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 create_na_channel():
"""Create the Na channel.
This will create the Na channel and save it to a file.
It will also validate this file.
returns: name of the created file
"""
na_channel = IonChannelHH(id="na_channel", notes="Sodium channel for HH cell", conductance="10pS", species="na")
gate_m = GateHHRates(id="na_m", instances="3", notes="m gate for na channel")
m_forward_rate = HHRate(type="HHExpLinearRate", rate="1per_ms", midpoint="-40mV", scale="10mV")
m_reverse_rate = HHRate(type="HHExpRate", rate="4per_ms", midpoint="-65mV", scale="-18mV")
gate_m.forward_rate = m_forward_rate
gate_m.reverse_rate = m_reverse_rate
na_channel.gate_hh_rates.append(gate_m)
gate_h = GateHHRates(id="na_h", instances="1", notes="h gate for na channel")
h_forward_rate = HHRate(type="HHExpRate", rate="0.07per_ms", midpoint="-65mV", scale="-20mV")
h_reverse_rate = HHRate(type="HHSigmoidRate", rate="1per_ms", midpoint="-35mV", scale="10mV")
gate_h.forward_rate = h_forward_rate
gate_h.reverse_rate = h_reverse_rate
na_channel.gate_hh_rates.append(gate_h)
na_channel_doc = NeuroMLDocument(id="na_channel", notes="Na channel for HH neuron")
na_channel_fn = "HH_example_na_channel.nml"
na_channel_doc.ion_channel_hhs.append(na_channel)
pynml.write_neuroml2_file(nml2_doc=na_channel_doc, nml2_file_name=na_channel_fn, validate=True)
return na_channel_fn
def create_leak_channel():
"""Create a leak channel
This will create the leak channel and save it to a file.
It will also validate this file.
:returns: name of leak channel nml file
"""
leak_channel = IonChannelHH(id="leak_channel", conductance="10pS", notes="Leak conductance")
leak_channel_doc = NeuroMLDocument(id="leak_channel", notes="leak channel for HH neuron")
leak_channel_fn = "HH_example_leak_channel.nml"
leak_channel_doc.ion_channel_hhs.append(leak_channel)
pynml.write_neuroml2_file(nml2_doc=leak_channel_doc, nml2_file_name=leak_channel_fn, validate=True)
return leak_channel_fn
def create_k_channel():
"""Create the K channel
This will create the K channel and save it to a file.
It will also validate this file.
:returns: name of the K channel file
"""
k_channel = IonChannelHH(id="k_channel", notes="Potassium channel for HH cell", conductance="10pS", species="k")
gate_n = GateHHRates(id="k_n", instances="4", notes="n gate for k channel")
n_forward_rate = HHRate(type="HHExpLinearRate", rate="0.1per_ms", midpoint="-55mV", scale="10mV")
n_reverse_rate = HHRate(type="HHExpRate", rate="0.125per_ms", midpoint="-65mV", scale="-80mV")
gate_n.forward_rate = n_forward_rate
gate_n.reverse_rate = n_reverse_rate
k_channel.gate_hh_rates.append(gate_n)
k_channel_doc = NeuroMLDocument(id="k_channel", notes="k channel for HH neuron")
k_channel_fn = "HH_example_k_channel.nml"
k_channel_doc.ion_channel_hhs.append(k_channel)
pynml.write_neuroml2_file(nml2_doc=k_channel_doc, nml2_file_name=k_channel_fn, validate=True)
return k_channel_fn
notes+="Ignored channels = %s\n\n"%ignore_chans
notes+="For more information on this cell model see: "+\
"https://bbp.epfl.ch/nmc-portal/microcircuit#/metype/%s/details\n\n"%cell_info['me-type']
cell.notes = notes
for channel in incl_chans:
nml_doc.includes.append(neuroml.IncludeType(
href="%s" % channel))
if make_zips:
print("Copying %s to zip folder"%channel)
shutil.copyfile('../../NeuroML2/%s'%channel, '%s/%s'%(nml2_cell_dir,channel))
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
stim_ref = 'stepcurrent3'
stim_ref_hyp = '%s_hyp'%stim_ref
stim_sim_duration = 3000
stim_hyp_amp, stim_amp = get_stimulus_amplitudes(bbp_ref)
stim_del = '700ms'
stim_dur = '2000ms'
new_net_loc = "%s/%s.%s.net.nml"%(nml2_cell_dir,bbp_ref, stim_ref)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net_doc.notes = notes
#<pulseGenerator id="Gran_10pA" delay="100.0ms" duration="500.0ms" amplitude="1.0E-5uA"/>
ca_file.write(xml)
ca_file.close()
intracellular_properties = neuroml.IntracellularProperties(resistivities=resistivities, species=species)
biophysical_properties = neuroml.BiophysicalProperties(id="biophys",
intracellular_properties=intracellular_properties,
membrane_properties=membrane_properties)
cell.biophysical_properties = biophysical_properties
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
pynml.nml2_to_svg(nml_cell_loc)
pref_duration_ms = 2500
pref_dt_ms = 0.005 # used in Allen Neuron runs
new_nml_file_name = "Network_%s.net.nml"%model_id
new_net_loc = "%s/%s"%(nml2_cell_dir, new_nml_file_name)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net = new_net_doc.networks[0]
new_net_doc.notes = notes
def process_celldir(inputs):
"""Process cell directory"""
count, cell_dir, nml2_cell_dir, total_count = inputs
local_nml2_cell_dir = os.path.join("..", nml2_cell_dir)
print(
"\n\n************************************************************\n\n"
"Parsing %s (cell %i/%i)\n" % (cell_dir, count, total_count)
)
if os.path.isdir(cell_dir):
old_cwd = os.getcwd()
os.chdir(cell_dir)
else:
old_cwd = os.getcwd()
os.chdir("../" + cell_dir)
if make_zips:
nml2_cell_dir = "%s/%s" % (zips_dir, cell_dir)
if not os.path.isdir(nml2_cell_dir):
os.mkdir(nml2_cell_dir)
print("Generating into %s" % nml2_cell_dir)
bbp_ref = None
template_file = open("template.hoc", "r")
for line in template_file:
if line.startswith("begintemplate "):
bbp_ref = line.split(" ")[1].strip()
print(" > Assuming cell in directory %s is in a template named %s" % (cell_dir, bbp_ref))
load_cell_file = "loadcell.hoc"
variables = {}
variables["cell"] = bbp_ref
variables["groups_info_file"] = groups_info_file
template = """
///////////////////////////////////////////////////////////////////////////////
//
// NOTE: This file is not part of the original BBP cell model distribution
// It has been generated by ../ParseAll.py to facilitate loading of the cell
// into NEURON for exporting the model morphology to NeuroML2
//
//////////////////////////////////////////////////////////////////////////////
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
//======================== settings ===================================
v_init = -80
hyp_amp = -0.062866
step_amp = 0.3112968
tstop = 3000
//=================== creating cell object ===========================
load_file("import3d.hoc")
objref cell
// Using 1 to force loading of the file, in case file with same name was loaded
// before...
load_file(1, "constants.hoc")
load_file(1, "morphology.hoc")
load_file(1, "biophysics.hoc")
print "Loaded morphology and biophysics..."
load_file(1, "synapses/synapses.hoc")
load_file(1, "template.hoc")
print "Loaded template..."
load_file(1, "createsimulation.hoc")
create_cell(0)
print "Created new cell using loadcell.hoc: {{ cell }}"
define_shape()
wopen("{{ groups_info_file }}")
fprint("//Saving information on groups in this cell...\\n")
fprint("- somatic\\n")
forsec {{ cell }}[0].somatic {
fprint("%s\\n",secname())
}
fprint("- basal\\n")
forsec {{ cell }}[0].basal {
fprint("%s\\n",secname())
}
fprint("- axonal\\n")
forsec {{ cell }}[0].axonal {
fprint("%s\\n",secname())
}
fprint("- apical\\n")
forsec {{ cell }}[0].apical {
fprint("%s\\n",secname())
}
wopen()
"""
t = Template(template)
contents = t.render(variables)
load_cell = open(load_cell_file, "w")
load_cell.write(contents)
load_cell.close()
print(" > Written %s" % load_cell_file)
if os.path.isfile(load_cell_file):
cell_info = parse_cell_info_file(cell_dir)
nml_file_name = "%s.net.nml" % bbp_ref
nml_net_loc = "%s/%s" % (local_nml2_cell_dir, nml_file_name)
nml_cell_file = "%s_0_0.cell.nml" % bbp_ref
nml_cell_loc = "%s/%s" % (local_nml2_cell_dir, nml_cell_file)
print(" > Loading %s and exporting to %s" % (load_cell_file, nml_net_loc))
export_to_neuroml2(load_cell_file, nml_net_loc, separateCellFiles=True, includeBiophysicalProperties=False)
print(" > Exported to: %s and %s using %s" % (nml_net_loc, nml_cell_loc, load_cell_file))
nml_doc = pynml.read_neuroml2_file(nml_cell_loc)
cell = nml_doc.cells[0]
print(" > Adding groups from: %s" % groups_info_file)
groups = {}
current_group = None
for line in open(groups_info_file):
if not line.startswith("//"):
if line.startswith("- "):
current_group = line[2:-1]
print(" > Adding group: [%s]" % current_group)
groups[current_group] = []
else:
section = line.split(".")[1].strip()
segment_group = section.replace("[", "_").replace("]", "")
groups[current_group].append(segment_group)
for g in groups.keys():
new_seg_group = neuroml.SegmentGroup(id=g)
cell.morphology.segment_groups.append(new_seg_group)
for sg in groups[g]:
new_seg_group.includes.append(neuroml.Include(sg))
if g in ["basal", "apical"]:
new_seg_group.inhomogeneous_parameters.append(
neuroml.InhomogeneousParameter(
id="PathLengthOver_" + g,
variable="p",
metric="Path Length from root",
proximal=neuroml.ProximalDetails(translation_start="0"),
)
)
ignore_chans = [
"Ih",
"Ca_HVA",
"Ca_LVAst",
"Ca",
"SKv3_1",
"SK_E2",
"CaDynamics_E2",
"Nap_Et2",
"Im",
"K_Tst",
"NaTa_t",
"K_Pst",
"NaTs2_t",
]
# ignore_chans=['StochKv','StochKv_deterministic']
ignore_chans = []
bp, incl_chans = get_biophysical_properties(
cell_info["e-type"], ignore_chans=ignore_chans, templates_json="../templates.json"
)
cell.biophysical_properties = bp
print("Set biophysical properties")
notes = ""
notes += (
"\n\nExport of a cell model obtained from the BBP Neocortical"
"Microcircuit Collaboration Portal into NeuroML2"
"\n\n******************************************************\n*"
" This export to NeuroML2 has not yet been fully validated!!"
"\n* Use with caution!!\n***********************************"
"*******************\n\n"
)
if len(ignore_chans) > 0:
notes += "Ignored channels = %s\n\n" % ignore_chans
notes += (
"For more information on this cell model see: "
"https://bbp.epfl.ch/nmc-portal/microcircuit#/metype/%s/"
"details\n\n" % cell_info["me-type"]
)
cell.notes = notes
for channel in incl_chans:
nml_doc.includes.append(neuroml.IncludeType(href="%s" % channel))
if make_zips:
print("Copying %s to zip folder" % channel)
shutil.copyfile("../../NeuroML2/%s" % channel, "%s/%s" % (local_nml2_cell_dir, channel))
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
stim_ref = "stepcurrent3"
stim_ref_hyp = "%s_hyp" % stim_ref
stim_sim_duration = 3000
stim_hyp_amp, stim_amp = get_stimulus_amplitudes(bbp_ref)
stim_del = "700ms"
stim_dur = "2000ms"
new_net_loc = "%s/%s.%s.net.nml" % (local_nml2_cell_dir, bbp_ref, stim_ref)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net_doc.notes = notes
stim_hyp = neuroml.PulseGenerator(
id=stim_ref_hyp, delay="0ms", duration="%sms" % stim_sim_duration, amplitude=stim_hyp_amp
)
new_net_doc.pulse_generators.append(stim_hyp)
stim = neuroml.PulseGenerator(id=stim_ref, delay=stim_del, duration=stim_dur, amplitude=stim_amp)
new_net_doc.pulse_generators.append(stim)
new_net = new_net_doc.networks[0]
pop_id = new_net.populations[0].id
pop_comp = new_net.populations[0].component
input_list = neuroml.InputList(id="%s_input" % stim_ref_hyp, component=stim_ref_hyp, populations=pop_id)
syn_input = neuroml.Input(id=0, target="../%s/0/%s" % (pop_id, pop_comp), destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
input_list = neuroml.InputList(id="%s_input" % stim_ref, component=stim_ref, populations=pop_id)
syn_input = neuroml.Input(id=0, target="../%s/0/%s" % (pop_id, pop_comp), destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
pynml.write_neuroml2_file(new_net_doc, new_net_loc)
generate_lems_file_for_neuroml(
cell_dir,
new_net_loc,
"network",
stim_sim_duration,
0.025,
"LEMS_%s.xml" % cell_dir,
local_nml2_cell_dir,
copy_neuroml=False,
seed=1234,
)
pynml.nml2_to_svg(nml_net_loc)
clear_neuron()
pop = neuroml.Population(id="Pop_%s" % bbp_ref, component=bbp_ref + "_0_0", type="populationList")
inst = neuroml.Instance(id="0")
pop.instances.append(inst)
width = 6
X = count % width
Z = (count - X) / width
inst.location = neuroml.Location(x=300 * X, y=0, z=300 * Z)
count += 1
if make_zips:
zip_file = "%s/%s.zip" % (zips_dir, cell_dir)
print("Creating zip file: %s" % zip_file)
with zipfile.ZipFile(zip_file, "w") as myzip:
for next_file in os.listdir(local_nml2_cell_dir):
next_file = "%s/%s" % (local_nml2_cell_dir, next_file)
arcname = next_file[len(zips_dir) :]
print("Adding : %s as %s" % (next_file, arcname))
myzip.write(next_file, arcname)
os.chdir(old_cwd)
return nml_cell_file, pop
def create_GoC_network(duration, dt, seed, runid, run=False):
### ---------- Load Params
noPar = True
pfile = Path('params_file.pkl')
if pfile.exists():
print('Reading parameters from file:')
file = open('params_file.pkl', 'rb')
params_list = pkl.load(file)
if len(params_list) > runid:
p = params_list[runid]
file.close()
if noPar:
p = inp.get_simulation_params(runid)
### ---------- Component types
goc_filename = 'GoC.cell.nml' # Golgi cell with channels
goc_file = pynml.read_neuroml2_file(goc_filename)
goc_type = goc_file.cells[0]
goc_ref = nml.IncludeType(href=goc_filename)
gj = nml.GapJunction(id="GJ_0", conductance="426pS") # GoC synapse
### --------- Populations
# Build network to specify cells and connectivity
net = nml.Network(id="gocNetwork",
type="networkWithTemperature",
temperature="23 degC")
# Create GoC population
goc_pop = nml.Population(id=goc_type.id + "Pop",
component=goc_type.id,
type="populationList",
size=p["nGoC"])
for goc in range(p["nGoC"]):
inst = nml.Instance(id=goc)
goc_pop.instances.append(inst)
inst.location = nml.Location(x=p["GoC_pos"][goc, 0],
y=p["GoC_pos"][goc, 1],
z=p["GoC_pos"][goc, 2])
net.populations.append(goc_pop)
# Create NML document for network specification
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(goc_ref)
net_doc.gap_junctions.append(gj)
### ------------ Connectivity
### 1. Input Current to one cell
ctr = 0
for goc in p["Test_GoC"]:
for jj in range(p["nSteps"]):
input_id = 'stim_{}'.format(ctr)
istep = nml.PulseGenerator(
id=input_id,
delay='{} ms'.format(p["iDuration"] * jj + p["iRest"] *
(jj + 1)),
duration='{} ms'.format(p["iDuration"]),
amplitude='{} pA'.format(p["iAmp"][jj]))
net_doc.pulse_generators.append(istep)
input_list = nml.InputList(id='ilist_{}'.format(ctr),
component=istep.id,
populations=goc_pop.id)
curr_inj = nml.Input('0',
target="../%s[%i]" % (goc_pop.id, goc),
destination="synapses")
input_list.input.append(curr_inj)
net.input_lists.append(input_list)
ctr += 1
### 2. Electrical coupling between GoCs
GoCCoupling = nml.ElectricalProjection(id="gocGJ",
presynaptic_population=goc_pop.id,
postsynaptic_population=goc_pop.id)
net.electrical_projections.append(GoCCoupling)
dend_id = [1, 2, 5]
for jj in range(p["GJ_pairs"].shape[0]):
conn = nml.ElectricalConnectionInstanceW(
id=jj,
pre_cell='../{}/{}/{}'.format(goc_pop.id, p["GJ_pairs"][jj, 0],
goc_type.id),
pre_segment=dend_id[p["GJ_loc"][jj, 0]],
pre_fraction_along='0.5',
post_cell='../{}/{}/{}'.format(goc_pop.id, p["GJ_pairs"][jj, 1],
goc_type.id),
post_segment=dend_id[p["GJ_loc"][jj, 1]],
post_fraction_along='0.5',
synapse=gj.id,
weight=p["GJ_wt"][jj])
GoCCoupling.electrical_connection_instance_ws.append(conn)
### -------------- Write files
net_filename = 'gocNetwork.nml'
pynml.write_neuroml2_file(net_doc, net_filename)
simid = 'sim_gocnet_' + goc_type.id + '_run_{}'.format(runid)
ls = LEMSSimulation(simid, duration=duration, dt=dt, simulation_seed=seed)
ls.assign_simulation_target(net.id)
ls.include_neuroml2_file(net_filename)
ls.include_neuroml2_file(goc_filename)
# Specify outputs
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes" % simid, format='ID_TIME')
for jj in range(goc_pop.size):
ls.add_selection_to_event_output_file(
eof0, jj, '{}/{}/{}'.format(goc_pop.id, jj, goc_type.id), 'spike')
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % simid)
ctr = 0
for jj in p["Test_GoC"]:
ls.add_column_to_output_file(
of0, jj, '{}/{}/{}/v'.format(goc_pop.id, ctr, goc_type.id))
ctr += 1
#Create Lems file to run
lems_simfile = ls.save_to_file()
if run:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
nogui=True,
plot=False)
else:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
only_generate_scripts=True,
compile_mods=False,
nogui=True,
plot=False)
return res
def run_individual(self, sim_var, show=False):
"""
Run an individual simulation.
