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 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(neuron_params):
"""
"""
resolution = 1.
# np.random.seed(kernel['seeds'])
np.random.seed(13)
kernel["resolution"] = resolution * minute
# kernel["angles_in_radians"] = False
ds.set_kernel_status(kernel, simulation_id="van_pelt_branching")
neuron_params['growth_cone_model'] = gc_model
neuron_params["position"] = np.random.uniform(-2500, 2500,
(num_neurons, 2)) * um
gid = ds.create_neurons(n=num_neurons,
params=neuron_params,
num_neurites=3,
position=[])
step(6000. / resolution * hour, 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
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 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
def RunNetGrowth(n_samples,
sim_length,
n_procs,
neuron_params,
save_path="tmp_measure",
plot=False):
"""
Run NetGrowth simulation
"""
kernel = {
"seeds": [33, 57, 19, 37, 79, 87, 11][:n_procs],
"num_local_threads": n_procs,
"resolution": 1.
}
experiment_params = {}
experiment_params["num_neurons"] = n_samples
np.random.seed(kernel['seeds'])
ds.get_kernel_status(kernel, ds.generate_simulation_id())
culture = ds.CreateEnvironment(culture_file, min_x=0, max_x=cavity_x_max)
pos_left = culture.seed_neurons(neurons=experiment_params["num_neurons"],
xmax=neuron_x_max,
soma_radius=1.)
neuron_params['growth_cone_model'] = 'random_walk'
neuron_params['position'] = pos_left
gids = None
gids = ds.create_neurons(experiment_params["num_neurons"],
"random_walk",
culture=culture,
params=neuron_params,
num_neurites=1)
ds.simulate(sim_length)
# fig, ax = plt.subplots()
# ds.plot.plot_neurons(gid=range(experiment_params["num_neurons"]),
# culture = culture, soma_color="k",
# axon_color='g', axis=ax, show=True)
ds.save_json_info(filepath=save_path)
ds.SaveSwc(filepath=save_path, swc_resolution=1)
# ds.save_json_info(filepath=tmp_dir)
# ds.plot_neurons(show_nodes=True)
ds.reset_kernel()
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
dendrites_params=dendrite_params,
axon_params=axon_params)
fig, ax = plt.subplots()
# ds.plot.plot_neurons(gid=range(100), culture=culture, soma_alpha=0.8,
# axon_color='g', gc_color="r", axis=ax, show=False)
# ds.plot.plot_neurons(gid=range(100, 200), show_culture=False, axis=ax,
# soma_alpha=0.8, axon_color='darkorange', gc_color="r",
# show=True)
# step(4000, 0, False)
# ~ for loop_n in range(5):
# ~ step(500, loop_n, True)
duration = time.time() - start
# prepare the plot
ds.plot.plot_neurons(gid=range(200), culture=culture, soma_alpha=0.8,
axon_color='g', gc_color="r", axis=ax, show=False)
ds.plot.plot_neurons(gid=range(200, 400), show_culture=False, axis=ax, ymax=500,
soma_alpha=0.8, axon_color='darkorange', gc_color="r",
show=True)
ds.plot.plot_neurons(gid=range(200, 400), show_culture=False, axis=ax,
soma_alpha=0.8, axon_color='yellow', gc_color="r",
show=True)
plt.show(block=True)
print("SIMULATION ENDED")
# save
graph =ds.generate_network()
save_path = CleanFolder(os.path.join(os.getcwd(),"diode_double_swc"))
ds.save_json_info(filepath=save_path)
ds.SaveSwc(filepath=save_path,swc_resolution = 10)
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
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
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
