def lateral_branching(neuron_params):
ds.reset_kernel()
np.random.seed(kernel['seeds'])
ds.set_kernel_status(kernel, simulation_id="uniform_branching")
neuron_params['growth_cone_model'] = 'run_tumble'
neuron_params[use_type] = False
neuron_params["position"] = np.random.uniform(-500, 500,
(num_neurons, 2)) * um
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
num_neurites=2)
step(1 * hour, 1, False, False)
neuron_params[use_type] = True
ds.set_object_properties(gid, params=neuron_params)
# ~ axon_params=neuron_params)
step(2 * day, 1, False, False)
# neuron_params['use_lateral_branching'] = True
ds.SaveSwc(swc_resolution=5)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
return swc_file
def run_dense(kernel, neuron_params, ID, plot):
"""
"""
resolution = 1.
np.random.seed(kernel['seeds'])
kernel["resolution"] = resolution
kernel["angles_in_radians"] = True
ds.set_kernel_status(kernel, simulation_id=ID)
neuron_params['growth_cone_model'] = gc_model
neuron_params["position"] = np.random.uniform(-1000, 1000,
(num_neurons, 2))
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=axon_params,
dendrites_params=dendrite_params,
num_neurites=6,
position=[])
# ds.set_object_properties(gid, params=neuron_params,
# axon_params=neuron_params)
step(3. / resolution, 1, False, plot)
step(300. / resolution, 1, False, plot)
neuron_params['use_van_pelt'] = True
dendrite_params['use_van_pelt'] = True
axon_params['use_flpl_branching'] = False
axon_params['flpl_branching_rate'] = 0.001
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(6000. / resolution, 1, False, plot)
axon_migated = {
'use_van_pelt': True,
# "flpl_branching_rate" : 0.004,
"res_retraction_threshold": 0.4,
"res_elongation_threshold": 0.15,
"res_elongation_factor": 0.6,
# 'use_van_pelt' : True,
"res_neurite_generated": 4500.,
"res_neurite_delivery_tau": 50.,
"res_correlation": 0.15,
"res_variance": 0.02,
"res_use_ratio": 0.3,
}
axon_params.update(axon_migated)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(3000. / resolution, 1, False, plot)
ds.SaveSwc(swc_resolution=25)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
# ds.reset_kernel()
return swc_file
def resource_branching(neuron_params):
ds.reset_kernel()
np.random.seed(kernel['seeds'])
ds.set_kernel_status(kernel, simulation_id="van_pelt_branching")
neuron_params['growth_cone_model'] = 'run_tumble_critical'
neuron_params['res_branching_threshold'] = np.inf
neuron_params["position"] = np.random.uniform(-500, 500, (num_neurons, 2))
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=neuron_params,
num_neurites=1,
position=[])
step(10, 1, False, False)
neuron_params['res_branching_threshold'] = b_th
ds.set_object_properties(gid,
params=neuron_params,
axon_params=neuron_params)
step(5000, 1, False, False)
# neuron_params['use_lateral_branching'] = True
ds.SaveSwc(swc_resolution=5)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
return swc_file
def run_dense(neuron_params):
"""
"""
resolution = 1.
