|
|
@@ -1,12 +1,15 @@
|
|
|
#!/usr/bin/env python3
|
|
|
"""
|
|
|
-Parse population and connectivity information exported from circuit.py to
|
|
|
+Main script for creating scaled versions of NeuroML network and simulating it.
|
|
|
+
|
|
|
+This parses population and connectivity information exported from circuit.py to
|
|
|
create a NeuroML representation of the network.
|
|
|
|
|
|
-File: create_simulate.py
|
|
|
+File: nml_main.py
|
|
|
|
|
|
Copyright 2023 Ankur Sinha
|
|
|
"""
|
|
|
+
|
|
|
import copy
|
|
|
import logging
|
|
|
import math
|
|
|
@@ -25,7 +28,6 @@ import neuroml
|
|
|
import numpy
|
|
|
import pandas
|
|
|
import progressbar
|
|
|
-import pyneuroml
|
|
|
from neuroml.loaders import read_neuroml2_file
|
|
|
from neuroml.utils import component_factory
|
|
|
from neuroml.writers import NeuroMLHdf5Writer
|
|
|
@@ -35,9 +37,12 @@ from pyneuroml.nsgr import run_on_nsg
|
|
|
from pyneuroml.plot.Plot import generate_plot
|
|
|
from pyneuroml.plot.PlotMorphology import plot_2D
|
|
|
from pyneuroml.plot.PlotMorphologyVispy import plot_interactive_3D
|
|
|
-from pyneuroml.pynml import (reload_saved_data, run_lems_with,
|
|
|
- write_neuroml2_file,
|
|
|
- generate_sim_scripts_in_folder)
|
|
|
+from pyneuroml.pynml import (
|
|
|
+ reload_saved_data,
|
|
|
+ run_lems_with,
|
|
|
+ write_neuroml2_file,
|
|
|
+ generate_sim_scripts_in_folder,
|
|
|
+)
|
|
|
from pyneuroml.utils import rotate_cell
|
|
|
from pyneuroml.utils.units import get_value_in_si, convert_to_units
|
|
|
|
|
|
@@ -53,7 +58,6 @@ def round_to_sig(x):
|
|
|
|
|
|
|
|
|
class HL23Net(object):
|
|
|
-
|
|
|
"""HL23 network"""
|
|
|
|
|
|
def __init__(
|
|
|
@@ -156,7 +160,9 @@ class HL23Net(object):
|
|
|
}
|
|
|
self.simulation_id = f"HL23Sim_{self.network_scale}"
|
|
|
if self.rotate_cells is True:
|
|
|
- self.lems_simulation_file = f"LEMS_HL23_{self.network_scale}_Sim.rotated.xml"
|
|
|
+ self.lems_simulation_file = (
|
|
|
+ f"LEMS_HL23_{self.network_scale}_Sim.rotated.xml"
|
|
|
+ )
|
|
|
else:
|
|
|
self.lems_simulation_file = f"LEMS_HL23_{self.network_scale}_Sim.xml"
|
|
|
self.netdoc = None
|
|
|
@@ -170,7 +176,10 @@ class HL23Net(object):
|
|
|
self.lems_components_file_name = f"lems_components_{self.network_scale}.xml"
|
|
|
self.stim_start = "200ms"
|
|
|
self.sim_length = "1000ms"
|
|
|
- self.sim_end = convert_to_units(f"{get_value_in_si(self.stim_start) + get_value_in_si(self.sim_length)} s", "ms")
|
|
|
+ self.sim_end = convert_to_units(
|
|
|
+ f"{get_value_in_si(self.stim_start) + get_value_in_si(self.sim_length)} s",
|
|
|
+ "ms",
|
|
|
+ )
|
|
|
self.dt = "0.025ms"
|
|
|
self.seed = 4587
|
|
|
|
|
|
@@ -230,8 +239,9 @@ class HL23Net(object):
|
|
|
|
|
|
print(f"Writing {self.netdoc_file_name} ")
|
|
|
if self.hdf5:
|
|
|
- NeuroMLHdf5Writer.write(self.netdoc, self.netdoc_file_name,