The candidate data has been flattened into the sim_var dict. The
sim_var dict contains parameter:value key value pairs, which are
applied to the model before it is simulated.
"""
nml_doc = read_neuroml2_file(self.neuroml_file,
include_includes=True,
verbose = True,
already_included = [])
for var_name in sim_var.keys():
words = var_name.split('/')
type, id1 = words[0].split(':')
if ':' in words[1]:
variable, id2 = words[1].split(':')
else:
variable = words[1]
id2 = None
units = words[2]
value = sim_var[var_name]
print_comment_v(' Changing value of %s (%s) in %s (%s) to: %s %s'%(variable, id2, type, id1, value, units))
if type == 'cell':
cell = None
for c in nml_doc.cells:
if c.id == id1:
cell = c
if variable == 'channelDensity':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities:
if cd.id == id2:
chanDens = cd
chanDens.cond_density = '%s %s'%(value, units)
elif variable == 'erev_id': # change all values of erev in channelDensity elements with only this id
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities:
if cd.id == id2:
chanDens = cd
chanDens.erev = '%s %s'%(value, units)
elif variable == 'erev_ion': # change all values of erev in channelDensity elements with this ion
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities:
if cd.ion == id2:
chanDens = cd
chanDens.erev = '%s %s'%(value, units)
elif variable == 'specificCapacitance':
specCap = None
for sc in cell.biophysical_properties.membrane_properties.specific_capacitances:
if (sc.segment_groups == None and id2 == 'all') or sc.segment_groups == id2 :
specCap = sc
specCap.value = '%s %s'%(value, units)
else:
print_comment_v('Unknown variable (%s) in variable expression: %s'%(variable, var_name))
exit()
elif type == 'izhikevich2007Cell':
izhcell = None
for c in nml_doc.izhikevich2007_cells:
if c.id == id1:
izhcell = c
izhcell.__setattr__(variable, '%s %s'%(value, units))
else:
print_comment_v('Unknown type (%s) in variable expression: %s'%(type, var_name))
new_neuroml_file = '%s/%s'%(self.generate_dir,os.path.basename(self.neuroml_file))
if new_neuroml_file == self.neuroml_file:
print_comment_v('Cannot use a directory for generating into (%s) which is the same location of the NeuroML file (%s)!'% \
(self.neuroml_file, self.generate_dir))
write_neuroml2_file(nml_doc, new_neuroml_file)
sim = NeuroMLSimulation(self.ref,
neuroml_file = new_neuroml_file,
target = self.target,
sim_time = self.sim_time,
dt = self.dt,
simulator = self.simulator,
generate_dir = self.generate_dir)
sim.go()
if show:
sim.show()
return sim.t, sim.volts
amplitude=stim)
nml_doc.pulse_generators.append(pg)
pop = 'Pop_Mitral_0_%i'%i
# Add these to cells
input_list = nml.InputList(id=input_id,
component=pg.id,
populations=pop)
input = nml.Input(id='0',
target="../%s[%i]"%(pop, i),
destination="synapses")
input_list.input.append(input)
nml_doc.networks[0].input_lists.append(input_list)
pynml.write_neuroml2_file(nml_doc, nml_file1)
plots = {}
saves = {}
for i in range(number_cells):
p = []
plots['Mitral_0_%i'%i] = p
p.append('Pop_Mitral_0_%i/0/Mitral_0_%i/0/v'%(i,i))
#p.append('Pop_Mitral_0_%i/0/Mitral_0_%i/681/v'%(i,i))
#p.append('Pop_Mitral_0_%i/0/Mitral_0_%i/20/v'%(i,i))
p.append('Pop_Mitral_0_%i/0/Mitral_0_%i/43/v'%(i,i))
#save_plot.append('Pop_Mitral_0_%i/0/Mitral_0_%i/682/v'%(i,i))
#save_plot.append('Pop_Mitral_0_%i/0/Mitral_0_%i/20/v'%(i,i))
#save_plot.append('Pop_Mitral_0_%i/0/Mitral_0_%i/43/v'%(i,i))
print plots
def exportToNML(cells):
nml_net_file = "../NeuroML2/GranuleCells/Exported/GCnet%iG.net.nml" % len(cells)
export_to_neuroml2(None,
nml_net_file,
includeBiophysicalProperties=False,
separateCellFiles=True)
# Rename files so their cell GIDs are preserved
for gcid in cells.keys():
fileId = cells[gcid]['index']
oldFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml' % fileId
newFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml_TEMP' % gcid
# Using TEMP files to avoid naming conflicts
os.rename(oldFile, newFile)
# Remove temp files after all have been renamed
for gcid in cells.keys():
oldFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml_TEMP' % gcid
newFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml' % gcid
os.rename(oldFile, newFile)
for gcid in cells.keys():
cell, nml_doc, nml_cell_file = readGCnml(gcid)
print("Loaded GC cell %i with %i segments"%(gcid, len(cell.morphology.segments)))
# Change cell ID to preserve GCID
cell.id = "Granule_0_%i" % gcid
# Change segment ids to start at 0 and increment
exportHelper.resetRoot(cell)
# Replace ModelViewParmSubset_N groups with all, axon, soma, dendrite groups
buildStandardSegmentGroups(cell)
# Add channel placeholders
nml_doc.includes.append(neuroml.IncludeType(href="channelIncludesPLACEHOLDER"))
cell.biophysical_properties = neuroml.BiophysicalProperties(id="biophysPLACEHOLDER")
cell.morphology.segments.append(neuroml.Segment(id="spinePLACEHOLDER"))
# Save the new NML
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
# Replace placeholders with contents from GranuleCell...xml files
replaceChannelPlaceholders(nml_cell_file)
cell, nml_doc, nml_cell_file = readGCnml(gcid)
# Fix the fractionAlong parent segment bug ( https://github.com/NeuroML/org.neuroml.export/issues/46 )
exportHelper.splitSegmentAlongFraction(cell, "Seg0_priden", "priden", 0.8, "Seg0_priden2_0")
# Orient cell along the versor
versor = granules.granule_position_orientation(gcid)[1]
for seg in cell.morphology.segments:
segLength = seg.length
if seg.parent is not None:
parentDistal = [parent for parent in cell.morphology.segments if parent.id == seg.parent.segments][0].distal
seg.proximal.x = parentDistal.x
seg.proximal.y = parentDistal.y
seg.proximal.z = parentDistal.z
seg.distal = setAlongVersor(seg.distal, versor, seg.proximal, segLength)
# Make sure spine is in the all group
[group for group in cell.morphology.segment_groups if group.id == 'all'][0]\
.includes\
.append(neuroml.Include(segment_groups='spine_group'))\
# and Dendrite group
[group for group in cell.morphology.segment_groups if group.id == 'dendrite_group'][0]\
.includes\
.append(neuroml.Include(segment_groups='spine_group'))
# Save orientation
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
print(nml_cell_file)
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
def __main__():
num_cells_to_export = 1
cells = []
for mgid in range(num_cells_to_export):
print mgid
cells.append(mkmitral(mgid))
nml_net_file = "../NeuroML2/MitralCells/Exported/PartialBulb_%iMTCells.net.nml" % num_cells_to_export
export_to_neuroml2(None,
nml_net_file,
includeBiophysicalProperties=False,
separateCellFiles=True)
for i in range(num_cells_to_export):
print("Processing cell %i out of %i"%(i, num_cells_to_export))
nml_cell_file = "../NeuroML2/MitralCells/Exported/Mitral_0_%i.cell.nml" % i
nml_doc = pynml.read_neuroml2_file(nml_cell_file)
cell = nml_doc.cells[0]
import pydevd
pydevd.settrace('10.211.55.3', port=4200, stdoutToServer=True, stderrToServer=True, suspend=True)
# Set root to id=0 and increment others
exportHelper.resetRoot(cell)
somaSeg = [seg for seg in cell.morphology.segments if seg.name == "Seg0_soma"][0]
initialSeg = [seg for seg in cell.morphology.segments if seg.name == "Seg0_initialseg"][0]
hillockSeg = [seg for seg in cell.morphology.segments if seg.name == "Seg0_hillock"][0]
# Fix initial and hillock segs by moving them to the soma
hillockSeg.proximal = pointMovedByOffset(hillockSeg.proximal, somaSeg.distal)
hillockSeg.distal = pointMovedByOffset(hillockSeg.distal, somaSeg.distal)
initialSeg.proximal = pointMovedByOffset(initialSeg.proximal, somaSeg.distal)
initialSeg.distal = pointMovedByOffset(initialSeg.distal, somaSeg.distal)
# Move everything back to the origin
originOffset = type("", (), dict(x = -somaSeg.proximal.x, y = -somaSeg.proximal.y, z = -somaSeg.proximal.z ))()
for seg in cell.morphology.segments:
seg.proximal = pointMovedByOffset(seg.proximal, originOffset)
seg.distal = pointMovedByOffset(seg.distal, originOffset)
# Replace ModelViewParmSubset_N groups with all, axon, soma, dendrite groups
buildStandardSegmentGroups(cell)
# Add channel placeholders
nml_doc.includes.append(neuroml.IncludeType(href="channelIncludesPLACEHOLDER"))
cell.biophysical_properties = neuroml.BiophysicalProperties(id="biophysPLACEHOLDER")
# Save the new NML
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
# Replace placeholders with contents from MitralCell...xml files
replaceChannelPlaceholders(nml_cell_file)
print("COMPLETED: " + nml_cell_file)
print("DONE")
def generate_current_vs_frequency_curve(nml2_file,
cell_id,
start_amp_nA,
end_amp_nA,
step_nA,
analysis_duration,
analysis_delay,
dt = 0.05,
temperature = "32degC",
spike_threshold_mV=0.,
plot_voltage_traces=False,
plot_if=True,
plot_iv=False,
xlim_if = None,
ylim_if = None,
xlim_iv = None,
ylim_iv = None,
show_plot_already=True,
save_if_figure_to=None,
save_iv_figure_to=None,
simulator="jNeuroML",
include_included=True):
from pyelectro.analysis import max_min
from pyelectro.analysis import mean_spike_frequency
import numpy as np
print_comment_v("Generating FI curve for cell %s in %s using %s (%snA->%snA; %snA steps)"%
(cell_id, nml2_file, simulator, start_amp_nA, end_amp_nA, step_nA))
sim_id = 'iv_%s'%cell_id
duration = analysis_duration+analysis_delay
ls = LEMSSimulation(sim_id, duration, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
stims = []
amp = start_amp_nA
while amp<=end_amp_nA :
stims.append(amp)
amp+=step_nA
number_cells = len(stims)
pop = nml.Population(id="population_of_%s"%cell_id,
component=cell_id,
size=number_cells)
# create network and add populations
net_id = "network_of_%s"%cell_id
net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature)
ls.assign_simulation_target(net_id)
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(nml2_file))
net.populations.append(pop)
for i in range(number_cells):
stim_amp = "%snA"%stims[i]
input_id = ("input_%s"%stim_amp).replace('.','_').replace('-','min')
pg = nml.PulseGenerator(id=input_id,
delay="0ms",
duration="%sms"%duration,
amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
# Add these to cells
input_list = nml.InputList(id=input_id,
component=pg.id,
populations=pop.id)
input = nml.Input(id='0',
target="../%s[%i]"%(pop.id, i),
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = '%s.net.nml'%sim_id
pynml.write_neuroml2_file(net_doc, net_file_name)
ls.include_neuroml2_file(net_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0,"Voltages", "-90", "50")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%sim_id)
for i in range(number_cells):
ref = "v_cell%i"%i
quantity = "%s[%i]/v"%(pop.id, i)
ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color())
ls.add_column_to_output_file(of0, ref, quantity)
lems_file_name = ls.save_to_file()
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
#print(results.keys())
if_results = {}
iv_results = {}
for i in range(number_cells):
t = np.array(results['t'])*1000
v = np.array(results["%s[%i]/v"%(pop.id, i)])*1000
mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV)
spike_times = mm['maxima_times']
freq = 0
if len(spike_times) > 2:
count = 0
for s in spike_times:
if s >= analysis_delay and s < (analysis_duration+analysis_delay):
count+=1
freq = 1000 * count/float(analysis_duration)
mean_freq = mean_spike_frequency(spike_times)
# print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
if_results[stims[i]] = freq
if freq == 0:
iv_results[stims[i]] = v[-1]
if plot_if:
stims = sorted(if_results.keys())
stims_pA = [ii*1000 for ii in stims]
freqs = [if_results[s] for s in stims]
pynml.generate_plot([stims_pA],
[freqs],
"Frequency versus injected current for: %s"%nml2_file,
colors = ['k'],
linestyles=['-'],
markers=['o'],
xaxis = 'Input current (pA)',
yaxis = 'Firing frequency (Hz)',
xlim = xlim_if,
ylim = ylim_if,
grid = True,
show_plot_already=False,
save_figure_to = save_if_figure_to)
if plot_iv:
stims = sorted(iv_results.keys())
stims_pA = [ii*1000 for ii in sorted(iv_results.keys())]
vs = [iv_results[s] for s in stims]
pynml.generate_plot([stims_pA],
[vs],
"Final membrane potential versus injected current for: %s"%nml2_file,
colors = ['k'],
linestyles=['-'],
markers=['o'],
xaxis = 'Input current (pA)',
yaxis = 'Membrane potential (mV)',
xlim = xlim_iv,
ylim = ylim_iv,
grid = True,
show_plot_already=False,
save_figure_to = save_iv_figure_to)
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
return if_results
def create_GoC_network( duration, dt, seed, N_goc=0, run=False, prob_type='Boltzmann', GJw_type='Vervaeke2010' ):
goc_filename = 'GoC.cell.nml'
goc_file = pynml.read_neuroml2_file( goc_filename )
goc_type = goc_file.cells[0]
GJ_filename = 'GapJuncCML.nml'
GJ_file = pynml.read_neuroml2_file( GJ_filename )
GJ_type = GJ_file.gap_junctions[0]
MFSyn_filename = 'MF_GoC_Syn.nml'
mfsyn_file = pynml.read_neuroml2_file( MFSyn_filename )
MFSyn_type = mfsyn_file.exp_three_synapses[0]
MF20Syn_filename = 'MF_GoC_SynMult.nml'
mf20syn_file = pynml.read_neuroml2_file( MF20Syn_filename )
MF20Syn_type = mf20syn_file.exp_three_synapses[0]
# Distribute cells in 3D
if N_goc>0:
GoC_pos = nu.GoC_locate(N_goc)
else:
GoC_pos = nu.GoC_density_locate()
N_goc = GoC_pos.shape[0]
# get GJ connectivity
GJ_pairs, GJWt = nu.GJ_conn( GoC_pos, prob_type, GJw_type )
tmp1, tmp2 = valnet.gapJuncAnalysis( GJ_pairs, GJWt )
print("Number of gap junctions per cell: ", tmp1)
print("Net GJ conductance per cell:", tmp2)
# Create pop List
goc_pop = nml.Population( id=goc_type.id+"Pop", component = goc_type.id, type="populationList", size=N_goc )
# Create NML document for network specification
net = nml.Network( id="gocNetwork", type="networkWithTemperature" , temperature="23 degC" )
net_doc = nml.NeuroMLDocument( id=net.id )
net_doc.networks.append( net )
net_doc.includes.append( goc_type )
net.populations.append( goc_pop )
#Add locations for GoC instances in the population:
for goc in range(N_goc):
inst = nml.Instance( id=goc )
goc_pop.instances.append( inst )
inst.location = nml.Location( x=GoC_pos[goc,0], y=GoC_pos[goc,1], z=GoC_pos[goc,2] )
# Define input spiketrains
input_type = 'spikeGenerator'#'spikeGeneratorPoisson'
lems_inst_doc = lems.Model()
mf_inputs = lems.Component( "MF_Input", input_type)
mf_inputs.set_parameter("period", "2000 ms" )
#mf_inputs.set_parameter("averageRate", "50 Hz")
lems_inst_doc.add( mf_inputs )
