGabaII = oc.add_exp_one_syn(nml_doc,
id="gabaII",
gbase="%snS" % abs(Bii),
erev="-80mV",
tau_decay="2ms")
synAmpaBkg = oc.add_exp_one_syn(nml_doc,
id="ampaBkg",
gbase="%snS" % Be_bkg,
erev="0mV",
tau_decay="1ms")
#synAmpaStim = oc.add_exp_one_syn(nml_doc, id="ampaStim", gbase="%snS"%Be_stim,
# erev="0mV", tau_decay="1ms")
synAmpaEE2 = oc.add_exp_one_syn(nml_doc,
id="ampaEE2",
gbase="%snS" % Bee2,
erev="0mV",
tau_decay="10ms")
synGabaIE2 = oc.add_exp_one_syn(nml_doc,
id="gabaIE2",
gbase="%snS" % abs(Bie2),
erev="-80mV",
tau_decay="10ms")
##### Input types
'''tpfsA = oc.add_transient_poisson_firing_synapse(nml_doc,
id="tpsfA",
average_rate="%s Hz"%r_bkg,
delay = '0ms',
duration = '%sms'%(Ttrans+Tblank),
synapse_id=synAmpaBkg.id)
tpfsB = oc.add_transient_poisson_firing_synapse(nml_doc,
id="tpsfB",
average_rate="%s Hz"%r_bkg,
delay = '%sms'%(Ttrans+Tblank),
duration = '%sms'%(Tstim),
synapse_id=synAmpaBkg.id)
tpfsC = oc.add_transient_poisson_firing_synapse(nml_doc,
id="tpsfC",
average_rate="%s Hz"%(r_bkg+r_stim),
delay = '%sms'%(Ttrans+Tblank),
duration = '%sms'%(Tstim),
synapse_id=synAmpaStim.id)'''
tpfsExtExc = oc.add_transient_poisson_firing_synapse(
nml_doc,
id="tpfsExtExc",
average_rate="%s Hz" % r_bkg_ExtExc,
delay='0ms',
duration='%sms' % (Ttrans + Tblank + Tstim + Tpost),
synapse_id=synAmpaBkg.id)
tpfsExtExc2 = oc.add_transient_poisson_firing_synapse(
nml_doc,
id="tpfsExtExc2",
average_rate="%s Hz" % r_bkg_ExtExc2,
delay='0ms',
duration='%sms' % (Ttrans + Tblank + Tstim + Tpost),
synapse_id=synAmpaBkg.id)
tpfsExtInh = oc.add_transient_poisson_firing_synapse(
nml_doc,
id="tpfsExtInh",
average_rate="%s Hz" % r_bkg_ExtInh,
delay='0ms',
duration='%sms' % (Ttrans + Tblank + Tstim + Tpost),
synapse_id=synAmpaBkg.id)
tpfsPertInh_before = oc.add_transient_poisson_firing_synapse(
nml_doc,
id="tpfsPertInh_before",
average_rate="%s Hz" % r_bkg_ExtInh,
delay='0ms',
duration='%sms' % (Ttrans + Tblank),
synapse_id=synAmpaBkg.id)
tpfsPertInh_during = oc.add_transient_poisson_firing_synapse(
nml_doc,
id="tpfsPertInh_during",
average_rate="%s Hz" % (r_bkg_ExtInh + r_stim),
delay='%sms' % (Ttrans + Tblank),
duration='%sms' % (Tstim),
synapse_id=synAmpaBkg.id)
tpfsPertInh_after = oc.add_transient_poisson_firing_synapse(
nml_doc,
id="tpfsPertInh_after",
average_rate="%s Hz" % r_bkg_ExtInh,
delay='%sms' % (Ttrans + Tblank + Tstim),
duration='%sms' % (Tpost),
synapse_id=synAmpaBkg.id)
##### Populations
popExc = oc.add_population_in_rectangular_region(network,
'popExc',
exc_cell_id,
num_exc,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color=exc_color)
from neuroml import Property
popExc.properties.append(Property('type', 'E'))
allExc = [popExc]
if num_exc2 > 0:
popExc2 = oc.add_population_in_rectangular_region(network,
'popExc2',
exc2_cell_id,
num_exc2,
xs,
yDim / 2,
zs,
xDim,
yDimExc2,
zDim,
color=exc2_color)
popExc2.properties.append(Property('type', 'E'))
allExc.append(popExc2)
popInh = oc.add_population_in_rectangular_region(network,
'popInh',
inh_cell_id,
num_inh,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color=inh_color)
popInh.properties.append(Property('type', 'I'))
allInh = [popInh]
if num_inh2 > 0:
popInh2 = oc.add_population_in_rectangular_region(network,
'popInh2',
inh2_cell_id,
num_inh2,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color=inh2_color)
allInh.append(popInh2)
##### Projections
if connections:
weight_expr = 'abs(normal(1,0.5))'
for popEpr in allExc:
for popEpo in allExc:
proj = add_projection(network, "projEE", popEpr, popEpo,