np.random.seed(kernel['seeds'])
kernel["resolution"] = resolution * minute
ds.set_kernel_status(kernel, simulation_id="van_pelt_branching")
neuron_params['growth_cone_model'] = gc_model
neuron_params["position"] = np.random.uniform(
-500, 500, (num_neurons, 2)) * um
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=axon_params,
num_neurites=3,
position=[]
)
neuron_params['use_van_pelt'] = False
neuron_params['use_flpl_branching'] = False
ds.set_object_properties(gid, params=neuron_params,
axon_params=axon_params)
step(200./resolution * minute, 1, False, True)
neuron_params['use_van_pelt'] = True
# neuron_params['use_flpl_branching'] = True
# neuron_params["flpl_branching_rate"] = 0.001
ds.set_object_properties(gid, params=neuron_params,
axon_params=neuron_params)
step(1000./resolution * minute, 1, False, True)
step(1000./resolution * minute, 1, False, True)
step(1000./resolution * minute, 1, False, True)
step(1000./resolution * minute, 1, False, True)
# step(180, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(4080, 1, False, True)
# neuron_params['use_van_pelt'] = True
# ds.set_object_properties(gid,params = neuron_params,
# axon_params=neuron_params)
# step(10, 1, False, True)
# step(10, 1, False, False)
# neuron_params['use_lateral_branching'] = True
ds.SaveSwc(swc_resolution=5)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
# ds.reset_kernel()
return swc_file
ds.simulate(5 * day)
ds.plot.plot_recording(rec, show=False)
ds.plot.plot_neurons(show=True)
lb = {
# 'res_branching_threshold': np.inf,
"use_flpl_branching": True,
"flpl_branching_rate": 0.003 * cph,
"lateral_branching_angle_mean": 50. * deg
}
lb_axon = lb.copy()
lb_axon["flpl_branching_rate"] = 0.012 * cph
ds.set_object_properties(n, axon_params=lb_axon, dendrites_params=lb)
ds.simulate(15 * day)
ds.plot.plot_neurons(show=True)
end_branching = {
"res_branching_threshold": 0.2 * uM,
'res_branching_proba': 0.05
}
ds.set_object_properties(n,
axon_params=end_branching,
dendrites_params=end_branching)
ds.simulate(10 * day)
ds.plot.plot_recording(rec2, show=False)
vp_dend = {
# branching choice and parameters
"use_van_pelt": True,
"gc_split_angle_mean": 30. * deg,
"gc_split_angle_std": 5. * deg,
"B": 5. * cpm,
"E": 0.1,
"S": 1.5, # large S leads to core dump
"T": 7 * day,
}
neurite_params = {"axon": vp_axon, "dendrites": vp_dend}
# update the parameters lists of the neurons 'gids'
ds.set_object_properties(gids, neurite_params=neurite_params)
ds.simulate(7 * day + 2 * day)
print("Simulation time : {}".format(dense.get_kernel_status('time')))
ds.plot.plot_neurons(mode="mixed", show=True)
'''
Change the parameters to include lateral branching
'''
print("\nLateral branching ON\n")
lat_params = {
"speed_growth_cone": 0.05 * um / minute,
"use_van_pelt": False,
"use_uniform_branching": True,
n = ds.create_neurons(n=num_neurons,
params=neuron_params,
dendrites_params=dend_params,
num_neurites=8)
ds.simulate(1. * day)
ds.plot.plot_neurons()
dend_params = {
# "use_uniform_branching": True,
# "uniform_branching_rate": 0.02*cph,
# "lateral_branching_angle_mean": 70.*deg,
}
ds.set_object_properties(n, dendrites_params=dend_params)
ds.simulate(1. * day)
ds.plot.plot_neurons()
dend_params = {
"speed_growth_cone": 0.005 * um / minute,
"somatropic_scale": 30. * um,
"somatropic_factor": 0.1,
# branching
"uniform_branching_rate": 0.02 * cph,
"uniform_split_rate": 0.05 * cph,
"gc_split_angle_mean": 90. * deg,
"gc_split_angle_std": 3. * deg,
}
def run_dense(neuron_params):
"""
"""
resolution = 1.
np.random.seed(kernel['seeds'])
kernel["resolution"] = resolution
kernel["angles_in_radians"] = True
ds.set_kernel_status(kernel, simulation_id="case_neuron")
neuron_params['growth_cone_model'] = gc_model
neuron_params["position"] = np.random.uniform(-1000, 1000,
(num_neurons, 2))
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=axon_params,
dendrites_params=dendrite_params,
num_neurites=4,
position=[])
step(100. / resolution, 1, False, True)
arborization = {
"use_van_pelt": True,
"gc_split_angle_mean": 0.50,
"B": 11.,
"T": 200.
}
arborization_axon = {
"use_van_pelt": True,
"gc_split_angle_mean": 1.0,
"B": 19.,
"T": 5000.
