pop0 = oc.add_population_in_rectangular_region(network,
'pop0',
'pyr_4_sym',
5,
0,offset,0,
xDim,yDim,zDim)
offset+=yDim
##### Projections
oc.add_probabilistic_projection(network,
"proj0",
pop0,
pop0,
synAmpa1.id,
0.5)
##### Inputs
oc.add_inputs_to_population(network, "Stim0",
pop0, pg0.id,
only_cells=[0])
oc.add_inputs_to_population(network, "Stim1",
pop0, pg1.id,
only_cells=[1])
oc.add_inputs_to_population(network, "Stim2",
'HH_471141261',
scale_pop_size(40),
xs,ys,zs,
xDim,yDim,zDim)
'''
popBBP = oc.add_single_cell_population(network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
z=200)'''
##### Projections
oc.add_probabilistic_projection(network, "proj0",
popExc, popExc,
synAmpa1.id, 0.3)
oc.add_probabilistic_projection(network, "proj1",
popExc, popInh,
synAmpa1.id, 0.5)
oc.add_probabilistic_projection(network, "proj3",
popInh, popExc,
synGaba1.id, 0.7)
oc.add_probabilistic_projection(network, "proj4",
popInh, popInh,
synGaba1.id, 0.5)
def generate(reference = "L23TraubDemo",
num_rs =2,
num_bask =2,
scalex=1,
scaley=1,
scalez=1,
connections=True,
poisson_inputs=True,
offset_curents=False,
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.add_cell_and_channels(nml_doc, 'Thalamocortical/L23PyrRS.cell.nml','L23PyrRS')
oc.add_cell_and_channels(nml_doc, 'Thalamocortical/SupBasket.cell.nml','SupBasket')
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)
if offset_curents:
pg0 = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="0ms",
duration="%sms"%duration,
amplitude="0.5nA")
oc.add_inputs_to_population(network,
"Stim0",
pop_rs,
pg0.id,
all_cells=True)
oc.add_inputs_to_population(network,
"Stim0",
pop_bask,
pg0.id,
all_cells=True)
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network,
"proj0",
pop_rs,
pop_bask,
syn1.id,
0.3,
weight=0.05,
delay=global_delay)
if proj:
total_conns += len(proj.connection_wds)
if num_rs != 2 or num_bask!=2:
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(reference = "Balanced",
num_bbp =1,
scalePops = 1,
scalex=1,
scaley=1,
scalez=1,
connections=True,
duration = 1000,
global_delay = 0,
format='xml'):
num_exc = scale_pop_size(80,scalePops)
num_inh = scale_pop_size(40,scalePops)
nml_doc, network = oc.generate_network(reference)
oc.add_cell_and_channels(nml_doc, 'AllenInstituteCellTypesDB_HH/HH_464198958.cell.nml','HH_464198958')
oc.add_cell_and_channels(nml_doc, 'AllenInstituteCellTypesDB_HH/HH_471141261.cell.nml','HH_471141261')
if num_bbp>0:
oc.add_cell_and_channels(nml_doc, 'BlueBrainProject_NMC/cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml', 'cADpyr229_L23_PC_5ecbf9b163_0_0')
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="150 Hz",
synapse_id=synAmpa1.id)
##### Populations
popExc = oc.add_population_in_rectangular_region(network,
'popExc',
'HH_464198958',
num_exc,
xs,ys,zs,
xDim,yDim,zDim)
popInh = oc.add_population_in_rectangular_region(network,
'popInh',
'HH_471141261',
num_inh,
xs,ys,zs,
xDim,yDim,zDim)
if num_bbp == 1:
popBBP = oc.add_single_cell_population(network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
z=200)
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)
##### Projections
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network, "proj0",
popExc, popExc,
synAmpa1.id, 0.3, delay = global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network, "proj1",
popExc, popInh,
synAmpa1.id, 0.5, 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':
lems_file_name = oc.generate_lems_simulation(nml_doc, network,
nml_file_name,
duration = duration,
dt = 0.025)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
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_pre,
pfs.id,
all_cells=True)
oc.add_probabilistic_projection(network,
"proj0",
pop_pre,
pop_post,
syn1.id,
0.3,
weight=0.05,
delay=5)
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 = 300,
dt = 0.025)