synAmpaEE.id, exc_exc_conn_prob,
global_delay, exc_target_dendrites,
weight_expr)
for popIpo in allInh:
proj = add_projection(network, "projEI", popEpr, popIpo,
synAmpaEI.id, exc_inh_conn_prob,
global_delay, exc_target_dendrites,
weight_expr)
for popIpr in allInh:
for popEpo in allExc:
proj = add_projection(network, "projIE", popIpr, popEpo,
synGabaIE.id, inh_exc_conn_prob,
global_delay, inh_target_dendrites,
weight_expr)
for popIpo in allInh:
proj = add_projection(network, "projII", popIpr, popIpo,
synGabaII.id, inh_inh_conn_prob,
global_delay, inh_target_dendrites,
weight_expr)
elif connections2:
weight_expr = 'abs(normal(1,0.5))'
proj = add_projection(network, "projEE", popExc, popExc, synAmpaEE.id,
exc_exc_conn_prob, global_delay,
exc_target_dendrites, weight_expr)
proj = add_projection(network, "projEI", popExc, popInh, synAmpaEI.id,
exc_inh_conn_prob, global_delay,
exc_target_dendrites, weight_expr)
proj = add_projection(network, "projIE", popInh, popExc, synGabaIE.id,
inh_exc_conn_prob, global_delay,
inh_target_dendrites, weight_expr)
proj = add_projection(network, "projII", popInh, popInh, synGabaII.id,
inh_inh_conn_prob, global_delay,
inh_target_dendrites, weight_expr)
proj = add_projection(network, "projEE2", popExc, popExc2,
synAmpaEE2.id, ee2_conn_prob, global_delay,
exc_target_dendrites, weight_expr)
proj = add_projection(network, "projIE2", popInh, popExc2,
synGabaIE2.id, ie2_conn_prob, global_delay,
inh_target_dendrites, weight_expr)
##### Inputs
oc.add_inputs_to_population(network,
"Stim_E",
popExc,
tpfsExtExc.id,
all_cells=True)
if num_exc2 > 0:
oc.add_inputs_to_population(network,
"Stim_E2",
popExc2,
tpfsExtExc2.id,
all_cells=True)
num_inh_pert = int(popInh.get_size() * fraction_inh_pert)
oc.add_inputs_to_population(network,
"Stim_I_nonpert",
popInh,
tpfsExtInh.id,
all_cells=False,
only_cells=range(num_inh_pert,
popInh.get_size()))
oc.add_inputs_to_population(network,
"Stim_I_pert_before",
popInh,
tpfsPertInh_before.id,
all_cells=False,
only_cells=range(0, num_inh_pert))
oc.add_inputs_to_population(network,
"Stim_I_pert_during",
popInh,
tpfsPertInh_during.id,
all_cells=False,
only_cells=range(0, num_inh_pert))
oc.add_inputs_to_population(network,
"Stim_I_pert_after",
popInh,
tpfsPertInh_after.id,
all_cells=False,
only_cells=range(0, num_inh_pert))
# injecting noise in the soma of detailed neurons to insert some variability
'''oc.add_targeted_inputs_to_population(network,
"PG_noise",
popExc2,
'noisyCurrentSource1', # from ../../../NoisyCurrentSource.xml
segment_group='soma_group',
number_per_cell = 1,
all_cells=True)
oc.add_inputs_to_population(network, "Stim_pre_ExtExc_%s"%popExc.id,
popExc, tpfsExtExc.id,
all_cells=True)
for pop in allExc:
#oc.add_inputs_to_population(network, "Stim_pre_ExtExc_%s"%pop.id,
# pop, tpfsExtExc.id,
# all_cells=True)
oc.add_inputs_to_population(network, "Stim_pre_%s"%pop.id,
pop, tpfsA.id,
all_cells=True)
oc.add_inputs_to_population(network, "Stim_E_%s"%pop.id,
pop, tpfsB.id,
all_cells=True)
for pop in allInh:
num_inh_pert = int(pop.get_size()*fraction_inh_pert)
oc.add_inputs_to_population(network, "Stim_pre_ExtInh_%s"%pop.id,
pop, tpfsExtInh.id,
all_cells=True)
oc.add_inputs_to_population(network, "Stim_pre_%s"%pop.id,
pop, tpfsA.id,
all_cells=True)
oc.add_inputs_to_population(network, "Stim_I_pert_%s"%pop.id,
pop, tpfsC.id,
all_cells=False,
only_cells=range(0,num_inh_pert))
oc.add_inputs_to_population(network, "Stim_I_nonpert_%s"%pop.id,
pop, tpfsB.id,
all_cells=False,
only_cells=range(num_inh_pert,pop.get_size())) '''