}
dendrite_params.update(arborization)
axon_params.update(arborization_axon)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(1000. / resolution, 1, False, True)
elongation = {
"use_van_pelt": False,
"persistence_length": 30.,
"res_retraction_factor": 0.01,
"res_elongation_factor": 0.1,
# "res_leakage": 0.05,
"res_retraction_threshold": 0.1,
"res_elongation_threshold": 0.2,
"res_leakage": 30.0,
"res_neurite_generated": 4500.,
"res_correlation": 0.05,
"res_variance": 0.002,
"res_use_ratio": 0.1,
}
elongation_axon = {"res_elongation_factor": 0.5}
dendrite_params.update(elongation)
axon_params.update(elongation_axon)
print("elongation")
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(2002. / resolution, 1, False, True)
# stop the dendrites
stortignation = {
"use_van_pelt": False,
"persistence_length": 10.,
"res_retraction_factor": 0.0,
"res_elongation_factor": 0.0,
"res_retraction_threshold": 0.000,
"res_elongation_threshold": 10.,
}
stortignation_axon = {
"use_van_pelt": False,
"sensing_angle": 0.20,
"persistence_length": 91.,
"flpl_branching_rate": 0.002,
"use_flpl_branching": True,
"res_retraction_factor": 0.02,
"res_elongation_factor": 0.2910,
# "res_leakage": 0.05,
"res_retraction_threshold": 0.0001,
"res_elongation_threshold": 0.10,
"res_leakage": 10.0,
"res_neurite_generated": 45000.,
"res_correlation": 0.05,
"res_weight_diameter": 0.,
"res_variance": 0.001,
"res_use_ratio": 0.1,
}
print("stortignation")
dendrite_params.update(stortignation)
axon_params.update(stortignation_axon)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(2000. / resolution, 1, False, True)
ds.SaveSwc(swc_resolution=5)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
# ds.reset_kernel()
return swc_file
n = ds.create_neurons(n=num_neurons,
params=neuron_params,
neurite_params=dend_params,
num_neurites=8)
ds.simulate(1. * day)
ds.plot.plot_neurons()
dend_params = {
# "use_uniform_branching": True,
# "uniform_branching_rate": 0.02*cph,
# "lateral_branching_angle_mean": 70.*deg,
}
ds.set_object_properties(n, neurite_params=dend_params)
ds.simulate(1. * day)
ds.plot.plot_neurons()
dend_params = {
"speed_growth_cone": 0.005 * um / minute,
"somatropic_scale": 30. * um,
"somatropic_factor": 0.1,
# branching
"uniform_branching_rate": 0.02 * cph,
"uniform_split_rate": 0.05 * cph,
"gc_split_angle_mean": 90. * deg,
"gc_split_angle_std": 3. * deg,
}
step(1 * day, 0, True)
dendrite_params.update({
"speed_growth_cone": 0.04 * um / minute,
"use_van_pelt": False
})
axon_params.update({
"speed_growth_cone": 0.1 * um / minute,
"use_van_pelt": False,
"use_uniform_branching": True,
"uniform_branching_rate": 0.1 * cph,
})
ds.set_object_properties(gids,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
# ~ for i in range(2):
# ~ step(4.*day, 0, True)
# ~ step(16.25*hour, 0, True)
step(simtime, 0, True)
# ~ step(5.*hour, 0, True)
duration = time.time() - start
print(ds.get_kernel_status("time"))
# prepare the plot
plt.show(block=True)
print("SIMULATION ENDED")
# save
# Turn branching on
#~ vp_branching = {'use_van_pelt': True}
resource_branching = {
'res_branching_threshold': 80.,
'res_branching_proba': 0.0005
}
d_rsrc_branching = {
'res_branching_threshold': 60.,
'res_branching_proba': 0.0003
}
#~ ds.set_object_properties(n, params=vp_branching)
#~ ds.set_object_properties(n, params=resource_branching)
ds.set_object_properties(n,
axon_params=resource_branching,
dendrites_params=d_rsrc_branching)
ds.simulate(20000)
ds.plot.plot_neurons(show=True)
lb = {
"use_van_pelt": False,
'res_branching_threshold': np.inf,
"use_flpl_branching": True,
"flpl_branching_rate": 0.001,
"lateral_branching_angle_mean": 45.
