def create_olm_network():
"""Create the network
:returns: name of network nml file
"""
net_doc = NeuroMLDocument(id="network", notes="OLM cell network")
net_doc_fn = "olm_example_net.nml"
net_doc.includes.append(IncludeType(href=create_olm_cell()))
# Create a population: convenient to create many cells of the same type
pop = Population(id="pop0",
notes="A population for our cell",
component="olm",
size=1,
type="populationList")
pop.instances.append(Instance(id=1, location=Location(0., 0., 0.)))
# Input
pulsegen = PulseGenerator(id="pg_olm",
notes="Simple pulse generator",
delay="100ms",
duration="100ms",
amplitude="0.08nA")
exp_input = ExplicitInput(target="pop0[0]", input="pg_olm")
net = Network(id="single_olm_cell_network",
note="A network with a single population")
net_doc.pulse_generators.append(pulsegen)
net.explicit_inputs.append(exp_input)
net.populations.append(pop)
net_doc.networks.append(net)
pynml.write_neuroml2_file(nml2_doc=net_doc,
nml2_file_name=net_doc_fn,
validate=True)
return net_doc_fn
def run():
######################## Build the network ####################################
nml_doc = NeuroMLDocument(id="IafNet")
IaFCell0 = IaFCell(id="iaf0", C="1.0 nF", thresh = "-50mV", reset="-65mV", leak_conductance="10 nS", leak_reversal="-65mV")
nml_doc.iaf_cells.append(IaFCell0)
IaFCell1 = IaFCell(id="iaf1", C="1.0 nF", thresh = "-50mV", reset="-65mV", leak_conductance="20 nS", leak_reversal="-65mV")
nml_doc.iaf_cells.append(IaFCell1)
syn0 = ExpOneSynapse(id="syn0", gbase="65nS", erev="0mV", tau_decay="3ms")
nml_doc.exp_one_synapses.append(syn0)
net = Network(id="IafNet")
nml_doc.networks.append(net)
size0 = 5
pop0 = Population(id="IafPop0", component=IaFCell0.id, size=size0)
net.populations.append(pop0)
size1 = 5
pop1 = Population(id="IafPop1", component=IaFCell0.id, size=size1)
net.populations.append(pop1)
prob_connection = 0.5
for pre in range(0,size0):
pg = PulseGenerator(id="pulseGen_%i"%pre, delay="0ms", duration="100ms", amplitude="%f nA"%(0.1*random()))
nml_doc.pulse_generators.append(pg)
net.explicit_inputs.append(ExplicitInput(target="%s[%i]"%(pop0.id,pre), input=pg.id))
for post in range(0,size1):
# fromxx is used since from is Python keyword
if random() <= prob_connection:
net.synaptic_connections.append(SynapticConnection(from_="%s[%i]"%(pop0.id,pre), synapse=syn0.id, to="%s[%i]"%(pop1.id,post)))
nml_file = 'tmp/testnet.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Written network file to: "+nml_file)
###### Validate the NeuroML ######
from utils import validateNeuroML2
validateNeuroML2(nml_file)
def add_new_input(nml_doc, cell, delay, duration, amplitude, params):
stim = PulseGenerator(id="stim_" + cell,
delay=delay,
duration=duration,
amplitude=amplitude)
nml_doc.pulse_generators.append(stim)
populations_without_location = isinstance(params.elec_syn, GapJunction)
target = "%s/0/%s" % (cell, params.generic_cell.id)
if populations_without_location:
target = "%s[0]" % (cell)
exp_input = ExplicitInput(target=target, input=stim.id)
nml_doc.networks[0].explicit_inputs.append(exp_input)
def generate(net_id, params, cells = None, cells_to_plot=None, cells_to_stimulate=None, duration=500, dt=0.01, vmin=-75, vmax=20):
nml_doc = NeuroMLDocument(id=net_id)
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...
