def generate(cell_id, duration, reference, iRange):
cell_file = '%s.cell.nml'%cell_id
#cell_id = 'Cell0'
#cell_file = 'L23_morph.cell.nml'
nml_doc, network = oc.generate_network(reference, temperature='35degC')
oc.include_neuroml2_cell_and_channels(nml_doc,cell_file,cell_id)
population_size = len(iRange)
pop = oc.add_population_in_rectangular_region(network,
'L23_pop',
cell_id,
population_size,
0,0,0,
1000,100,1000)
for i in range(iRange.size):
stim_id = ("Stim_%i"%i)
pg = oc.add_pulse_generator(nml_doc,
id=stim_id,
delay="50ms",
duration="250ms",
amplitude="%fnA"%iRange[i])
oc.add_inputs_to_population(network,
stim_id,
pop,
pg.id,
all_cells=False,
only_cells=[i])
nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc, nml_file_name, validate=True)
oc.generate_lems_simulation(nml_doc,
network,
nml_file_name,
duration,
dt = 0.025)
def generate(reference = "DendConn",
num_pyr = 4,
num_bask = 0,
scalex=1,
scaley=1,
scalez=1,
connections=True,
global_delay = 0,
duration = 500,
segments_to_plot_record = {'pop_pyr':[0],'pop_bask':[0]},
format='xml'):
nml_doc, network = oc.generate_network(reference)
oc.include_opencortex_cell(nml_doc, 'acnet2/pyr_4_sym.cell.nml')
oc.include_opencortex_cell(nml_doc, 'acnet2/bask.cell.nml')
xDim = 500*scalex
yDim = 50*scaley
zDim = 500*scalez
pop_pyr = oc.add_population_in_rectangular_region(network, 'pop_pyr',
'pyr_4_sym', num_pyr,
0,0,0, xDim,yDim,zDim)
pop_bask = oc.add_population_in_rectangular_region(network, 'pop_bask',
'bask', num_bask,
0,yDim,0, xDim,yDim+yDim,zDim)
ampa_syn = oc.add_exp_two_syn(nml_doc, id="AMPA_syn",
gbase="30e-9S", erev="0mV",
tau_rise="0.003s", tau_decay="0.0031s")
pg = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="10ms",
duration="300ms",
amplitude="0.7nA")
total_conns = 0
if connections:
this_syn=ampa_syn.id
proj = oc.add_targeted_projection(network,
"Proj_pyr_pyr",
pop_pyr,
pop_pyr,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'dendrite_group',
number_conns_per_cell=1,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
oc.add_targeted_inputs_to_population(network, "Stim0",
pop_pyr, pg.id,
segment_group='soma_group',
number_per_cell = 1,
all_cells=False,
only_cells=[0])
nml_file_name = '%s.net.%s'%(network.id,'nml.h5' if format == 'hdf5' else 'nml')
target_dir = 'HDF5/' if format == 'hdf5' else './'
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format,
target_dir=target_dir)
gen_plots_for_quantities = {} # Dict with displays vs lists of quantity paths
gen_saves_for_quantities = {} # Dict with file names vs lists of quantity paths
for pop in segments_to_plot_record.keys():
pop_nml = network.get_by_id(pop)
if pop_nml is not None and pop_nml.size>0:
group = len(segments_to_plot_record[pop]) == 1
if group:
display = 'Display_%s_v'%(pop)
file_ = 'Sim_%s.%s.v.dat'%(nml_doc.id,pop)
gen_plots_for_quantities[display] = []
gen_saves_for_quantities[file_] = []
for i in range(int(pop_nml.size)):
if not group:
display = 'Display_%s_%i_v'%(pop,i)
file_ = 'Sim_%s.%s.%i.v.dat'%(nml_doc.id,pop,i)
gen_plots_for_quantities[display] = []
gen_saves_for_quantities[file_] = []
for seg in segments_to_plot_record[pop]:
quantity = '%s/%i/%s/%i/v'%(pop,i,pop_nml.component,seg)
gen_plots_for_quantities[display].append(quantity)
gen_saves_for_quantities[file_].append(quantity)
lems_file_name = oc.generate_lems_simulation(nml_doc,
network,
target_dir+nml_file_name,
duration = duration,
dt = 0.025,
gen_plots_for_all_v = False,
gen_plots_for_quantities = gen_plots_for_quantities,
gen_saves_for_all_v = False,
gen_saves_for_quantities = gen_saves_for_quantities,
target_dir=target_dir)
