def generate(reference = "SimpleNet",
scale=1,
format='xml'):
population_size = scale_pop_size(3,scale)
nml_doc, network = oc.generate_network(reference)
oc.add_cell_and_channels(nml_doc, 'izhikevich/RS.cell.nml','RS')
pop = oc.add_population_in_rectangular_region(network,
'RS_pop',
'RS',
population_size,
0,0,0,
100,100,100)
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)
def generate(reference="SimpleNet", scale=1, format='xml'):
population_size = scale_pop_size(3, scale)
nml_doc, network = oc.generate_network(reference)
oc.add_cell_and_channels(nml_doc, 'izhikevich/RS.cell.nml', 'RS')
pop = oc.add_population_in_rectangular_region(network, 'RS_pop', 'RS',
population_size, 0, 0, 0,
100, 100, 100)
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)
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",
pop0, pfs.id,
all_cells=True)
nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc, nml_file_name, validate=True)
offset+=yDim
##### Projections
oc.add_probabilistic_projection(network,
"proj0",
popIaf,
popIzh,
synAmpa1.id,
0.5)
##### Inputs
oc.add_inputs_to_population(network, "Stim0",
popIzh, pfsStrong.id,
all_cells=True)
oc.add_inputs_to_population(network, "Stim1",
popIaf, pfs200.id,
all_cells=True)
oc.add_inputs_to_population(network, "Stim2",
popIafRef, pfs200.id,
all_cells=True)
oc.add_inputs_to_population(network, "Stim3",
popPyrS, pfs100.id,
all_cells=True)
oc.add_inputs_to_population(network, "Stim4",
def generate_lems(glif_dir, curr_pA, show_plot=True):
os.chdir(glif_dir)
with open('model_metadata.json', "r") as json_file:
model_metadata = json.load(json_file)
with open('neuron_config.json', "r") as json_file:
neuron_config = json.load(json_file)
with open('ephys_sweeps.json', "r") as json_file:
ephys_sweeps = json.load(json_file)
template_cell = '''<Lems>
<%s %s/>
</Lems>
'''
type = '???'
print(model_metadata['name'])
if '(LIF)' in model_metadata['name']:
type = 'glifCell'
if '(LIF-ASC)' in model_metadata['name']:
type = 'glifAscCell'
if '(LIF-R)' in model_metadata['name']:
type = 'glifRCell'
if '(LIF-R-ASC)' in model_metadata['name']:
type = 'glifRAscCell'
if '(LIF-R-ASC-A)' in model_metadata['name']:
type = 'glifRAscATCell'
cell_id = 'GLIF_%s'%glif_dir
attributes = ""
attributes +=' id="%s"'%cell_id
attributes +='\n C="%s F"'%neuron_config["C"]
attributes +='\n leakReversal="%s V"'%neuron_config["El"]
attributes +='\n reset="%s V"'%neuron_config["El"]
attributes +='\n thresh="%s V"'%( float(neuron_config["th_inf"]) * float(neuron_config["coeffs"]["th_inf"]))
attributes +='\n leakConductance="%s S"'%(1/float(neuron_config["R_input"]))
if 'Asc' in type:
attributes +='\n tau1="%s s"'%neuron_config["asc_tau_array"][0]
attributes +='\n tau2="%s s"'%neuron_config["asc_tau_array"][1]
attributes +='\n amp1="%s A"'% ( float(neuron_config["asc_amp_array"][0]) * float(neuron_config["coeffs"]["asc_amp_array"][0]) )
attributes +='\n amp2="%s A"'% ( float(neuron_config["asc_amp_array"][1]) * float(neuron_config["coeffs"]["asc_amp_array"][1]) )
if 'glifR' in type:
attributes +='\n bs="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["b_spike"]
attributes +='\n deltaThresh="%s V"'%neuron_config["threshold_dynamics_method"]["params"]["a_spike"]
attributes +='\n fv="%s"'%neuron_config["voltage_reset_method"]["params"]["a"]
attributes +='\n deltaV="%s V"'%neuron_config["voltage_reset_method"]["params"]["b"]
if 'glifRAscATCell' in type:
attributes +='\n bv="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["b_voltage"]
attributes +='\n a="%s per_s"'%neuron_config["threshold_dynamics_method"]["params"]["a_voltage"]
file_contents = template_cell%(type, attributes)
print(file_contents)
cell_file_name = '%s.xml'%(cell_id)
cell_file = open(cell_file_name,'w')
cell_file.write(file_contents)
cell_file.close()
import opencortex.build as oc
nml_doc, network = oc.generate_network("Test_%s"%glif_dir)
pop = oc.add_single_cell_population(network,
'pop_%s'%glif_dir,
cell_id)
pg = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="100ms",
duration="1000ms",
amplitude="%s pA"%curr_pA)
oc.add_inputs_to_population(network,
"Stim0",
pop,
pg.id,
all_cells=True)
nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc, nml_file_name, validate=True)
thresh = 'thresh'
if 'glifR' in type:
thresh = 'threshTotal'
lems_file_name = oc.generate_lems_simulation(nml_doc,
network,
nml_file_name,
include_extra_lems_files = [cell_file_name,'../GLIFs.xml'],
duration = 1200,
dt = 0.01,
gen_saves_for_quantities = {'thresh.dat':['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]},
gen_plots_for_quantities = {'Threshold':['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]})
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True)
print("Ran simulation; results reloaded for: %s"%results.keys())
info = "Model %s; %spA stimulation"%(glif_dir,curr_pA)
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/v'%(glif_dir,glif_dir)]]
labels = ['LEMS - jNeuroML']
original_model_v = 'original.v.dat'
if os.path.isfile(original_model_v):
data, indices = pynml.reload_standard_dat_file(original_model_v)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Membrane potential; %s"%info,
xaxis = "Time (s)",
yaxis = "Voltage (V)",
labels = labels,
grid = True,
show_plot_already=False,
save_figure_to='Comparison_%ipA.png'%(curr_pA))
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/%s'%(glif_dir,glif_dir,thresh)]]
labels = ['LEMS - jNeuroML']
original_model_th = 'original.thresh.dat'
if os.path.isfile(original_model_th):
data, indeces = pynml.reload_standard_dat_file(original_model_th)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Threshold; %s"%info,
xaxis = "Time (s)",
yaxis = "Voltage (V)",
labels = labels,
grid = True,
show_plot_already=show_plot,
save_figure_to='Comparison_Threshold_%ipA.png'%(curr_pA))
readme = '''
## Model: %(id)s
### Original model
%(name)s
[Allen Cell Types DB electrophysiology page for specimen](http://celltypes.brain-map.org/mouse/experiment/electrophysiology/%(spec)s)
[Neuron configuration](neuron_config.json); [model metadata](model_metadata.json); [electrophysiology summary](ephys_sweeps.json)
#### Original traces:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)
### Conversion to NeuroML 2
LEMS version of this model: [GLIF_%(id)s.xml](GLIF_%(id)s.xml)
[Definitions of LEMS Component Types](../GLIFs.xml) for GLIFs.
This model can be run locally by installing [jNeuroML](https://github.com/NeuroML/jNeuroML) and running:
jnml LEMS_Test_%(id)s.xml
#### Comparison:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)'''
readme_file = open('README.md','w')
curr_str = str(curr_pA)
# @type curr_str str
if curr_str.endswith('.0'):
curr_str = curr_str[:-2]
readme_file.write(readme%{"id":glif_dir,"name":model_metadata['name'],"spec":model_metadata["specimen_id"],"curr":curr_str})
readme_file.close()
os.chdir('..')