#synapse_type = 'alphaCurrentSynapse'
#alpha_syn = lems.Component( "AlphaSyn", synapse_type)
#alpha_syn.set_parameter("tau", "30 ms" )
#alpha_syn.set_parameter("ibase", "200 pA")
#lems_inst_doc.add( alpha_syn )
# Define MF input population
N_mf = 15
#MF_pop = nml.Population(id=mf_inputs.id+"_pop", component=mf_inputs.id, type="populationList", size=N_mf)
#net.populations.append( MF_pop )
mf_type2 = 'spikeGeneratorPoisson'
#mf_poisson = lems.Component( "MF_Poisson", mf_type2)
#mf_poisson.set_parameter("averageRate", "5 Hz")
#lems_inst_doc.add( mf_poisson )
# adding in neuroml document instead of mf_poisson
mf_poisson = nml.SpikeGeneratorPoisson( id = "MF_Poisson", average_rate="5 Hz" )
net_doc.spike_generator_poissons.append( mf_poisson )
net_doc.includes.append( goc_type )
MF_Poisson_pop = nml.Population(id=mf_poisson.id+"_pop", component=mf_poisson.id, type="populationList", size=N_mf)
net.populations.append( MF_Poisson_pop )
MF_pos = nu.GoC_locate( N_mf )
for mf in range( N_mf ):
inst = nml.Instance(id=mf)
MF_Poisson_pop.instances.append( inst )
inst.location = nml.Location( x=MF_pos[mf,0], y=MF_pos[mf,1], z=MF_pos[mf,2] )
# Setup Mf->GoC synapses
#MFprojection = nml.Projection(id="MFtoGoC", presynaptic_population=MF_pop.id, postsynaptic_population=goc_pop.id, synapse=alpha_syn.id)
#net.projections.append(MFprojection)
MF2projection = nml.Projection(id="MF2toGoC", presynaptic_population=MF_Poisson_pop.id, postsynaptic_population=goc_pop.id, synapse=MFSyn_type.id)#alpha_syn.id
net.projections.append(MF2projection)
#Get list of MF->GoC synapse
mf_synlist = nu.randdist_MF_syn( N_mf, N_goc, pConn=0.3)
nMFSyn = mf_synlist.shape[1]
for syn in range( nMFSyn ):
mf, goc = mf_synlist[:, syn]
conn2 = nml.Connection(id=syn, pre_cell_id='../{}/{}/{}'.format(MF_Poisson_pop.id, mf, mf_poisson.id), post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), post_segment_id='0', post_fraction_along="0.5")
MF2projection.connections.append(conn2)
# Burst of MF input (as explicit input)
mf_bursttype = 'transientPoissonFiringSynapse'
mf_burst = lems.Component( "MF_Burst", mf_bursttype)
mf_burst.set_parameter( "averageRate", "100 Hz" )
mf_burst.set_parameter( "delay", "2000 ms" )
mf_burst.set_parameter( "duration", "500 ms" )
mf_burst.set_parameter( "synapse", MF20Syn_type.id )
mf_burst.set_parameter( "spikeTarget", './{}'.format(MF20Syn_type.id) )
lems_inst_doc.add( mf_burst )
# Add few burst inputs
n_bursts = 4
gocPerm = np.random.permutation( N_goc )
ctr = 0
for gg in range(4):
goc = gocPerm[gg]
for jj in range( n_bursts ):
inst = nml.ExplicitInput( id=ctr, target='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), input=mf_burst.id, synapse=MF20Syn_type.id, spikeTarget='./{}'.format(MF20Syn_type.id))
net.explicit_inputs.append( inst )
ctr += 1
'''
one-to-one pairing of MF and GoC -> no shared inputs
for goc in range(N_mf):
#inst = nml.Instance(id=goc)
#MF_pop.instances.append( inst )
#inst.location = nml.Location( x=GoC_pos[goc,0], y=GoC_pos[goc,1], z=GoC_pos[goc,2]+100 )
#conn = nml.Connection(id=goc, pre_cell_id='../{}/{}/{}'.format(MF_pop.id, goc, mf_inputs.id), post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), post_segment_id='0', post_fraction_along="0.5")
#MFprojection.connections.append(conn)
goc2 = N_goc-goc-1
inst2 = nml.Instance(id=goc)
MF_Poisson_pop.instances.append( inst2 )
inst2.location = nml.Location( x=GoC_pos[goc2,0], y=GoC_pos[goc2,1], z=GoC_pos[goc2,2]+100 )
conn2 = nml.Connection(id=goc, pre_cell_id='../{}/{}/{}'.format(MF_Poisson_pop.id, goc, mf_poisson.id), post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc2, goc_type.id), post_segment_id='0', post_fraction_along="0.5")
MF2projection.connections.append(conn2)
'''
# Add electrical synapses
GoCCoupling = nml.ElectricalProjection( id="gocGJ", presynaptic_population=goc_pop.id, postsynaptic_population=goc_pop.id )
#print(GJ_pairs)
gj = nml.GapJunction( id="GJ_0", conductance="426pS" )
net_doc.gap_junctions.append(gj)
nGJ = GJ_pairs.shape[0]
for jj in range( nGJ ):
#gj.append( lems.Component( "GJ_%d"%jj, 'gapJunction') )
#gj[jj].set_parameter( "conductance", "%fnS"%(GJWt[jj]) )
#gj = nml.GapJunction(id="GJ_%d"%jj, conductance="%fnS"%(GJWt[jj]))
#net_doc.gap_junctions.append(gj)
#lems_inst_doc.add( gj[jj] )
#print("%fnS"%(GJWt[jj]*0.426))
conn = nml.ElectricalConnectionInstanceW( id=jj, pre_cell='../{}/{}/{}'.format(goc_pop.id, GJ_pairs[jj,0], goc_type.id), pre_segment='1', pre_fraction_along='0.5', post_cell='../{}/{}/{}'.format(goc_pop.id, GJ_pairs[jj,1], goc_type.id), post_segment='1', post_fraction_along='0.5', synapse=gj.id, weight=GJWt[jj] )#synapse="GapJuncCML" synapse=gj.id , conductance="100E-9mS"
# ------------ need to create GJ component
GoCCoupling.electrical_connection_instance_ws.append( conn )
net.electrical_projections.append( GoCCoupling )
net_filename = 'gocNetwork.nml'
pynml.write_neuroml2_file( net_doc, net_filename )
lems_filename = 'instances.xml'
pynml.write_lems_file( lems_inst_doc, lems_filename, validate=False )
simid = 'sim_gocnet'+goc_type.id
ls = LEMSSimulation( simid, duration=duration, dt=dt, simulation_seed=seed )
ls.assign_simulation_target( net.id )
#ls.include_lems_file( 'Synapses.xml', include_included=False)
#ls.include_lems_file( 'Inputs.xml', include_included=False)
ls.include_neuroml2_file( net_filename)
ls.include_neuroml2_file( goc_filename)
ls.include_neuroml2_file( GJ_filename)
ls.include_neuroml2_file( MFSyn_filename)
ls.include_neuroml2_file( MF20Syn_filename)
ls.include_lems_file( lems_filename, include_included=False)
# Specify outputs
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes"%simid,format='ID_TIME')
for jj in range( goc_pop.size):
ls.add_selection_to_event_output_file( eof0, jj, '{}/{}/{}'.format( goc_pop.id, jj, goc_type.id), 'spike' )
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%simid)
for jj in range( goc_pop.size ):
ls.add_column_to_output_file(of0, jj, '{}/{}/{}/v'.format( goc_pop.id, jj, goc_type.id))
#Create Lems file to run
lems_simfile = ls.save_to_file()
#res = pynml.run_lems_with_jneuroml( lems_simfile, max_memory="1G",nogui=True, plot=False)
#res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="2G", only_generate_scripts = True, compile_mods = False, nogui=True, plot=False)
res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="2G", compile_mods = False,nogui=True, plot=False)
#res=True
return res
def generate_grc_layer_network(
runID,
correlationRadius,
NADT,
duration,
dt,
minimumISI, # ms
ONRate, # Hz
OFFRate, # Hz
run=False):
########################################
# Load parameters for this run
file = open('../params_file.pkl', 'r')
p = pkl.load(file)
N_syn = p['N_syn'][int(runID) - 1]
f_mf = p['f_mf'][int(runID) - 1]
run_num = p['run_num'][int(runID) - 1]
file.close()
#################################################################################
# Get connectivity matrix between cells
file = open('../../network_structures/GCLconnectivity_' + str(N_syn) +
'.pkl')
p = pkl.load(file)
conn_mat = p['conn_mat']
N_mf, N_grc = conn_mat.shape
assert (np.all(conn_mat.sum(
axis=0) == N_syn)), 'Connectivity matrix is incorrect.'
# Get MF activity pattern
if correlationRadius == 0: # Activate MFs randomly
N_mf_ON = int(N_mf * f_mf)
mf_indices_ON = random.sample(range(N_mf), N_mf_ON)
mf_indices_ON.sort()
elif correlationRadius > 0: # Spatially correlated MFs
f_mf_range = np.linspace(.05, .95, 19)
f_mf_ix = np.where(f_mf_range == f_mf)[0][0]
p = io.loadmat('../../input_statistics/mf_patterns_r' +
str(correlationRadius) + '.mat')
R = p['Rs'][:, :, f_mf_ix]
g = p['gs'][f_mf_ix]
t = np.dot(R.transpose(), np.random.randn(N_mf))
S = (t > -g * np.ones(N_mf))
mf_indices_ON = np.where(S)[0]
N_mf_ON = len(mf_indices_ON)
#
N_mf_OFF = N_mf - N_mf_ON
mf_indices_OFF = [x for x in range(N_mf) if x not in mf_indices_ON]
mf_indices_OFF.sort()
#################################################################################
# load NeuroML components, LEMS components and LEMS componentTypes from external files
# Spike generator (for Poisson MF spiking)
spike_generator_file_name = "../../grc_lemsDefinitions/spikeGenerators.xml"
spike_generator_doc = pynml.read_lems_file(spike_generator_file_name)
# Integrate-and-fire GC model
# if NADT = 1, loads model GC
iaf_nml2_file_name = "../../grc_lemsDefinitions/IaF_GrC.nml" if NADT == 0 else "../../grc_lemsDefinitions/IaF_GrC_" + '{:.2f}'.format(
f_mf) + ".nml"
iaF_GrC_doc = pynml.read_neuroml2_file(iaf_nml2_file_name)
iaF_GrC = iaF_GrC_doc.iaf_ref_cells[0]
# AMPAR and NMDAR mediated synapses
ampa_syn_filename = "../../grc_lemsDefinitions/RothmanMFToGrCAMPA_" + str(
N_syn) + ".xml"
nmda_syn_filename = "../../grc_lemsDefinitions/RothmanMFToGrCNMDA_" + str(
N_syn) + ".xml"
rothmanMFToGrCAMPA_doc = pynml.read_lems_file(ampa_syn_filename)
rothmanMFToGrCNMDA_doc = pynml.read_lems_file(nmda_syn_filename)
#
# Define components from the componentTypes we just loaded
# Refractory poisson input -- representing active MF
spike_generator_ref_poisson_type = spike_generator_doc.component_types[
'spikeGeneratorRefPoisson']
lems_instances_doc = lems.Model()
spike_generator_on = lems.Component("mossySpikerON",
spike_generator_ref_poisson_type.name)
spike_generator_on.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_on.set_parameter("averageRate", "%s Hz" % ONRate)
lems_instances_doc.add(spike_generator_on)
# Refractory poisson input -- representing silent MF
spike_generator_off = lems.Component("mossySpikerOFF",
spike_generator_ref_poisson_type.name)
spike_generator_off.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_off.set_parameter("averageRate", "%s Hz" % OFFRate)
lems_instances_doc.add(spike_generator_off)
# Synapses
rothmanMFToGrCAMPA = rothmanMFToGrCAMPA_doc.components[
'RothmanMFToGrCAMPA'].id
rothmanMFToGrCNMDA = rothmanMFToGrCNMDA_doc.components[
'RothmanMFToGrCNMDA'].id
#
# Create ON MF, OFF MF, and GC populations
GrCPop = nml.Population(id="GrCPop", component=iaF_GrC.id, size=N_grc)
mossySpikersPopON = nml.Population(id=spike_generator_on.id + "Pop",
component=spike_generator_on.id,
size=N_mf_ON)
mossySpikersPopOFF = nml.Population(id=spike_generator_off.id + "Pop",
component=spike_generator_off.id,
size=N_mf_OFF)
#
# Create network and add populations
net = nml.Network(id="network")
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net.populations.append(GrCPop)
net.populations.append(mossySpikersPopON)
net.populations.append(mossySpikersPopOFF)
#
# MF-GC connectivity
# First connect ON MFs to GCs
for mf_ix_ON in range(N_mf_ON):
mf_ix = mf_indices_ON[mf_ix_ON]
# Find which GCs are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
# Add AMPAR and NMDAR mediated synapses
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.SynapticConnection(
from_='{}[{}]'.format(mossySpikersPopON.id, mf_ix_ON),
synapse=synapse,
to='GrCPop[{}]'.format(grc_ix))
net.synaptic_connections.append(connection)
#
# Now connect OFF MFs to GCs
for mf_ix_OFF in range(N_mf_OFF):
mf_ix = mf_indices_OFF[mf_ix_OFF]
# Find which GCs are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
# Add AMPAR and NMDAR mediated synapses
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.SynapticConnection(
from_='{}[{}]'.format(mossySpikersPopOFF.id, mf_ix_OFF),
synapse=synapse,
to='GrCPop[{}]'.format(grc_ix))
net.synaptic_connections.append(connection)
#
# Write network to file
net_file_name = 'generated_network_' + runID + '.net.nml'
pynml.write_neuroml2_file(net_doc, net_file_name)
# Write LEMS instances to file
lems_instances_file_name = 'instances_' + runID + '.xml'
pynml.write_lems_file(lems_instances_doc,
lems_instances_file_name,
validate=False)
# Create a LEMSSimulation to manage creation of LEMS file
ls = LEMSSimulation('sim_' + runID,
duration,
dt,
lems_seed=int(np.round(1000 * random.random())))
# Point to network as target of simulation
ls.assign_simulation_target(net.id)
# Include generated/existing NeuroML2 files
ls.include_neuroml2_file(iaf_nml2_file_name)
ls.include_lems_file(spike_generator_file_name, include_included=False)
ls.include_lems_file(lems_instances_file_name)
ls.include_lems_file(ampa_syn_filename, include_included=False)
ls.include_lems_file(nmda_syn_filename, include_included=False)
ls.include_neuroml2_file(net_file_name)
# Specify Displays and Output Files
# Details for saving output files
basedir = '../data_r' + str(
correlationRadius) + '/' if NADT == 0 else '../data_r' + str(
correlationRadius) + '_NADT/'
end_filename = str(N_syn) + '_{:.2f}'.format(f_mf) + '_' + str(
run_num) # Add parameter values to spike time filename
# Save MF spike times under basedir + MF_spikes_ + end_filename
eof0 = 'MFspikes_file'
ls.create_event_output_file(eof0,
basedir + "MF_spikes_" + end_filename + ".dat")
# ON MFs
for i in range(mossySpikersPopON.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_ON[i], "%s[%i]" % (mossySpikersPopON.id, i),
'spike')
# OFF MFs
for i in range(mossySpikersPopOFF.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_OFF[i], "%s[%i]" % (mossySpikersPopOFF.id, i),
'spike')
# Save GC spike times under basedir + GrC_spikes_ + end_filename
eof1 = 'GrCspikes_file'
ls.create_event_output_file(
eof1, basedir + "GrC_spikes_" + end_filename + ".dat")
#
for i in range(GrCPop.size):
ls.add_selection_to_event_output_file(eof1, i,
"%s[%i]" % (GrCPop.id, i),
'spike')
#
lems_file_name = ls.save_to_file()
#
if run:
results = pynml.run_lems_with_jneuroml(lems_file_name,
max_memory="8G",
nogui=True,
load_saved_data=False,
plot=False)
return results
def generate_grc_layer_network(
p_mf_ON,
duration,
dt,
minimumISI, # ms
ONRate, # Hz
OFFRate, # Hz
run=False):
# Load connectivity matrix
file = open('GCLconnectivity.pkl')
p = pkl.load(file)
conn_mat = p['conn_mat']
N_mf, N_grc = conn_mat.shape
assert (np.all(conn_mat.sum(
axis=0) == 4)), 'Connectivity matrix is incorrect.'
# Load GrC and MF rosette positions
grc_pos = p['grc_pos']
glom_pos = p['glom_pos']
# Choose which mossy fibers are on, which are off
N_mf_ON = int(N_mf * p_mf_ON)
mf_indices_ON = random.sample(range(N_mf), N_mf_ON)
mf_indices_ON.sort()
N_mf_OFF = N_mf - N_mf_ON
mf_indices_OFF = [x for x in range(N_mf) if x not in mf_indices_ON]
mf_indices_OFF.sort()
# load NeuroML components, LEMS components and LEMS componentTypes from external files
##spikeGeneratorRefPoisson is now a standard nml type...