cell_names, conns = SpreadsheetDataReader.readDataFromSpreadsheet("../../../")
cell_names.sort()
# To hold all Cell NeuroML objects vs. names
all_cells = {}
# lems_file = ""
lems_info = {"reference": net_id,
"duration": duration,
"dt": dt,
"vmin": vmin,
"vmax": vmax,
"cell_component": params.generic_cell.id}
lems_info["plots"] = []
lems_info["cells"] = []
for cell in cell_names:
if cells is None or cell in cells:
# build a Population data structure out of the cell name
pop0 = Population(id=cell,
component=params.generic_cell.id,
type="populationList")
inst = Instance(id="0")
pop0.instances.append(inst)
# put that Population into the Network data structure from above
net.populations.append(pop0)
# also use the cell name to grab the morphology file, as a NeuroML data structure
# into the 'all_cells' dict
cell_file = '../../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))
exp_input = ExplicitInput(target="%s/0/%s"%(pop0.id, params.generic_cell.id),
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()
lems_info["plots"].append(plot)
lems_info["cells"].append(cell)
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)
syn0 = params.exc_syn
if 'GABA' in conn.synclass:
syn0 = params.inh_syn
syn_new = create_n_connection_synapse(syn0, conn.number, nml_doc)
proj0 = Projection(id=proj_id, \
presynaptic_population=conn.pre_cell,
postsynaptic_population=conn.post_cell,
synapse=syn_new.id)
# Get the corresponding Cell for each
# pre_cell = all_cells[conn.pre_cell]
# post_cell = all_cells[conn.post_cell]
net.projections.append(proj0)
# Add a Connection with the closest locations
conn0 = Connection(id="0", \
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))
proj0.connections.append(conn0)
write_to_file(nml_doc, lems_info, net_id)
pop1 = Population(id="IafPop1", component=IafCell0.id, size=size1)
net.populations.append(pop1)
prob_connection = 0.5
for pre in range(0, size0):
pg = PulseGenerator(id="pulseGen_%i" % pre,
delay="0ms",
duration="100ms",
amplitude="%f nA" % (0.1 * random()))
nml_doc.pulse_generators.append(pg)
exp_input = ExplicitInput(target="%s[%i]" % (pop0.id, pre), input=pg.id)
net.explicit_inputs.append(exp_input)
for post in range(0, size1):
# fromxx is used since from is Python keyword
if random() <= prob_connection:
syn = SynapticConnection(from_="%s[%i]" % (pop0.id, pre),
synapse=syn0.id,
to="%s[%i]" % (pop1.id, post))
net.synaptic_connections.append(syn)
nml_file = 'tmp/testnet.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Written network file to: " + nml_file)
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'))
for n_pop in range(n_pops[pop_idx]):
inst = Instance(id=n_pop)
population.instances.append(inst)
inst.location = Location(x=-20 if 'E' in pop else 20, y=0, z=0)
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], net)
# Add inputs
for pop_idx, pop in enumerate(pops):
for n_idx in range(n_pops[pop_idx]):
exp_input = ExplicitInput(target='%sPop/%i/%s' % (pop, n_idx, pop),
input='mod_%s' % pops[pop_idx],
destination='synapses')
net.explicit_inputs.append(exp_input)
nml_file = 'WC_%s%s.net.nml' % (baseline, dl_str)
writers.NeuroMLWriter.write(nml_doc, nml_file)
print('Written NeuroML file: %s' % nml_file)
generatePopulationSimulationLEMS(n_pops, baseline, pops, duration, dt, dl)
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