return nml_doc, nml_file_name, lems_file_name
def generate(reference = "Balanced",
scalePops = 1,
scalex=1,
scaley=1,
scalez=1,
connections=True,
connections_scaling=1,
duration = 1000,
global_delay = 2,
max_in_pop_to_plot_and_save = 5,
gen_spike_saves_for_all_somas = True,
deterministic = True,
format='xml'):
num_exc = scale_pop_size(80,scalePops)
num_inh = scale_pop_size(40,scalePops)
if scalePops!=1:
reference += '_%s'%scalePops
nml_doc, network = oc.generate_network(reference)
oc.include_opencortex_cell(nml_doc, 'AllenInstituteCellTypesDB_HH/HH_477127614.cell.nml')
oc.include_opencortex_cell(nml_doc, 'AllenInstituteCellTypesDB_HH/HH_476686112.cell.nml')
xDim = 400*scalex
yDim = 500*scaley
zDim = 300*scalez
xs = -200
ys = -150
zs = 100
##### Synapses
synAmpa1 = oc.add_exp_two_syn(nml_doc, id="synAmpa1", gbase="1nS",
erev="0mV", tau_rise="0.5ms", tau_decay="5ms")
synGaba1 = oc.add_exp_two_syn(nml_doc, id="synGaba1", gbase="2nS",
erev="-80mV", tau_rise="1ms", tau_decay="20ms")
##### Input types
if not deterministic:
pfs1 = oc.add_poisson_firing_synapse(nml_doc,
id="psf1",
average_rate="150 Hz",
synapse_id=synAmpa1.id)
##### Populations
popExc = oc.add_population_in_rectangular_region(network,
'popExc',
'HH_477127614',
num_exc,
xs,ys,zs,
xDim,yDim,zDim)
popInh = oc.add_population_in_rectangular_region(network,
'popInh',
'HH_476686112',
num_inh,
xs,ys,zs,
xDim,yDim,zDim)
##### Projections
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network, "proj0",
popExc, popExc,
synAmpa1.id, connections_scaling*0.3, delay = global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network, "proj1",
popExc, popInh,
synAmpa1.id, connections_scaling*0.5, delay = global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network, "proj3",
popInh, popExc,
synGaba1.id, connections_scaling*0.7, delay = global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network, "proj4",
popInh, popInh,
synGaba1.id, connections_scaling*0.5, delay = global_delay)
total_conns += len(proj.connection_wds)
##### Inputs
if not deterministic:
oc.add_inputs_to_population(network, "Stim0",
popExc, pfs1.id,
all_cells=True)
else:
for i in range(num_exc):
pg = oc.add_pulse_generator(nml_doc,
id="pg_%i"%i,
delay="0ms",
duration="10000ms",
amplitude="%snA"%(random()*0.5))
oc.add_inputs_to_population(network, "Stim_%i"%i,
popExc, pg.id,
all_cells=False,
only_cells=[i])
##### Save NeuroML and LEMS Simulation files
nml_file_name = '%s%s.net.%s'%('XH_' if format == 'xml_hdf5' else '', network.id,'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format)
plot_v = {popExc.id:[],popInh.id:[]}
save_v = {'%s_v.dat'%popExc.id:[],'%s_v.dat'%popInh.id:[]}
for i in range(min(max_in_pop_to_plot_and_save,num_exc)):
plot_v[popExc.id].append("%s/%i/%s/v"%(popExc.id,i,popExc.component))
save_v['%s_v.dat'%popExc.id].append("%s/%i/%s/v"%(popExc.id,i,popExc.component))
for i in range(min(max_in_pop_to_plot_and_save,num_inh)):
plot_v[popInh.id].append("%s/%i/%s/v"%(popInh.id,i,popInh.component))
save_v['%s_v.dat'%popInh.id].append("%s/%i/%s/v"%(popInh.id,i,popInh.component))
lems_file_name = "LEMS_%s.xml"%network.id
if format != 'xml':
lems_file_name = "LEMS_%s_%s.xml"%(network.id,format)
lems_file_name = oc.generate_lems_simulation(nml_doc, network,
nml_file_name,
duration = duration,
dt = 0.025,
gen_plots_for_all_v = False,
gen_plots_for_quantities = plot_v,
gen_saves_for_all_v = False,
gen_saves_for_quantities = save_v,
gen_spike_saves_for_all_somas = gen_spike_saves_for_all_somas,
lems_file_name = lems_file_name)
return nml_doc, nml_file_name, lems_file_name
def generate(reference="DendConn",
num_pyr=4,
num_bask=0,
scalex=1,