}
no_b = {"use_van_pelt": False}
def run_dense(neuron_params):
"""
"""
kernel["resolution"] = resolution * minute
ds.set_kernel_status(kernel, simulation_id="case_neuron")
neuron_params['growth_cone_model'] = gc_model
print(neuron_params['growth_cone_model'])
neuron_params["position"] = np.random.uniform(-1000, 1000,
(num_neurons, 2)) * um
print(neuron_params['growth_cone_model'])
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=axon_params,
dendrites_params=dendrite_params,
num_neurites=1)
print(neuron_params['growth_cone_model'])
# ~ rec = ds.create_recorders(gid, ["speed", "resource"], levels="growth_cone")
rec = ds.create_recorders(gid, ["resource"], levels="growth_cone")
print(neuron_params['growth_cone_model'])
# ds.set_object_properties(gid, params=neuron_params,
# axon_params=neuron_params)
step(3. / resolution * minute, 1, False, False)
step(500. / resolution * minute, 1, False, False)
print(neuron_params['growth_cone_model'])
neuron_params['use_van_pelt'] = True
dendrite_params['use_van_pelt'] = True
axon_params['use_flpl_branching'] = True
axon_params['flpl_branching_rate'] = 0.001 * cpm
neuron_params.pop('growth_cone_model')
print(dendrite_params)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(2000. / resolution * minute, 1, False, True)
axon_migated = {
# 'use_flpl_branching' : True,
# "flpl_branching_rate" : 0.004 * cpm,
"res_retraction_threshold": 0.4 * uM,
"res_elongation_threshold": 0.15 * uM,
"res_elongation_factor": 0.4 * um / minute,
# 'use_van_pelt' : True,
"res_neurite_generated": 4500. * uM,
"res_neurite_delivery_tau": 50. * minute,
"res_correlation": 0.15,
"res_variance": 0.02 * uM / minute**0.5,
"res_use_ratio": 0.3,
}
axon_params.update(axon_migated)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(3000. / resolution * minute, 1, False, True)
# neuron_params['use_flpl_branching'] = True
# neuron_params["flpl_branching_rate"] = 0.001 * cpm
# ds.set_object_properties(gid,params = neuron_params,
# axon_params= neuron_params)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(180, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(4080, 1, False, True)
# neuron_params['use_van_pelt'] = True
# ds.set_object_properties(gid,params = neuron_params,
# axon_params=neuron_params)
# step(10, 1, False, True)
# step(10, 1, False, False)
# neuron_params['use_lateral_branching'] = True
#~ ds.SaveSwc(swc_resolution=5)
#~ ds.save_json_info()
ds.plot.plot_recording(rec, show=False)
swc_file = ds.get_simulation_id()
# print(swc_file)
# ds.reset_kernel()
return swc_file
"T": 3*day,
}
axon_vp = {
"use_van_pelt": True,
# Best model
"gc_split_angle_mean": 30.*deg,
"B": 5.*cpm,
"E": 0.,
"S": 1.,
"T": 3*day,
}
neurite_params = {"axon": axon_vp, "dendrites": dend_vp}
ds.set_object_properties(n, neurite_params=neurite_params)
ds.simulate(12*day)
# ds.save_to_swc("chandelier-cell.swc", gid=n, resolution=50)
ds.plot.plot_neurons()
lb_a = {
"use_van_pelt": False,
#~ 'res_branching_threshold': np.inf,
"use_flpl_branching": True,
"flpl_branching_rate": 0.1*cph,
"lateral_branching_angle_mean": 45.*deg
}
# ~ ds.plot.plot_neurons(show=True)
lb = {
# 'res_branching_threshold': np.inf,
"use_flpl_branching": True,
"flpl_branching_rate": 0.003*cph,
"lateral_branching_angle_mean": 50.*deg
}
lb_axon = lb.copy()
lb_axon["flpl_branching_rate"] = 0.012*cph
neurite_params = {"axon": lb_axon, "dendrites": lb}