|
|
|
- embed_xml=False, compress=True)
|
|
|
+ NeuroMLHdf5Writer.write(
|
|
|
+ self.netdoc, self.netdoc_file_name, embed_xml=False, compress=True
|
|
|
+ )
|
|
|
else:
|
|
|
write_neuroml2_file(self.netdoc, self.netdoc_file_name, validate=False)
|
|
|
|
|
|
@@ -274,9 +284,7 @@ class HL23Net(object):
|
|
|
|
|
|
self.nml_cell[ctype] = neuroml.loaders.read_neuroml2_file(
|
|
|
f"{ctype}.cell.nml"
|
|
|
- ).cells[
|
|
|
- 0
|
|
|
- ] # type: neuroml.Cell
|
|
|
+ ).cells[0] # type: neuroml.Cell
|
|
|
# replace biophys with empty object
|
|
|
if self.biophysics is False:
|
|
|
self.nml_cell[
|
|
|
@@ -306,8 +314,9 @@ class HL23Net(object):
|
|
|
)
|
|
|
unrotated_cell_doc.add(unrotated_cell)
|
|
|
if self.biophysics is True:
|
|
|
- self.__add_tonic_inhibition(ctype, unrotated_cell,
|
|
|
- unrotated_cell_doc)
|
|
|
+ self.__add_tonic_inhibition(
|
|
|
+ ctype, unrotated_cell, unrotated_cell_doc
|
|
|
+ )
|
|
|
write_neuroml2_file(
|
|
|
unrotated_cell_doc, unrotated_cell_file, validate=False
|
|
|
)
|
|
|
@@ -346,7 +355,10 @@ class HL23Net(object):
|
|
|
f"{self.temp_cell_dir}/{self.nml_cell[ctype].id}_{gid}.cell.nml"
|
|
|
)
|
|
|
rotated_cell_id = self.nml_cell[ctype].id + f"_{gid}"
|
|
|
- if self.new_cells is False and pathlib.Path(rotated_cell_file).exists():
|
|
|
+ if (
|
|
|
+ self.new_cells is False
|
|
|
+ and pathlib.Path(rotated_cell_file).exists()
|
|
|
+ ):
|
|
|
print(f"{rotated_cell_file} already exists, not overwriting.")
|
|
|
print("Set new_cells=True to regenerate all rotated cell files")
|
|
|
else:
|
|
|
@@ -364,8 +376,9 @@ class HL23Net(object):
|
|
|
)
|
|
|
|
|
|
if self.biophysics is True:
|
|
|
- self.__add_tonic_inhibition(ctype, rotated_cell,
|
|
|
- rotated_cell_doc)
|
|
|
+ self.__add_tonic_inhibition(
|
|
|
+ ctype, rotated_cell, rotated_cell_doc
|
|
|
+ )
|
|
|
|
|
|
rotated_cell_doc.add(rotated_cell)
|
|
|
write_neuroml2_file(
|
|
|
@@ -564,7 +577,9 @@ class HL23Net(object):
|
|
|
# show a progress bar so we have some idea of what's going on
|
|
|
max_value_possible = int(conndataset.shape[0])
|
|
|
# rounded = round_to_sig(approx_max_value)
|
|
|
- bar = progressbar.ProgressBar(max_val=max_value_possible, poll_interval=30)
|
|
|
+ bar = progressbar.ProgressBar(
|
|
|
+ max_val=max_value_possible, poll_interval=30
|
|
|
+ )
|
|
|
bar_count = 0
|
|
|
for conn in conndataset:
|
|
|
precell = conn[0]
|
|
|
@@ -575,7 +590,10 @@ class HL23Net(object):
|
|
|
sectionx = conn[5]
|
|
|
|
|
|
# if both cells are not in our population, skip this connection
|
|
|
- if precell not in self.cell_list.keys() or postcell not in self.cell_list.keys():
|
|
|
+ if (
|
|
|
+ precell not in self.cell_list.keys()
|
|
|
+ or postcell not in self.cell_list.keys()
|
|
|
+ ):
|
|
|
logger.debug(
|
|
|
f"{precell} or {postcell} are not included in the network. Skipping"