scaley=1,
scalez=1,
connections=True,
global_delay=0,
duration=500,
segments_to_plot_record={
'pop_pyr': [0],
'pop_bask': [0]
},
format='xml'):
nml_doc, network = oc.generate_network(reference)
oc.include_opencortex_cell(nml_doc, 'acnet2/pyr_4_sym.cell.nml')
oc.include_opencortex_cell(nml_doc, 'acnet2/bask.cell.nml')
xDim = 500 * scalex
yDim = 50 * scaley
zDim = 500 * scalez
pop_pyr = oc.add_population_in_rectangular_region(network, 'pop_pyr',
'pyr_4_sym', num_pyr, 0,
0, 0, xDim, yDim, zDim)
pop_bask = oc.add_population_in_rectangular_region(network, 'pop_bask',
'bask', num_bask, 0,
yDim, 0, xDim,
yDim + yDim, zDim)
ampa_syn = oc.add_exp_two_syn(nml_doc,
id="AMPA_syn",
gbase="30e-9S",
erev="0mV",
tau_rise="0.003s",
tau_decay="0.0031s")
pg = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="10ms",
duration="300ms",
amplitude="0.7nA")
total_conns = 0
if connections:
this_syn = ampa_syn.id
proj = oc.add_targeted_projection(network,
"Proj_pyr_pyr",
pop_pyr,
pop_pyr,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group='soma_group',
post_segment_group='dendrite_group',
number_conns_per_cell=1,
delays_dict={this_syn: global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
oc.add_targeted_inputs_to_population(network,
"Stim0",
pop_pyr,
pg.id,
segment_group='soma_group',
number_per_cell=1,
all_cells=False,
only_cells=[0])
nml_file_name = '%s.net.%s' % (network.id,
'nml.h5' if format == 'hdf5' else 'nml')
target_dir = 'HDF5/' if format == 'hdf5' else './'
oc.save_network(nml_doc,
nml_file_name,
validate=(format == 'xml'),
format=format,
target_dir=target_dir)
gen_plots_for_quantities = {
} # Dict with displays vs lists of quantity paths
gen_saves_for_quantities = {
} # Dict with file names vs lists of quantity paths
for pop in segments_to_plot_record.keys():
pop_nml = network.get_by_id(pop)
if pop_nml is not None and pop_nml.size > 0:
group = len(segments_to_plot_record[pop]) == 1
if group:
display = 'Display_%s_v' % (pop)
file_ = 'Sim_%s.%s.v.dat' % (nml_doc.id, pop)
gen_plots_for_quantities[display] = []
gen_saves_for_quantities[file_] = []
for i in range(int(pop_nml.size)):
if not group:
display = 'Display_%s_%i_v' % (pop, i)
file_ = 'Sim_%s.%s.%i.v.dat' % (nml_doc.id, pop, i)
gen_plots_for_quantities[display] = []
gen_saves_for_quantities[file_] = []
for seg in segments_to_plot_record[pop]:
quantity = '%s/%i/%s/%i/v' % (pop, i, pop_nml.component,
seg)
gen_plots_for_quantities[display].append(quantity)
gen_saves_for_quantities[file_].append(quantity)
lems_file_name = oc.generate_lems_simulation(
nml_doc,
network,
target_dir + nml_file_name,
duration=duration,
dt=0.025,
gen_plots_for_all_v=False,
gen_plots_for_quantities=gen_plots_for_quantities,
gen_saves_for_all_v=False,
gen_saves_for_quantities=gen_saves_for_quantities,
target_dir=target_dir)
return nml_doc, nml_file_name, lems_file_name
popIzh = oc.add_single_cell_population(network, 'popIzh', 'RS')
popHH = oc.add_single_cell_population(network,
'popHH',
'pyr_4_sym_soma',
z=100)
'''
popBBP = oc.add_single_cell_population(network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
z=200)'''
pgIzh = oc.add_pulse_generator(nml_doc,
id="pgIzh",
delay="100ms",
duration="300ms",
amplitude="0.5nA")
pgHH = oc.add_pulse_generator(nml_doc,
id="pgHH",
delay="100ms",
duration="300ms",
amplitude="0.7nA")
'''
pgBBP = oc.add_pulse_generator(nml_doc,
id="pgBBP",
delay="100ms",
duration="300ms",
amplitude="0.7nA")'''
def generate(reference="Balanced",
scalePops=1,
scalex=1,
scaley=1,
scalez=1,
connections=True,
connections_scaling=1,
duration=1000,
global_delay=2,
max_in_pop_to_plot_and_save=5,