ds.set_object_properties(n, neurite_params=neurite_params)
ds.simulate(15*day)
# ~ ds.plot.plot_recording(rec, show=False)
# ~ ds.plot.plot_neurons(show=True)
end_branching = {
"res_branching_threshold": 0.2*uM,
'res_branching_proba': 0.05
}
ds.set_object_properties(n, neurite_params=end_branching)
ds.simulate(10*day)
def run_dense(neuron_params):
"""
"""
resolution = 30. * minute
np.random.seed(kernel['seeds']) #Seeds the random number generator
kernel["resolution"] = resolution
# kernel["angles_in_radians"] = True
ds.set_kernel_status(kernel, simulation_id="multipolar")
neuron_params["position"] = np.random.uniform(-1000, 1000,
(num_neurons, 2)) * um
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=axon_params,
dendrites_params=dendrite_params,
num_neurites=4)
# ds.set_object_properties(gid, params=neuron_params,
# axon_params=neuron_params)
step(3 * day, 1, False, True)
axon_params['use_van_pelt'] = True
axon_params["B"] = 90. * cpm
axon_params["E"] = 0.2
axon_params["S"] = 1.
axon_params["T"] = 10000. * minute
dendrite_params['use_van_pelt'] = True
dendrite_params["B"] = 90. * cpm
dendrite_params["E"] = 0.2
dendrite_params["S"] = 1.
dendrite_params["T"] = 10000. * minute
axon_params['use_flpl_branching'] = False
axon_params['flpl_branching_rate'] = 0.001 * cpm
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(6 * day, 1, False, True)
axon_migated = {
'use_van_pelt': True,
# "flpl_branching_rate" : 0.004,
"res_retraction_threshold": 0.4 * uM,
"res_elongation_threshold": 0.15 * uM,
"res_elongation_factor": 0.6 * um / minute,
# 'use_van_pelt' : True,
"res_neurite_generated": 4500. * uM,
"res_neurite_delivery_tau": 50. * minute,
"res_correlation": 0.15,
"res_variance": 0.02 * uM / minute**0.5,
"res_use_ratio": 0.3 * cpm,
}
axon_params.update(axon_migated)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(3 * day, 1, False, True)
# neuron_params['use_flpl_branching'] = True
# neuron_params["flpl_branching_rate"] = 0.001
# ds.set_object_properties(gid,params = neuron_params,
# axon_params= neuron_params)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(180, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(4080, 1, False, True)
# neuron_params['use_van_pelt'] = True
# ds.set_object_properties(gid,params = neuron_params,
# axon_params=neuron_params)
# step(10, 1, False, True)
# step(10, 1, False, False)
# neuron_params['use_lateral_branching'] = True
ds.io.save_to_swc(resolution=25)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
# ds.reset_kernel()
return swc_file
"use_van_pelt": False,
"use_flpl_branching": True,
"flpl_branching_rate": 0.00025 * cpm,
"lateral_branching_angle_mean": 45. * deg,
}
dend_params = {
"use_van_pelt": False,
"use_uniform_branching": True,
"uniform_branching_rate": 0.0002 * cpm,
"persistence_length": 100. * um,
"speed_growth_cone": 0.01 * um / minute,
"lateral_branching_angle_mean": 40. * deg,
}
ds.set_object_properties(n, dendrites_params=dend_params, axon_params=lb_axon)
ds.simulate(30000 * minute)
ds.plot.plot_neurons()
# then further branching
vp_axon = {
"use_flpl_branching": False,
#~ "use_van_pelt": True,
#~ "B": 5.,
#~ "T": 40000.,
#~ "gc_split_angle_mean": 30.,
}
dend_params = {
dendrites_params=dend_params,
num_neurites=6)
ds.simulate(2 * day)
ds.plot.plot_neurons(show=True, subsample=50)
lb_axon = {
"use_van_pelt": False,
"use_flpl_branching": True,