|
|
|
)
|
|
|
@@ -591,16 +609,21 @@ class HL23Net(object):
|
|
|
# it
|
|
|
try:
|
|
|
ord_segs = ordered_segments[f"{posttype}"][neuroml_seggrp_id]
|
|
|
- cumul_lengths = cumulative_lengths[f"{posttype}"][neuroml_seggrp_id]
|
|
|
+ cumul_lengths = cumulative_lengths[f"{posttype}"][
|
|
|
+ neuroml_seggrp_id
|
|
|
+ ]
|
|
|
except KeyError:
|
|
|
[
|
|
|
ord_segs,
|
|
|
cumul_lengths,
|
|
|
] = anml_cell.get_ordered_segments_in_groups(
|
|
|
- group_list=[neuroml_seggrp_id], include_cumulative_lengths=True
|
|
|
+ group_list=[neuroml_seggrp_id],
|
|
|
+ include_cumulative_lengths=True,
|
|
|
)
|
|
|
ordered_segments[f"{posttype}"][neuroml_seggrp_id] = ord_segs
|
|
|
- cumulative_lengths[f"{posttype}"][neuroml_seggrp_id] = cumul_lengths
|
|
|
+ cumulative_lengths[f"{posttype}"][neuroml_seggrp_id] = (
|
|
|
+ cumul_lengths
|
|
|
+ )
|
|
|
|
|
|
list_ord_segs = ord_segs[neuroml_seggrp_id]
|
|
|
list_cumul_lengths = cumul_lengths[neuroml_seggrp_id]
|
|
|
@@ -740,14 +763,21 @@ class HL23Net(object):
|
|
|
|
|
|
# create input component for 0.5
|
|
|
g_e0 = cell_type_ge0[cell_type] * math.exp(0.5)
|
|
|
- gfluct_component = lems.Component(id_=f"Gfluct_{cell_type}_0_5",
|
|
|
- type_="Gfluct",
|
|
|
- start=self.stim_start,
|
|
|
- stop=self.sim_end, dt=self.dt,
|
|
|
- E_e="0mV",
|
|
|
- E_i="-80mV", g_e0=f"{g_e0} uS", g_i0="0pS",
|
|
|
- tau_e="65ms", tau_i="20ms",
|
|
|
- std_e=f"{g_e0} uS", std_i="0pS")
|
|
|
+ gfluct_component = lems.Component(
|
|
|
+ id_=f"Gfluct_{cell_type}_0_5",
|
|
|
+ type_="Gfluct",
|
|
|
+ start=self.stim_start,
|
|
|
+ stop=self.sim_end,
|
|
|
+ dt=self.dt,
|
|
|
+ E_e="0mV",
|
|
|
+ E_i="-80mV",
|
|
|
+ g_e0=f"{g_e0} uS",
|
|
|
+ g_i0="0pS",
|
|
|
+ tau_e="65ms",
|
|
|
+ tau_i="20ms",
|
|
|
+ std_e=f"{g_e0} uS",
|
|
|
+ std_i="0pS",
|
|
|
+ )
|
|
|
self.lems_components.add(gfluct_component)
|
|
|
|
|
|
# get segments to place input at
|
|
|
@@ -770,25 +800,38 @@ class HL23Net(object):
|
|
|
# create the input component
|
|
|
g_e0 = cell_type_ge0[cell_type] * math.exp(d)
|
|
|
# create input component for use at each distance point
|
|
|
- gfluct_component = lems.Component(id_=f"Gfluct_HL23PYR_{str(d).replace('.', '_')}",
|
|
|
- type_="Gfluct",
|
|
|
- start=self.stim_start,
|
|
|
- stop=self.sim_end, dt=self.dt,
|
|
|
- E_e="0mV",
|
|
|
- E_i="-80mV", g_e0=f"{g_e0} uS", g_i0="0pS",
|
|
|
- tau_e="65ms", tau_i="20ms",
|
|
|
- std_e=f"{g_e0} uS", std_i="0pS")
|
|
|
+ gfluct_component = lems.Component(
|
|
|
+ id_=f"Gfluct_HL23PYR_{str(d).replace('.', '_')}",
|
|
|
+ type_="Gfluct",
|
|
|
+ start=self.stim_start,
|
|
|
+ stop=self.sim_end,
|
|
|
+ dt=self.dt,
|
|
|
+ E_e="0mV",
|
|
|
+ E_i="-80mV",
|
|
|
+ g_e0=f"{g_e0} uS",
|
|
|
+ g_i0="0pS",
|
|
|
+ tau_e="65ms",
|
|
|
+ tau_i="20ms",
|
|
|
+ std_e=f"{g_e0} uS",
|
|
|
+ std_i="0pS",
