def generate(reference = "SimpleNet",
scale=1,
format='xml'):
population_size = scale_pop_size(3,scale)
nml_doc, network = oc.generate_network(reference)
oc.include_opencortex_cell(nml_doc, 'izhikevich/RS.cell.nml')
pop = oc.add_population_in_rectangular_region(network,
'RS_pop',
'RS',
population_size,
0,0,0,
100,100,100,
color='0 .8 0')
import neuroml
pop.properties.append(neuroml.Property('radius',10))
syn = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="2nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="50 Hz",
synapse_id=syn.id)
oc.add_inputs_to_population(network,
"Stim0",
pop,
pfs.id,
all_cells=True)
nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format)
if format=='xml':
oc.generate_lems_simulation(nml_doc,
network,
nml_file_name,
duration = 500,
dt = 0.025,
report_file_name='report.simple.txt')
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
import opencortex.core as oc
nml_doc, network = oc.generate_network("IClamps")
oc.include_opencortex_cell(nml_doc, 'izhikevich/RS.cell.nml')
oc.include_opencortex_cell(nml_doc, 'acnet2/pyr_4_sym_soma.cell.nml')
#oc.include_opencortex_cell(nml_doc, '../NeuroML2/prototypes/BlueBrainProject_NMC/cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml')
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")
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="GapJunctions",
num_pre=5,
num_post=2,
connections=True,
duration=1000,
segments_to_plot_record={
'pop_pre': [0],
'pop_post': [0]
},
format='xml'):
nml_doc, network = oc.generate_network(reference)
oc.include_opencortex_cell(nml_doc, 'acnet2/pyr_4_sym.cell.nml')
xDim = 500
yDim = 50
zDim = 500
pop_pre = oc.add_population_in_rectangular_region(network, 'pop_pre',
'pyr_4_sym', num_pre, 0,
0, 0, xDim, yDim, zDim)
pop_post = oc.add_population_in_rectangular_region(network, 'pop_post',
'pyr_4_sym', num_post,
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")
gj_syn = oc.add_gap_junction_synapse(nml_doc, id="gj0", conductance="5nS")
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="30 Hz",
synapse_id=ampa_syn.id)
oc.add_inputs_to_population(network,
"Stim0",
pop_pre,
pfs.id,
all_cells=True)
total_conns = 0
if connections:
this_syn = gj_syn.id
proj = oc.add_targeted_electrical_projection(
nml_doc,
network,
"Proj0",
pop_pre,
pop_post,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group='soma_group',
post_segment_group='dendrite_group',
number_conns_per_cell=3)
if proj:
total_conns += len(proj[0].electrical_connections) + len(
proj[0].electrical_connection_instances)
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:
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,
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
import opencortex.core as oc
population_size0 = 10
population_size1 = 10
nml_doc, network = oc.generate_network("SpikingNet")
oc.include_opencortex_cell(nml_doc, 'izhikevich/RS.cell.nml')
pop_pre = oc.add_population_in_rectangular_region(network,
'pop_pre',
'RS',
population_size0,
0,0,0,
100,100,100,
color='.8 0 0')
import neuroml
pop_pre.properties.append(neuroml.Property('radius',10))
pop_post = oc.add_population_in_rectangular_region(network,
'pop_post',
'RS',
population_size1,
0,100,0,
100,200,100,
color='0 0 .8')
pop_post.properties.append(neuroml.Property('radius',10))
syn0 = oc.add_exp_two_syn(nml_doc,
def generate(scalePops=1,
percentage_exc_detailed=0,
scalex=1,
scaley=1,
scalez=1,
ratio_inh_exc=2,
connections=True,
duration=1000,
input_rate=150,
global_delay=2,
max_in_pop_to_plot_and_save=5,
format='xml',
run_in_simulator=None):
reference = ("Multiscale__g%s__i%s" % (ratio_inh_exc, input_rate)).replace(
'.', '_')
num_exc = scale_pop_size(80, scalePops)
num_exc2 = int(0.5 + num_exc * percentage_exc_detailed / 100.0)
num_exc -= num_exc2
num_inh = scale_pop_size(40, 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')
oc.include_opencortex_cell(nml_doc,
'L23Pyr_SmithEtAl2013/L23_NoHotSpot.cell.nml')
xDim = 1000 * scalex
yDim = 300 * scaley
zDim = 1000 * scalez
xs = -200
ys = -150
zs = 100
##### Synapses
exc_syn_nS = 1.