"flpl_branching_rate": 0.1 * cph,
"speed_growth_cone": 0.02 * um / minute,
"lateral_branching_angle_mean": 45. * deg,
}
ds.set_object_properties(n, axon_params=lb_axon)
ds.simulate(5 * day)
ds.io.save_to_swc("granule-cell.swc", gid=n)
ds.plot.plot_neurons(show=True, subsample=50)
import neurom as nm
from neurom import viewer
nrn = nm.load_neuron("granule-cell.swc")
fig, _ = viewer.draw(nrn)
for ax in fig.axes:
ax.set_title("")
def run_dense(kernel, neuron_params, ID, plot=True):
"""
"""
np.random.seed(kernel['seeds'])
resolution = kernel["resolution"]
kernel["angles_in_radians"] = True
ds.set_kernel_status(kernel, simulation_id=ID)
neuron_params['growth_cone_model'] = gc_model
neuron_params["position"] = np.random.uniform(-1000, 1000,
(num_neurons, 2))
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=axon_params,
dendrites_params=dendrite_params,
num_neurites=4,
position=[])
step(100. / resolution, 1, False, plot)
arborization = {
"use_van_pelt": True,
"gc_split_angle_mean": 0.50,
"B": 11.,
"T": 200.
}
arborization_axon = {
"use_van_pelt": True,
"gc_split_angle_mean": 1.0,
"B": 19.,
"T": 2000.
}
dendrite_params.update(arborization)
axon_params.update(arborization_axon)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(1000. / resolution, 1, False, plot)
elongation = {
"use_van_pelt": False,
"persistence_length": 30.,
}
dendrite_params.update(elongation)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(2002. / resolution, 1, False, plot)
stortignation = {
"use_van_pelt": False,
"persistence_length": 10.,
}
stortignation_axon = {
"use_van_pelt": False,
# "sensing_angle": 0.20,
"use_flpl_branching": True,
"persistence_length": 91.,
"flpl_branching_rate": 0.0066,
}
# dendrite_params.update(stortignation)
# axon_params.update(stortignation_axon)
# ds.set_object_properties(gid,
# params=neuron_params,
# dendrites_params=dendrite_params,
# axon_params=axon_params)
step(2000. / resolution, 1, False, plot)
# stortignation = {"use_van_pelt": False,
# "persistence_length":10.,
# }
# stortignation_axon={
# "use_van_pelt": False,
# "use_flpl_branching": True,
# "flpl_branching_rate" : 0.003,
# "sensing_angle": 0.63,
# "persistence_length": 40.,
# }
# dendrite_params.update(stortignation)
# axon_params.update(stortignation_axon)
# ds.set_object_properties(gid,
# params=neuron_params,
# dendrites_params=dendrite_params,
# axon_params=axon_params)
# step(2000./resolution, 1, False, True)
# step(2000./resolution, 1, False, True)
ds.SaveSwc(swc_resolution=5)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
# ds.reset_kernel()
return swc_file
ds.set_kernel_status(kernel)
pyr_neuron = ds.create_neurons(1,
params=pyr_nrn,
axon_params=pyr_axon_i,
dendrites_params=pyr_dend_i,
num_neurites=3)
# initial extension (5 days)
ds.simulate(sim_time)
ds.plot.plot_neurons(mode="mixed")
print(ds.get_object_properties(pyr_neuron))
# Extension and branching period (5 days)
ds.set_object_properties(pyr_neuron,
axon_params=pyr_axon_lb,
dendrites_params=pyr_dend_lb)
ds.simulate(sim_time)
ds.plot.plot_neurons(mode="mixed")
# Extension and branching period (5 more days)
ds.simulate(sim_time)
ds.plot.plot_neurons(mode="mixed")
# Termination period (10 days)
ds.set_object_properties(pyr_neuron,
axon_params=axon_t,
dendrites_params=dend_t)
ds.simulate(2 * sim_time)
ds.plot.plot_neurons(mode="mixed")
def run_dense(neuron_params):
"""
"""
resolution = 1.
