def add_projection(network,
proj_id,
pop_pre,
pop_post,
syn_id,
conn_prob,
delay,
target_dendrites):
if pop_post.size > pop_pre.size:
num_connections = pop_pre.size * conn_prob
targeting_mode='convergent'
else:
num_connections = pop_post.size * conn_prob
targeting_mode='divergent'
post_segment_group = 'soma_group'
if '2' in pop_post.id and target_dendrites:
post_segment_group = 'dendrite_group'
proj = oc.add_targeted_projection(network,
proj_id,
pop_pre,
pop_post,
targeting_mode=targeting_mode,
synapse_list=[syn_id],
pre_segment_group = 'soma_group',
post_segment_group = post_segment_group,
number_conns_per_cell=num_connections,
delays_dict = {syn_id:delay})
return proj
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="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
scale_pop_size(100),
0,offset,0,
xDim,yDim,zDim,
color='.8 0 .8')
popStim.properties.append(neuroml.Property('radius',3))
offset+=yDim
##### Projections
proj0 = oc.add_targeted_projection(network,
prefix="proj0",
presynaptic_population=popStim,
postsynaptic_population=popPyr,
targeting_mode="convergent",
synapse_list=[synAmpa1.id],
number_conns_per_cell=2,
pre_segment_group=None,
post_segment_group="dendrite_group",
delays_dict={synAmpa1.id:3},
weights_dict={synAmpa1.id:1})
proj1 = oc.add_probabilistic_projection(network,
"proj1",
popIaf,
popIzh,
synAmpa1.id,
0.5)