def generateExmplicitInput(population, idx, net):
exp_input = ExplicitInput(target='%sPop[%d]' % (population, idx),
input='baseline_%s%d' % (population, idx),
destination='synapses')
net.explicit_inputs.append(exp_input)
def createModel(self):
# File names of all components
pyr_file_name = "../ACnet2_NML2/Cells/pyr_4_sym.cell.nml"
bask_file_name = "../ACnet2_NML2/Cells/bask.cell.nml"
exc_exc_syn_names = '../ACnet2_NML2/Synapses/AMPA_syn.synapse.nml'
exc_inh_syn_names = '../ACnet2_NML2/Synapses/AMPA_syn_inh.synapse.nml'
inh_exc_syn_names = '../ACnet2_NML2/Synapses/GABA_syn.synapse.nml'
inh_inh_syn_names = '../ACnet2_NML2/Synapses/GABA_syn_inh.synapse.nml'
bg_exc_syn_names = '../ACnet2_NML2/Synapses/bg_AMPA_syn.synapse.nml'
nml_doc = NeuroMLDocument(id=self.filename + '_doc')
net = Network(id=self.filename + '_net')
nml_doc.networks.append(net)
nml_doc.includes.append(IncludeType(pyr_file_name))
nml_doc.includes.append(IncludeType(bask_file_name))
nml_doc.includes.append(IncludeType(exc_exc_syn_names))
nml_doc.includes.append(IncludeType(exc_inh_syn_names))
nml_doc.includes.append(IncludeType(inh_exc_syn_names))
nml_doc.includes.append(IncludeType(inh_inh_syn_names))
nml_doc.includes.append(IncludeType(bg_exc_syn_names))
# Create a LEMSSimulation to manage creation of LEMS file
ls = LEMSSimulation(self.filename, self.sim_time, self.dt)
# Point to network as target of simulation
ls.assign_simulation_target(net.id)
# The names of the cell type/component used in the Exc & Inh populations
exc_group_component = "pyr_4_sym"
inh_group_component = "bask"
# The names of the Exc & Inh groups/populations
exc_group = "pyramidals" #"pyramidals48x48"
inh_group = "baskets" #"baskets24x24"
# The names of the network connections
net_conn_exc_exc = "pyr_pyr"
net_conn_exc_inh = "pyr_bask"
net_conn_inh_exc = "bask_pyr"
net_conn_inh_inh = "bask_bask"
# The names of the synapse types
exc_exc_syn = "AMPA_syn"
exc_exc_syn_seg_id = 3 # Middle apical dendrite
exc_inh_syn = "AMPA_syn_inh"
exc_inh_syn_seg_id = 1 # Dendrite
inh_exc_syn = "GABA_syn"
inh_exc_syn_seg_id = 6 # Basal dendrite
inh_inh_syn = "GABA_syn_inh"
inh_inh_syn_seg_id = 0 # Soma
aff_exc_syn = "AMPA_aff_syn"
aff_exc_syn_seg_id = 5 # proximal apical dendrite
bg_exc_syn = "bg_AMPA_syn"
bg_exc_syn_seg_id = 7 # Basal dendrite
# Excitatory Parameters
XSCALE_ex = 24 #48
ZSCALE_ex = 24 #48
xSpacing_ex = 40 # 10^-6m
zSpacing_ex = 40 # 10^-6m
# Inhibitory Parameters
XSCALE_inh = 12 #24
ZSCALE_inh = 12 #24
xSpacing_inh = 80 # 10^-6m
zSpacing_inh = 80 # 10^-6m
numCells_ex = XSCALE_ex * ZSCALE_ex
numCells_inh = XSCALE_inh * ZSCALE_inh
# Connection probabilities (initial value)
connection_probability_ex_ex = 0.15
connection_probability_ex_inh = 0.45
connection_probability_inh_ex = 0.6
connection_probability_inh_inh = 0.6
# Generate excitatory cells
exc_pop = Population(id=exc_group,
component=exc_group_component,
type="populationList",
size=XSCALE_ex * ZSCALE_ex)
net.populations.append(exc_pop)
exc_pos = np.zeros((XSCALE_ex * ZSCALE_ex, 2))
for i in range(0, XSCALE_ex):
for j in range(0, ZSCALE_ex):
# create cells
x = i * xSpacing_ex
z = j * zSpacing_ex
index = i * ZSCALE_ex + j
inst = Instance(id=index)
exc_pop.instances.append(inst)
inst.location = Location(x=x, y=0, z=z)
exc_pos[index, 0] = x
exc_pos[index, 1] = z
# Generate inhibitory cells
inh_pop = Population(id=inh_group,