save_v = {}
plot_v = {}
# Work in progress...
# General idea: clamp one (or more) exc cell at rev pot of inh syn and see only exc inputs
#
if v_clamp:
levels = {'IPSC': synAmpaEE.erev, 'EPSC': synGabaIE.erev}
for l in levels:
cell_index = levels.keys().index(l)
pop = 'popExc2'
plot = 'IClamp_i_%s' % (l)
for seg_id in [0, 2953,
1406]: # 2953: end of axon; 1406 on dendrite
clamp_id = "vclamp_cell%s_seg%s_%s" % (cell_index, seg_id, l)
v_clamped = levels[l]
vc = oc.add_voltage_clamp_triple(
nml_doc,
id=clamp_id,
delay='0ms',
duration='%sms' % duration,
conditioning_voltage=v_clamped,
testing_voltage=v_clamped,
return_voltage=v_clamped,
simple_series_resistance="1e2ohm",
active="1")
vc_dat_file = 'v_clamps_i_seg%s_%s.%s.dat' % (seg_id, l,
simulation_seed)
seg_file = '%s_seg%s_%s_v.dat' % (pop, seg_id, l)
save_v[vc_dat_file] = []
plot_v[plot] = []
oc.add_inputs_to_population(network,
"vclamp_seg%s_%s" % (seg_id, l),
network.get_by_id(pop),
vc.id,
all_cells=False,
only_cells=[cell_index],
segment_ids=[seg_id])
# record at seg
q = '%s/%s/%s/%s/%s/i' % (pop, cell_index,
network.get_by_id(pop).component,
seg_id, clamp_id)
save_v[vc_dat_file].append(q)
plot_v[plot].append(q)
if seg_id != 0:
save_v[seg_file] = []
q = '%s/%s/%s/%s/v' % (pop, cell_index,
network.get_by_id(pop).component,
seg_id)
save_v[seg_file].append(q)
##### Save NeuroML and LEMS Simulation files
nml_file_name = '%s.net.%s' % (network.id,
'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format == 'xml'),
format=format,
target_dir=target_dir)
print("Saved to: %s" % nml_file_name)
if num_exc > 0:
exc_traces = '%s_%s_v.dat' % (network.id, popExc.id)
save_v[exc_traces] = []
plot_v[popExc.id] = []
if num_inh > 0:
inh_traces = '%s_%s_v.dat' % (network.id, popInh.id)
save_v[inh_traces] = []
plot_v[popInh.id] = []
if num_exc2 > 0:
exc2_traces = '%s_%s_v.dat' % (network.id, popExc2.id)
save_v[exc2_traces] = []
plot_v[popExc2.id] = []
if num_inh2 > 0:
inh2_traces = '%s_%s_v.dat' % (network.id, popInh2.id)
save_v[inh2_traces] = []
plot_v[popInh2.id] = []
for i in range(min(max_in_pop_to_plot_and_save, num_exc)):
plot_v[popExc.id].append("%s/%i/%s/v" %
(popExc.id, i, popExc.component))
save_v[exc_traces].append("%s/%i/%s/v" %
(popExc.id, i, popExc.component))
for i in range(min(max_in_pop_to_plot_and_save, num_exc2)):
plot_v[popExc2.id].append("%s/%i/%s/v" %
(popExc2.id, i, popExc2.component))
save_v[exc2_traces].append("%s/%i/%s/v" %
(popExc2.id, i, popExc2.component))
for i in range(min(max_in_pop_to_plot_and_save, num_inh)):
plot_v[popInh.id].append("%s/%i/%s/v" %
(popInh.id, i, popInh.component))
save_v[inh_traces].append("%s/%i/%s/v" %
(popInh.id, i, popInh.component))
for i in range(min(max_in_pop_to_plot_and_save, num_inh2)):
plot_v[popInh2.id].append("%s/%i/%s/v" %
(popInh2.id, i, popInh2.component))
save_v[inh2_traces].append("%s/%i/%s/v" %
(popInh2.id, i, popInh2.component))
gen_spike_saves_for_all_somas = True
lems_file_name, lems_sim = oc.generate_lems_simulation(
nml_doc,
network,
target_dir + nml_file_name,
duration=duration,
dt=dt,
gen_plots_for_all_v=False,
gen_plots_for_quantities=plot_v,
gen_saves_for_all_v=False,
gen_saves_for_quantities=save_v,
gen_spike_saves_for_all_somas=gen_spike_saves_for_all_somas,