gen_spike_saves_for_all_somas=True,
deterministic=True,
format='xml'):
num_exc = scale_pop_size(80, scalePops)
num_inh = scale_pop_size(40, scalePops)
if scalePops != 1:
reference += '_%s' % scalePops
nml_doc, network = oc.generate_network(reference)
oc.include_opencortex_cell(
nml_doc, 'AllenInstituteCellTypesDB_HH/HH_477127614.cell.nml')
oc.include_opencortex_cell(
nml_doc, 'AllenInstituteCellTypesDB_HH/HH_476686112.cell.nml')
xDim = 400 * scalex
yDim = 500 * scaley
zDim = 300 * scalez
xs = -200
ys = -150
zs = 100
##### Synapses
synAmpa1 = oc.add_exp_two_syn(nml_doc,
id="synAmpa1",
gbase="1nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="5ms")
synGaba1 = oc.add_exp_two_syn(nml_doc,
id="synGaba1",
gbase="2nS",
erev="-80mV",
tau_rise="1ms",
tau_decay="20ms")
##### Input types
if not deterministic:
pfs1 = oc.add_poisson_firing_synapse(nml_doc,
id="psf1",
average_rate="150 Hz",
synapse_id=synAmpa1.id)
##### Populations
popExc = oc.add_population_in_rectangular_region(network, 'popExc',
'HH_477127614', num_exc,
xs, ys, zs, xDim, yDim,
zDim)
popInh = oc.add_population_in_rectangular_region(network, 'popInh',
'HH_476686112', num_inh,
xs, ys, zs, xDim, yDim,
zDim)
##### Projections
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network,
"proj0",
popExc,
popExc,
synAmpa1.id,
connections_scaling * 0.3,
delay=global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network,
"proj1",
popExc,
popInh,
synAmpa1.id,
connections_scaling * 0.5,
delay=global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network,
"proj3",
popInh,
popExc,
synGaba1.id,
connections_scaling * 0.7,
delay=global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network,
"proj4",
popInh,
popInh,
synGaba1.id,
connections_scaling * 0.5,
delay=global_delay)
total_conns += len(proj.connection_wds)
##### Inputs
if not deterministic:
oc.add_inputs_to_population(network,
"Stim0",
popExc,
pfs1.id,
all_cells=True)
else:
for i in range(num_exc):
pg = oc.add_pulse_generator(nml_doc,
id="pg_%i" % i,
delay="0ms",
duration="10000ms",
amplitude="%snA" % (random() * 0.5))
oc.add_inputs_to_population(network,
"Stim_%i" % i,
popExc,
pg.id,
all_cells=False,
only_cells=[i])
##### Save NeuroML and LEMS Simulation files
nml_file_name = '%s%s.net.%s' % ('XH_' if format == 'xml_hdf5' else '',
network.id,
'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format == 'xml'),
format=format)
plot_v = {popExc.id: [], popInh.id: []}
save_v = {'%s_v.dat' % popExc.id: [], '%s_v.dat' % popInh.id: []}
for i in range(min(max_in_pop_to_plot_and_save, num_exc)):
plot_v[popExc.id].append("%s/%i/%s/v" %
(popExc.id, i, popExc.component))
save_v['%s_v.dat' % popExc.id].append("%s/%i/%s/v" %
(popExc.id, i, popExc.component))
for i in range(min(max_in_pop_to_plot_and_save, num_inh)):
plot_v[popInh.id].append("%s/%i/%s/v" %
(popInh.id, i, popInh.component))
save_v['%s_v.dat' % popInh.id].append("%s/%i/%s/v" %
(popInh.id, i, popInh.component))
lems_file_name = "LEMS_%s.xml" % network.id
if format != 'xml':
lems_file_name = "LEMS_%s_%s.xml" % (network.id, format)
lems_file_name = oc.generate_lems_simulation(
nml_doc,
network,
nml_file_name,
duration=duration,
dt=0.025,
gen_plots_for_all_v=False,
gen_plots_for_quantities=plot_v,
gen_saves_for_all_v=False,
gen_saves_for_quantities=save_v,
gen_spike_saves_for_all_somas=gen_spike_saves_for_all_somas,
lems_file_name=lems_file_name)
return nml_doc, nml_file_name, lems_file_name
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
synAmpa2 = oc.add_exp_two_syn(nml_doc,
id="synAmpa2",
gbase="0.5nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="5ms")