component=inh_group_component,
type="populationList",
size=XSCALE_inh * ZSCALE_inh)
net.populations.append(inh_pop)
inh_pos = np.zeros((XSCALE_inh * ZSCALE_inh, 2))
for i in range(0, XSCALE_inh):
for j in range(0, ZSCALE_inh):
# create cells
x = i * xSpacing_inh
z = j * zSpacing_inh
index = i * ZSCALE_inh + j
inst = Instance(id=index)
inh_pop.instances.append(inst)
inst.location = Location(x=x, y=0, z=z)
inh_pos[index, 0] = x
inh_pos[index, 1] = z
proj_exc_exc = Projection(id=net_conn_exc_exc,
presynaptic_population=exc_group,
postsynaptic_population=exc_group,
synapse=exc_exc_syn)
net.projections.append(proj_exc_exc)
proj_exc_inh = Projection(id=net_conn_exc_inh,
presynaptic_population=exc_group,
postsynaptic_population=inh_group,
synapse=exc_inh_syn)
net.projections.append(proj_exc_inh)
proj_inh_exc = Projection(id=net_conn_inh_exc,
presynaptic_population=inh_group,
postsynaptic_population=exc_group,
synapse=inh_exc_syn)
net.projections.append(proj_inh_exc)
proj_inh_inh = Projection(id=net_conn_inh_inh,
presynaptic_population=inh_group,
postsynaptic_population=inh_group,
synapse=inh_inh_syn)
net.projections.append(proj_inh_inh)
# Generate exc -> * connections
exc_exc_conn = np.zeros((numCells_ex, numCells_ex))
exc_inh_conn = np.zeros((numCells_ex, numCells_inh))
count_exc_exc = 0
count_exc_inh = 0
for i in range(0, XSCALE_ex):
for j in range(0, ZSCALE_ex):
x = i * xSpacing_ex
y = j * zSpacing_ex
index = i * ZSCALE_ex + j
#print("Looking at connections for exc cell at (%i, %i)"%(i,j))
# exc -> exc connections
conn_type = net_conn_exc_exc
for k in range(0, XSCALE_ex):
for l in range(0, ZSCALE_ex):
# calculate distance from pre- to post-synaptic neuron
xk = k * xSpacing_ex
yk = l * zSpacing_ex
distance = math.sqrt((x - xk)**2 + (y - yk)**2)
connection_probability = connection_probability_ex_ex * math.exp(
-(distance / (10.0 * xSpacing_ex))**2)
# create a random number between 0 and 1, if it is <= connection_probability
# accept connection otherwise refuse
a = random.random()
if 0 < a <= connection_probability:
index2 = k * ZSCALE_ex + l
count_exc_exc += 1
add_connection(proj_exc_exc, count_exc_exc,
exc_group, exc_group_component,
index, 0, exc_group,
exc_group_component, index2,
exc_exc_syn_seg_id)
exc_exc_conn[index, index2] = 1
# exc -> inh connections
conn_type = net_conn_exc_inh
for k in range(0, XSCALE_inh):
for l in range(0, ZSCALE_inh):
# calculate distance from pre- to post-synaptic neuron
xk = k * xSpacing_inh
yk = l * zSpacing_inh
distance = math.sqrt((x - xk)**2 + (y - yk)**2)
connection_probability = connection_probability_ex_inh * math.exp(
-(distance / (10.0 * xSpacing_ex))**2)
# create a random number between 0 and 1, if it is <= connection_probability
# accept connection otherwise refuse
a = random.random()
if 0 < a <= connection_probability:
index2 = k * ZSCALE_inh + l
count_exc_inh += 1
add_connection(proj_exc_inh, count_exc_inh,
exc_group, exc_group_component,
index, 0, inh_group,
inh_group_component, index2,
exc_inh_syn_seg_id)
exc_inh_conn[index, index2] = 1
inh_exc_conn = np.zeros((numCells_inh, numCells_ex))
inh_inh_conn = np.zeros((numCells_inh, numCells_inh))
count_inh_exc = 0
count_inh_inh = 0
for i in range(0, XSCALE_inh):
for j in range(0, ZSCALE_inh):
x = i * xSpacing_inh
y = j * zSpacing_inh
index = i * ZSCALE_inh + j
#print("Looking at connections for inh cell at (%i, %i)"%(i,j))