synAmpa1 = oc.add_exp_two_syn(nml_doc,
id="synAmpa1",
gbase="%snS" % exc_syn_nS,
erev="0mV",
tau_rise="0.5ms",
tau_decay="5ms")
synGaba1 = oc.add_exp_two_syn(nml_doc,
id="synGaba1",
gbase="%snS" % (exc_syn_nS * ratio_inh_exc),
erev="-80mV",
tau_rise="1ms",
tau_decay="20ms")
##### Input types
pfs1 = oc.add_poisson_firing_synapse(nml_doc,
id="psf1",
average_rate="%s Hz" % input_rate,
synapse_id=synAmpa1.id)
##### Populations
popExc = oc.add_population_in_rectangular_region(network,
'popExc',
'HH_477127614',
num_exc,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color='0 0 1')
popExc2 = oc.add_population_in_rectangular_region(network,
'popExc2',
'L23_NoHotSpot',
num_exc2,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color='0 1 0')
allExc = [popExc, popExc2]
popInh = oc.add_population_in_rectangular_region(network,
'popInh',
'HH_476686112',
num_inh,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color='1 0 0')
##### Projections
if connections:
for pop1 in allExc:
for pop2 in allExc:
proj = oc.add_probabilistic_projection(network,
"proj0",
pop1,
pop2,
synAmpa1.id,
0.5,
delay=global_delay)
proj = oc.add_probabilistic_projection(network,
"proj1",
pop1,
popInh,
synAmpa1.id,
0.7,
delay=global_delay)
proj = oc.add_probabilistic_projection(network,
"proj2",
popInh,
pop1,
synGaba1.id,
0.7,
delay=global_delay)
proj = oc.add_probabilistic_projection(network,
"proj3",
popInh,
popInh,
synGaba1.id,
0.5,
delay=global_delay)
##### Inputs
for pop in allExc:
oc.add_inputs_to_population(network,
"Stim_%s" % pop.id,
pop,
pfs1.id,
all_cells=True)
##### Save NeuroML and LEMS Simulation files
target_dir = './temp/'
nml_file_name = '%s.net.%s' % (network.id,
'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format == 'xml'),
format=format,
target_dir=target_dir)
if format == 'xml':
plot_v = {popExc.id: [], popExc2.id: [], popInh.id: []}
exc_traces = '%s_%s_v.dat' % (network.id, popExc.id)
exc2_traces = '%s_%s_v.dat' % (network.id, popExc2.id)
inh_traces = '%s_%s_v.dat' % (network.id, popInh.id)
save_v = {exc_traces: [], inh_traces: [], exc2_traces: []}
for i in range(min(max_in_pop_to_plot_and_save, num_exc)):
plot_v[popExc.id].append("%s/%i/%s/v" %
(popExc.id, i, popExc.component))
save_v[exc_traces].append("%s/%i/%s/v" %
(popExc.id, i, popExc.component))
for i in range(min(max_in_pop_to_plot_and_save, num_exc2)):
plot_v[popExc2.id].append("%s/%i/%s/v" %
(popExc2.id, i, popExc2.component))
save_v[exc2_traces].append("%s/%i/%s/v" %
(popExc2.id, i, popExc2.component))
for i in range(min(max_in_pop_to_plot_and_save, num_inh)):
plot_v[popInh.id].append("%s/%i/%s/v" %
(popInh.id, i, popInh.component))
save_v[inh_traces].append("%s/%i/%s/v" %
(popInh.id, i, popInh.component))
gen_spike_saves_for_all_somas = run_in_simulator != 'jNeuroML_NetPyNE'
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=plot_v,
gen_saves_for_all_v=False,
gen_saves_for_quantities=save_v,
gen_spike_saves_for_all_somas=gen_spike_saves_for_all_somas,
target_dir=target_dir)
if run_in_simulator:
print("Running %s in %s" % (lems_file_name, run_in_simulator))
traces, events = oc.simulate_network(lems_file_name,
run_in_simulator,
max_memory='4000M',
nogui=True,
load_saved_data=True,
reload_events=True,
plot=False,
verbose=False)
print("Reloaded traces: %s" % traces.keys())
#print("Reloaded events: %s"%events.keys())
use_events_for_rates = False
exc_rate = 0
inh_rate = 0
if use_events_for_rates:
if (run_in_simulator == 'jNeuroML_NetPyNE'):
raise (
'Saving of spikes (and so calculation of rates) not yet supported in jNeuroML_NetPyNE'
)
for ek in events.keys():
rate = 1000 * len(events[ek]) / float(duration)
print("Cell %s has rate %s Hz" % (ek, rate))
if 'popExc' in ek:
exc_rate += rate / num_exc
if 'popInh' in ek:
inh_rate += rate / num_inh
else:
tot_exc_rate = 0
exc_cells = 0
tot_inh_rate = 0
inh_cells = 0
tt = [t * 1000 for t in traces['t']]
for tk in traces.keys():
if tk != 't':
rate = get_rate_from_trace(
tt, [v * 1000 for v in traces[tk]])
print("Cell %s has rate %s Hz" % (tk, rate))
if 'popExc' in tk:
tot_exc_rate += rate
exc_cells += 1
if 'popInh' in tk:
tot_inh_rate += rate
inh_cells += 1
exc_rate = tot_exc_rate / exc_cells
inh_rate = tot_inh_rate / inh_cells
print("Run %s: Exc rate: %s Hz; Inh rate %s Hz" %
(reference, exc_rate, inh_rate))
return exc_rate, inh_rate, traces
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
def generate(reference = "Weights",
num_each = 6,
connections=True,
duration = 1000,
format='xml'):
nml_doc, network = oc.generate_network(reference)
cell_id = 'HH_477127614'
cell = oc.include_opencortex_cell(nml_doc, 'AllenInstituteCellTypesDB_HH/%s.cell.nml'%cell_id)
xDim = 500
yDim = 500
zDim = 30
pop_pre = oc.add_population_in_rectangular_region(network, 'pop_pre',
cell_id, num_each,
0,0,0, xDim,yDim,zDim,
color='.8 0 0')
pop_post_chem_exc = oc.add_population_in_rectangular_region(network, 'pop_post_chem_exc',
cell_id, num_each+1,
0,yDim,0, xDim,yDim,zDim,
color='0 0 .8')
pop_post_chem_inh = oc.add_population_in_rectangular_region(network, 'pop_post_chem_inh',
cell_id, num_each+2,
xDim,yDim,0, xDim,yDim,zDim,
color='0 .8 .8')
pop_post_cont = oc.add_population_in_rectangular_region(network, 'pop_post_cont',
cell_id, num_each+3,
xDim,0,0, xDim,yDim,zDim,
color='0 .8 0')
ampa_syn = oc.add_exp_two_syn(nml_doc, id="AMPA_syn",
gbase="10nS", erev="0mV",
tau_rise="2ms", tau_decay="10ms")
gaba_syn = oc.add_exp_two_syn(nml_doc, id="GABA_syn",
gbase="10nS", erev="-80mV",
tau_rise="3ms", tau_decay="30ms")
gj_syn = oc.add_gap_junction_synapse(nml_doc, id="gj0",
conductance=".05nS")
analog_syn = GradedSynapse(id='analog_syn',
conductance="10nS",
delta="5mV",
Vth="-35mV",
k="0.025per_ms",
erev="0mV")
silent_syn = SilentSynapse(id="silent1")
nml_doc.graded_synapses.append(analog_syn)
nml_doc.silent_synapses.append(silent_syn)
pfs = oc.add_poisson_firing_synapse(nml_doc, id="poissonFiringSyn",
average_rate="10 Hz", synapse_id=ampa_syn.id)
oc.add_inputs_to_population(network, "Stim0",
pop_pre, pfs.id, all_cells=True)
if connections:
proj_chem_exc = oc.add_probabilistic_projection(network,
"proj_chem_exc",
pop_pre,
pop_post_chem_exc,
ampa_syn.id,
0.7,
weight=1,
delay=5)
for conn in proj_chem_exc.connection_wds:
if conn.get_pre_cell_id() < 3 and conn.get_post_cell_id() < 3:
conn.weight = 0.5
proj_chem_inh = oc.add_probabilistic_projection(network,
"proj_chem_inh",
pop_pre,
pop_post_chem_inh,
gaba_syn.id,
0.7,
weight=1,
delay=5)
for conn in proj_chem_inh.connection_wds:
if conn.get_pre_cell_id() < 3 and conn.get_post_cell_id() < 3:
conn.weight = 2
proj_cont = ContinuousProjection(id='proj_cont', \
presynaptic_population=pop_pre.id,
postsynaptic_population=pop_post_cont.id)
network.continuous_projections.append(proj_cont)
for i in range(pop_pre.get_size()):
for j in range(pop_post_cont.get_size()):
