Alexander/P-unit_model
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

add way to test other parameters

Browse Source
This commit is contained in:
a.ott 2020-01-17 17:29:38 +01:00
parent 29fdee009e
commit 61605cbb9e
1 changed files with 34 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

55
RelationAdaptionVariables.py
View File

@ -10,15 +10,22 @@ import functions as fu
def main(): def main():
values = np.arange(2, 18, 1)
parameter = "threshold"
for value in values:
lifac_model = LIFACModel({"delta_a": 0}) lifac_model = LIFACModel({"delta_a": 0})
stimulus_strengths = np.arange(20, 32, 1) lifac_model.set_variable(parameter, value)
stimulus_strengths = np.arange(50, 60, 1)
line_vars, boltzmann_vars = find_fitting_boltzmann(lifac_model, stimulus_strengths) line_vars, boltzmann_vars = find_fitting_boltzmann(lifac_model, stimulus_strengths)
find_relation(line_vars, boltzmann_vars) relation = find_relation(lifac_model, line_vars, boltzmann_vars, stimulus_strengths)
print("threshold:", value)
print(relation)
def find_fitting_boltzmann(lifac_model, stimulus_strengths): def find_fitting_boltzmann(lifac_model, stimulus_strengths):
# Requieres a lifac model with adaption delta_a = 0, so just the base is fit # Requires a lifac model with adaption delta_a = 0, so just the base is fit
frequencies = [] frequencies = []
duration = 0.2 duration = 0.2
@ -44,26 +51,23 @@ def find_fitting_boltzmann(lifac_model, stimulus_strengths):
return popt, popt2 return popt, popt2
def find_relation(line_vars, boltzmann_vars, stimulus_strengths): def find_relation(lifac, line_vars, boltzmann_vars, stimulus_strengths, use_line=True):
# boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01] # boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
# line_vars = [5.10369405, -29.79774806] # line_vars = [5.10369405, -29.79774806]
# example values for base lifac (15.1.20) and stimulus 20-32 # example values for base lifac (15.1.20) and stimulus 20-32
stimulus_step_size = 2 duration = 0.1
stimulus_range = np.arange(20, 32, stimulus_step_size)
lifac_adaption_strength_range = np.arange(0, 3.1, 0.5)
duration = 0.2
lifac_adaption_strength_range = np.arange(0, 3, 0.5)
firerate_adaption_variables = [] firerate_adaption_variables = []
for lifac_adaption_strength in lifac_adaption_strength_range: for lifac_adaption_strength in lifac_adaption_strength_range:
print(lifac_adaption_strength) print(lifac_adaption_strength)
lifac = LIFACModel({"delta_a": lifac_adaption_strength, "tau_a": 20}) lifac.set_variable("delta_a", lifac_adaption_strength)
lifac.set_variable("tau_a", 10)
adapted_frequencies = [] adapted_frequencies = []
for stim in stimulus_range: for stim in stimulus_strengths:
print("stim:", stim) #print("stim:", stim)
stimulus = StepStimulus(0, duration, stim) stimulus = StepStimulus(0, duration, stim)
lifac.simulate(stimulus, duration) lifac.simulate(stimulus, duration)
spiketimes = lifac.get_spiketimes() spiketimes = lifac.get_spiketimes()
@ -75,16 +79,18 @@ def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
for i in range(len(adapted_frequencies)): for i in range(len(adapted_frequencies)):
goal_adapted_freq = adapted_frequencies[i] goal_adapted_freq = adapted_frequencies[i]
# stimulus_strength_after_adaption = fu.inverse_full_boltzmann(goal_adapted_freq, if use_line:
# boltzmann_vars[0], # assume fitted linear firing rate as basis of the fire-rate model:
# boltzmann_vars[1],
# boltzmann_vars[2],
# boltzmann_vars[3],)
# assume fitted line as basis of the fire-rate model:
stimulus_strength_after_adaption = fu.inverse_line(goal_adapted_freq, line_vars[0], line_vars[1]) stimulus_strength_after_adaption = fu.inverse_line(goal_adapted_freq, line_vars[0], line_vars[1])
else:
# assume fitted boltzmann firing rate as basis of the fire-rate model:
stimulus_strength_after_adaption = fu.inverse_full_boltzmann(goal_adapted_freq,
boltzmann_vars[0],
boltzmann_vars[1],
boltzmann_vars[2],
boltzmann_vars[3],)
adaption_strength = stimulus_range[i] - stimulus_strength_after_adaption adaption_strength = stimulus_strengths[i] - stimulus_strength_after_adaption
firerate_adaption = adaption_strength / goal_adapted_freq firerate_adaption = adaption_strength / goal_adapted_freq
firerate_adaption_strengths.append(firerate_adaption) firerate_adaption_strengths.append(firerate_adaption)
@ -95,7 +101,7 @@ def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
# plt.show() # plt.show()
for i in range(len(lifac_adaption_strength_range)): for i in range(len(lifac_adaption_strength_range)):
plt.plot([lifac_adaption_strength_range[i]+p*0.01 for p in range(len(stimulus_range))], firerate_adaption_variables[i]) plt.plot([lifac_adaption_strength_range[i]+p*0.01 for p in range(len(stimulus_strengths))], firerate_adaption_variables[i])
mean_firerate_adaption_value = [np.mean(strengths) for strengths in firerate_adaption_variables] mean_firerate_adaption_value = [np.mean(strengths) for strengths in firerate_adaption_variables]
@ -103,11 +109,12 @@ def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
plt.title("Relation of adaption strength variables:\n Small 'subplots' value for different stimulus strength") plt.title("Relation of adaption strength variables:\n Small 'subplots' value for different stimulus strength")
plt.xlabel("lifac adaption strength: delta_a") plt.xlabel("lifac adaption strength: delta_a")
plt.ylabel("firerate adaption strength: alpha") plt.ylabel("firerate adaption strength: alpha")
plt.savefig("figures/adaption_relation_stimulus_strength_stepsize_0-0001.png") plt.savefig("figures/adaption_relation_threshold_" + str(lifac.get_parameters()["threshold"]) + ".png")
plt.close() plt.close()
popt, pcov = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value) popt, pcov = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
print(popt) # print(popt)
return popt
def test_firerate_model( boltzmann_vars): def test_firerate_model( boltzmann_vars):