|
|
|
+ )
|
|
|
self.lems_components.add(gfluct_component)
|
|
|
|
|
|
# get segments to place at
|
|
|
- segs_apical = pyr_cell.get_segments_at_distance(d * longest_apical_branch_length)
|
|
|
+ segs_apical = pyr_cell.get_segments_at_distance(
|
|
|
+ d * longest_apical_branch_length
|
|
|
+ )
|
|
|
for seg, frac_along in segs_apical.items():
|
|
|
if seg in pyr_apical_segs:
|
|
|
try:
|
|
|
- cell_type_input_locations["HL23PYR"][d].append((seg, frac_along))
|
|
|
+ cell_type_input_locations["HL23PYR"][d].append(
|
|
|
+ (seg, frac_along)
|
|
|
+ )
|
|
|
except KeyError:
|
|
|
cell_type_input_locations["HL23PYR"][d] = []
|
|
|
- cell_type_input_locations["HL23PYR"][d].append((seg, frac_along))
|
|
|
+ cell_type_input_locations["HL23PYR"][d].append(
|
|
|
+ (seg, frac_along)
|
|
|
+ )
|
|
|
|
|
|
# create a new input list for each population, and each location
|
|
|
# because input list takes a component as an argument, and a different
|
|
|
@@ -802,24 +845,35 @@ class HL23Net(object):
|
|
|
|
|
|
for rel_dist, seginfos in input_segs.items():
|
|
|
# one input list per population per component
|
|
|
- inputlist = self.network.add("InputList", id=f"Gfluct_{input_list_ctr}",
|
|
|
- component=f"Gfluct_{cell_type}_{str(rel_dist).replace('.', '_')}",
|
|
|
- populations=pop.id,
|
|
|
- validate=False)
|
|
|
+ inputlist = self.network.add(
|
|
|
+ "InputList",
|
|
|
+ id=f"Gfluct_{input_list_ctr}",
|
|
|
+ component=f"Gfluct_{cell_type}_{str(rel_dist).replace('.', '_')}",
|
|
|
+ populations=pop.id,
|
|
|
+ validate=False,
|
|
|
+ )
|
|
|
input_list_ctr += 1
|
|
|
- for (seg, frac_along) in seginfos:
|
|
|
+ for seg, frac_along in seginfos:
|
|
|
if self.rotate_cells is True:
|
|
|
- inputlist.add("Input", id=f"{input_ctr}",
|
|
|
- target=f"../{pop.id}/0/{pop.component}",
|
|
|
- destination="synapses", segment_id=seg,
|
|
|
- fraction_along=frac_along)
|
|
|
+ inputlist.add(
|
|
|
+ "Input",
|
|
|
+ id=f"{input_ctr}",
|
|
|
+ target=f"../{pop.id}/0/{pop.component}",
|
|
|
+ destination="synapses",
|
|
|
+ segment_id=seg,
|
|
|
+ fraction_along=frac_along,
|
|
|
+ )
|
|
|
input_ctr += 1
|
|
|
else:
|
|
|
for inst in pop.instances:
|
|
|
- inputlist.add("Input", id=f"{input_ctr}",
|
|
|
- target=f"../{pop.id}/{inst.id}/{pop.component}",
|
|
|
- destination="synapses", segment_id=seg,
|
|
|
- fraction_along=frac_along)
|
|
|
+ inputlist.add(
|
|
|
+ "Input",
|
|
|
+ id=f"{input_ctr}",
|
|
|
+ target=f"../{pop.id}/{inst.id}/{pop.component}",
|
|
|
+ destination="synapses",
|
|
|
+ segment_id=seg,
|
|
|
+ fraction_along=frac_along,
|
|
|
+ )
|
|
|
input_ctr += 1
|
|
|
|
|
|
end = time.time()
|
|
|
@@ -960,7 +1014,9 @@ class HL23Net(object):
|
|
|
|
|
|
def visualize_network(self, min_cells=5):
|
|
|
"""Generate morph plots"""
|
|
|