np.random.seed(kernel['seeds'])
kernel["resolution"] = resolution
kernel["angles_in_radians"] = True
ds.set_kernel_status(kernel, simulation_id="case_neuron")
neuron_params['growth_cone_model'] = gc_model
neuron_params["position"] = np.random.uniform(-1000, 1000,
(num_neurons, 2))
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
axon_params=axon_params,
dendrites_params=dendrite_params,
num_neurites=4,
position=[])
step(2000. / resolution, 1, False, True)
splitting_dendrites = {
'use_van_pelt': True,
"persistence_length": 60.0,
"gc_split_angle_mean": 1.,
'use_flpl_branching': True,
"flpl_branching_rate": 0.0036,
"B": 30.,
"E": 0.9,
"S": 1.0,
"T": 10000.,
}
dendrite_params.update(splitting_dendrites)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params)
step(2000. / resolution, 1, False, True)
arborization = {
'use_van_pelt': False,
'use_flpl_branching': False,
"persistence_length": 30.0,
"res_weight_diameter": 0.1,
"res_retraction_threshold": 0.01,
"res_elongation_threshold": 0.12,
"res_elongation_factor": 0.0612,
"res_retraction_factor": 10.,
# 'use_van_pelt' : True,
"res_neurite_generated": 9500.,
"res_neurite_delivery_tau": 50.,
"res_correlation": 0.15,
"res_variance": 0.02,
"res_use_ratio": 0.3,
}
dendrite_params.update(arborization)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(2000. / resolution, 1, False, True)
arborization = {
'use_van_pelt': True,
# 'use_flpl_branching' : True,
"flpl_branching_rate": 0.00036,
"persistence_length": 5.0,
"res_retraction_threshold": 0.1,
"res_weight_diameter": 0.001,
"res_elongation_threshold": 0.14,
"res_elongation_factor": 0.12,
# 'use_van_pelt' : True,
"res_neurite_generated": 9500.,
"res_neurite_delivery_tau": 50.,
"res_correlation": 0.5,
"res_variance": 0.2,
"res_use_ratio": 0.4,
}
dendrite_params.update(arborization)
ds.set_object_properties(gid,
params=neuron_params,
dendrites_params=dendrite_params,
axon_params=axon_params)
step(2000. / resolution, 1, False, True)
step(2000. / resolution, 1, False, True)
# neuron_params['use_flpl_branching'] = True
# neuron_params["flpl_branching_rate"] = 0.001
# ds.set_object_properties(gid,params = neuron_params,
# axon_params= neuron_params)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(1000./resolution, 1, False, True)
# step(180, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(1080, 1, False, True)
# step(4080, 1, False, True)
# neuron_params['use_van_pelt'] = True
# ds.set_object_properties(gid,params = neuron_params,
# axon_params=neuron_params)
# step(10, 1, False, True)
# step(10, 1, False, False)
# neuron_params['use_lateral_branching'] = True
ds.SaveSwc(swc_resolution=15)
ds.save_json_info()
swc_file = ds.get_simulation_id()
# print(swc_file)
# ds.reset_kernel()
return swc_file
}
vp_dend = {
# branching choice and parameters
"use_van_pelt": True,
"gc_split_angle_mean": 30. * deg,
"gc_split_angle_std": 5. * deg,
"B": 900. * cpm,
"E": 0.1,
"S": 1.5, # large S leads to core dump
"T": 8.6e2 * minute,
}
# update the parameters lists of the neurons 'gids'
ds.set_object_properties(gids,
axon_params=vp_axon,
dendrites_params=vp_dend)
# pprint(ds.get_object_properties(gids))
ds.simulate(7 * day + 2 * day)
print("Simulation time : {}".format(dense.get_kernel_status('time')))
ds.plot.plot_neurons(mode="mixed", show=True)
'''
Change the parameters to include lateral branching
'''
print("\nLateral branching ON\n")
lat_params = {
"speed_growth_cone": 0.005 * um / minute,