conn0 = ContinuousConnectionInstanceW(id='%s'%(j+i*pop_pre.get_size()), \
pre_cell='../%s/%s/%s'%(pop_pre.id,i,cell_id),
post_cell='../%s/%s/%s'%(pop_post_cont.id,j,cell_id),
pre_component=silent_syn.id,
post_component=analog_syn.id,
weight=(i+j)/10.0)
proj_cont.continuous_connection_instance_ws.append(conn0)
gj_pops = [pop_pre, pop_post_chem_exc, pop_post_chem_inh, pop_post_cont]
for pre in gj_pops:
for post in gj_pops:
proj_gap = oc.add_targeted_electrical_projection(nml_doc,
network,
"proj_",
pre,
post,
targeting_mode='convergent',
synapse_list=[gj_syn.id],
pre_segment_group = 'soma_group',
post_segment_group = 'soma_group',
number_conns_per_cell=3)
for conn in network.electrical_projections[-1].electrical_connection_instance_ws:
conn.weight = conn.get_pre_cell_id() + conn.get_post_cell_id()
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)
if format=='xml':
lems_file_name = oc.generate_lems_simulation(nml_doc, network,
nml_file_name,
duration = duration,
dt = 0.025,
gen_plots_for_all_v = True,
gen_saves_for_all_v = True)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
def generate(reference="VClamp",
poisson_inputs=True,
use_vclamp=False,
duration=500,
format='xml'):
nml_doc, network = oc.generate_network(reference)
oc.include_opencortex_cell(nml_doc, 'Thalamocortical/L23PyrRS.cell.nml')
num_cells = 4
pop_rs = oc.add_population_in_rectangular_region(network,
'popRS',
'L23PyrRS',
num_cells,
0,
0,
0,
1000,
20,
20,
color='.8 0 0')
syn0 = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="1nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
if poisson_inputs:
pfs = oc.add_transient_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="20 Hz",
delay="50 ms",
duration="400 ms",
synapse_id=syn0.id)
oc.add_targeted_inputs_to_population(network,
"pfs_noise",
pop_rs,
pfs.id,
segment_group='dendrite_group',
number_per_cell=100,
all_cells=True)
all_vclamp_segs = [0, 142, 87]
vclamp_segs = {0: [], 1: [0], 2: [0, 142], 3: all_vclamp_segs}
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
if use_vclamp:
v_clamped = '-70mV'
for cell_id in vclamp_segs:
for seg_id in vclamp_segs[cell_id]:
vc = oc.add_voltage_clamp_triple(
nml_doc,
id='vclamp_cell%i_seg%i' % (cell_id, seg_id),
delay='0ms',
duration='%sms' % duration,
conditioning_voltage=v_clamped,
testing_voltage=v_clamped,
return_voltage=v_clamped,
simple_series_resistance="1e1ohm",
active="1")
vc_dat_file = 'v_clamps_i_cell%s_seg%s.dat' % (cell_id, seg_id)
gen_saves_for_quantities[vc_dat_file] = []
oc.add_inputs_to_population(network,
"input_vClamp_cell%i_seg%i" %
(cell_id, seg_id),
pop_rs,
vc.id,
all_cells=False,
only_cells=[cell_id],
segment_ids=[seg_id])
q = '%s/%s/%s/%s/%s/i' % (pop_rs.id, cell_id, pop_rs.component,
seg_id, vc.id)
gen_saves_for_quantities[vc_dat_file].append(q)
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)
segments_to_plot_record = {pop_rs.id: all_vclamp_segs + [20, 50, 99, 139]}
if format == 'xml':
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
'''
Generates a NeuroML 2 file with a LEMS file recording many details of the network
'''
import opencortex.core as oc
nml_doc, network = oc.generate_network("Recording")
##### Cells
oc.include_opencortex_cell(nml_doc, 'acnet2/pyr_4_sym.cell.nml')
xDim = 500
yDim = 100
zDim = 500
offset = 0
##### Synapses
synAmpa1 = oc.add_exp_two_syn(nml_doc,
id="synAmpa1",
gbase="1nS",
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",
def generate(reference="Balanced",