##spike_generator_doc = pynml.read_lems_file(spike_generator_file_name)
iaF_GrC = nml.IafRefCell(id="iaF_GrC",
refract="2ms",
C="3.22pF",
thresh="-40mV",
reset="-63mV",
leak_conductance="1.498nS",
leak_reversal="-79.67mV")
ampa_syn_filename = "RothmanMFToGrCAMPA.xml"
nmda_syn_filename = "RothmanMFToGrCNMDA.xml"
rothmanMFToGrCAMPA_doc = pynml.read_lems_file(ampa_syn_filename)
rothmanMFToGrCNMDA_doc = pynml.read_lems_file(nmda_syn_filename)
# define some components from the componentTypes we just loaded
##spike_generator_ref_poisson_type = spike_generator_doc.component_types['spikeGeneratorRefPoisson']
lems_instances_doc = lems.Model()
spike_generator_ref_poisson_type_name = 'spikeGeneratorRefPoisson'
spike_generator_on = lems.Component("mossySpikerON",
spike_generator_ref_poisson_type_name)
spike_generator_on.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_on.set_parameter("averageRate", "%s Hz" % ONRate)
lems_instances_doc.add(spike_generator_on)
spike_generator_off = lems.Component(
"mossySpikerOFF", spike_generator_ref_poisson_type_name)
spike_generator_off.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_off.set_parameter("averageRate", "%s Hz" % OFFRate)
lems_instances_doc.add(spike_generator_off)
rothmanMFToGrCAMPA = rothmanMFToGrCAMPA_doc.components[
'RothmanMFToGrCAMPA'].id
rothmanMFToGrCNMDA = rothmanMFToGrCNMDA_doc.components[
'RothmanMFToGrCNMDA'].id
# create populations
GrCPop = nml.Population(id=iaF_GrC.id + "Pop",
component=iaF_GrC.id,
type="populationList",
size=N_grc)
GrCPop.properties.append(nml.Property(tag='color', value='0 0 0.8'))
GrCPop.properties.append(nml.Property(tag='radius', value=2))
mossySpikersPopON = nml.Population(id=spike_generator_on.id + "Pop",
component=spike_generator_on.id,
type="populationList",
size=N_mf_ON)
mossySpikersPopON.properties.append(
nml.Property(tag='color', value='0.8 0 0'))
mossySpikersPopON.properties.append(nml.Property(tag='radius', value=2))
mossySpikersPopOFF = nml.Population(id=spike_generator_off.id + "Pop",
component=spike_generator_off.id,
size=N_mf_OFF)
mossySpikersPopOFF.properties.append(
nml.Property(tag='color', value='0 0.8 0'))
mossySpikersPopOFF.properties.append(nml.Property(tag='radius', value=2))
# create network and add populations
net = nml.Network(id="network")
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.iaf_ref_cells.append(iaF_GrC)
net.populations.append(GrCPop)
net.populations.append(mossySpikersPopON)
net.populations.append(mossySpikersPopOFF)
#net_doc.includes.append(nml.IncludeType(href=iaf_nml2_file_name))
# Add locations for GCs
for grc in range(N_grc):
inst = nml.Instance(id=grc)
GrCPop.instances.append(inst)
inst.location = nml.Location(x=grc_pos[grc, 0],
y=grc_pos[grc, 1],
z=grc_pos[grc, 2])
# ON MFs: locations and connectivity
ONprojectionAMPA = nml.Projection(
id="ONProjAMPA",
presynaptic_population=mossySpikersPopON.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCAMPA)
ONprojectionNMDA = nml.Projection(
id="ONProjNMDA",
presynaptic_population=mossySpikersPopON.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCNMDA)
net.projections.append(ONprojectionAMPA)
net.projections.append(ONprojectionNMDA)
ix = 0
for mf_ix_ON in range(N_mf_ON):
mf_ix = mf_indices_ON[mf_ix_ON]
inst = nml.Instance(id=mf_ix_ON)
mossySpikersPopON.instances.append(inst)
inst.location = nml.Location(x=glom_pos[mf_ix, 0],
y=glom_pos[mf_ix, 1],
z=glom_pos[mf_ix, 2])
# find which granule cells are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.Connection(
id=ix,
pre_cell_id='../{}/{}/{}'.format(mossySpikersPopON.id,
mf_ix_ON,
spike_generator_on.id),
post_cell_id='../{}/{}/{}'.format(GrCPop.id, grc_ix,
iaF_GrC.id))
ONprojectionAMPA.connections.append(connection)
ONprojectionNMDA.connections.append(connection)
ix = ix + 1
# OFF MFs: locations and connectivity
OFFprojectionAMPA = nml.Projection(
id="OFFProjAMPA",
presynaptic_population=mossySpikersPopOFF.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCAMPA)
OFFprojectionNMDA = nml.Projection(
id="OFFProjNMDA",
presynaptic_population=mossySpikersPopOFF.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCNMDA)
net.projections.append(OFFprojectionAMPA)
net.projections.append(OFFprojectionNMDA)
ix = 0
for mf_ix_OFF in range(N_mf_OFF):
mf_ix = mf_indices_OFF[mf_ix_OFF]
inst = nml.Instance(id=mf_ix_OFF)
mossySpikersPopOFF.instances.append(inst)
inst.location = nml.Location(x=glom_pos[mf_ix, 0],
y=glom_pos[mf_ix, 1],
z=glom_pos[mf_ix, 2])
# find which granule cells are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.Connection(
id=ix,
pre_cell_id='../{}/{}/{}'.format(mossySpikersPopOFF.id,
mf_ix_OFF,
spike_generator_on.id),
post_cell_id='../{}/{}/{}'.format(GrCPop.id, grc_ix,
iaF_GrC.id))
OFFprojectionAMPA.connections.append(connection)
OFFprojectionNMDA.connections.append(connection)
ix = ix + 1
# Write network to file
net_file_name = 'OSBnet.nml'
pynml.write_neuroml2_file(net_doc, net_file_name)
# Write LEMS instances to file
lems_instances_file_name = 'instances.xml'
pynml.write_lems_file(lems_instances_doc,
lems_instances_file_name,
validate=False)
# Create a LEMSSimulation to manage creation of LEMS file
ls = LEMSSimulation(
'sim', duration, dt,
simulation_seed=123) # int(np.round(1000*random.random())))
# Point to network as target of simulation
ls.assign_simulation_target(net.id)
# Include generated/existing NeuroML2 files
###ls.include_lems_file(spike_generator_file_name, include_included=False)
ls.include_lems_file(lems_instances_file_name)
ls.include_lems_file(ampa_syn_filename, include_included=False)
ls.include_lems_file(nmda_syn_filename, include_included=False)
ls.include_neuroml2_file(net_file_name)
# Specify Displays and Output Files
basedir = ''
eof0 = 'Volts_file'
ls.create_event_output_file(eof0, basedir + "MF_spikes.dat")
for i in range(mossySpikersPopON.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_ON[i], '{}/{}/{}'.format(mossySpikersPopON.id, i,
spike_generator_on.id),
'spike')
for i in range(mossySpikersPopOFF.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_OFF[i],
'{}/{}/{}'.format(mossySpikersPopOFF.id, i,
spike_generator_on.id), 'spike')
eof1 = 'GrCspike_file'
ls.create_event_output_file(eof1, basedir + "GrC_spikes.dat")
for i in range(GrCPop.size):
ls.add_selection_to_event_output_file(
eof1, i, '{}/{}/{}'.format(GrCPop.id, i, iaF_GrC.id), 'spike')
lems_file_name = ls.save_to_file()
if run:
print('Running the generated LEMS file: %s for simulation of %sms' %
(lems_file_name, duration))
results = pynml.run_lems_with_jneuroml(lems_file_name,
max_memory="8G",
nogui=True,
load_saved_data=False,
plot=False)
return results
def generate_network_for_sweeps(cell_type, dataset_id, cell_file_name, cell_id, target_dir, data_dir="../../data"):
target_sweep_numbers = DH.DATASET_TARGET_SWEEPS[dataset_id]
net_id = "network_%s_%s"%(dataset_id, cell_type)
net = neuroml.Network(id=net_id, type="networkWithTemperature", temperature=DH.SIMULATION_TEMPERATURE)
net_doc = neuroml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(neuroml.IncludeType(cell_file_name))
number_cells = len(target_sweep_numbers)
pop = neuroml.Population(id="Pop0",
component=cell_id,
size=number_cells,
type="populationList")
net.populations.append(pop)
for i in range(number_cells):
location = neuroml.Location(x=100*i,y=0,z=0)
pop.instances.append(neuroml.Instance(id=i,location=location))
print target_sweep_numbers
f = "%s/%s_analysis.json"%(data_dir,dataset_id)
with open(f, "r") as json_file:
data = json.load(json_file)
id = data['data_set_id']
sweeps = data['sweeps']
print("Looking at data analysis in %s (dataset: %s)"%(f,id))
index = 0
for s in target_sweep_numbers:
current = float(sweeps['%i'%s]["sweep_metadata"]["aibs_stimulus_amplitude_pa"])
print("Sweep %s (%s pA)"%(s, current))
stim_amp = "%s pA"%current
input_id = ("input_%i"%s)
pg = neuroml.PulseGenerator(id=input_id,
delay="270ms",
duration="1000ms",
amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
input_list = neuroml.InputList(id=input_id,
component=pg.id,
populations=pop.id)
input = neuroml.Input(id='0',
target="../%s/%i/%s"%(pop.id, index, cell_id),
destination="synapses")
index+=1
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = '%s/%s.net.nml'%(target_dir,net_id)
print("Saving generated network to: %s"%net_file_name)
pynml.write_neuroml2_file(net_doc, net_file_name)
return net_file_name
def create_GoC_network(duration=2000,
dt=0.025,
seed=123,
runid=0,
run=False,
minI=-75,
maxI=200,
iStep=25,
iDur=400,
iRest=500):
file = open('useParams_SpontFreq_7_pm_2.pkl', 'rb')
use_params = pkl.load(file)["useParams"]
file.close()
runid = use_params[0][runid]
print('Using parameter set = ', runid)
### ---------- Component types
gocID = 'GoC_' + format(runid, '05d')
goc_filename = '{}.cell.nml'.format(gocID)
goc_type = pynml.read_neuroml2_file(goc_filename).cells[0]
### --------- Populations
# Build network to specify cells and connectivity
net = nml.Network(id='GoCNet_' + format(runid, '05d'),
type="networkWithTemperature",
temperature="23 degC")
# Create GoC population
goc_pop = nml.Population(id=goc_type.id + "Pop",
component=goc_type.id,
type="populationList",
size=1)
inst = nml.Instance(id=0)
goc_pop.instances.append(inst)
inst.location = nml.Location(x=0, y=0, z=0)
net.populations.append(goc_pop)
# Create NML document for network specification
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(href=goc_filename))
# Add Current Injection
ctr = 0
goc = 0
p = {
"iAmp": np.arange(minI, maxI + iStep / 2, iStep),
"iDuration": iDur,
"iRest": iRest
}
p["nSteps"] = p["iAmp"].shape[0]
for jj in range(p["nSteps"]):
input_id = 'stim_{}'.format(ctr)
istep = nml.PulseGenerator(id=input_id,
delay='{} ms'.format(p["iDuration"] * jj +
p["iRest"] * (jj + 1)),
duration='{} ms'.format(p["iDuration"]),
amplitude='{} pA'.format(p["iAmp"][jj]))
net_doc.pulse_generators.append(istep)
input_list = nml.InputList(id='ilist_{}'.format(ctr),
component=istep.id,
populations=goc_pop.id)
curr_inj = nml.Input('0',
target="../%s[%i]" % (goc_pop.id, goc),
destination="synapses")
input_list.input.append(curr_inj)
net.input_lists.append(input_list)
ctr += 1
### -------------- Write files
net_filename = 'GoCNet_istep_' + format(runid, '05d') + '.nml'
pynml.write_neuroml2_file(net_doc, net_filename)
simid = 'sim_gocnet_istep_' + goc_type.id
ls = LEMSSimulation(simid, duration=duration, dt=dt, simulation_seed=seed)
ls.assign_simulation_target(net.id)
ls.include_neuroml2_file(net_filename)
ls.include_neuroml2_file(goc_filename)
# Specify outputs
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes" % simid, format='ID_TIME')
for jj in range(goc_pop.size):
ls.add_selection_to_event_output_file(
eof0, jj, '{}/{}/{}'.format(goc_pop.id, jj, goc_type.id), 'spike')
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % simid)
for jj in range(goc_pop.size):
ls.add_column_to_output_file(
of0, jj, '{}/{}/{}/v'.format(goc_pop.id, jj, goc_type.id))
#Create Lems file to run
lems_simfile = ls.save_to_file()
if run:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
nogui=True,
plot=False)
else:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
only_generate_scripts=True,
compile_mods=False,
nogui=True,
plot=False)
return res
def generate_current_vs_frequency_curve(nml2_file,
cell_id,
start_amp_nA = -0.1,
end_amp_nA = 0.1,
step_nA = 0.01,
custom_amps_nA = [],
analysis_duration = 1000,
analysis_delay = 0,
pre_zero_pulse = 0,
post_zero_pulse = 0,
dt = 0.05,
temperature = "32degC",
spike_threshold_mV = 0.,
plot_voltage_traces = False,
plot_if = True,
plot_iv = False,
xlim_if = None,
ylim_if = None,
xlim_iv = None,
ylim_iv = None,
label_xaxis = True,
label_yaxis = True,
show_volts_label = True,
grid = True,
font_size = 12,
if_iv_color = 'k',
linewidth = 1,
bottom_left_spines_only = False,
show_plot_already = True,
save_voltage_traces_to = None,
save_if_figure_to = None,
save_iv_figure_to = None,
save_if_data_to = None,
save_iv_data_to = None,
simulator = "jNeuroML",
num_processors = 1,
include_included = True,
title_above_plot = False,
return_axes = False,
verbose = False):
print_comment("Running generate_current_vs_frequency_curve() on %s (%s)"%(nml2_file,os.path.abspath(nml2_file)), verbose)
from pyelectro.analysis import max_min
from pyelectro.analysis import mean_spike_frequency
import numpy as np
traces_ax = None
if_ax = None
iv_ax = None
sim_id = 'iv_%s'%cell_id
total_duration = pre_zero_pulse+analysis_duration+analysis_delay+post_zero_pulse
pulse_duration = analysis_duration+analysis_delay
end_stim = pre_zero_pulse+analysis_duration+analysis_delay
ls = LEMSSimulation(sim_id, total_duration, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
stims = []
if len(custom_amps_nA)>0:
stims = [float(a) for a in custom_amps_nA]
stim_info = ['%snA'%float(a) for a in custom_amps_nA]
else:
amp = start_amp_nA
while amp<=end_amp_nA :
stims.append(amp)
amp+=step_nA
stim_info = '(%snA->%snA; %s steps of %snA; %sms)'%(start_amp_nA, end_amp_nA, len(stims), step_nA, total_duration)
print_comment_v("Generating an IF curve for cell %s in %s using %s %s"%
(cell_id, nml2_file, simulator, stim_info))
number_cells = len(stims)
pop = nml.Population(id="population_of_%s"%cell_id,
component=cell_id,
size=number_cells)
# create network and add populations
net_id = "network_of_%s"%cell_id
net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature)
ls.assign_simulation_target(net_id)
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(nml2_file))
net.populations.append(pop)
for i in range(number_cells):
stim_amp = "%snA"%stims[i]
input_id = ("input_%s"%stim_amp).replace('.','_').replace('-','min')
pg = nml.PulseGenerator(id=input_id,
delay="%sms"%pre_zero_pulse,
duration="%sms"%pulse_duration,
amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
# Add these to cells
input_list = nml.InputList(id=input_id,
component=pg.id,
populations=pop.id)
input = nml.Input(id='0',
target="../%s[%i]"%(pop.id, i),
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = '%s.net.nml'%sim_id
pynml.write_neuroml2_file(net_doc, net_file_name)
ls.include_neuroml2_file(net_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0,"Voltages", "-90", "50")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%sim_id)
for i in range(number_cells):
ref = "v_cell%i"%i
quantity = "%s[%i]/v"%(pop.id, i)
ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color())
ls.add_column_to_output_file(of0, ref, quantity)
lems_file_name = ls.save_to_file()
print_comment("Written LEMS file %s (%s)"%(lems_file_name,os.path.abspath(lems_file_name)), verbose)
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NetPyNE":
results = pynml.run_lems_with_jneuroml_netpyne(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
num_processors = num_processors,
verbose=verbose)
else:
raise Exception("Sorry, cannot yet run current vs frequency analysis using simulator %s"%simulator)
print_comment("Completed run in simulator %s (results: %s)"%(simulator,results.keys()), verbose)
#print(results.keys())
times_results = []
volts_results = []
volts_labels = []
if_results = {}
iv_results = {}
for i in range(number_cells):
t = np.array(results['t'])*1000
v = np.array(results["%s[%i]/v"%(pop.id, i)])*1000
if plot_voltage_traces:
times_results.append(t)
volts_results.append(v)
volts_labels.append("%s nA"%stims[i])
mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV)
spike_times = mm['maxima_times']
freq = 0
if len(spike_times) > 2:
count = 0
for s in spike_times:
if s >= pre_zero_pulse + analysis_delay and s < (pre_zero_pulse + analysis_duration+analysis_delay):
count+=1
freq = 1000 * count/float(analysis_duration)
mean_freq = mean_spike_frequency(spike_times)
#print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
if_results[stims[i]] = freq
if freq == 0:
if post_zero_pulse==0:
iv_results[stims[i]] = v[-1]
else:
v_end = None
for j in range(len(t)):
if v_end==None and t[j]>=end_stim:
v_end = v[j]
iv_results[stims[i]] = v_end
if plot_voltage_traces:
traces_ax = pynml.generate_plot(times_results,
volts_results,
"Membrane potential traces for: %s"%nml2_file,
xaxis = 'Time (ms)' if label_xaxis else ' ',
yaxis = 'Membrane potential (mV)' if label_yaxis else '',
xlim = [total_duration*-0.05,total_duration*1.05],
show_xticklabels = label_xaxis,
font_size = font_size,
bottom_left_spines_only = bottom_left_spines_only,
grid = False,
labels = volts_labels if show_volts_label else [],
show_plot_already=False,
save_figure_to = save_voltage_traces_to,
title_above_plot = title_above_plot,
verbose=verbose)
if plot_if:
stims = sorted(if_results.keys())
stims_pA = [ii*1000 for ii in stims]
freqs = [if_results[s] for s in stims]
if_ax = pynml.generate_plot([stims_pA],
[freqs],
"Firing frequency versus injected current for: %s"%nml2_file,
colors = [if_iv_color],
linestyles=['-'],
markers=['o'],
linewidths = [linewidth],
xaxis = 'Input current (pA)' if label_xaxis else ' ',
yaxis = 'Firing frequency (Hz)' if label_yaxis else '',
xlim = xlim_if,
ylim = ylim_if,
show_xticklabels = label_xaxis,
show_yticklabels = label_yaxis,
font_size = font_size,
bottom_left_spines_only = bottom_left_spines_only,
grid = grid,
show_plot_already=False,
save_figure_to = save_if_figure_to,
title_above_plot = title_above_plot,
verbose=verbose)
if save_if_data_to:
with open(save_if_data_to,'w') as if_file:
for i in range(len(stims_pA)):
if_file.write("%s\t%s\n"%(stims_pA[i],freqs[i]))
if plot_iv:
stims = sorted(iv_results.keys())
stims_pA = [ii*1000 for ii in sorted(iv_results.keys())]
vs = [iv_results[s] for s in stims]
xs = []
ys = []
xs.append([])
ys.append([])
for si in range(len(stims)):
stim = stims[si]
if len(custom_amps_nA)==0 and si>1 and (stims[si]-stims[si-1])>step_nA*1.01:
xs.append([])
ys.append([])
xs[-1].append(stim*1000)
ys[-1].append(iv_results[stim])
iv_ax = pynml.generate_plot(xs,
ys,
"V at %sms versus I below threshold for: %s"%(end_stim,nml2_file),
colors = [if_iv_color for s in xs],
linestyles=['-' for s in xs],
markers=['o' for s in xs],
xaxis = 'Input current (pA)' if label_xaxis else '',
yaxis = 'Membrane potential (mV)' if label_yaxis else '',
xlim = xlim_iv,
ylim = ylim_iv,
show_xticklabels = label_xaxis,
show_yticklabels = label_yaxis,
font_size = font_size,
linewidths = [linewidth for s in xs],
bottom_left_spines_only = bottom_left_spines_only,
grid = grid,
show_plot_already=False,
save_figure_to = save_iv_figure_to,
title_above_plot = title_above_plot,
verbose=verbose)
if save_iv_data_to:
with open(save_iv_data_to,'w') as iv_file:
for i in range(len(stims_pA)):
iv_file.write("%s\t%s\n"%(stims_pA[i],vs[i]))
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
if return_axes:
return traces_ax, if_ax, iv_ax
return if_results
def create_GoC_network(duration=2000, dt=0.025, seed=123, runid=0, run=False):
keepFile = open('useParams_FI_14_25.pkl', 'rb')
runid = pkl.load(keepFile)[runid]
keepFile.close()
### ---------- Component types
gocID = 'Golgi_040408_C1_' + format(runid, '05d')
goc_filename = '{}.cell.nml'.format(gocID)
goc_type = pynml.read_neuroml2_file(goc_filename).cells[0]
### --------- Populations
# Build network to specify cells and connectivity
net = nml.Network(id='MorphoNet_' + format(runid, '05d'),
type="networkWithTemperature",
temperature="23 degC")
# Create GoC population
goc_pop = nml.Population(id=goc_type.id + "Pop",
component=goc_type.id,
type="populationList",
size=1)
inst = nml.Instance(id=0)
goc_pop.instances.append(inst)
inst.location = nml.Location(x=0, y=0, z=0)
net.populations.append(goc_pop)
# Create NML document for network specification
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(href=goc_filename))
### -------------- Write files
net_filename = 'Morpho1_' + format(runid, '05d') + '.nml'
pynml.write_neuroml2_file(net_doc, net_filename)
simid = 'sim_morpho1_' + goc_type.id
ls = LEMSSimulation(simid, duration=duration, dt=dt, simulation_seed=seed)
ls.assign_simulation_target(net.id)
ls.include_neuroml2_file(net_filename)
ls.include_neuroml2_file(goc_filename)
# Specify outputs
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes" % simid, format='ID_TIME')
for jj in range(goc_pop.size):
ls.add_selection_to_event_output_file(
eof0, jj, '{}/{}/{}'.format(goc_pop.id, jj, goc_type.id), 'spike')
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % simid)
for jj in range(goc_pop.size):
ls.add_column_to_output_file(
of0, jj, '{}/{}/{}/v'.format(goc_pop.id, jj, goc_type.id))
#Create Lems file to run
lems_simfile = ls.save_to_file()
if run:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
nogui=True,
plot=False)
else:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
only_generate_scripts=True,
compile_mods=False,
nogui=True,
plot=False)
return res
def run_individual(self, sim_var, show=False):
"""
Run an individual simulation.