- print(f"Visualising network at scale {self.network_scale} with {min_cells} in full")
|
|
|
+ print(
|
|
|
+ f"Visualising network at scale {self.network_scale} with {min_cells} in full"
|
|
|
+ )
|
|
|
# if the network has been constructed, copy over the populations and
|
|
|
# include the cells
|
|
|
if self.netdoc is not None:
|
|
|
@@ -1015,7 +1071,9 @@ class HL23Net(object):
|
|
|
PV_point_cells = random.sample(PV_cells, len(PV_cells) - min_cells)
|
|
|
if (len(VIP_cells) - min_cells) > 0:
|
|
|
VIP_point_cells = random.sample(VIP_cells, len(VIP_cells) - min_cells)
|
|
|
- print(f"Picked {len(PYR_point_cells) + len(SST_point_cells) + len(PV_point_cells) + len(VIP_point_cells)} point cells")
|
|
|
+ print(
|
|
|
+ f"Picked {len(PYR_point_cells) + len(SST_point_cells) + len(PV_point_cells) + len(VIP_point_cells)} point cells"
|
|
|
+ )
|
|
|
|
|
|
"""
|
|
|
print("Removing axons from PYR cells")
|
|
|
@@ -1071,9 +1129,17 @@ class HL23Net(object):
|
|
|
}
|
|
|
)
|
|
|
"""
|
|
|
- plot_interactive_3D(self.netdoc_file_name, plot_type="detailed", plot_spec={
|
|
|
- "point_cells": PYR_point_cells + SST_point_cells + VIP_point_cells + PV_point_cells
|
|
|
- }, min_width=1.0)
|
|
|
+ plot_interactive_3D(
|
|
|
+ self.netdoc_file_name,
|
|
|
+ plot_type="detailed",
|
|
|
+ plot_spec={
|
|
|
+ "point_cells": PYR_point_cells
|
|
|
+ + SST_point_cells
|
|
|
+ + VIP_point_cells
|
|
|
+ + PV_point_cells
|
|
|
+ },
|
|
|
+ min_width=1.0,
|
|
|
+ )
|
|
|
|
|
|
def create_simulation(self, dt=None, seed=123, record_data=True):
|
|
|
"""Create simulation, record data.
|
|
|
@@ -1096,19 +1162,23 @@ class HL23Net(object):
|
|
|
sim_id=self.simulation_id,
|
|
|
duration=float(self.sim_length.replace("ms", "")),
|
|
|
dt=float(dt.replace("ms", "")),
|
|
|
- simulation_seed=seed
|
|
|
+ simulation_seed=seed,
|
|
|
)
|
|
|
simulation.assign_simulation_target(self.network_id)
|
|
|
simulation.include_neuroml2_file(f"HL23Net_{self.network_scale}.net.nml")
|
|
|
simulation.include_lems_file(self.lems_components_file_name)
|
|
|
|
|
|
if record_data is True:
|
|
|
- simulation.create_output_file("output1", f"HL23Net_{self.network_scale}.v.dat")
|
|
|
+ simulation.create_output_file(
|
|
|
+ "output1", f"HL23Net_{self.network_scale}.v.dat"
|
|
|
+ )
|
|
|
print(f"Saving data to: HL23Net_{self.network_scale}.v.dat")
|
|
|
for apop in self.network.populations:
|
|
|
for inst in apop.instances:
|
|
|
simulation.add_column_to_output_file(
|
|
|
- "output1", f"{apop.id}_{inst.id}", f"{apop.id}/{inst.id}/{apop.component}/0/v"
|
|
|
+ "output1",
|
|
|
+ f"{apop.id}_{inst.id}",
|
|
|
+ f"{apop.id}/{inst.id}/{apop.component}/0/v",
|
|
|
)
|
|
|
|
|
|
simulation.save_to_file(self.lems_simulation_file)
|
|
|
@@ -1116,9 +1186,12 @@ class HL23Net(object):
|
|
|
end = time.time()
|
|
|
print(f"Creating simulation took: {(end - start)} seconds.")
