def create_test_goc1():
simid = 'sim_goc2Pools'
ls = LEMSSimulation(simid, duration=1500, dt=0.025, target='network')
#Load NeuroML components
GoC_file_name = 'GoC.cell.nml'
ls.include_neuroml2_file(GoC_file_name)
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % simid)
ls.add_column_to_output_file(of0, 'v', "Golgi[0]/v")
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes" % simid, format='ID_TIME')
ls.add_selection_to_event_output_file(eof0, '0', "Golgi[0]", "spike")
#Create Lems file to run
lems_simfile = ls.save_to_file()
#res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="1G", compile_mods =False, nogui=True, plot=False)
res = pynml.run_lems_with_jneuroml_neuron(lems_simfile,
max_memory="2G",
nogui=True,
plot=False)
return res
'''
from pyneuroml.lems import LEMSSimulation
from pyneuroml.lems import generate_lems_file_for_neuroml
import os
import sys
import pprint; pp = pprint.PrettyPrinter(depth=6)
if __name__ == '__main__':
############################################
### Create a LEMS file "manually"...
sim_id = 'HHSim'
ls = LEMSSimulation(sim_id, 500, 0.05, 'net1')
ls.include_neuroml2_file('NML2_SingleCompHHCell.nml')
disp0 = 'display0'
ls.create_display(disp0,"Voltages", "-90", "50")
ls.add_line_to_display(disp0, "v", "hhpop[0]/v", "1mV", "#ffffff")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%sim_id)
ls.add_column_to_output_file(of0, 'v', "hhpop[0]/v")
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes"%sim_id,format='ID_TIME')
ls.add_selection_to_event_output_file(eof0, '0', "hhpop[0]", "spike")
from pyneuroml.lems import LEMSSimulation
from pyneuroml.lems import generate_lems_file_for_neuroml
import os
import sys
import pprint
pp = pprint.PrettyPrinter(depth=6)
if __name__ == '__main__':
############################################
### Create a LEMS file "manually"...
sim_id = 'HHSim'
ls = LEMSSimulation(sim_id, 500, 0.05, 'net1')
ls.include_neuroml2_file('NML2_SingleCompHHCell.nml')
disp0 = 'display0'
ls.create_display(disp0, "Voltages", "-90", "50")
ls.add_line_to_display(disp0, "v", "hhpop[0]/v", "1mV", "#ffffff")
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat" % sim_id)
ls.add_column_to_output_file(of0, 'v', "hhpop[0]/v")
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes" % sim_id, format='ID_TIME')
ls.add_selection_to_event_output_file(eof0, '0', "hhpop[0]", "spike")
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 main():
"""Main function
Include the NeuroML model into a LEMS simulation file, run it, plot some
data.
"""
# Simulation bits
sim_id = "HH_single_compartment_example_sim"
simulation = LEMSSimulation(sim_id=sim_id, duration=300, dt=0.01, simulation_seed=123)
# Include the NeuroML model file
simulation.include_neuroml2_file(create_network())
# Assign target for the simulation
simulation.assign_simulation_target("single_hh_cell_network")
# Recording information from the simulation
simulation.create_output_file(id="output0", file_name=sim_id + ".dat")
simulation.add_column_to_output_file("output0", column_id="pop0[0]/v", quantity="pop0[0]/v")
simulation.add_column_to_output_file("output0", column_id="pop0[0]/iChannels", quantity="pop0[0]/iChannels")
simulation.add_column_to_output_file("output0", column_id="pop0[0]/na/iDensity", quantity="pop0[0]/hh_b_prop/membraneProperties/na_channels/iDensity/")
simulation.add_column_to_output_file("output0", column_id="pop0[0]/k/iDensity", quantity="pop0[0]/hh_b_prop/membraneProperties/k_channels/iDensity/")
# Save LEMS simulation to file
sim_file = simulation.save_to_file()
# Run the simulation using the default jNeuroML simulator
pynml.run_lems_with_jneuroml(sim_file, max_memory="2G", nogui=True, plot=False)
# Plot the data
plot_data(sim_id)
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
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_test_goc1(): simid = 'sim_goc1' ls = LEMSSimulation( simid, duration=150, dt=0.025, target='net1' ) #Load NeuroML components GoC_file_name = 'test_channel.cell.nml'#'Golgi.cell.nml'#'test_channel.cell.nml' #'simple_cell.cell.nml' #Cell_Golgi.cell.nml ls.include_neuroml2_file( GoC_file_name ) disp0 = 'dispaly0' ls.create_display(disp0, "Voltage", "-90", "50" ) ls.add_line_to_display(disp0, "v", "gocpop[0]/v", "1mV", "#ffffff") of0 = 'Volts_file' ls.create_output_file(of0, "%s.v.dat"%simid) ls.add_column_to_output_file(of0, 'v', "gocpop[0]/v") #of1 = 'Na_file' #ls.create_output_file(of1, "%s.na.dat"%simid) #ls.add_column_to_output_file(of1, '0', "gocpop[0]/ina") eof0 = 'Events_file' ls.create_event_output_file(eof0, "%s.v.spikes"%simid,format='ID_TIME') ls.add_selection_to_event_output_file(eof0, '0', "gocpop[0]", "spike") #Create Lems file to run lems_simfile = ls.save_to_file() #res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="1G", compile_mods =False, nogui=True, plot=False) res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="1G", nogui=True, plot=False) #res=True return res