# inh -> exc connections
conn_type = net_conn_inh_exc
for k in range(0, XSCALE_ex):
for l in range(0, ZSCALE_ex):
# calculate distance from pre- to post-synaptic neuron
xk = k * xSpacing_ex
yk = l * zSpacing_ex
distance = math.sqrt((x - xk)**2 + (y - yk)**2)
connection_probability = connection_probability_inh_ex * math.exp(
-(distance / (10.0 * xSpacing_ex))**2)
# create a random number between 0 and 1, if it is <= connection_probability
# accept connection otherwise refuse
a = random.random()
if 0 < a <= connection_probability:
index2 = k * ZSCALE_ex + l
count_inh_exc += 1
add_connection(proj_inh_exc, count_inh_exc,
inh_group, inh_group_component,
index, 0, exc_group,
exc_group_component, index2,
inh_exc_syn_seg_id)
inh_exc_conn[index, index2] = 1
# inh -> inh connections
conn_type = net_conn_inh_inh
for k in range(0, XSCALE_inh):
for l in range(0, ZSCALE_inh):
# calculate distance from pre- to post-synaptic neuron
xk = k * xSpacing_inh
yk = l * zSpacing_inh
distance = math.sqrt((x - xk)**2 + (y - yk)**2)
connection_probability = connection_probability_inh_inh * math.exp(
-(distance / (10.0 * xSpacing_ex))**2)
# create a random number between 0 and 1, if it is <= connection_probability
# accept connection otherwise refuse
a = random.random()
if 0 < a <= connection_probability:
index2 = k * ZSCALE_inh + l
count_inh_inh += 1
add_connection(proj_inh_inh, count_inh_inh,
inh_group, inh_group_component,
index, 0, inh_group,
inh_group_component, index2,
inh_inh_syn_seg_id)
inh_inh_conn[index, index2] = 1
print(
"Generated network with %i exc_exc, %i exc_inh, %i inh_exc, %i inh_inh connections"
% (count_exc_exc, count_exc_inh, count_inh_exc, count_inh_inh))
####### Create Input ######
# Create a sine generator
sgE = SineGenerator(id="sineGen_0",
phase="0",
delay="0ms",
duration=str(self.sim_time) + "ms",
amplitude=str(self.Edrive_weight) + "nA",
period=str(self.Drive_period) + "ms")
sgI = SineGenerator(id="sineGen_1",
phase="0",
delay="0ms",
duration=str(self.sim_time) + "ms",
amplitude=str(self.Idrive_weight) + "nA",
period=str(self.Drive_period) + "ms")
nml_doc.sine_generators.append(sgE)
nml_doc.sine_generators.append(sgI)
# Create an input object for each excitatory cell
for i in range(0, XSCALE_ex):
exp_input = ExplicitInput(target="%s[%i]" % (exc_pop.id, i),
input=sgE.id)
net.explicit_inputs.append(exp_input)
# Create an input object for a percentage of inhibitory cells
input_probability = 0.65
for i in range(0, XSCALE_inh):
ran = random.random()
if 0 < ran <= input_probability:
inh_input = ExplicitInput(target="%s[%i]" % (inh_pop.id, i),
input=sgI.id)
net.explicit_inputs.append(inh_input)
# Define Poisson noise input
# Ex
#nml_doc.includes.append(IncludeType('Synapses/bg_AMPA_syn.synapse.nml'))
pfs1 = PoissonFiringSynapse(id="poissonFiringSyn1",
average_rate=str(self.bg_noise_frequency) +
"Hz",
synapse=bg_exc_syn,
spike_target="./%s" % bg_exc_syn)
nml_doc.poisson_firing_synapses.append(pfs1)
pfs_input_list1 = InputList(id="pfsInput1",
component=pfs1.id,
populations=exc_pop.id)
net.input_lists.append(pfs_input_list1)
for i in range(0, numCells_ex):
pfs_input_list1.input.append(
Input(id=i,
target='../%s/%i/%s' %
(exc_pop.id, i, exc_group_component),
segment_id=bg_exc_syn_seg_id,
destination="synapses"))
####### Write to file ######
print("Saving to file...")