The candidate data has been flattened into the sim_var dict. The
sim_var dict contains parameter:value key value pairs, which are
applied to the model before it is simulated.
"""
nml_doc = read_neuroml2_file(self.neuroml_file,
include_includes=True,
verbose=True,
already_included=[])
for var_name in sim_var.keys():
words = var_name.split('/')
type, id1 = words[0].split(':')
if ':' in words[1]:
variable, id2 = words[1].split(':')
else:
variable = words[1]
id2 = None
units = words[2]
value = sim_var[var_name]
print_comment_v(
' Changing value of %s (%s) in %s (%s) to: %s %s' %
(variable, id2, type, id1, value, units))
if type == 'cell':
cell = None
for c in nml_doc.cells:
if c.id == id1:
cell = c
if variable == 'channelDensity':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities:
if cd.id == id2:
chanDens = cd
chanDens.cond_density = '%s %s' % (value, units)
elif variable == 'erev_id': # change all values of erev in channelDensity elements with only this id
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities:
if cd.id == id2:
chanDens = cd
chanDens.erev = '%s %s' % (value, units)
elif variable == 'erev_ion': # change all values of erev in channelDensity elements with this ion
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities:
if cd.ion == id2:
chanDens = cd
chanDens.erev = '%s %s' % (value, units)
elif variable == 'specificCapacitance':
specCap = None
for sc in cell.biophysical_properties.membrane_properties.specific_capacitances:
if (sc.segment_groups == None
and id2 == 'all') or sc.segment_groups == id2:
specCap = sc
specCap.value = '%s %s' % (value, units)
else:
print_comment_v(
'Unknown variable (%s) in variable expression: %s' %
(variable, var_name))
exit()
elif type == 'izhikevich2007Cell':
izhcell = None
for c in nml_doc.izhikevich2007_cells:
if c.id == id1:
izhcell = c
izhcell.__setattr__(variable, '%s %s' % (value, units))
else:
print_comment_v(
'Unknown type (%s) in variable expression: %s' %
(type, var_name))
new_neuroml_file = '%s/%s' % (self.generate_dir,
os.path.basename(self.neuroml_file))
if new_neuroml_file == self.neuroml_file:
print_comment_v('Cannot use a directory for generating into (%s) which is the same location of the NeuroML file (%s)!'% \
(self.neuroml_file, self.generate_dir))
write_neuroml2_file(nml_doc, new_neuroml_file)
sim = NeuroMLSimulation(self.ref,
neuroml_file=new_neuroml_file,
target=self.target,
sim_time=self.sim_time,
dt=self.dt,
simulator=self.simulator,
generate_dir=self.generate_dir)
sim.go()
if show:
sim.show()
return sim.t, sim.volts
def generate_current_vs_frequency_curve(
nml2_file,
cell_id,
start_amp_nA,
end_amp_nA,
step_nA,
analysis_duration,
analysis_delay,
dt=0.05,
temperature="32degC",
spike_threshold_mV=0.0,
plot_voltage_traces=False,
plot_if=True,
simulator="jNeuroML",
):
from pyelectro.analysis import max_min
from pyelectro.analysis import mean_spike_frequency
import numpy as np
sim_id = "iv_%s" % cell_id
duration = analysis_duration + analysis_delay
ls = LEMSSimulation(sim_id, duration, dt)
ls.include_neuroml2_file(nml2_file)
stims = []
amp = start_amp_nA
while amp <= end_amp_nA:
stims.append(amp)
amp += step_nA
number_cells = len(stims)
pop = nml.Population(id="population_of_%s" % cell_id, component=cell_id, size=number_cells)
# create network and add populations
net_id = "network_of_%s" % cell_id
net = nml.Network(id=net_id, type="networkWithTemperature", temperature=temperature)
ls.assign_simulation_target(net_id)
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net.populations.append(pop)
for i in range(number_cells):
stim_amp = "%snA" % stims[i]
input_id = ("input_%s" % stim_amp).replace(".", "_")
pg = nml.PulseGenerator(id=input_id, delay="0ms", duration="%sms" % duration, amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
# Add these to cells
input_list = nml.InputList(id=input_id, component=pg.id, populations=pop.id)
input = nml.Input(id="0", target="../%s[%i]" % (pop.id, i), destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = "%s.net.nml" % sim_id
pynml.write_neuroml2_file(net_doc, net_file_name)
ls.include_neuroml2_file(net_file_name)
disp0 = "Voltage_display"
ls.create_display(disp0, "Voltages", "-90", "50")
of0 = "Volts_file"
ls.create_output_file(of0, "%s.v.dat" % sim_id)
for i in range(number_cells):
ref = "v_cell%i" % i
quantity = "%s[%i]/v" % (pop.id, i)
ls.add_line_to_display(disp0, ref, quantity, "1mV", pynml.get_next_hex_color())
ls.add_column_to_output_file(of0, ref, quantity)
lems_file_name = ls.save_to_file()
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(
lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces
)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(
lems_file_name, nogui=True, load_saved_data=True, plot=plot_voltage_traces
)
# print(results.keys())
if_results = {}
for i in range(number_cells):
t = np.array(results["t"]) * 1000
v = np.array(results["%s[%i]/v" % (pop.id, i)]) * 1000
mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV)
spike_times = mm["maxima_times"]
freq = 0
if len(spike_times) > 2:
count = 0
for s in spike_times:
if s >= analysis_delay and s < (analysis_duration + analysis_delay):
count += 1
freq = 1000 * count / float(analysis_duration)
mean_freq = mean_spike_frequency(spike_times)
# print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
if_results[stims[i]] = freq
if plot_if:
from matplotlib import pyplot as plt
plt.xlabel("Input current (nA)")
plt.ylabel("Firing frequency (Hz)")
plt.grid("on")
stims = sorted(if_results.keys())
freqs = []
for s in stims:
freqs.append(if_results[s])
plt.plot(stims, freqs, "o")
plt.show()
return if_results
def export(num_cells_to_export=5):
cells = []
for mgid in range(num_cells_to_export):
print mgid
cells.append(mkmitral(mgid))
nml_net_file = "../NeuroML2/MitralCells/Exported/PartialBulb_%iMTCells.net.nml" % num_cells_to_export
export_to_neuroml2(None,
nml_net_file,
includeBiophysicalProperties=False,
separateCellFiles=True)
for i in range(num_cells_to_export):
print("Processing cell %i out of %i" % (i, num_cells_to_export))
nml_cell_file = "../NeuroML2/MitralCells/Exported/Mitral_0_%i.cell.nml" % i
nml_doc = pynml.read_neuroml2_file(nml_cell_file)
cell = nml_doc.cells[0]
soma_seg = next(seg for seg in cell.morphology.segments
if seg.name == "Seg0_soma")
initial_seg = next(seg for seg in cell.morphology.segments
if seg.name == "Seg0_initialseg")
hillock_seg = next(seg for seg in cell.morphology.segments
if seg.name == "Seg0_hillock")
# Ensure hillock parent is soma
hillock_seg.parent.segments = soma_seg.id
# Fix initial and hillock segs by moving them to the soma
hillock_seg.proximal = pointMovedByOffset(hillock_seg.proximal,
soma_seg.distal)
hillock_seg.distal = pointMovedByOffset(hillock_seg.distal,
soma_seg.distal)
initial_seg.proximal = pointMovedByOffset(initial_seg.proximal,
soma_seg.distal)
initial_seg.distal = pointMovedByOffset(initial_seg.distal,
soma_seg.distal)
# Set root to id=0 and increment others
exportHelper.resetRoot(cell)
# TODO: cell.position(x,y,z) used for cell positioning in networks does not work as expected
# See: https://github.com/NeuroML/jNeuroML/issues/55
# Skipping the translation for now
# # Move everything back to the origin
# originOffset = type("", (), dict(x = -soma_seg.proximal.x, y = -soma_seg.proximal.y, z = -soma_seg.proximal.z ))()
#
# for seg in cell.morphology.segments:
# seg.proximal = pointMovedByOffset(seg.proximal, originOffset)
# seg.distal = pointMovedByOffset(seg.distal, originOffset)
# Replace ModelViewParmSubset_N groups with all, axon, soma, dendrite groups
buildStandardSegmentGroups(cell)
# Add channel placeholders
nml_doc.includes.append(
neuroml.IncludeType(href="channelIncludesPLACEHOLDER"))
cell.biophysical_properties = neuroml.BiophysicalProperties(
id="biophysPLACEHOLDER")
# Save the new NML
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
# Replace placeholders with contents from MitralCell...xml files
replaceChannelPlaceholders(nml_cell_file)
print("COMPLETED: " + nml_cell_file)
print("DONE")
cell = nml_doc.izhikevich2007_cells[0]
print("Extracted cell: %s from tuned model"%cell.id)
new_id = '%s_%s'%(type, dataset)
new_cell_doc = neuroml.NeuroMLDocument(id=new_id)
cell.id = new_id
cell.notes = "Cell model tuned to Allen Institute Cell Types Database, dataset: "+ \
"%s\n\nTuning procedure metadata:\n\n%s\n"%(dataset, pp.pformat(r2))
new_cell_doc.izhikevich2007_cells.append(cell)
new_cell_file = 'tuned_cells/%s.cell.nml'%new_id
pynml.write_neuroml2_file(new_cell_doc, new_cell_file)
#### Run a 'quick' optimisation for HH cell model
elif '-quick' in sys.argv:
simulator = 'jNeuroML_NEURON'
dataset = 471141261
ref = 'network_%s_HH'%(dataset)
report = run_one_optimisation('AllenTestQ',
1234,
parameters = parameters_hh,
max_constraints = max_constraints_hh,
def process_celldir(inputs):
"""Process cell directory"""
count, cell_dir, nml2_cell_dir, total_count = inputs
local_nml2_cell_dir = os.path.join("..", nml2_cell_dir)
print(
'\n\n************************************************************\n\n'
'Parsing %s (cell %i/%i)\n' % (cell_dir, count, total_count))
if os.path.isdir(cell_dir):
old_cwd = os.getcwd()
os.chdir(cell_dir)
else:
old_cwd = os.getcwd()
os.chdir('../' + cell_dir)
if make_zips:
nml2_cell_dir = '%s/%s' % (zips_dir, cell_dir)
if not os.path.isdir(nml2_cell_dir):
os.mkdir(nml2_cell_dir)
print("Generating into %s" % nml2_cell_dir)
bbp_ref = None
template_file = open('template.hoc', 'r')
for line in template_file:
if line.startswith('begintemplate '):
bbp_ref = line.split(' ')[1].strip()
print(
' > Assuming cell in directory %s is in a template named %s' %
(cell_dir, bbp_ref))
load_cell_file = 'loadcell.hoc'
variables = {}
variables['cell'] = bbp_ref
variables['groups_info_file'] = groups_info_file
template = """
///////////////////////////////////////////////////////////////////////////////
//
// NOTE: This file is not part of the original BBP cell model distribution
// It has been generated by ../ParseAll.py to facilitate loading of the cell
// into NEURON for exporting the model morphology to NeuroML2
//
//////////////////////////////////////////////////////////////////////////////
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
//======================== settings ===================================
v_init = -80
hyp_amp = -0.062866
step_amp = 0.3112968
tstop = 3000
//=================== creating cell object ===========================
load_file("import3d.hoc")
objref cell
// Using 1 to force loading of the file, in case file with same name was loaded
// before...
load_file(1, "constants.hoc")
load_file(1, "morphology.hoc")
load_file(1, "biophysics.hoc")
print "Loaded morphology and biophysics..."
load_file(1, "synapses/synapses.hoc")
load_file(1, "template.hoc")
print "Loaded template..."
load_file(1, "createsimulation.hoc")
create_cell(0)
print "Created new cell using loadcell.hoc: {{ cell }}"
define_shape()
wopen("{{ groups_info_file }}")
fprint("//Saving information on groups in this cell...\\n")
fprint("- somatic\\n")
forsec {{ cell }}[0].somatic {
fprint("%s\\n",secname())
}
fprint("- basal\\n")
forsec {{ cell }}[0].basal {
fprint("%s\\n",secname())
}
fprint("- axonal\\n")
forsec {{ cell }}[0].axonal {
fprint("%s\\n",secname())
}
fprint("- apical\\n")
forsec {{ cell }}[0].apical {
fprint("%s\\n",secname())
}
wopen()
"""
t = Template(template)
contents = t.render(variables)
load_cell = open(load_cell_file, 'w')
load_cell.write(contents)
load_cell.close()
print(' > Written %s' % load_cell_file)
if os.path.isfile(load_cell_file):
cell_info = parse_cell_info_file(cell_dir)
nml_file_name = "%s.net.nml" % bbp_ref
nml_net_loc = "%s/%s" % (local_nml2_cell_dir, nml_file_name)
nml_cell_file = "%s_0_0.cell.nml" % bbp_ref
nml_cell_loc = "%s/%s" % (local_nml2_cell_dir, nml_cell_file)
print(' > Loading %s and exporting to %s' %
(load_cell_file, nml_net_loc))
export_to_neuroml2(load_cell_file,
nml_net_loc,
separateCellFiles=True,
includeBiophysicalProperties=False)
print(' > Exported to: %s and %s using %s' %
(nml_net_loc, nml_cell_loc, load_cell_file))
nml_doc = pynml.read_neuroml2_file(nml_cell_loc)
cell = nml_doc.cells[0]
print(' > Adding groups from: %s' % groups_info_file)
groups = {}
current_group = None
for line in open(groups_info_file):
if not line.startswith('//'):
if line.startswith('- '):
current_group = line[2:-1]
print(' > Adding group: [%s]' % current_group)
groups[current_group] = []
else:
section = line.split('.')[1].strip()
segment_group = section.replace('[', '_').replace(']', '')
groups[current_group].append(segment_group)
for g in groups.keys():
new_seg_group = neuroml.SegmentGroup(id=g)
cell.morphology.segment_groups.append(new_seg_group)
for sg in groups[g]:
new_seg_group.includes.append(neuroml.Include(sg))
if g in ['basal', 'apical']:
new_seg_group.inhomogeneous_parameters.append(
neuroml.InhomogeneousParameter(
id="PathLengthOver_" + g,
variable="p",
metric="Path Length from root",
proximal=neuroml.ProximalDetails(
translation_start="0")))
ignore_chans = [
'Ih', 'Ca_HVA', 'Ca_LVAst', 'Ca', "SKv3_1", "SK_E2",
"CaDynamics_E2", "Nap_Et2", "Im", "K_Tst", "NaTa_t", "K_Pst",
"NaTs2_t"
]
# ignore_chans=['StochKv','StochKv_deterministic']
ignore_chans = []
bp, incl_chans = get_biophysical_properties(
cell_info['e-type'],
ignore_chans=ignore_chans,
templates_json="../templates.json")
cell.biophysical_properties = bp
print("Set biophysical properties")
notes = ''
notes += \
"\n\nExport of a cell model obtained from the BBP Neocortical" \
"Microcircuit Collaboration Portal into NeuroML2" \
"\n\n******************************************************\n*" \
" This export to NeuroML2 has not yet been fully validated!!" \
"\n* Use with caution!!\n***********************************" \
"*******************\n\n"
if len(ignore_chans) > 0:
notes += "Ignored channels = %s\n\n" % ignore_chans
notes += "For more information on this cell model see: " \
"https://bbp.epfl.ch/nmc-portal/microcircuit#/metype/%s/" \
"details\n\n" % cell_info['me-type']
cell.notes = notes
for channel in incl_chans:
nml_doc.includes.append(neuroml.IncludeType(href="%s" % channel))
if make_zips:
print("Copying %s to zip folder" % channel)
shutil.copyfile('../../NeuroML2/%s' % channel,
'%s/%s' % (local_nml2_cell_dir, channel))
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
stim_ref = 'stepcurrent3'
stim_ref_hyp = '%s_hyp' % stim_ref
stim_sim_duration = 3000
stim_hyp_amp, stim_amp = get_stimulus_amplitudes(bbp_ref)
stim_del = '700ms'
stim_dur = '2000ms'
new_net_loc = "%s/%s.%s.net.nml" % (local_nml2_cell_dir, bbp_ref,
stim_ref)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net_doc.notes = notes
stim_hyp = neuroml.PulseGenerator(id=stim_ref_hyp,
delay="0ms",
duration="%sms" % stim_sim_duration,
amplitude=stim_hyp_amp)
new_net_doc.pulse_generators.append(stim_hyp)
stim = neuroml.PulseGenerator(id=stim_ref,
delay=stim_del,
duration=stim_dur,
amplitude=stim_amp)
new_net_doc.pulse_generators.append(stim)
new_net = new_net_doc.networks[0]
pop_id = new_net.populations[0].id
pop_comp = new_net.populations[0].component
input_list = neuroml.InputList(id="%s_input" % stim_ref_hyp,
component=stim_ref_hyp,
populations=pop_id)
syn_input = neuroml.Input(id=0,
target="../%s/0/%s" % (pop_id, pop_comp),
destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
input_list = neuroml.InputList(id="%s_input" % stim_ref,
component=stim_ref,
populations=pop_id)
syn_input = neuroml.Input(id=0,
target="../%s/0/%s" % (pop_id, pop_comp),
destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
pynml.write_neuroml2_file(new_net_doc, new_net_loc)
generate_lems_file_for_neuroml(cell_dir,
new_net_loc,
"network",
stim_sim_duration,
0.025,
"LEMS_%s.xml" % cell_dir,
local_nml2_cell_dir,
copy_neuroml=False,
seed=1234)
pynml.nml2_to_svg(nml_net_loc)
clear_neuron()
pop = neuroml.Population(id="Pop_%s" % bbp_ref,
component=bbp_ref + '_0_0',
type="populationList")
inst = neuroml.Instance(id="0")
pop.instances.append(inst)
width = 6
X = count % width
Z = (count - X) / width
inst.location = neuroml.Location(x=300 * X, y=0, z=300 * Z)
count += 1
if make_zips:
zip_file = "%s/%s.zip" % (zips_dir, cell_dir)
print("Creating zip file: %s" % zip_file)
with zipfile.ZipFile(zip_file, 'w') as myzip:
for next_file in os.listdir(local_nml2_cell_dir):
next_file = '%s/%s' % (local_nml2_cell_dir, next_file)
arcname = next_file[len(zips_dir):]
print("Adding : %s as %s" % (next_file, arcname))
myzip.write(next_file, arcname)
os.chdir(old_cwd)
return nml_cell_file, pop
def create_GoC_network(duration,
dt,
seed,
N_goc=0,
N_mf=15,
run=False,
prob_type='Boltzmann',
GJw_type='Vervaeke2010'):
### ---------- Component types
goc_filename = 'GoC.cell.nml' # Golgi cell with channels
goc_file = pynml.read_neuroml2_file(goc_filename)
goc_type = goc_file.cells[0]
goc_ref = nml.IncludeType(href=goc_filename)
MFSyn_filename = 'MF_GoC_Syn.nml' # small conductance synapse for background inputs
mfsyn_file = pynml.read_neuroml2_file(MFSyn_filename)
MFSyn_type = mfsyn_file.exp_three_synapses[0]
mfsyn_ref = nml.IncludeType(href=MFSyn_filename)
MF20Syn_filename = 'MF_GoC_SynMult.nml' # multi-syn conductance for strong/coincident transient input
mf20syn_file = pynml.read_neuroml2_file(MF20Syn_filename)
MF20Syn_type = mf20syn_file.exp_three_synapses[0]
mf20syn_ref = nml.IncludeType(href=MF20Syn_filename)
mf_type2 = 'spikeGeneratorPoisson' # Spike source for background inputs
mf_poisson = nml.SpikeGeneratorPoisson(
id="MF_Poisson", average_rate="5 Hz") # Not tuned to any data - qqq !