##### Input types
pg0 = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="10ms",
duration="300ms",
amplitude="0.3nA")
pg1 = oc.add_pulse_generator(nml_doc,
id="pg1",
delay="50ms",
duration="400ms",
amplitude="0.35nA")
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="150 Hz",
synapse_id=synAmpa2.id)
##### Populations
oc.include_neuroml2_file(nml_doc,'AMPA_NMDA.synapse.nml')
##### Input types
pfs100 = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn100",
average_rate="100 Hz",
synapse_id='AMPA_noplast')
pg0 = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="1000ms",
duration="1000ms",
amplitude="0.2nA")
##### Populations
pop0 = oc.add_population_in_rectangular_region(network,
'pop0',
'RS',
scale_pop_size(1),
0,offset,0,
xDim,yDim,zDim)
offset+=yDim
pop1 = oc.add_population_in_rectangular_region(network,
def generate(reference="L23TraubDemo",
num_rs=DEFAULT_RS_POP_SIZE,
num_bask=DEFAULT_BASK_POP_SIZE,
scalex=1,
scaley=1,
scalez=1,
connections=False,
poisson_inputs=True,
offset_curent_range_pA=None,
global_delay=0,
duration=300,
segments_to_plot_record={
'pop_rs': [0],
'pop_bask': [0]
},
format='xml'):
nml_doc, network = oc.generate_network(reference)
#oc.add_cell_and_channels(nml_doc, 'acnet2/pyr_4_sym.cell.nml','pyr_4_sym')
oc.include_opencortex_cell(nml_doc, 'Thalamocortical/L23PyrRS.cell.nml')
oc.include_opencortex_cell(nml_doc, 'Thalamocortical/SupBasket.cell.nml')
xDim = 500 * scalex
yDim = 200 * scaley
zDim = 500 * scalez
pop_rs = oc.add_population_in_rectangular_region(network, 'pop_rs',
'L23PyrRS', num_rs, 0, 0,
0, xDim, yDim, zDim)
pop_bask = oc.add_population_in_rectangular_region(network, 'pop_bask',
'SupBasket', num_bask,
0, 0, 0, xDim, yDim,
zDim)
syn0 = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="1nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
syn1 = oc.add_exp_two_syn(nml_doc,
id="syn1",
gbase="2nS",
erev="0mV",
tau_rise="1ms",
tau_decay="15ms")
if poisson_inputs:
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="150 Hz",
synapse_id=syn0.id)
oc.add_inputs_to_population(network,
"Stim0",
pop_rs,
pfs.id,
all_cells=True)
'''
oc.add_inputs_to_population(network,
"Stim1",
pop_bask,
pfs.id,
all_cells=True)'''
if offset_curent_range_pA:
for pop_id in offset_curent_range_pA:
pop = next(p for p in network.populations if p.id == pop_id)
pg0 = oc.add_pulse_generator(nml_doc,
id="offset_current_%s" % pop.id,
delay="0ms",
duration="%sms" % duration,
amplitude="1pA")
import neuroml
import random
input_list = neuroml.InputList(id="inputs_offset_current_%s" %
pop.id,
component=pg0.id,
populations=pop.id)
network.input_lists.append(input_list)
min_, max_ = offset_curent_range_pA[pop_id]
for i in range(pop.get_size()):
input = neuroml.InputW(
id=i,
target="../%s/%i/%s" % (pop.id, i, pop.component),
destination="synapses",
weight=(min_ + (max_ - min_) * random.random()))
input_list.input_ws.append(input)
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network,
"proj0",
pop_rs,
pop_bask,
syn1.id,
1,
weight=1,
delay=global_delay)
'''
proj = oc.add_targeted_projection(nml_doc,
network,
"proj0",
presynaptic_population = pop_rs,
postsynaptic_population = pop_bask,
targeting_mode = 'convergent',
synapse_list = [syn1.id],
number_conns_per_cell = 1,
pre_segment_group = 'axon_group',
post_segment_group = 'dendrite_group',
delays_dict = {syn1.id:2},
weights_dict = {syn1.id:1})'''
if proj:
total_conns += len(proj.connection_wds)
if num_rs != DEFAULT_RS_POP_SIZE or num_bask != DEFAULT_BASK_POP_SIZE:
new_reference = '%s_%scells_%sconns' % (nml_doc.id, num_rs + num_bask,
total_conns)
network.id = new_reference
nml_doc.id = new_reference
nml_file_name = '%s.net.%s' % (network.id,