target_dir=target_dir,
include_extra_lems_files=['./NoisyCurrentSource.xml'],
report_file_name='report.txt',
simulation_seed=simulation_seed)
if run_in_simulator:
print("Running %s for %sms in %s" %
(lems_file_name, duration, run_in_simulator))
traces, events = oc.simulate_network(lems_file_name,
run_in_simulator,
max_memory='4000M',
nogui=True,
load_saved_data=True,
reload_events=True,
plot=False,
verbose=True,
num_processors=num_processors)
print("Reloaded traces: %s" % traces.keys())
#print("Reloaded events: %s"%events.keys())
use_events_for_rates = False
exc_rate = 0
inh_rate = 0
if use_events_for_rates:
if (run_in_simulator == 'jNeuroML_NetPyNE'):
raise (
'Saving of spikes (and so calculation of rates) not yet supported in jNeuroML_NetPyNE'
)
for ek in events.keys():
rate = 1000 * len(events[ek]) / float(duration)
print("Cell %s has a rate %s Hz" % (ek, rate))
if 'popExc' in ek:
exc_rate += rate / num_exc
if 'popInh' in ek:
inh_rate += rate / num_inh
else:
tot_exc_rate = 0
exc_cells = 0
tot_inh_rate = 0
inh_cells = 0
tt = [t * 1000 for t in traces['t']]
for tk in traces.keys():
if tk != 't':
rate = get_rate_from_trace(tt,
[v * 1000 for v in traces[tk]])
print("Cell %s has rate %s Hz" % (tk, rate))
if 'popExc' in tk:
tot_exc_rate += rate
exc_cells += 1
if 'popInh' in tk:
tot_inh_rate += rate
inh_cells += 1
exc_rate = tot_exc_rate / exc_cells
inh_rate = tot_inh_rate / inh_cells
print("Run %s: Exc rate: %s Hz; Inh rate %s Hz" %
(reference, exc_rate, inh_rate))
return exc_rate, inh_rate, traces
return nml_doc, nml_file_name, lems_file_name
def create_olm_cell():
"""Create the complete cell.
:returns: cell object
"""
nml_cell_doc = NeuroMLDocument(id="oml_cell")
cell = create_cell("olm")
nml_cell_file = cell.id + ".cell.nml"
# Add two soma segments
diam = 10.0
soma_0 = add_segment(cell,
prox=[0.0, 0.0, 0.0, diam],
dist=[0.0, 10., 0.0, diam],
name="Seg0_soma_0",
group="soma_0")
soma_1 = add_segment(cell,
prox=None,
dist=[0.0, 10. + 10., 0.0, diam],
name="Seg1_soma_0",
parent=soma_0,
group="soma_0")
# Add axon segments
diam = 1.5
axon_0 = add_segment(cell,
prox=[0.0, 0.0, 0.0, diam],
dist=[0.0, -75, 0.0, diam],
name="Seg0_axon_0",
parent=soma_0,
fraction_along=0.0,
group="axon_0")
axon_1 = add_segment(cell,
prox=None,
dist=[0.0, -150, 0.0, diam],
name="Seg1_axon_0",
parent=axon_0,
group="axon_0")
# Add 2 dendrite segments
diam = 3.0
dend_0_0 = add_segment(cell,
prox=[0.0, 20, 0.0, diam],
dist=[100, 120, 0.0, diam],
name="Seg0_dend_0",
parent=soma_1,
fraction_along=1,
group="dend_0")
dend_1_0 = add_segment(cell,
prox=None,
dist=[177, 197, 0.0, diam],
name="Seg1_dend_0",
parent=dend_0_0,
fraction_along=1,
group="dend_0")
dend_0_1 = add_segment(cell,
prox=[0.0, 20, 0.0, diam],
dist=[-100, 120, 0.0, diam],
name="Seg0_dend_1",
parent=soma_1,
fraction_along=1,
group="dend_1")
dend_1_1 = add_segment(cell,
prox=None,
dist=[-177, 197, 0.0, diam],
name="Seg1_dend_1",
parent=dend_0_1,
fraction_along=1,
group="dend_1")
# XXX: For segment groups to be correctly mapped to sections in NEURON,
# they must include the correct neurolex ID
for section_name in ["soma_0", "axon_0", "dend_0", "dend_1"]:
section_group = get_seg_group_by_id(section_name, cell)