nml_file = '../ACnet2_NML2/' + self.filename + '_doc' + '.net.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)
print "-----------------------------------"
###### Output #######
# Output membrane potential
# Ex population
Ex_potentials = 'V_Ex'
ls.create_output_file(Ex_potentials,
"../ACnet2_NML2/Results/v_exc.dat")
for j in range(numCells_ex):
quantity = "%s[%i]/v" % (exc_pop.id, j)
v = 'v' + str(j)
ls.add_column_to_output_file(Ex_potentials, v, quantity)
# Inh population
#Inh_potentials = 'V_Inh'
#ls.create_output_file(Inh_potentials, "../ACnet2_NML2/Results/v_inh.dat")
#for j in range(numCells_inh):
# quantity = "%s[%i]/v"%(inh_pop.id, j)
# v = 'v'+str(j)
# ls.add_column_to_output_file(Inh_potentials,v, quantity)
# include generated network
ls.include_neuroml2_file(nml_file)
# Save to LEMS XML file
lems_file_name = ls.save_to_file(file_name='../ACnet2_NML2/LEMS_' +
self.filename + '.xml')
def tune_izh_model(acq_list: List, metrics_from_data: Dict,
currents: Dict) -> Dict:
"""Tune networks model against the data.
Here we generate a network with the necessary number of Izhikevich cells,
one for each current stimulus, and tune them against the experimental data.
:param acq_list: list of indices of acquisitions/sweeps to tune against
:type acq_list: list
:param metrics_from_data: dictionary with the sweep number as index, and
the dictionary containing metrics generated from the analysis
:type metrics_from_data: dict
:param currents: dictionary with sweep number as index and stimulus current
value
"""
# length of simulation of the cells---should match the length of the
# experiment
sim_time = 1500.0
# Create a NeuroML template network simulation file that we will use for
# the tuning
template_doc = NeuroMLDocument(id="IzhTuneNet")
# Add an Izhikevich cell with some parameters to the document
template_doc.izhikevich2007_cells.append(
Izhikevich2007Cell(
id="Izh2007",
C="100pF",
v0="-60mV",
k="0.7nS_per_mV",
vr="-60mV",
vt="-40mV",
vpeak="35mV",
a="0.03per_ms",
b="-2nS",
c="-50.0mV",
d="100pA",
))
template_doc.networks.append(Network(id="Network0"))
# Add a cell for each acquisition list
popsize = len(acq_list)
template_doc.networks[0].populations.append(
Population(id="Pop0", component="Izh2007", size=popsize))
# Add a current source for each cell, matching the currents that
# were used in the experimental study.
counter = 0
for acq in acq_list:
template_doc.pulse_generators.append(
PulseGenerator(
id="Stim{}".format(counter),
delay="80ms",
duration="1000ms",
amplitude="{}pA".format(currents[acq]),
))
template_doc.networks[0].explicit_inputs.append(
ExplicitInput(target="Pop0[{}]".format(counter),
input="Stim{}".format(counter)))
counter = counter + 1
# Print a summary
print(template_doc.summary())