'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format == 'xml'),
format=format)
if format == 'xml':
gen_plots_for_quantities = {
} # Dict with displays vs lists of quantity paths
gen_saves_for_quantities = {
} # Dict with file names vs lists of quantity paths
for pop in segments_to_plot_record.keys():
pop_nml = network.get_by_id(pop)
if pop_nml is not None and pop_nml.size > 0:
for i in range(int(pop_nml.size)):
gen_plots_for_quantities['Display_%s_%i_v' % (pop, i)] = []
gen_saves_for_quantities['Sim_%s.%s.%i.v.dat' %
(nml_doc.id, pop, i)] = []
for seg in segments_to_plot_record[pop]:
quantity = '%s/%i/%s/%i/v' % (pop, i,
pop_nml.component, seg)
gen_plots_for_quantities['Display_%s_%i_v' %
(pop, i)].append(quantity)
gen_saves_for_quantities['Sim_%s.%s.%i.v.dat' %
(nml_doc.id, pop,
i)].append(quantity)
lems_file_name = oc.generate_lems_simulation(
nml_doc,
network,
nml_file_name,
duration=duration,
dt=0.025,
gen_plots_for_all_v=False,
gen_plots_for_quantities=gen_plots_for_quantities,
gen_saves_for_all_v=False,
gen_saves_for_quantities=gen_saves_for_quantities)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
def generate(cell_id,
duration,
reference,
format='hdf5',
num_cells=10,
simulator=None):
#Insyn = int(Ensyn * 0.2)
#bInsyn = int(bEnsyn * 0.2)
cell_file = '../%s.cell.nml' % cell_id
if '/' in cell_id:
cell_id = cell_id.split('/')[-1]
nml_doc, network = oc.generate_network(reference, temperature='35degC')
oc.include_neuroml2_cell_and_channels(nml_doc, cell_file, cell_id)
pop = oc.add_population_in_rectangular_region(network, 'L23_pop', cell_id,
num_cells, 0, 0, 0, 100, 100,
100)
to_plot = {'Some_voltages': []}
to_save = {'%s_%s_voltages.dat' % (reference, cell_id): []}
interesting_seg_ids = [0, 200, 1000, 2000, 2500,
2949] # [soma, .. some dends .. , axon]
interesting_seg_ids = [0] # [soma, .. some dends .. , axon]
pg0 = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="200ms",
duration="600ms",
amplitude="0.4nA")
pg1 = oc.add_pulse_generator(nml_doc,
id="pg1",
delay="100ms",
duration="400ms",
amplitude="0.02nA")
oc.add_targeted_inputs_to_population(network,
"PG_fixed",
pop,
pg0.id,
segment_group='soma_group',
number_per_cell=1,
all_cells=True)
oc.add_targeted_inputs_to_population(
network,
"PG_variable",
pop,
'cond0', # from ../../../ConductanceClamp.xml
segment_group='soma_group',
number_per_cell=1,
all_cells=True,
weights='random()')
for i in range(num_cells):
for seg_id in interesting_seg_ids:
to_plot.values()[0].append('%s/%s/%s/%s/v' %
(pop.id, i, pop.component, seg_id))
to_save.values()[0].append('%s/%s/%s/%s/v' %
(pop.id, i, pop.component, seg_id))
nml_file_name = '%s.net.nml' % network.id + ('.h5'
if format == 'hdf5' else '')
target_dir = './'
oc.save_network(nml_doc,
nml_file_name,
validate=False,
use_subfolder=True,
target_dir=target_dir,
format=format)
lems_file_name, lems_sim = oc.generate_lems_simulation(
nml_doc,
network,
nml_file_name,
duration,
dt=0.025,
target_dir=target_dir,
include_extra_lems_files=['../../../ConductanceClamp.xml'],
gen_plots_for_all_v=False,
plot_all_segments=False,
gen_plots_for_quantities=
to_plot, # Dict with displays vs lists of quantity paths
gen_saves_for_all_v=False,
save_all_segments=False,
gen_saves_for_quantities=
to_save, # Dict with file names vs lists of quantity paths
verbose=True)
if simulator:
print("Running %s for %sms in %s" %
(lems_file_name, duration, simulator))
traces, events = oc.simulate_network(lems_file_name,
simulator,
max_memory='4000M',
nogui=True,
load_saved_data=True,
reload_events=True,
plot=False,
verbose=True,
num_processors=min(num_cells, 16))