mf_bursttype = 'transientPoissonFiringSynapse' # Burst of MF input (as explicit input)
mf_burst = nml.TransientPoissonFiringSynapse(id="MF_Burst",
average_rate="100 Hz",
delay="2000 ms",
duration="500 ms",
synapse=MF20Syn_type.id,
spike_target='./{}'.format(
MF20Syn_type.id))
gj = nml.GapJunction(id="GJ_0", conductance="426pS") # GoC synapse
### --------- Populations
# Build network to specify cells and connectivity
net = nml.Network(id="gocNetwork",
type="networkWithTemperature",
temperature="23 degC")
### Golgi cells
if N_goc > 0:
GoC_pos = nu.GoC_locate(N_goc)
else:
GoC_pos = nu.GoC_density_locate()
N_goc = GoC_pos.shape[0]
# Create GoC population
goc_pop = nml.Population(id=goc_type.id + "Pop",
component=goc_type.id,
type="populationList",
size=N_goc)
for goc in range(N_goc):
inst = nml.Instance(id=goc)
goc_pop.instances.append(inst)
inst.location = nml.Location(x=GoC_pos[goc, 0],
y=GoC_pos[goc, 1],
z=GoC_pos[goc, 2])
net.populations.append(goc_pop)
### MF population
MF_Poisson_pop = nml.Population(id=mf_poisson.id + "_pop",
component=mf_poisson.id,
type="populationList",
size=N_mf)
MF_pos = nu.GoC_locate(N_mf)
for mf in range(N_mf):
inst = nml.Instance(id=mf)
MF_Poisson_pop.instances.append(inst)
inst.location = nml.Location(x=MF_pos[mf, 0],
y=MF_pos[mf, 1],
z=MF_pos[mf, 2])
net.populations.append(MF_Poisson_pop)
# Create NML document for network specification
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(goc_ref)
net_doc.includes.append(mfsyn_ref)
net_doc.includes.append(mf20syn_ref)
net_doc.spike_generator_poissons.append(mf_poisson)
net_doc.transient_poisson_firing_synapses.append(mf_burst)
net_doc.gap_junctions.append(gj)
### ------------ Connectivity
### background excitatory inputs: MF to GoC populations
MFProjection = nml.Projection(id="MFtoGoC",
presynaptic_population=MF_Poisson_pop.id,
postsynaptic_population=goc_pop.id,
synapse=MFSyn_type.id)
net.projections.append(MFProjection)
#Get list of MF->GoC synapse
mf_synlist = nu.randdist_MF_syn(N_mf, N_goc,
pConn=0.3) # Not tuned to any data - qqq!
nMFSyn = mf_synlist.shape[1]
for syn in range(nMFSyn):
mf, goc = mf_synlist[:, syn]
conn2 = nml.Connection(
id=syn,
pre_cell_id='../{}/{}/{}'.format(MF_Poisson_pop.id, mf,
mf_poisson.id),
post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id),
post_segment_id='0',
post_fraction_along="0.5") #on soma
MFProjection.connections.append(conn2)
### Add few burst inputs
n_bursts = 4
gocPerm = np.random.permutation(
N_goc) # change to central neurons later -qqq !!!
ctr = 0
for gg in range(4):
goc = gocPerm[gg]
for jj in range(n_bursts):
inst = nml.ExplicitInput(
id=ctr,
target='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id),
input=mf_burst.id,
synapse=MF20Syn_type.id,
spikeTarget='./{}'.format(MF20Syn_type.id))
net.explicit_inputs.append(inst)
ctr += 1
### Electrical coupling between GoCs
# get GJ connectivity
GJ_pairs, GJWt = nu.GJ_conn(GoC_pos, prob_type, GJw_type)
#tmp1, tmp2 = valnet.gapJuncAnalysis( GJ_pairs, GJWt )
#print("Number of gap junctions per cell: ", tmp1)
#print("Net GJ conductance per cell:", tmp2)
# Add electrical synapses
GoCCoupling = nml.ElectricalProjection(id="gocGJ",
presynaptic_population=goc_pop.id,
postsynaptic_population=goc_pop.id)
nGJ = GJ_pairs.shape[0]
for jj in range(nGJ):
conn = nml.ElectricalConnectionInstanceW(
id=jj,
pre_cell='../{}/{}/{}'.format(goc_pop.id, GJ_pairs[jj, 0],
goc_type.id),
pre_segment='1',
pre_fraction_along='0.5',
post_cell='../{}/{}/{}'.format(goc_pop.id, GJ_pairs[jj, 1],
goc_type.id),
post_segment='1',
post_fraction_along='0.5',
synapse=gj.id,
weight=GJWt[jj])
GoCCoupling.electrical_connection_instance_ws.append(conn)
net.electrical_projections.append(GoCCoupling)
### -------------- Write files
net_filename = 'gocNetwork.nml'
pynml.write_neuroml2_file(net_doc, net_filename)
#lems_filename = 'instances.xml'
#pynml.write_lems_file( lems_inst_doc, lems_filename, validate=False )
simid = 'sim_gocnet' + goc_type.id
ls = LEMSSimulation(simid, duration=duration, dt=dt, simulation_seed=seed)
ls.assign_simulation_target(net.id)
ls.include_neuroml2_file(net_filename)
ls.include_neuroml2_file(goc_filename)
ls.include_neuroml2_file(MFSyn_filename)
ls.include_neuroml2_file(MF20Syn_filename)
#ls.include_lems_file( lems_filename, include_included=False)
# Specify outputs
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes" % simid, format='ID_TIME')
for jj in range(goc_pop.size):
ls.add_selection_to_event_output_file(
eof0, jj, '{}/{}/{}'.format(goc_pop.id, jj, goc_type.id), 'spike')
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % simid)
for jj in range(goc_pop.size):
ls.add_column_to_output_file(
of0, jj, '{}/{}/{}/v'.format(goc_pop.id, jj, goc_type.id))
#Create Lems file to run
lems_simfile = ls.save_to_file()
if run:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
nogui=True,
plot=False)
else:
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
only_generate_scripts=True,
compile_mods=False,
nogui=True,
plot=False)
return res
def create_GoC_network( duration, dt, seed, runid, run=False):
### ---------- Load Params
noPar = True
pfile = Path('params_file.pkl')
if pfile.exists():
print('Reading parameters from file:')
file = open('params_file.pkl','rb')
params_list = pkl.load(file)
if len(params_list)>runid:
p = params_list[runid]
file.close()
if noPar:
p = inp.get_simulation_params( runid )
### ---------- Component types
goc_filename = 'GoC.cell.nml' # Golgi cell with channels
goc_file = pynml.read_neuroml2_file( goc_filename )
goc_type = goc_file.cells[0]
goc_ref = nml.IncludeType( href=goc_filename )
MFSyn_filename = 'MF_GoC_Syn.nml' # small conductance synapse for background inputs
mfsyn_file = pynml.read_neuroml2_file( MFSyn_filename )
MFSyn_type = mfsyn_file.exp_three_synapses[0]
mfsyn_ref = nml.IncludeType( href=MFSyn_filename )
MF20Syn_filename = 'MF_GoC_SynMult.nml' # multi-syn conductance for strong/coincident transient input
mf20syn_file = pynml.read_neuroml2_file( MF20Syn_filename )
MF20Syn_type = mf20syn_file.exp_three_synapses[0]
mf20syn_ref = nml.IncludeType( href=MF20Syn_filename )
mf_type2 = 'spikeGeneratorPoisson' # Spike source for background inputs
mf_poisson = nml.SpikeGeneratorPoisson( id = "MF_Poisson", average_rate="5 Hz" ) # Not tuned to any data - qqq !
mf_bursttype = 'transientPoissonFiringSynapse' # Burst of MF input (as explicit input)
mf_burst = nml.TransientPoissonFiringSynapse( id="MF_Burst", average_rate="100 Hz", delay="2000 ms", duration="500 ms", synapse=MF20Syn_type.id, spike_target='./{}'.format(MF20Syn_type.id) )
gj = nml.GapJunction( id="GJ_0", conductance="426pS" ) # GoC synapse
### --------- Populations
# Build network to specify cells and connectivity
net = nml.Network( id="gocNetwork", type="networkWithTemperature" , temperature="23 degC" )
# Create GoC population
goc_pop = nml.Population( id=goc_type.id+"Pop", component = goc_type.id, type="populationList", size=p["nGoC"] )
for goc in range( p["nGoC"] ):
inst = nml.Instance( id=goc )
goc_pop.instances.append( inst )
inst.location = nml.Location( x=p["GoC_pos"][goc,0], y=p["GoC_pos"][goc,1], z=p["GoC_pos"][goc,2] )
net.populations.append( goc_pop )
### MF population
MF_Poisson_pop = nml.Population(id=mf_poisson.id+"_pop", component=mf_poisson.id, type="populationList", size=p["nMF"])
for mf in range( p["nMF"] ):
inst = nml.Instance(id=mf)
MF_Poisson_pop.instances.append( inst )
inst.location = nml.Location( x=p["MF_pos"][mf,0], y=p["MF_pos"][mf,1], z=p["MF_pos"][mf,2] )
net.populations.append( MF_Poisson_pop )
# Create NML document for network specification
net_doc = nml.NeuroMLDocument( id=net.id )
net_doc.networks.append( net )
net_doc.includes.append( goc_ref )
net_doc.includes.append( mfsyn_ref )
net_doc.includes.append( mf20syn_ref )
net_doc.spike_generator_poissons.append( mf_poisson )
net_doc.transient_poisson_firing_synapses.append( mf_burst )
net_doc.gap_junctions.append(gj)
### ------------ Connectivity
### 1. Background excitatory inputs: MF to GoC populations
MFProjection = nml.Projection(id="MFtoGoC", presynaptic_population=MF_Poisson_pop.id, postsynaptic_population=goc_pop.id, synapse=MFSyn_type.id)
net.projections.append(MFProjection)
# MF_> GoC synapses (with syn_count equivalent to integer scaling of Mf synapse strength)
nMFSyn = p["MF_GoC_pairs"].shape[1]
ctr=0
for syn in range( nMFSyn ):
mf, goc = p["MF_GoC_pairs"][:, syn]
for syn_count in range(p["MF_GoC_wt"][ctr]):
conn2 = nml.Connection(id=ctr, pre_cell_id='../{}/{}/{}'.format(MF_Poisson_pop.id, mf, mf_poisson.id), post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), post_segment_id='0', post_fraction_along="0.5") #on soma
MFProjection.connections.append(conn2)
ctr+=1
### 2. Perturbation as High Freq MF Inputs
ctr=0
for goc in p["Burst_GoC"]:
for jj in range( p["nBurst"] ): # Each Perturbed GoC gets nBurst random Burst sources
inst = nml.ExplicitInput( id=ctr, target='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), input=mf_burst.id, synapse=MF20Syn_type.id, spikeTarget='./{}'.format(MF20Syn_type.id))
net.explicit_inputs.append( inst )
ctr += 1
### 3. Electrical coupling between GoCs
GoCCoupling = nml.ElectricalProjection( id="gocGJ", presynaptic_population=goc_pop.id, postsynaptic_population=goc_pop.id )
net.electrical_projections.append( GoCCoupling )
dend_id = [1,2,5]
for jj in range( p["GJ_pairs"].shape[0] ):
conn = nml.ElectricalConnectionInstanceW( id=jj, pre_cell='../{}/{}/{}'.format(goc_pop.id, p["GJ_pairs"][jj,0], goc_type.id), pre_segment=dend_id[p["GJ_loc"][jj,0]], pre_fraction_along='0.5', post_cell='../{}/{}/{}'.format(goc_pop.id, p["GJ_pairs"][jj,1], goc_type.id), post_segment=dend_id[p["GJ_loc"][jj,1]], post_fraction_along='0.5', synapse=gj.id, weight=p["GJ_wt"][jj] )
GoCCoupling.electrical_connection_instance_ws.append( conn )
### -------------- Write files
net_filename = 'gocNetwork.nml'
pynml.write_neuroml2_file( net_doc, net_filename )
simid = 'sim_gocnet_'+goc_type.id+'_run_{}'.format(runid)
ls = LEMSSimulation( simid, duration=duration, dt=dt, simulation_seed=seed )
ls.assign_simulation_target( net.id )
ls.include_neuroml2_file( net_filename)
ls.include_neuroml2_file( goc_filename)
ls.include_neuroml2_file( MFSyn_filename)
ls.include_neuroml2_file( MF20Syn_filename)
# Specify outputs
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes"%simid,format='ID_TIME')
for jj in range( goc_pop.size):
ls.add_selection_to_event_output_file( eof0, jj, '{}/{}/{}'.format( goc_pop.id, jj, goc_type.id), 'spike' )
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%simid)
for jj in range( goc_pop.size ):
ls.add_column_to_output_file(of0, jj, '{}/{}/{}/v'.format( goc_pop.id, jj, goc_type.id))
#Create Lems file to run
lems_simfile = ls.save_to_file()
if run:
res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="2G", nogui=True, plot=False)
else:
res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="2G", only_generate_scripts = True, compile_mods = False, nogui=True, plot=False)
return res
cell = nml_doc.izhikevich2007_cells[0]
print("Extracted cell: %s from tuned model" % cell.id)
new_id = '%s_%s' % (type, dataset)
new_cell_doc = neuroml.NeuroMLDocument(id=new_id)
cell.id = new_id
cell.notes = "Cell model tuned to Allen Institute Cell Types Database, dataset: "+ \
"%s\n\nTuning procedure metadata:\n\n%s\n"%(dataset, pp.pformat(r2))
new_cell_doc.izhikevich2007_cells.append(cell)
new_cell_file = 'tuned_cells/%s.cell.nml' % new_id
pynml.write_neuroml2_file(new_cell_doc, new_cell_file)
#### Run a 'quick' optimisation for HH cell model
elif '-quick' in sys.argv:
simulator = 'jNeuroML_NEURON'
dataset = 471141261
ref = 'network_%s_HH' % (dataset)
report = run_one_optimisation('AllenTestQ',
1234,
parameters=parameters_hh,
max_constraints=max_constraints_hh,
min_constraints=min_constraints_hh,
population_size=10,
def generate_Vm_vs_time_plot(nml2_file,
cell_id,
inj_amp_nA=80,
delay_ms=20,
inj_dur_ms=60,
sim_dur_ms=100,
dt=0.05,
plot_voltage_traces=False,
show_plot_already=True,
simulator="jNeuroML",
include_included=True):
ref = "Test"
print_comment_v(
"Generating Vm(mV) vs Time(ms) plot for cell %s in %s using %s (Inj %snA / %sms dur after %sms delay)"
% (cell_id, nml2_file, simulator, inj_amp_nA, inj_dur_ms, delay_ms))
sim_id = 'Vm_%s' % ref
duration = sim_dur_ms
ls = LEMSSimulation(sim_id, sim_dur_ms, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
ls.assign_simulation_target('network')
nml_doc = nml.NeuroMLDocument(id=cell_id)
nml_doc.includes.append(nml.IncludeType(href=nml2_file))
net = nml.Network(id="network")
nml_doc.networks.append(net)
input_id = ("input_%s" % str(inj_amp_nA).replace('.', '_'))
pg = nml.PulseGenerator(id=input_id,
delay="%sms" % delay_ms,
duration='%sms' % inj_dur_ms,
amplitude='%spA' % inj_amp_nA)
nml_doc.pulse_generators.append(pg)
pop_id = 'hhpop'
pop = nml.Population(id=pop_id,
component='hhcell',
size=1,
type="populationList")
inst = nml.Instance(id=0)
pop.instances.append(inst)
inst.location = nml.Location(x=0, y=0, z=0)
net.populations.append(pop)
# Add these to cells
input_list = nml.InputList(id='il_%s' % input_id,
component=pg.id,
populations=pop_id)
input = nml.Input(id='0',
target='../hhpop/0/hhcell',
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
sim_file_name = '%s.sim.nml' % sim_id
pynml.write_neuroml2_file(nml_doc, sim_file_name)
ls.include_neuroml2_file(sim_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0, "Voltages", "-90", "50")
ls.add_line_to_display(disp0, "V", "hhpop/0/hhcell/v", scale='1mV')
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % sim_id)
ls.add_column_to_output_file(of0, "V", "hhpop/0/hhcell/v")
lems_file_name = ls.save_to_file()
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
return of0
def generate_current_vs_frequency_curve(nml2_file,
cell_id,
start_amp_nA=-0.1,
end_amp_nA=0.1,
step_nA=0.01,
custom_amps_nA=[],
analysis_duration=1000,
analysis_delay=0,
pre_zero_pulse=0,
post_zero_pulse=0,
dt=0.05,
temperature="32degC",
spike_threshold_mV=0.,
plot_voltage_traces=False,
plot_if=True,
plot_iv=False,
xlim_if=None,
ylim_if=None,
xlim_iv=None,
ylim_iv=None,
label_xaxis=True,
label_yaxis=True,
show_volts_label=True,
grid=True,
font_size=12,
if_iv_color='k',
linewidth=1,
bottom_left_spines_only=False,
show_plot_already=True,
save_voltage_traces_to=None,
save_if_figure_to=None,
save_iv_figure_to=None,
save_if_data_to=None,
save_iv_data_to=None,
simulator="jNeuroML",
num_processors=1,
include_included=True,
title_above_plot=False,
return_axes=False,
verbose=False):
print_comment(
"Running generate_current_vs_frequency_curve() on %s (%s)" %
(nml2_file, os.path.abspath(nml2_file)), verbose)
from pyelectro.analysis import max_min
from pyelectro.analysis import mean_spike_frequency
import numpy as np
traces_ax = None
if_ax = None
iv_ax = None
sim_id = 'iv_%s' % cell_id
total_duration = pre_zero_pulse + analysis_duration + analysis_delay + post_zero_pulse
pulse_duration = analysis_duration + analysis_delay
end_stim = pre_zero_pulse + analysis_duration + analysis_delay
ls = LEMSSimulation(sim_id, total_duration, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
stims = []
if len(custom_amps_nA) > 0:
stims = [float(a) for a in custom_amps_nA]
stim_info = ['%snA' % float(a) for a in custom_amps_nA]
else:
amp = start_amp_nA
while amp <= end_amp_nA:
stims.append(amp)
amp += step_nA
stim_info = '(%snA->%snA; %s steps of %snA; %sms)' % (
start_amp_nA, end_amp_nA, len(stims), step_nA, total_duration)
print_comment_v("Generating an IF curve for cell %s in %s using %s %s" %
(cell_id, nml2_file, simulator, stim_info))
number_cells = len(stims)
pop = nml.Population(id="population_of_%s" % cell_id,
component=cell_id,
size=number_cells)
# create network and add populations
net_id = "network_of_%s" % cell_id
net = nml.Network(id=net_id,
type="networkWithTemperature",
temperature=temperature)
