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general changes in search for relation

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a.ott 2020-01-20 16:22:08 +01:00
parent 6cd4e13da4
commit d274f19f3c
1 changed files with 47 additions and 35 deletions
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78
RelationAdaptionVariables.py
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@ -10,21 +10,21 @@ import functions as fu
def main(): def main():
values = np.arange(2, 18, 1) values = [1] # np.arange(5, 40, 1)
parameter = "threshold" parameter = "currently not active"
for value in values: for value in values:
lifac_model = LIFACModel({"delta_a": 0}) lifac_model = LIFACModel({"delta_a": 0})
lifac_model.set_variable(parameter, value) # lifac_model.set_variable(parameter, value)
stimulus_strengths = np.arange(50, 60, 1) stimulus_strengths = np.arange(50, 60, 1)
line_vars, boltzmann_vars = find_fitting_boltzmann(lifac_model, stimulus_strengths) line_vars = find_fitting_line(lifac_model, stimulus_strengths)
relation = find_relation(lifac_model, line_vars, boltzmann_vars, stimulus_strengths) relation = find_relation(lifac_model, line_vars, stimulus_strengths, confirm=True)
print("threshold:", value) print(parameter, value)
print(relation) print(relation)
def find_fitting_boltzmann(lifac_model, stimulus_strengths): def find_fitting_line(lifac_model, stimulus_strengths):
# Requires 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 = []
@ -41,31 +41,32 @@ def find_fitting_boltzmann(lifac_model, stimulus_strengths):
frequencies.append(freq[-1]) frequencies.append(freq[-1])
popt, pcov = curve_fit(fu.line, stimulus_strengths, frequencies) popt, pcov = curve_fit(fu.line, stimulus_strengths, frequencies)
popt2, pcov = curve_fit(fu.full_boltzmann, stimulus_strengths, frequencies, p0=[700, 0, 5, 25], bounds=([0, 0, -np.inf, -np.inf], [3000, 0.001, np.inf, np.inf]))
print("line:", popt) print("line:", popt)
print("boltzmann:", popt2) # popt2, pcov = curve_fit(fu.full_boltzmann, stimulus_strengths, frequencies, p0=[700, 0, 5, 25], bounds=([0, 0, -np.inf, -np.inf], [3000, 0.001, np.inf, np.inf]))
# print("boltzmann:", popt2)
# plt.plot(stimulus_strengths, frequencies) # plt.plot(stimulus_strengths, frequencies)
# plt.plot(stimulus_strengths, [fu.line(x, popt[0], popt[1]) for x in stimulus_strengths], '.') # plt.plot(stimulus_strengths, [fu.line(x, popt[0], popt[1]) for x in stimulus_strengths], '.')
# plt.plot(stimulus_strengths, [fu.full_boltzmann(x, popt2[0], popt2[1], popt2[2], popt2[3]) for x in stimulus_strengths], 'o') # plt.plot(stimulus_strengths, [fu.full_boltzmann(x, popt2[0], popt2[1], popt2[2], popt2[3]) for x in stimulus_strengths], 'o')
# plt.show() # plt.show()
return popt, popt2 return popt # , popt2
def find_relation(lifac, line_vars, boltzmann_vars, stimulus_strengths, use_line=True): def find_relation(lifac, line_vars, stimulus_strengths, parameter="", value=0, confirm=False):
# 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
duration = 0.1 duration = 0.4
lifac_adaption_strength_range = np.arange(0, 3.1, 0.5) lifac_adaption_strength_range = np.arange(0, 0.31, 0.05)
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.set_variable("delta_a", lifac_adaption_strength) lifac.set_variable("delta_a", lifac_adaption_strength)
lifac.set_variable("tau_a", 10) lifac.set_variable("tau_a", 40)
adapted_frequencies = [] adapted_frequencies = []
firerate_adaption_strengths = []
for stim in stimulus_strengths: for stim in stimulus_strengths:
#print("stim:", stim) #print("stim:", stim)
stimulus = StepStimulus(0, duration, stim) stimulus = StepStimulus(0, duration, stim)
@ -74,25 +75,20 @@ def find_relation(lifac, line_vars, boltzmann_vars, stimulus_strengths, use_line
time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac.get_sampling_interval()/1000) time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac.get_sampling_interval()/1000)
adapted_frequencies.append(freq[-1]) adapted_frequencies.append(freq[-1])
goal_adapted_freq = freq[-1]
firerate_adaption_strengths = []
for i in range(len(adapted_frequencies)):
goal_adapted_freq = adapted_frequencies[i]
if use_line:
# assume fitted linear firing rate as basis of the fire-rate model: # assume fitted linear firing rate 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_strengths[i] - stimulus_strength_after_adaption
# needed adaption strength
adaption_strength = stim - stimulus_strength_after_adaption
# adaption variable in model:
firerate_adaption = adaption_strength / goal_adapted_freq firerate_adaption = adaption_strength / goal_adapted_freq
# test in model if calculated
if confirm:
test_adaption_strength_in_firerate_model(line_vars, firerate_adaption, stimulus, goal_adapted_freq)
firerate_adaption_strengths.append(firerate_adaption) firerate_adaption_strengths.append(firerate_adaption)
firerate_adaption_variables.append(firerate_adaption_strengths) firerate_adaption_variables.append(firerate_adaption_strengths)
@ -101,15 +97,20 @@ def find_relation(lifac, line_vars, boltzmann_vars, stimulus_strengths, use_line
# 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_strengths))], firerate_adaption_variables[i]) plt.plot([lifac_adaption_strength_range[i]+p*0.001 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.median(strengths) for strengths in firerate_adaption_variables]
plt.plot(lifac_adaption_strength_range, mean_firerate_adaption_value) l_vars, x = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
plt.title("Relation of adaption strength variables:\n Small 'subplots' value for different stimulus strength") plt.plot(lifac_adaption_strength_range, mean_firerate_adaption_value, label="slope:" + str(round(l_vars[0], 5)))
plt.title("Relation of adaption strength variables:\n Colored points values for different stimulus strengths")
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_threshold_" + str(lifac.get_parameters()["threshold"]) + ".png") plt.legend()
if parameter != "":
plt.savefig("figures/adaption_relation_" + parameter + "_" + str(value) + ".png")
else:
plt.savefig("figures/adaption_relation.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)
@ -117,6 +118,17 @@ def find_relation(lifac, line_vars, boltzmann_vars, stimulus_strengths, use_line
return popt return popt
def test_adaption_strength_in_firerate_model(line_vars, adaption_strength, stimulus, expected_freq):
params = {"function_params": line_vars, "adaptation_factor": adaption_strength, "a_tau": 10}
model = FirerateModel(params)
model.simulate(stimulus, 0.2)
freq = model.get_frequency()[-1]
diff = expected_freq - freq
if diff > 0.00001 * expected_freq:
print("expected freq:", expected_freq, "=?=", str(freq), ":", str(diff < 0.00001 * expected_freq))
def test_firerate_model( boltzmann_vars): def test_firerate_model( boltzmann_vars):
fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0}) fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0})