num_bbp=1,
scalePops=1,
scalex=1,
scaley=1,
scalez=1,
connections=True,
duration=1000,
input_rate=150,
global_delay=0,
max_in_pop_to_plot_and_save=5,
gen_spike_saves_for_all_somas=True,
format='xml'):
num_exc = scale_pop_size(80, scalePops)
num_inh = scale_pop_size(40, 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')
if num_bbp > 0:
oc.include_opencortex_cell(
nml_doc,
'BlueBrainProject_NMC/cADpyr229_L23_PC_5ecbf9b163_0_0.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
pfs1 = oc.add_poisson_firing_synapse(nml_doc,
id="psf1",
average_rate="%s Hz" % input_rate,
synapse_id=synAmpa1.id)
##### Populations
popExc = oc.add_population_in_rectangular_region(network,
'popExc',
'HH_477127614',
num_exc,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color='.8 0 0')
popInh = oc.add_population_in_rectangular_region(network,
'popInh',
'HH_476686112',
num_inh,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color='0 0 .8')
if num_bbp == 1:
popBBP = oc.add_single_cell_population(
network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
z=200,
color='0 .8 0')
elif num_bbp > 1:
popBBP = oc.add_population_in_rectangular_region(
network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
num_bbp,
xs,
ys,
zs,
xDim,
yDim,
zDim,
color='0 .8 0')
##### Projections
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network,
"proj0",
popExc,
popExc,
synAmpa1.id,
0.5,
delay=global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network,
"proj1",
popExc,
popInh,
synAmpa1.id,
0.7,
delay=global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network,
"proj3",
popInh,
popExc,
synGaba1.id,
0.7,
delay=global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network,
"proj4",
popInh,
popInh,
synGaba1.id,
0.5,
delay=global_delay)
total_conns += len(proj.connection_wds)
if num_bbp > 0:
proj = oc.add_probabilistic_projection(network,
"proj5",
popExc,
popBBP,
synAmpa1.id,
0.5,
delay=global_delay)
total_conns += len(proj.connection_wds)
##### Inputs
oc.add_inputs_to_population(network,
"Stim0",
popExc,
pfs1.id,
all_cells=True)
##### Save NeuroML and LEMS Simulation files
if num_bbp != 1:
new_reference = 'Balanced_%scells_%sconns' % (num_bbp + num_exc +
num_inh, 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':
plot_v = {popExc.id: [], popInh.id: []}
save_v = {'%s_v.dat' % popExc.id: [], '%s_v.dat' % popInh.id: []}
if num_bbp > 0:
plot_v[popBBP.id] = []
save_v['%s_v.dat' % popBBP.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))
for i in range(min(max_in_pop_to_plot_and_save, num_bbp)):
plot_v[popBBP.id].append("%s/%i/%s/v" %
(popBBP.id, i, popBBP.component))
save_v['%s_v.dat' % popBBP.id].append(
"%s/%i/%s/v" % (popBBP.id, i, popBBP.component))
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)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
nml_doc, network = oc.generate_network("SimpleNet")
scale = 7
min_pop_size = 1
def scale_pop_size(baseline):
return max(min_pop_size, int(baseline*scale))
xDim = 500
yDim = 100
zDim = 500
offset = 0
##### Cells
oc.include_opencortex_cell(nml_doc, 'izhikevich/RS.cell.nml')
# TODO: add method oc.add_spike_generator_poisson(...)
spike_gen = neuroml.SpikeGeneratorPoisson(id="poissonInput",
average_rate="50Hz")
nml_doc.spike_generator_poissons.append(spike_gen)
##### Synapses
oc.include_neuroml2_file(nml_doc,'AMPA_NMDA.synapse.nml')
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