ls.assign_simulation_target(net_id)
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(nml2_file))
net.populations.append(pop)
for i in range(number_cells):
stim_amp = "%snA" % stims[i]
input_id = ("input_%s" % stim_amp).replace('.',
'_').replace('-', 'min')
pg = nml.PulseGenerator(id=input_id,
delay="%sms" % pre_zero_pulse,
duration="%sms" % pulse_duration,
amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
# Add these to cells
input_list = nml.InputList(id=input_id,
component=pg.id,
populations=pop.id)
input = nml.Input(id='0',
target="../%s[%i]" % (pop.id, i),
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = '%s.net.nml' % sim_id
pynml.write_neuroml2_file(net_doc, net_file_name)
ls.include_neuroml2_file(net_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0, "Voltages", "-90", "50")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % sim_id)
for i in range(number_cells):
ref = "v_cell%i" % i
quantity = "%s[%i]/v" % (pop.id, i)
ls.add_line_to_display(disp0, ref, quantity, "1mV",
pynml.get_next_hex_color())
ls.add_column_to_output_file(of0, ref, quantity)
lems_file_name = ls.save_to_file()
print_comment(
"Written LEMS file %s (%s)" %
(lems_file_name, os.path.abspath(lems_file_name)), verbose)
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
verbose=verbose)
elif simulator == "jNeuroML_NetPyNE":
results = pynml.run_lems_with_jneuroml_netpyne(
lems_file_name,
nogui=True,
load_saved_data=True,
plot=False,
show_plot_already=False,
num_processors=num_processors,
verbose=verbose)
else:
raise Exception(
"Sorry, cannot yet run current vs frequency analysis using simulator %s"
% simulator)
print_comment(
"Completed run in simulator %s (results: %s)" %
(simulator, results.keys()), verbose)
#print(results.keys())
times_results = []
volts_results = []
volts_labels = []
if_results = {}
iv_results = {}
for i in range(number_cells):
t = np.array(results['t']) * 1000
v = np.array(results["%s[%i]/v" % (pop.id, i)]) * 1000
if plot_voltage_traces:
times_results.append(t)
volts_results.append(v)
volts_labels.append("%s nA" % stims[i])
mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV)
spike_times = mm['maxima_times']
freq = 0
if len(spike_times) > 2:
count = 0
for s in spike_times:
if s >= pre_zero_pulse + analysis_delay and s < (
pre_zero_pulse + analysis_duration + analysis_delay):
count += 1
freq = 1000 * count / float(analysis_duration)
mean_freq = mean_spike_frequency(spike_times)
#print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
if_results[stims[i]] = freq
if freq == 0:
if post_zero_pulse == 0:
iv_results[stims[i]] = v[-1]
else:
v_end = None
for j in range(len(t)):
if v_end == None and t[j] >= end_stim:
v_end = v[j]
iv_results[stims[i]] = v_end
if plot_voltage_traces:
traces_ax = pynml.generate_plot(
times_results,
volts_results,
"Membrane potential traces for: %s" % nml2_file,
xaxis='Time (ms)' if label_xaxis else ' ',
yaxis='Membrane potential (mV)' if label_yaxis else '',
xlim=[total_duration * -0.05, total_duration * 1.05],
show_xticklabels=label_xaxis,
font_size=font_size,
bottom_left_spines_only=bottom_left_spines_only,
grid=False,
labels=volts_labels if show_volts_label else [],
show_plot_already=False,
save_figure_to=save_voltage_traces_to,
title_above_plot=title_above_plot,
verbose=verbose)
if plot_if:
stims = sorted(if_results.keys())
stims_pA = [ii * 1000 for ii in stims]
freqs = [if_results[s] for s in stims]
if_ax = pynml.generate_plot(
[stims_pA], [freqs],
"Firing frequency versus injected current for: %s" % nml2_file,
colors=[if_iv_color],
linestyles=['-'],
markers=['o'],
linewidths=[linewidth],
xaxis='Input current (pA)' if label_xaxis else ' ',
yaxis='Firing frequency (Hz)' if label_yaxis else '',
xlim=xlim_if,
ylim=ylim_if,
show_xticklabels=label_xaxis,
show_yticklabels=label_yaxis,
font_size=font_size,
bottom_left_spines_only=bottom_left_spines_only,
grid=grid,
show_plot_already=False,
save_figure_to=save_if_figure_to,
title_above_plot=title_above_plot,
verbose=verbose)
if save_if_data_to:
with open(save_if_data_to, 'w') as if_file:
for i in range(len(stims_pA)):
if_file.write("%s\t%s\n" % (stims_pA[i], freqs[i]))
if plot_iv:
stims = sorted(iv_results.keys())
stims_pA = [ii * 1000 for ii in sorted(iv_results.keys())]
vs = [iv_results[s] for s in stims]
xs = []
ys = []
xs.append([])
ys.append([])
for si in range(len(stims)):
stim = stims[si]
if len(custom_amps_nA) == 0 and si > 1 and (
stims[si] - stims[si - 1]) > step_nA * 1.01:
xs.append([])
ys.append([])
xs[-1].append(stim * 1000)
ys[-1].append(iv_results[stim])
iv_ax = pynml.generate_plot(
xs,
ys,
"V at %sms versus I below threshold for: %s" %
(end_stim, nml2_file),
colors=[if_iv_color for s in xs],
linestyles=['-' for s in xs],
markers=['o' for s in xs],
xaxis='Input current (pA)' if label_xaxis else '',
yaxis='Membrane potential (mV)' if label_yaxis else '',
xlim=xlim_iv,
ylim=ylim_iv,
show_xticklabels=label_xaxis,
show_yticklabels=label_yaxis,
font_size=font_size,
linewidths=[linewidth for s in xs],
bottom_left_spines_only=bottom_left_spines_only,
grid=grid,
show_plot_already=False,
save_figure_to=save_iv_figure_to,
title_above_plot=title_above_plot,
verbose=verbose)
if save_iv_data_to:
with open(save_iv_data_to, 'w') as iv_file:
for i in range(len(stims_pA)):
iv_file.write("%s\t%s\n" % (stims_pA[i], vs[i]))
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
if return_axes:
return traces_ax, if_ax, iv_ax
return if_results
def run_individual(self, sim_var, show=False):
"""
Run an individual simulation.
The candidate data has been flattened into the sim_var dict. The
sim_var dict contains parameter:value key value pairs, which are
applied to the model before it is simulated.
"""
nml_doc = read_neuroml2_file(self.neuroml_file,
include_includes=True,
verbose = True)
for var_name in sim_var.keys():
words = var_name.split('/')
type, id1 = words[0].split(':')
variable, id2 = words[1].split(':')
units = words[2]
value = sim_var[var_name]
print('Changing value of %s (%s) in %s (%s) to: %s %s'%(variable, id2, type, id1, value, units))
if type == 'cell':
cell = None
for c in nml_doc.cells:
if c.id == id1:
cell = c
if variable == 'channelDensity':
chanDens = None
for cd in cell.biophysical_properties.membrane_properties.channel_densities:
if cd.id == id2:
chanDens = cd
chanDens.cond_density = '%s %s'%(value, units)
else:
print('Unknown variable (%s) in variable expression: %s'%(variable, var_name))
else:
print('Unknown type (%s) in variable expression: %s'%(type, var_name))
new_neuroml_file = '%s/%s'%(self.generate_dir,os.path.basename(self.neuroml_file))
if new_neuroml_file == self.neuroml_file:
print('Cannot use a directory for generating into (%s) which is the same location of the NeuroML file (%s)!'% \
(self.neuroml_file, self.generate_dir))
write_neuroml2_file(nml_doc, new_neuroml_file)
sim = NeuroMLSimulation(self.ref,
neuroml_file = new_neuroml_file,
target = self.target,
sim_time = self.sim_time,
dt = self.dt,
simulator = self.simulator,
generate_dir = self.generate_dir)
sim.go()
if show:
sim.show()
return sim.t, sim.volts
def generate_Vm_vs_time_plot(nml2_file,
cell_id,
inj_amp_nA = 80,
delay_ms = 20,
inj_dur_ms = 60,
sim_dur_ms = 100,
dt = 0.05,
temperature = "32degC",
spike_threshold_mV=0.,
plot_voltage_traces=False,
show_plot_already=True,
simulator="jNeuroML",
include_included=True):
# simulation parameters
nogui = '-nogui' in sys.argv # Used to supress GUI in tests for Travis-CI
ref = "Test"
print_comment_v("Generating Vm(mV) vs Time(ms) plot for cell %s in %s using %s (Inj %snA / %sms dur after %sms delay)"%
(cell_id, nml2_file, simulator, inj_amp_nA, inj_dur_ms, delay_ms))
sim_id = 'Vm_%s'%ref
duration = sim_dur_ms
ls = LEMSSimulation(sim_id, sim_dur_ms, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
ls.assign_simulation_target('network')
nml_doc = nml.NeuroMLDocument(id=cell_id)
nml_doc.includes.append(nml.IncludeType(href=nml2_file))
net = nml.Network(id="network")
nml_doc.networks.append(net)
input_id = ("input_%s"%str(inj_amp_nA).replace('.','_'))
pg = nml.PulseGenerator(id=input_id,
delay="%sms"%delay_ms,
duration='%sms'%inj_dur_ms,
amplitude='%spA'%inj_amp_nA)
nml_doc.pulse_generators.append(pg)
pop_id = 'hhpop'
pop = nml.Population(id=pop_id, component='hhcell', size=1, type="populationList")
inst = nml.Instance(id=0)
pop.instances.append(inst)
inst.location = nml.Location(x=0, y=0, z=0)
net.populations.append(pop)
# Add these to cells
input_list = nml.InputList(id='il_%s'%input_id,
component=pg.id,
populations=pop_id)
input = nml.Input(id='0', target='../hhpop/0/hhcell',
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
sim_file_name = '%s.sim.nml'%sim_id
pynml.write_neuroml2_file(nml_doc, sim_file_name)
ls.include_neuroml2_file(sim_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0,"Voltages", "-90", "50")
ls.add_line_to_display(disp0, "V", "hhpop/0/hhcell/v", scale='1mV')
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%sim_id)
ls.add_column_to_output_file(of0, "V", "hhpop/0/hhcell/v")
lems_file_name = ls.save_to_file()
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
return of0
def generate_current_vs_frequency_curve(nml2_file,
cell_id,
start_amp_nA,
end_amp_nA,
step_nA,
analysis_duration,
analysis_delay,
dt=0.05,
temperature="32degC",
spike_threshold_mV=0.,
plot_voltage_traces=False,
plot_if=True,
plot_iv=False,
xlim_if=None,
ylim_if=None,
xlim_iv=None,
ylim_iv=None,
show_plot_already=True,
save_if_figure_to=None,
save_iv_figure_to=None,
simulator="jNeuroML",
include_included=True):
from pyelectro.analysis import max_min
from pyelectro.analysis import mean_spike_frequency
import numpy as np
print_comment_v(
"Generating FI curve for cell %s in %s using %s (%snA->%snA; %snA steps)"
% (cell_id, nml2_file, simulator, start_amp_nA, end_amp_nA, step_nA))
sim_id = 'iv_%s' % cell_id
duration = analysis_duration + analysis_delay
ls = LEMSSimulation(sim_id, duration, dt)
ls.include_neuroml2_file(nml2_file, include_included=include_included)
stims = []
amp = start_amp_nA
while amp <= end_amp_nA:
stims.append(amp)
amp += step_nA
number_cells = len(stims)
pop = nml.Population(id="population_of_%s" % cell_id,
component=cell_id,
size=number_cells)
# create network and add populations
net_id = "network_of_%s" % cell_id
net = nml.Network(id=net_id,
type="networkWithTemperature",
temperature=temperature)
ls.assign_simulation_target(net_id)
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.includes.append(nml.IncludeType(nml2_file))
net.populations.append(pop)
for i in range(number_cells):
stim_amp = "%snA" % stims[i]
input_id = ("input_%s" % stim_amp).replace('.',
'_').replace('-', 'min')
pg = nml.PulseGenerator(id=input_id,
delay="0ms",
duration="%sms" % duration,
amplitude=stim_amp)
net_doc.pulse_generators.append(pg)
# Add these to cells
input_list = nml.InputList(id=input_id,
component=pg.id,
populations=pop.id)
input = nml.Input(id='0',
target="../%s[%i]" % (pop.id, i),
destination="synapses")
input_list.input.append(input)
net.input_lists.append(input_list)
net_file_name = '%s.net.nml' % sim_id
pynml.write_neuroml2_file(net_doc, net_file_name)
ls.include_neuroml2_file(net_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0, "Voltages", "-90", "50")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % sim_id)
for i in range(number_cells):
ref = "v_cell%i" % i
quantity = "%s[%i]/v" % (pop.id, i)
ls.add_line_to_display(disp0, ref, quantity, "1mV",
pynml.get_next_hex_color())
ls.add_column_to_output_file(of0, ref, quantity)
lems_file_name = ls.save_to_file()
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
#print(results.keys())
if_results = {}
iv_results = {}
for i in range(number_cells):
t = np.array(results['t']) * 1000
v = np.array(results["%s[%i]/v" % (pop.id, i)]) * 1000
mm = max_min(v, t, delta=0, peak_threshold=spike_threshold_mV)
spike_times = mm['maxima_times']
freq = 0
if len(spike_times) > 2:
count = 0
for s in spike_times:
if s >= analysis_delay and s < (analysis_duration +
analysis_delay):
count += 1
freq = 1000 * count / float(analysis_duration)
mean_freq = mean_spike_frequency(spike_times)
# print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
if_results[stims[i]] = freq
if freq == 0:
iv_results[stims[i]] = v[-1]
if plot_if:
stims = sorted(if_results.keys())
stims_pA = [ii * 1000 for ii in stims]
freqs = [if_results[s] for s in stims]
pynml.generate_plot([stims_pA], [freqs],
"Frequency versus injected current for: %s" %
nml2_file,
colors=['k'],
linestyles=['-'],
markers=['o'],
xaxis='Input current (pA)',
yaxis='Firing frequency (Hz)',
xlim=xlim_if,
ylim=ylim_if,
grid=True,
show_plot_already=False,
save_figure_to=save_if_figure_to)
if plot_iv:
stims = sorted(iv_results.keys())
stims_pA = [ii * 1000 for ii in sorted(iv_results.keys())]
vs = [iv_results[s] for s in stims]
pynml.generate_plot(
[stims_pA], [vs],
"Final membrane potential versus injected current for: %s" %
nml2_file,
colors=['k'],
linestyles=['-'],
markers=['o'],
xaxis='Input current (pA)',
yaxis='Membrane potential (mV)',
xlim=xlim_iv,
ylim=ylim_iv,
grid=True,
show_plot_already=False,
save_figure_to=save_iv_figure_to)
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
return if_results
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)
def create_GoC(runid):
### ---------- Load Params
noPar = True
pfile = Path('cellparams_file.pkl')
keepFile = open('useParams_FI_14_25.pkl', 'rb')
runid = pkl.load(keepFile)[runid]
keepFile.close()
print('Running morphology for parameter set = ', runid)
if pfile.exists():
print('Reading parameters from file:')
file = open('cellparams_file.pkl', 'rb')
params_list = pkl.load(file)
if len(params_list) > runid:
p = params_list[runid]
file.close()
if noPar:
p = icp.get_channel_params(runid)
# Creating document for cell
gocID = 'Golgi_040408_C1_' + format(runid, '05d')
goc = nml.Cell(id=gocID) #--------simid
cell_doc = nml.NeuroMLDocument(id=gocID)
cell_doc.cells.append(goc)
### Load morphology
morpho_fname = 'Golgi_040408_C1.cell.nml'
morpho_file = pynml.read_neuroml2_file(morpho_fname)
morpho = morpho_file.cells[0].morphology
cell_doc.includes.append(nml.IncludeType(href=morpho_fname))
goc.morphology = morpho
### ---------- Channels
na_fname = 'Golgi_Na.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=na_fname))
nar_fname = 'Golgi_NaR.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=nar_fname))
nap_fname = 'Golgi_NaP.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=nap_fname))
ka_fname = 'Golgi_KA.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=ka_fname))
sk2_fname = 'Golgi_SK2.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=sk2_fname))
km_fname = 'Golgi_KM.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=km_fname))
kv_fname = 'Golgi_KV.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=kv_fname))
bk_fname = 'Golgi_BK.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=bk_fname))
cahva_fname = 'Golgi_CaHVA.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=cahva_fname))
calva_fname = 'Golgi_CaLVA.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=calva_fname))
hcn1f_fname = 'Golgi_HCN1f.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=hcn1f_fname))
hcn1s_fname = 'Golgi_HCN1s.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=hcn1s_fname))
hcn2f_fname = 'Golgi_HCN2f.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=hcn2f_fname))
hcn2s_fname = 'Golgi_HCN2s.channel.nml'
cell_doc.includes.append(nml.IncludeType(href=hcn2s_fname))
leak_fname = 'Golgi_lkg.channel.nml'
#leak_ref = nml.IncludeType( href=leak_fname)
cell_doc.includes.append(nml.IncludeType(href=leak_fname))
calc_fname = 'Golgi_CALC.nml'
cell_doc.includes.append(nml.IncludeType(href=calc_fname))
calc = pynml.read_neuroml2_file(
calc_fname).decaying_pool_concentration_models[0]
calc2_fname = 'Golgi_CALC2.nml'