return model_metadata, neuron_config, ephys_sweeps
popInh, popExc,
synGaba1.id, 0.7)
oc.add_probabilistic_projection(network, "proj4",
popInh, popInh,
synGaba1.id, 0.5)
'''
oc.add_probabilistic_projection(network, "proj5",
popExc, popBBP,
synAmpa1.id, 1)'''
##### Inputs
oc.add_inputs_to_population(network, "Stim0",
popExc, pfs1.id,
all_cells=True)
##### Save NeuroML and LEMS Simulation files
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 = 1000,
dt = 0.025)
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
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
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")'''
oc.add_inputs_to_population(network,
"Stim0",
popIzh,
pgIzh.id,
all_cells=True)
oc.add_inputs_to_population(network,
"Stim1",
popHH,
pgHH.id,
all_cells=True)
'''
oc.add_inputs_to_population(network,
"Stim2",
popBBP,
pgBBP.id,
all_cells=True)'''
def generate(reference = "ACNet",
num_pyr = 48,
num_bask = 12,
scalex=1,
scaley=1,
scalez=1,
connections=True,
global_delay = 0,
duration = 300,
segments_to_plot_record = {'pop_pyr':[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, 'acnet2/bask.cell.nml','bask')
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")
ampa_syn_inh = oc.add_exp_two_syn(nml_doc, id="AMPA_syn_inh",
gbase="0.15e-9S", erev="0mV",
tau_rise="0.003s", tau_decay="0.0031s")
gaba_syn = oc.add_exp_two_syn(nml_doc, id="GABA_syn",
gbase="0.6e-9S", erev="-0.080V",
tau_rise="0.005s", tau_decay="0.012s")
gaba_syn_inh = oc.add_exp_two_syn(nml_doc, id="GABA_syn_inh",
gbase="0S", erev="-0.080V",
tau_rise="0.003s", tau_decay="0.008s")
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_pyr, pfs.id, all_cells=True)
total_conns = 0
if connections:
this_syn=ampa_syn.id
proj = oc.add_chem_projection0(nml_doc,
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=7,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
this_syn=ampa_syn_inh.id
proj = oc.add_chem_projection0(nml_doc,
network,
"Proj_pyr_bask",
pop_pyr,
pop_bask,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'all',
number_conns_per_cell=21,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
this_syn=gaba_syn.id
proj = oc.add_chem_projection0(nml_doc,
network,
"Proj_bask_pyr",
pop_bask,
pop_pyr,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'all',
number_conns_per_cell=21,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
this_syn=gaba_syn_inh.id
proj = oc.add_chem_projection0(nml_doc,
network,
"Proj_bask_bask",
pop_bask,
pop_bask,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'all',
number_conns_per_cell=5,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
if num_pyr != 48 or num_bask!=12:
new_reference = '%s_%scells_%sconns'%(nml_doc.id,num_pyr+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:
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
##### Projections
oc.add_probabilistic_projection(network,
"proj0",
pop_iaf,
pop_rs,
synAmpa1.id,
0.5)
##### Inputs
oc.add_inputs_to_population(network, "Stim0",
pop_iaf, pg0.id,
only_cells=[i for i in [0,2] if i<pop_iaf.size])
oc.add_inputs_to_population(network, "Stim1",
pop_iaf, pg1.id,
only_cells=[i for i in [3,4] if i<pop_iaf.size])
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 = 500,
def generate_lems(glif_dir, curr_pA, show_plot=True):
os.chdir(glif_dir)
with open('model_metadata.json', "r") as json_file:
model_metadata = json.load(json_file)
with open('neuron_config.json', "r") as json_file:
neuron_config = json.load(json_file)
with open('ephys_sweeps.json', "r") as json_file:
ephys_sweeps = json.load(json_file)
template_cell = '''<Lems>
<%s %s/>
</Lems>
'''
type = '???'
print(model_metadata['name'])
if '(LIF)' in model_metadata['name']:
type = 'glifCell'
if '(LIF-ASC)' in model_metadata['name']:
type = 'glifAscCell'
if '(LIF-R)' in model_metadata['name']:
type = 'glifRCell'
if '(LIF-R-ASC)' in model_metadata['name']:
type = 'glifRAscCell'
if '(LIF-R-ASC-A)' in model_metadata['name']:
type = 'glifRAscATCell'
cell_id = 'GLIF_%s' % glif_dir
attributes = ""
attributes += ' id="%s"' % cell_id
attributes += '\n C="%s F"' % neuron_config["C"]
attributes += '\n leakReversal="%s V"' % neuron_config["El"]
attributes += '\n reset="%s V"' % neuron_config["El"]
attributes += '\n thresh="%s V"' % (float(
neuron_config["th_inf"]) * float(neuron_config["coeffs"]["th_inf"]))
attributes += '\n leakConductance="%s S"' % (
1 / float(neuron_config["R_input"]))
if 'Asc' in type:
attributes += '\n tau1="%s s"' % neuron_config[