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 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
syn = Connection(id=count,
pre_cell_id="../%s[%i]" % (pop0.id, pre),
synapse=syn0.id,
post_cell_id="../%s[%i]" % (pop1.id, post))
proj.connections.append(syn)
count += 1
nml_file = 'izhikevich2007_network.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Written network file to: " + nml_file)
pynml.validate_neuroml2(nml_file)
simulation_id = "example_izhikevich2007network_sim"
simulation = LEMSSimulation(sim_id=simulation_id,
duration=1000,
dt=0.1,
simulation_seed=123)
simulation.assign_simulation_target(net.id)
simulation.include_neuroml2_file(nml_file)
simulation.create_event_output_file("pop0",
"%s.0.spikes.dat" % simulation_id,
format='ID_TIME')
for pre in range(0, size0):
simulation.add_selection_to_event_output_file("pop0", pre,
'IzPop0[{}]'.format(pre),
'spike')
simulation.create_event_output_file("pop1",
"%s.1.spikes.dat" % simulation_id,
format='ID_TIME')
from pyneuroml.lems import LEMSSimulation
ls = LEMSSimulation('sim1', 500, 0.05, 'net1')
ls.include_neuroml2_file('NML2_SingleCompHHCell.nml')
ls.create_display('display0', "Voltages", "-90", "50")
ls.add_line_to_display('display0', "v", "hhpop[0]/v", "1mV", "#ffffff")
ls.create_output_file('Volts_file', "v.dat")
ls.add_column_to_output_file('Volts_file', 'v', "hhpop[0]/v")
ls.save_to_file()
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 main():
"""Main function
Include the NeuroML model into a LEMS simulation file, run it, plot some
data.
"""
# Simulation bits
sim_id = "olm_example_sim"
simulation = LEMSSimulation(sim_id=sim_id,
duration=600,
dt=0.01,
simulation_seed=123)
# Include the NeuroML model file
simulation.include_neuroml2_file(create_olm_network())
# Assign target for the simulation
simulation.assign_simulation_target("single_olm_cell_network")
# Recording information from the simulation
simulation.create_output_file(id="output0", file_name=sim_id + ".dat")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v",
quantity="pop0[0]/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg0_soma_0",
quantity="pop0/0/olm/0/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg1_soma_0",
quantity="pop0/0/olm/1/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg0_axon_0",
quantity="pop0/0/olm/2/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg1_axon_0",
quantity="pop0/0/olm/3/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg0_dend_0",
quantity="pop0/0/olm/4/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg1_dend_0",
quantity="pop0/0/olm/6/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg0_dend_1",
quantity="pop0/0/olm/5/v")
simulation.add_column_to_output_file("output0",
column_id="pop0_0_v_Seg1_dend_1",
quantity="pop0/0/olm/7/v")
# Save LEMS simulation to file
sim_file = simulation.save_to_file()
# Run the simulation using the NEURON simulator
pynml.run_lems_with_jneuroml_neuron(sim_file,
max_memory="2G",
nogui=True,
plot=False,
skip_run=False)
# Plot the data
plot_data(sim_id)
# Write the NeuroML model to a file
nml_file = 'izhikevich2007_single_cell_network.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Written network file to: " + nml_file)
# Validate the NeuroML model against the NeuroML schema
validate_neuroml2(nml_file)
################################################################################
## The NeuroML file has now been created and validated. The rest of the code
## involves writing a LEMS simulation file to run the model
# Create a simulation instance of the model
simulation_id = "example-single-izhikevich2007cell-sim"
simulation = LEMSSimulation(sim_id=simulation_id,
duration=1000, dt=0.1, simulation_seed=123)
simulation.assign_simulation_target(net.id)
simulation.include_neuroml2_file(nml_file)
# Define the output file to store simulation outputs
# we record the neuron's membrane potential
simulation.create_output_file(
"output0", "%s.v.dat" % simulation_id
)
simulation.add_column_to_output_file("output0", 'IzhPop0[0]', 'IzhPop0[0]/v')
# Save the simulation to a file
lems_simulation_file = simulation.save_to_file()
# Run the simulation using the jNeuroML simulator
pynml.run_lems_with_jneuroml(