|
|
|
|
|
|
- def run_sim(self, engine: str = "jneuroml_neuron",
|
|
|
- cluster: typing.Union[str, bool] = False,
|
|
|
- **kwargs):
|
|
|
+ def run_sim(
|
|
|
+ self,
|
|
|
+ engine: str = "jneuroml_neuron",
|
|
|
+ cluster: typing.Union[str, bool] = False,
|
|
|
+ **kwargs,
|
|
|
+ ):
|
|
|
"""Run the sim
|
|
|
|
|
|
:param engine: engine to use (jneuroml_neuron/jneuroml_netpyne)
|
|
|
@@ -1137,7 +1210,7 @@ class HL23Net(object):
|
|
|
max_memory=self.max_memory,
|
|
|
nogui=True,
|
|
|
show_plot_already=False,
|
|
|
- **kwargs
|
|
|
+ **kwargs,
|
|
|
)
|
|
|
elif cluster == "qsub":
|
|
|
print(f"Generating files but not running: {self.lems_simulation_file}")
|
|
|
@@ -1149,7 +1222,7 @@ class HL23Net(object):
|
|
|
max_memory=self.max_memory,
|
|
|
nogui=True,
|
|
|
show_plot_already=False,
|
|
|
- **kwargs
|
|
|
+ **kwargs,
|
|
|
)
|
|
|
# remove trailing backslash
|
|
|
if tdir[-1] == "/":
|
|
|
@@ -1157,7 +1230,7 @@ class HL23Net(object):
|
|
|
qsub_fn = f"{tdir}/{tdir}_generated/{self.lems_simulation_file}.sh"
|
|
|
netpyne_simfile = self.lems_simulation_file.replace(".xml", "_netpyne.py")
|
|
|
print(f"Generating qsub script for use on cluster: {qsub_fn}")
|
|
|
- with open(qsub_fn, 'w') as f:
|
|
|
+ with open(qsub_fn, "w") as f:
|
|
|
print(
|
|
|
inspect.cleandoc(
|
|
|
textwrap.dedent(
|
|
|
@@ -1175,23 +1248,37 @@ class HL23Net(object):
|
|
|
"""
|
|
|
)
|
|
|
),
|
|
|
- file=f
|
|
|
+ file=f,
|
|
|
)
|
|
|
elif cluster == "nsg_dry":
|
|
|
- print(f"Preparing to run on NSG (but not submitting): {self.lems_simulation_file}")
|
|
|
- run_on_nsg(engine, self.lems_simulation_file,
|
|
|
- dry_run=True, max_memory=self.max_memory, **kwargs)
|
|
|
+ print(
|
|
|
+ f"Preparing to run on NSG (but not submitting): {self.lems_simulation_file}"
|
|
|
+ )
|
|
|
+ run_on_nsg(
|
|
|
+ engine,
|
|
|
+ self.lems_simulation_file,
|
|
|
+ dry_run=True,
|
|
|
+ max_memory=self.max_memory,
|
|
|
+ **kwargs,
|
|
|
+ )
|
|
|
elif cluster == "nsg":
|
|
|
print(f"Running simulation on NSG: {self.lems_simulation_file}")
|
|
|
- run_on_nsg(engine, self.lems_simulation_file,
|
|
|
- max_memory=self.max_memory, **kwargs)
|
|
|
+ run_on_nsg(
|
|
|
+ engine, self.lems_simulation_file, max_memory=self.max_memory, **kwargs
|
|
|
+ )
|
|
|
print("Press Ctrl C to interrupt the waiting process")
|
|
|
- print("You can use `nsg_job -l` to check the status of the job and download the simulation reults")
|
|
|
+ print(
|
|
|
+ "You can use `nsg_job -l` to check the status of the job and download the simulation reults"
|
|
|
+ )
|
|
|
|
|
|
def plot_v_graphs(self):
|
|
|
"""Plot membrane potential graphs"""
|
|
|
- data = reload_saved_data(self.lems_simulation_file, plot=True,
|
|
|
- show_plot_already=True, reload_events=False)
|
|
|
+ data = reload_saved_data(
|
|
|
+ self.lems_simulation_file,
|
|
|
+ plot=True,
|
|
|
+ show_plot_already=True,
|
|
|
+ reload_events=False,
|
|
|
+ )
|
|
|
"""
|
|
|
logger.debug(data.keys())
|
|
|
xvals = [data["t"]]
|
|
|
@@ -1223,12 +1310,15 @@ class HL23Net(object):
|
|
|
"""
|
|
|
print("Adding step current to each cell")
|
|
|
# a single pulse generator
|
|
|
- pg = self.netdoc.add(neuroml.PulseGenerator, id="pg_0",
|
|
|
- delay="100ms", duration=self.sim_length,
|
|
|
- amplitude="0.2nA")
|
|
|
+ pg = self.netdoc.add(
|
|
|
+ neuroml.PulseGenerator,
|
|
|
+ id="pg_0",
|
|
|
+ delay="100ms",
|
|
|
+ duration=self.sim_length,
|
|
|
+ amplitude="0.2nA",
|
|
|
+ )
|
|
|
for pop in self.network.populations:
|
|
|
- self.network.add(neuroml.ExplicitInput, target=f"{pop.id}[0]",
|
|
|
- input=pg.id)
|
|
|
+ self.network.add(neuroml.ExplicitInput, target=f"{pop.id}[0]", input=pg.id)
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
Ankur Sinha (Ankur Sinha Gmail)