def uq_medaka():
"""
Perform the uncertainty quantification and sensitivity analysis of the
medaka model.
Returns
-------
data : uncertainpy.Data
A data object that contains the results from the uncertainty
quantification of the rat model.
"""
parameters = {
"g_K": 4.18e-4,
"g_Ca": 6.25e-5,
"g_SK": 4e-4,
"g_l": 2e-5,
"g_BK": 3.13e-4,
"g_Na": 2.19e-2,
}
parameters = un.Parameters(parameters)
# Set all parameters to have a uniform distribution
# within a +/- 50% interval around their original value
parameters.set_all_distributions(un.uniform(1))
# Set full g_BK distribution
parameters["g_BK"].distribution = cp.Uniform(0, 3.13e-4)
# Initialize the features
features = un.SpikingFeatures(
new_features=[is_bursting, is_regular, is_not_spiking],
features_to_run=features_to_run,
logger_level="error",
strict=False,
threshold=0.55,
end_threshold=-0.1,
normalize=True,
trim=False,
min_amplitude=min_spike_amplitude,
)
# Initialize the model and defining default options
model = un.NeuronModel(file="medaka.py", name="medaka", ignore=True)
# Perform the uncertainty quantification
UQ = un.UncertaintyQuantification(model,
parameters=parameters,
features=features)
# We set the seed to easier be able to reproduce the result
data = UQ.quantify(
seed=10,
filename="medaka",
polynomial_order=polynomial_order,
save=True,
plot=None,
)
return data
def perform_uncertainty_analysis():
"""
Perform an uncertainty quantification and sensitivity analysis of the model.
Returns
-------
data : uncertainpy.Data object
The results from the uncertainty quantification.
"""
parameters = {
"g_K": scale_conductance(G_K),
"g_Ca": scale_conductance(G_Ca),
"g_SK": scale_conductance(G_SK),
"g_l": scale_conductance(G_l),
"g_BK": scale_conductance(0.67)
} # Temporary value
parameters = un.Parameters(parameters)
# Set all conductances to have a uniform distribution
# within a +/- 50% interval around their original value
parameters.set_all_distributions(un.uniform(1))
parameters["g_BK"].distribution = cp.Uniform(scale_conductance(0),
scale_conductance(1))
# Initialize features with the correct algorithm
features = un.SpikingFeatures(new_features=burstiness_factor,
strict=False,
logger_level="error",
features_to_run=features_to_run,
threshold=0.55,
end_threshold=-0.1,
normalize=True,
trim=False,
min_amplitude=min_spike_amplitude)
# Initialize the model and define default options
model = un.NeuronModel(file="tabak.py",
name="tabak",
discard=discard,
noise_amplitude=noise_amplitude,
simulation_time=simulation_time,
ignore=True)
# Perform the uncertainty quantification
UQ = un.UncertaintyQuantification(model,
parameters=parameters,
features=features)
# We set the seed to be able to reproduce the result
data = UQ.quantify(seed=10,
plot=None,
data_folder=data_folder,
polynomial_order=polynomial_order)
return data
def go(parameter_list=[["a", 0.02, None], ["b", 0.2, None], ["c", -65, None],
["d", 8, None]]):
# Create the parameters
parameters = un.Parameters(parameter_list)
# Set all parameters to have a uniform distribution
# within a 50% interval around their fixed value
parameters.set_all_distributions(un.uniform(0.5))
# Create a model from coffee_cup function and add labels
model = un.Model(izhikevich, labels=["Time (ms)", "Voltage (mV)"])
# Initialize features
features = un.SpikingFeatures(features_to_run="all")
# Perform the uncertainty quantification
UQ = un.UncertaintyQuantification(model=model,
parameters=parameters,
features=features)
data = UQ.quantify()
super(NeuronModelBahl, self).__init__(adaptive=True,
path="bahl_neuron_model",
stimulus_start=stimulus_start,
stimulus_end=stimulus_end)
# Reimplement the set_parameters method used by run
def set_parameters(self, parameters):
for parameter in parameters:
self.h(parameter + " = " + str(parameters[parameter]))
# These commands must be added for this specific
# model to recalculate the parameters after they have been set
self.h("recalculate_passive_properties()")
self.h("recalculate_channel_densities()")
# Initialize the model with the start and end time of the stimulus
model = NeuronModelBahl(stimulus_start=100, stimulus_end=600)
# Define a parameter list and use it directly
parameters = {"e_pas": -80, cp.Uniform(-60, -85),
"apical Ra": 261, cp.Uniform(150, 300)}
# Initialize the features
features = un.SpikingFeatures()
# Perform the uncertainty quantification
UQ = un.UncertaintyQuantification(model=model,
parameters=parameters,
features=features)
data = UQ.quantify()
import uncertainpy as un
from izhikevich_class import Izhikevich
# Define a parameter list
parameter_list = [["a", 0.02, None], ["b", 0.2, None], ["c", -65, None],
["d", 8, None]]
# Create the parameters
parameters = un.Parameters(parameter_list)
# Set all parameters to have a uniform distribution
# within a 50% interval around their fixed value
parameters.set_all_distributions(un.uniform(0.5))
# Initialize the model
model = Izhikevich()
features = un.SpikingFeatures(features_to_run="all")
# Perform the uncertainty quantification
UQ = un.UncertaintyQuantification(model=model,
parameters=parameters,
features=features)
data = UQ.quantify()