cell_doc.includes.append(nml.IncludeType(href=calc2_fname))
goc_2pools_fname = 'GoC_2Pools.cell.nml'
### ------Biophysical Properties
biophys = nml.BiophysicalProperties(id='biophys_' + gocID)
goc.biophysical_properties = biophys
# Inproperties
'''
res = nml.Resistivity( p["ra"] ) # --------- "0.1 kohm_cm"
ca_species = nml.Species( id="ca", ion="ca", concentration_model=calc.id, initial_concentration ="5e-5 mM", initial_ext_concentration="2 mM" )
ca2_species = nml.Species( id="ca2", ion="ca2", concentration_model="Golgi_CALC2", initial_concentration ="5e-5 mM", initial_ext_concentration="2 mM" )
intracellular = nml.IntracellularProperties( )
intracellular.resistivities.append( res )
intracellular.species.append( ca_species )
'''
intracellular = pynml.read_neuroml2_file(goc_2pools_fname).cells[
0].biophysical_properties.intracellular_properties
biophys.intracellular_properties = intracellular
# Membrane properties ------- cond
memb = nml.MembraneProperties()
biophys.membrane_properties = memb
#pynml.read_neuroml2_file(leak_fname).ion_channel[0].id -> can't read ion channel passive
chan_leak = nml.ChannelDensity(ion_channel="LeakConductance",
cond_density=p["leak_cond"],
erev="-55 mV",
ion="non_specific",
id="Leak")
memb.channel_densities.append(chan_leak)
chan_na = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(na_fname).ion_channel[0].id,
cond_density=p["na_cond"],
erev="87.39 mV",
ion="na",
id="Golgi_Na_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_na)
chan_nap = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(nap_fname).ion_channel[0].id,
cond_density=p["nap_cond"],
erev="87.39 mV",
ion="na",
id="Golgi_NaP_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_nap)
chan_nar = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(nar_fname).ion_channel[0].id,
cond_density=p["nar_cond"],
erev="87.39 mV",
ion="na",
id="Golgi_NaR_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_nar)
chan_ka = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(ka_fname).ion_channel[0].id,
cond_density=p["ka_cond"],
erev="-84.69 mV",
ion="k",
id="Golgi_KA_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_ka)
chan_sk = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(sk2_fname).ion_channel_kses[0].id,
cond_density=p["sk2_cond"],
erev="-84.69 mV",
ion="k",
id="Golgi_KAHP_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_sk)
chan_kv = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(kv_fname).ion_channel[0].id,
cond_density=p["kv_cond"],
erev="-84.69 mV",
ion="k",
id="Golgi_KV_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_kv)
chan_km = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(km_fname).ion_channel[0].id,
cond_density=p["km_cond"],
erev="-84.69 mV",
ion="k",
id="Golgi_KM_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_km)
chan_bk = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(bk_fname).ion_channel[0].id,
cond_density=p["bk_cond"],
erev="-84.69 mV",
ion="k",
id="Golgi_BK_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_bk)
chan_h1f = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(hcn1f_fname).ion_channel[0].id,
cond_density=p["hcn1f_cond"],
erev="-20 mV",
ion="h",
id="Golgi_hcn1f_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_h1f)
chan_h1s = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(hcn1s_fname).ion_channel[0].id,
cond_density=p["hcn1s_cond"],
erev="-20 mV",
ion="h",
id="Golgi_hcn1s_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_h1s)
chan_h2f = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(hcn2f_fname).ion_channel[0].id,
cond_density=p["hcn2f_cond"],
erev="-20 mV",
ion="h",
id="Golgi_hcn2f_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_h2f)
chan_h2s = nml.ChannelDensity(
ion_channel=pynml.read_neuroml2_file(hcn2s_fname).ion_channel[0].id,
cond_density=p["hcn2s_cond"],
erev="-20 mV",
ion="h",
id="Golgi_hcn2s_soma_group",
segment_groups="soma_group")
memb.channel_densities.append(chan_h2s)
chan_hva = nml.ChannelDensityNernst(
ion_channel=pynml.read_neuroml2_file(cahva_fname).ion_channel[0].id,
cond_density=p["cahva_cond"],
ion="ca",
id="Golgi_Ca_HVA_soma_group",
segment_groups="soma_group")
memb.channel_density_nernsts.append(chan_hva)
chan_lva = nml.ChannelDensityNernst(
ion_channel=pynml.read_neuroml2_file(calva_fname).ion_channel[0].id,
cond_density=p["calva_cond"],
ion="ca2",
id="Golgi_Ca_LVA_soma_group",
segment_groups="soma_group")
memb.channel_density_nernsts.append(chan_lva)
memb.spike_threshes.append(nml.SpikeThresh("0 mV"))
memb.specific_capacitances.append(
nml.SpecificCapacitance("1.0 uF_per_cm2"))
memb.init_memb_potentials.append(nml.InitMembPotential("-60 mV"))
goc_filename = '{}.cell.nml'.format(gocID)
pynml.write_neuroml2_file(cell_doc, goc_filename)
return True
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 generate_Vm_vs_time_plot(NML2_file,
cell_id,
# inj_amp_nA = 80,
# delay_ms = 20,
# inj_dur_ms = 0.5,
sim_dur_ms = 1000,
dt = 0.05,
temperature = "35",
spike_threshold_mV=0.,
plot_voltage_traces=False,
show_plot_already=True,
simulator="jNeuroML_NEURON",
include_included=True):
# simulation parameters
nogui = '-nogui' in sys.argv # Used to supress GUI in tests for Travis-CI
ref = "iMC1_cell_1_origin"
print_comment_v("Generating Vm(mV) vs Time(ms) plot for cell %s in %s using %s"% # (Inj %snA / %sms dur after %sms delay)"%
(cell_id, NML2_file, simulator))#, inj_amp_nA, inj_dur_ms, delay_ms))
sim_id = 'Vm_%s'%ref
duration = sim_dur_ms
ls = LEMSSimulation(sim_id, sim_dur_ms, dt)
ls.include_neuroml2_file(NML2_file, include_included=include_included)
ls.assign_simulation_target('network')
nml_doc = nml.NeuroMLDocument(id=cell_id)
nml_doc.includes.append(nml.IncludeType(href=NML2_file))
net = nml.Network(id="network", type='networkWithTemperature', temperature='%sdegC'%temperature)
nml_doc.networks.append(net)
#input_id = ("input_%s"%str(inj_amp_nA).replace('.','_'))
#pg = nml.PulseGenerator(id=input_id,
# delay="%sms"%delay_ms,
# duration='%sms'%inj_dur_ms,
# amplitude='%spA'%inj_amp_nA)
#nml_doc.pulse_generators.append(pg)
pop_id = 'single_cell'
pop = nml.Population(id=pop_id, component='iMC1_cell_1_origin', size=1, type="populationList")
inst = nml.Instance(id=0)
pop.instances.append(inst)
inst.location = nml.Location(x=0, y=0, z=0)
net.populations.append(pop)
# Add these to cells
#input_list = nml.InputList(id='il_%s'%input_id,
# component=pg.id,
# populations=pop_id)
#input = nml.Input(id='0', target='../hhpop/0/hhcell',
# destination="synapses")
#input_list.input.append(input)
#net.input_lists.append(input_list)
sim_file_name = '%s.sim.nml'%sim_id
pynml.write_neuroml2_file(nml_doc, sim_file_name)
ls.include_neuroml2_file(sim_file_name)
disp0 = 'Voltage_display'
ls.create_display(disp0,"Voltages", "-90", "50")
ls.add_line_to_display(disp0, "V", "hhpop/0/hhcell/v", scale='1mV')
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%sim_id)
ls.add_column_to_output_file(of0, "V", "hhpop/0/hhcell/v")
lems_file_name = ls.save_to_file()
if simulator == "jNeuroML":
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
elif simulator == "jNeuroML_NEURON":
results = pynml.run_lems_with_jneuroml_neuron(lems_file_name,
nogui=True,
load_saved_data=True,
plot=plot_voltage_traces,
show_plot_already=False)
if show_plot_already:
from matplotlib import pyplot as plt
plt.show()
#plt.plot("t","V")
#plt.title("Vm(mV) vs Time(ms) plot for cell %s in %s using %s (Inj %snA / %sms dur after %sms delay)"%
# (cell_id, nml2_file, simulator, inj_amp_nA, inj_dur_ms, delay_ms))
#plt.xlabel('Time (ms)')
#plt.ylabel('Vmemb (mV)')
#plt.legend(['Test'], loc='upper right')
return of0
def create_cell():
"""Create the cell.
:returns: name of the cell nml file
"""
# Create the nml file and add the ion channels
hh_cell_doc = NeuroMLDocument(id="cell", notes="HH cell")
hh_cell_fn = "HH_example_cell.nml"
hh_cell_doc.includes.append(IncludeType(href=create_na_channel()))
hh_cell_doc.includes.append(IncludeType(href=create_k_channel()))
hh_cell_doc.includes.append(IncludeType(href=create_leak_channel()))
# Define a cell
hh_cell = Cell(id="hh_cell", notes="A single compartment HH cell")
# Define its biophysical properties
bio_prop = BiophysicalProperties(id="hh_b_prop")
# notes="Biophysical properties for HH cell")
# Membrane properties are a type of biophysical properties
mem_prop = MembraneProperties()
# Add membrane properties to the biophysical properties
bio_prop.membrane_properties = mem_prop
# Append to cell
hh_cell.biophysical_properties = bio_prop
# Channel density for Na channel
na_channel_density = ChannelDensity(id="na_channels", cond_density="120.0 mS_per_cm2", erev="50.0 mV", ion="na", ion_channel="na_channel")
mem_prop.channel_densities.append(na_channel_density)
# Channel density for k channel
k_channel_density = ChannelDensity(id="k_channels", cond_density="360 S_per_m2", erev="-77mV", ion="k", ion_channel="k_channel")
mem_prop.channel_densities.append(k_channel_density)
# Leak channel
leak_channel_density = ChannelDensity(id="leak_channels", cond_density="3.0 S_per_m2", erev="-54.3mV", ion="non_specific", ion_channel="leak_channel")
mem_prop.channel_densities.append(leak_channel_density)
# Other membrane properties
mem_prop.spike_threshes.append(SpikeThresh(value="-20mV"))
mem_prop.specific_capacitances.append(SpecificCapacitance(value="1.0 uF_per_cm2"))
mem_prop.init_memb_potentials.append(InitMembPotential(value="-65mV"))
intra_prop = IntracellularProperties()
intra_prop.resistivities.append(Resistivity(value="0.03 kohm_cm"))
# Add to biological properties
bio_prop.intracellular_properties = intra_prop
# Morphology
morph = Morphology(id="hh_cell_morph")
# notes="Simple morphology for the HH cell")
seg = Segment(id="0", name="soma", notes="Soma segment")
# We want a diameter such that area is 1000 micro meter^2
# surface area of a sphere is 4pi r^2 = 4pi diam^2
diam = math.sqrt(1000 / math.pi)
proximal = distal = Point3DWithDiam(x="0", y="0", z="0", diameter=str(diam))
seg.proximal = proximal
seg.distal = distal
morph.segments.append(seg)
hh_cell.morphology = morph
hh_cell_doc.cells.append(hh_cell)
pynml.write_neuroml2_file(nml2_doc=hh_cell_doc, nml2_file_name=hh_cell_fn, validate=True)
return hh_cell_fn
def __main__():
import customsim
import modeldata
MCs = 1
GCsPerMC = 1
networkTemplate = FileTemplate("../NeuroML2/Networks/NetworkTemplate.xml")
includeTemplate = FileTemplate("../NeuroML2/Networks/IncludeTemplate.xml")
populationTemplate = FileTemplate("../NeuroML2/Networks/PopulationTemplate.xml")
projectionTemplate = FileTemplate("../NeuroML2/Networks/ProjectionTemplate.xml")
customsim.setup(MCs, GCsPerMC)
model = modeldata.getmodel()
netFile = "../NeuroML2/Networks/Bulb_%iMC_%iGC.net.nml" % (len(model.mitral_gids), len(model.granule_gids))
includes = ""
populations = ""
projections = ""
mcNMLs = {}
gcNMLs = {}
#import pydevd
#pydevd.settrace('10.211.55.3', port=4200, stdoutToServer=True, stderrToServer=True)
# Make MC includes and populations
for mcgid in model.mitral_gids:
includes += includeTemplate.text\
.replace("[CellType]", "Mitral")\
.replace("[GID]", `mcgid`)
populations += populationTemplate.text\
.replace("[CellType]", "Mitral")\
.replace("[GID]", `mcgid`)\
.replace("[X]", `model.mitrals[mcgid].x`)\
.replace("[Y]", `model.mitrals[mcgid].y`)\
.replace("[Z]", `model.mitrals[mcgid].z`)
# Retain mitral cell NML
mcNML = pynml\
.read_neuroml2_file("../NeuroML2/MitralCells/Exported/Mitral_0_%i.cell.nml" % mcgid)\
.cells[0]
mcNMLs.update({mcgid:mcNML})
# Make GC includes and populations
import granules
from neuroml.nml.nml import NeuroMLDocument
for gcgid in model.granule_gids:
includes += includeTemplate.text\
.replace("[CellType]", "Granule")\
.replace("[GID]", `gcgid`)
populations += populationTemplate.text\
.replace("[CellType]", "Granule")\
.replace("[GID]", `gcgid`)\
.replace("[X]", `granules.gid2pos[gcgid][0]`)\
.replace("[Y]", `granules.gid2pos[gcgid][1]`)\
.replace("[Z]", `granules.gid2pos[gcgid][2]`)
# Retain granule cell NML
gcNML = pynml\
.read_neuroml2_file("../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml" % gcgid)\
gcNMLs.update({gcgid:gcNML})
# Add a projection for each synapse
synCount = len(model.mgrss.keys())
curSyn = 0
for sgid in model.mgrss.keys():
print('Building synapse %i of %i' % (curSyn+1,synCount))
synapse = model.mgrss[sgid]
nsecden = model.mitrals[synapse.mgid].secden[synapse.isec].nseg
secdenIndex = min(nsecden-1, int(synapse.xm * nsecden))
postSegmentId = [seg.id\
for seg in mcNMLs[synapse.mgid].morphology.segments\
if seg.name == "Seg%i_secden_%i"%(secdenIndex,synapse.isec)\
][0]
gcNML = gcNMLs[synapse.ggid].cells[0]
# Position the spine along the GC priden
import exportHelper
exportHelper.splitSegmentAlongFraction(gcNML,"Seg0_priden2_0","priden2_0",synapse.xg,'Seg0_neck')
pynml.write_neuroml2_file(gcNMLs[synapse.ggid], "../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml" % synapse.ggid)
# Add Dendro-dendritic synapses
# GC -> MC part
projections += projectionTemplate.text\
.replace("[ProjectionID]", `sgid`+'_G2M')\
.replace("[PreCellType]", "Granule")\
.replace("[PreGID]", `synapse.ggid`)\
.replace("[PreSegment]", `4`)\
.replace("[PreAlong]", `0.5`)\
.replace("[PostCellType]", "Mitral")\
.replace("[PostGID]", `synapse.mgid`)\
.replace("[PostSegment]", `postSegmentId`)\
.replace("[PostAlong]", "0.5")\
.replace("[Synapse]", "FIsyn")\
# MC -> GC part
projections += projectionTemplate.text\
.replace("[ProjectionID]", `sgid`+'_M2G')\
.replace("[PreCellType]", "Mitral")\
.replace("[PreGID]", `synapse.mgid`)\
.replace("[PreSegment]", `postSegmentId`)\
.replace("[PreAlong]", `0.5`)\
.replace("[PostCellType]", "Granule")\
.replace("[PostGID]", `synapse.ggid`)\
.replace("[PostSegment]", `4`)\
.replace("[PostAlong]", "0.5")\
.replace("[Synapse]", "AmpaNmdaSyn")\
curSyn += 1
network = networkTemplate.text\
.replace("[IncludesPlaceholder]", includes)\
.replace("[PopulationsPlaceholder]", populations)\
.replace("[ProjectionsPlaceholder]", projections)
with open(netFile, "w") as file:
file.write(network)
print('Net file saved to: ' + netFile)
def exportToNML(cells):
'''
GCs only vary these parameters:
id
priden: length (through y position of distal pt), number of subdivitions (nseg)
spine neck: location on parent priden2
'''
print("Exporting " + str(len(cells)) + "GCs...")
exported = []
for gid in cells.keys():
print("Exporting GC:" + ` gid ` + "...")
# Obtain the varying values from the network model
pridenLength = cells[gid].priden.L
pridenNseg = cells[gid].priden.nseg
neckLoc = cells[gid].priden2[0].children()[0].parentseg().x
# Read the cell template
from pyneuroml import pynml
nmldoc = pynml.read_neuroml2_file(
"../NeuroML2/GranuleCells/GranuleCellTemplate.xml")
cell = nmldoc.cells[0]
# Replace placeholders
cell.id = "Granule_0_" + str(gid)
next(seg for seg in cell.morphology.segments
if seg.name == 'priden_seg').distal.y = pridenLength
next(prop
for prop in next(section
for section in cell.morphology.segment_groups
if section.id == 'priden').properties
if prop.tag == 'numberInternalDivisions').value = pridenNseg
next(seg for seg in cell.morphology.segments
if seg.name == 'neck_seg').parent.fraction_along = neckLoc
# Align the GC along the bulb versor
import granules
versor = granules.granule_position_orientation(gid)[1]
for seg in cell.morphology.segments:
segLength = seg.length # retain the original length
if seg.parent is not None:
parent = next(parent for parent in cell.morphology.segments
if parent.id == seg.parent.segments)
seg.proximal.x = parent.distal.x
seg.proximal.y = parent.distal.y
seg.proximal.z = parent.distal.z
seg.distal = setAlongVersor(seg.distal, versor, seg.proximal,
segLength)
# Save the file
cellFile = "../NeuroML2/GranuleCells/Exported/Granule_0_" + str(
gid) + ".cell.nml"
pynml.write_neuroml2_file(nmldoc, cellFile)
exported.append(cellFile)
return exported