# Write to a neuroml file and validate it.
reference = "TuneIzhFergusonPyr3"
template_filename = "{}.net.nml".format(reference)
write_neuroml2_file(template_doc, template_filename, validate=True)
# Now for the tuning bits
# format is type:id/variable:id/units
# supported types: cell/channel/izhikevich2007cell
# supported variables:
# - channel: vShift
# - cell: channelDensity, vShift_channelDensity, channelDensityNernst,
# erev_id, erev_ion, specificCapacitance, resistivity
# - izhikevich2007Cell: all available attributes
# we want to tune these parameters within these ranges
# param: (min, max)
parameters = {
"izhikevich2007Cell:Izh2007/C/pF": (100, 300),
"izhikevich2007Cell:Izh2007/k/nS_per_mV": (0.01, 2),
"izhikevich2007Cell:Izh2007/vr/mV": (-70, -50),
"izhikevich2007Cell:Izh2007/vt/mV": (-60, 0),
"izhikevich2007Cell:Izh2007/vpeak/mV": (35, 70),
"izhikevich2007Cell:Izh2007/a/per_ms": (0.001, 0.4),
"izhikevich2007Cell:Izh2007/b/nS": (-10, 10),
"izhikevich2007Cell:Izh2007/c/mV": (-65, -10),
"izhikevich2007Cell:Izh2007/d/pA": (50, 500),
} # type: Dict[str, Tuple[float, float]]
# Set up our target data and so on
ctr = 0
target_data = {}
weights = {}
for acq in acq_list:
# data to fit to:
# format: path/to/variable:metric
# metric from pyelectro, for example:
# https://pyelectro.readthedocs.io/en/latest/pyelectro.html?highlight=mean_spike_frequency#pyelectro.analysis.mean_spike_frequency
mean_spike_frequency = "Pop0[{}]/v:mean_spike_frequency".format(ctr)
average_last_1percent = "Pop0[{}]/v:average_last_1percent".format(ctr)
first_spike_time = "Pop0[{}]/v:first_spike_time".format(ctr)
# each metric can have an associated weight
weights[mean_spike_frequency] = 1
weights[average_last_1percent] = 1
weights[first_spike_time] = 1
# value of the target data from our data set
target_data[mean_spike_frequency] = metrics_from_data[acq][
"{}:mean_spike_frequency".format(acq)]
target_data[average_last_1percent] = metrics_from_data[acq][
"{}:average_last_1percent".format(acq)]
target_data[first_spike_time] = metrics_from_data[acq][
"{}:first_spike_time".format(acq)]
# only add these if the experimental data includes them
# these are only generated for traces with spikes
if "{}:average_maximum".format(acq) in metrics_from_data[acq]:
average_maximum = "Pop0[{}]/v:average_maximum".format(ctr)
weights[average_maximum] = 1
target_data[average_maximum] = metrics_from_data[acq][
"{}:average_maximum".format(acq)]
if "{}:average_minimum".format(acq) in metrics_from_data[acq]:
average_minimum = "Pop0[{}]/v:average_minimum".format(ctr)
weights[average_minimum] = 1
target_data[average_minimum] = metrics_from_data[acq][
"{}:average_minimum".format(acq)]
ctr = ctr + 1
# simulator to use
simulator = "jNeuroML"
return run_optimisation(
# Prefix for new files
prefix="TuneIzh",
# Name of the NeuroML template file
neuroml_file=template_filename,
# Name of the network
target="Network0",
# Parameters to be fitted
parameters=list(parameters.keys()),
# Our max and min constraints
min_constraints=[v[0] for v in parameters.values()],
max_constraints=[v[1] for v in parameters.values()],
# Weights we set for parameters
weights=weights,
# The experimental metrics to fit to
target_data=target_data,
# Simulation time
sim_time=sim_time,
# EC parameters
population_size=100,
max_evaluations=500,
num_selected=30,
num_offspring=50,
mutation_rate=0.9,
num_elites=3,