"asc_tau_array"][0]
attributes += '\n tau2="%s s"' % neuron_config[
"asc_tau_array"][1]
attributes += '\n amp1="%s A"' % (
float(neuron_config["asc_amp_array"][0]) *
float(neuron_config["coeffs"]["asc_amp_array"][0]))
attributes += '\n amp2="%s A"' % (
float(neuron_config["asc_amp_array"][1]) *
float(neuron_config["coeffs"]["asc_amp_array"][1]))
if 'glifR' in type:
attributes += '\n bs="%s per_s"' % neuron_config[
"threshold_dynamics_method"]["params"]["b_spike"]
attributes += '\n deltaThresh="%s V"' % neuron_config[
"threshold_dynamics_method"]["params"]["a_spike"]
attributes += '\n fv="%s"' % neuron_config[
"voltage_reset_method"]["params"]["a"]
attributes += '\n deltaV="%s V"' % neuron_config[
"voltage_reset_method"]["params"]["b"]
if 'glifRAscATCell' in type:
attributes += '\n bv="%s per_s"' % neuron_config[
"threshold_dynamics_method"]["params"]["b_voltage"]
attributes += '\n a="%s per_s"' % neuron_config[
"threshold_dynamics_method"]["params"]["a_voltage"]
file_contents = template_cell % (type, attributes)
print(file_contents)
cell_file_name = '%s.xml' % (cell_id)
cell_file = open(cell_file_name, 'w')
cell_file.write(file_contents)
cell_file.close()
import opencortex.build as oc
nml_doc, network = oc.generate_network("Test_%s" % glif_dir)
pop = oc.add_single_cell_population(network, 'pop_%s' % glif_dir, cell_id)
pg = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="100ms",
duration="1000ms",
amplitude="%s pA" % curr_pA)
oc.add_inputs_to_population(network, "Stim0", pop, pg.id, all_cells=True)
nml_file_name = '%s.net.nml' % network.id
oc.save_network(nml_doc, nml_file_name, validate=True)
thresh = 'thresh'
if 'glifR' in type:
thresh = 'threshTotal'
lems_file_name = oc.generate_lems_simulation(
nml_doc,
network,
nml_file_name,
include_extra_lems_files=[cell_file_name, '../GLIFs.xml'],
duration=1200,
dt=0.01,
gen_saves_for_quantities={
'thresh.dat':
['pop_%s/0/GLIF_%s/%s' % (glif_dir, glif_dir, thresh)]
},
gen_plots_for_quantities={
'Threshold':
['pop_%s/0/GLIF_%s/%s' % (glif_dir, glif_dir, thresh)]
})
results = pynml.run_lems_with_jneuroml(lems_file_name,
nogui=True,
load_saved_data=True)
print("Ran simulation; results reloaded for: %s" % results.keys())
info = "Model %s; %spA stimulation" % (glif_dir, curr_pA)
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/v' % (glif_dir, glif_dir)]]
labels = ['LEMS - jNeuroML']
original_model_v = 'original.v.dat'
if os.path.isfile(original_model_v):
data, indices = pynml.reload_standard_dat_file(original_model_v)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Membrane potential; %s" % info,
xaxis="Time (s)",
yaxis="Voltage (V)",
labels=labels,
grid=True,
show_plot_already=False,
save_figure_to='Comparison_%ipA.png' % (curr_pA))
times = [results['t']]
vs = [results['pop_%s/0/GLIF_%s/%s' % (glif_dir, glif_dir, thresh)]]
labels = ['LEMS - jNeuroML']
original_model_th = 'original.thresh.dat'
if os.path.isfile(original_model_th):
data, indeces = pynml.reload_standard_dat_file(original_model_th)
times.append(data['t'])
vs.append(data[0])
labels.append('Allen SDK')
pynml.generate_plot(times,
vs,
"Threshold; %s" % info,
xaxis="Time (s)",
yaxis="Voltage (V)",
labels=labels,
grid=True,
show_plot_already=show_plot,
save_figure_to='Comparison_Threshold_%ipA.png' %
(curr_pA))
readme = '''
## Model: %(id)s
### Original model
%(name)s
[Allen Cell Types DB electrophysiology page for specimen](http://celltypes.brain-map.org/mouse/experiment/electrophysiology/%(spec)s)
[Neuron configuration](neuron_config.json); [model metadata](model_metadata.json); [electrophysiology summary](ephys_sweeps.json)
#### Original traces:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)
### Conversion to NeuroML 2
LEMS version of this model: [GLIF_%(id)s.xml](GLIF_%(id)s.xml)
[Definitions of LEMS Component Types](../GLIFs.xml) for GLIFs.
This model can be run locally by installing [jNeuroML](https://github.com/NeuroML/jNeuroML) and running:
jnml LEMS_Test_%(id)s.xml
#### Comparison:
**Membrane potential**
Current injection of %(curr)s pA
spA.png)
**Threshold**
spA.png)'''
readme_file = open('README.md', 'w')
curr_str = str(curr_pA)
# @type curr_str str
if curr_str.endswith('.0'):
curr_str = curr_str[:-2]
readme_file.write(
readme % {
"id": glif_dir,
"name": model_metadata['name'],
"spec": model_metadata["specimen_id"],
"curr": curr_str
})
readme_file.close()
os.chdir('..')
return model_metadata, neuron_config, ephys_sweeps