section_group.neuro_lex_id = 'sao864921383'
den_seg_group = get_seg_group_by_id("dendrite_group", cell)
den_seg_group.includes.append(Include(segment_groups="dend_0"))
den_seg_group.includes.append(Include(segment_groups="dend_1"))
den_seg_group.properties.append(Property(tag="color", value="0.8 0 0"))
ax_seg_group = get_seg_group_by_id("axon_group", cell)
ax_seg_group.includes.append(Include(segment_groups="axon_0"))
ax_seg_group.properties.append(Property(tag="color", value="0 0.8 0"))
soma_seg_group = get_seg_group_by_id("soma_group", cell)
soma_seg_group.includes.append(Include(segment_groups="soma_0"))
soma_seg_group.properties.append(Property(tag="color", value="0 0 0.8"))
# Other cell properties
set_init_memb_potential(cell, "-67mV")
set_resistivity(cell, "0.15 kohm_cm")
set_specific_capacitance(cell, "1.3 uF_per_cm2")
# channels
# leak
add_channel_density(cell,
nml_cell_doc,
cd_id="leak_all",
cond_density="0.01 mS_per_cm2",
ion_channel="leak_chan",
ion_chan_def_file="olm-example/leak_chan.channel.nml",
erev="-67mV",
ion="non_specific")
# HCNolm_soma
add_channel_density(cell,
nml_cell_doc,
cd_id="HCNolm_soma",
cond_density="0.5 mS_per_cm2",
ion_channel="HCNolm",
ion_chan_def_file="olm-example/HCNolm.channel.nml",
erev="-32.9mV",
ion="h",
group="soma_group")
# Kdrfast_soma
add_channel_density(cell,
nml_cell_doc,
cd_id="Kdrfast_soma",
cond_density="73.37 mS_per_cm2",
ion_channel="Kdrfast",
ion_chan_def_file="olm-example/Kdrfast.channel.nml",
erev="-77mV",
ion="k",
group="soma_group")
# Kdrfast_dendrite
add_channel_density(cell,
nml_cell_doc,
cd_id="Kdrfast_dendrite",
cond_density="105.8 mS_per_cm2",
ion_channel="Kdrfast",
ion_chan_def_file="olm-example/Kdrfast.channel.nml",
erev="-77mV",
ion="k",
group="dendrite_group")
# Kdrfast_axon
add_channel_density(cell,
nml_cell_doc,
cd_id="Kdrfast_axon",
cond_density="117.392 mS_per_cm2",
ion_channel="Kdrfast",
ion_chan_def_file="olm-example/Kdrfast.channel.nml",
erev="-77mV",
ion="k",
group="axon_group")
# KvAolm_soma
add_channel_density(cell,
nml_cell_doc,
cd_id="KvAolm_soma",
cond_density="4.95 mS_per_cm2",
ion_channel="KvAolm",
ion_chan_def_file="olm-example/KvAolm.channel.nml",
erev="-77mV",
ion="k",
group="soma_group")
# KvAolm_dendrite
add_channel_density(cell,
nml_cell_doc,
cd_id="KvAolm_dendrite",
cond_density="2.8 mS_per_cm2",
ion_channel="KvAolm",
ion_chan_def_file="olm-example/KvAolm.channel.nml",
erev="-77mV",
ion="k",
group="dendrite_group")
# Nav_soma
add_channel_density(cell,
nml_cell_doc,
cd_id="Nav_soma",
cond_density="10.7 mS_per_cm2",
ion_channel="Nav",
ion_chan_def_file="olm-example/Nav.channel.nml",
erev="50mV",
ion="na",
group="soma_group")
# Nav_dendrite
add_channel_density(cell,
nml_cell_doc,
cd_id="Nav_dendrite",
cond_density="23.4 mS_per_cm2",
ion_channel="Nav",
ion_chan_def_file="olm-example/Nav.channel.nml",
erev="50mV",
ion="na",
group="dendrite_group")
# Nav_axon
add_channel_density(cell,
nml_cell_doc,
cd_id="Nav_axon",
cond_density="17.12 mS_per_cm2",
ion_channel="Nav",
ion_chan_def_file="olm-example/Nav.channel.nml",
erev="50mV",
ion="na",
group="axon_group")
nml_cell_doc.cells.append(cell)
pynml.write_neuroml2_file(nml_cell_doc, nml_cell_file, True, True)
return nml_cell_file