# Seed value
seed=12345,
# Simulator
simulator=simulator,
dt=0.025,
show_plot_already='-nogui' not in sys.argv,
save_to_file="fitted_izhikevich_fitness.png",
save_to_file_scatter="fitted_izhikevich_scatter.png",
save_to_file_hist="fitted_izhikevich_hist.png",
save_to_file_output="fitted_izhikevich_output.png",
num_parallel_evaluations=4,
)
def run_fitted_cell_simulation(sweeps_to_tune_against: List,
tuning_report: Dict,
simulation_id: str) -> None:
"""Run a simulation with the values obtained from the fitting
:param tuning_report: tuning report from the optimser
:type tuning_report: Dict
:param simulation_id: text id of simulation
:type simulation_id: str
"""
# get the fittest variables
fittest_vars = tuning_report["fittest vars"]
C = str(fittest_vars["izhikevich2007Cell:Izh2007/C/pF"]) + "pF"
k = str(
fittest_vars["izhikevich2007Cell:Izh2007/k/nS_per_mV"]) + "nS_per_mV"
vr = str(fittest_vars["izhikevich2007Cell:Izh2007/vr/mV"]) + "mV"
vt = str(fittest_vars["izhikevich2007Cell:Izh2007/vt/mV"]) + "mV"
vpeak = str(fittest_vars["izhikevich2007Cell:Izh2007/vpeak/mV"]) + "mV"
a = str(fittest_vars["izhikevich2007Cell:Izh2007/a/per_ms"]) + "per_ms"
b = str(fittest_vars["izhikevich2007Cell:Izh2007/b/nS"]) + "nS"
c = str(fittest_vars["izhikevich2007Cell:Izh2007/c/mV"]) + "mV"
d = str(fittest_vars["izhikevich2007Cell:Izh2007/d/pA"]) + "pA"
# Create a simulation using our obtained parameters.
# Note that the tuner generates a graph with the fitted values already, but
# we want to keep a copy of our fitted cell also, so we'll create a NeuroML
# Document ourselves also.
sim_time = 1500.0
simulation_doc = NeuroMLDocument(id="FittedNet")
# Add an Izhikevich cell with some parameters to the document
simulation_doc.izhikevich2007_cells.append(
Izhikevich2007Cell(
id="Izh2007",
C=C,
v0="-60mV",
k=k,
vr=vr,
vt=vt,
vpeak=vpeak,
a=a,
b=b,
c=c,
d=d,
))
simulation_doc.networks.append(Network(id="Network0"))
# Add a cell for each acquisition list
popsize = len(sweeps_to_tune_against)
simulation_doc.networks[0].populations.append(
Population(id="Pop0", component="Izh2007", size=popsize))
# Add a current source for each cell, matching the currents that
# were used in the experimental study.
counter = 0
for acq in sweeps_to_tune_against:
simulation_doc.pulse_generators.append(
PulseGenerator(
id="Stim{}".format(counter),
delay="80ms",
duration="1000ms",
amplitude="{}pA".format(currents[acq]),
))
simulation_doc.networks[0].explicit_inputs.append(
ExplicitInput(target="Pop0[{}]".format(counter),
input="Stim{}".format(counter)))
counter = counter + 1
# Print a summary
print(simulation_doc.summary())
# Write to a neuroml file and validate it.
reference = "FittedIzhFergusonPyr3"
simulation_filename = "{}.net.nml".format(reference)
write_neuroml2_file(simulation_doc, simulation_filename, validate=True)
simulation = LEMSSimulation(
sim_id=simulation_id,
duration=sim_time,
dt=0.1,
target="Network0",
simulation_seed=54321,
)
simulation.include_neuroml2_file(simulation_filename)
simulation.create_output_file("output0", "{}.v.dat".format(simulation_id))
counter = 0
for acq in sweeps_to_tune_against:
simulation.add_column_to_output_file("output0",
"Pop0[{}]".format(counter),
"Pop0[{}]/v".format(counter))
counter = counter + 1
simulation_file = simulation.save_to_file()
# simulate
run_lems_with_jneuroml(simulation_file,
max_memory="2G",
nogui=True,
plot=False)
