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prepare for testing of other variables

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AlexanderOtt 2020-01-15 21:33:07 +01:00
parent 3eda710394
commit 29fdee009e
1 changed files with 47 additions and 39 deletions
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RelationAdaptionVariables.py
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@ -10,68 +10,60 @@ import functions as fu
def main(): def main():
#boltzmann_vars = find_fitting_boltzmann() lifac_model = LIFACModel({"delta_a": 0})
#test_firerate_model(boltzmann_vars) stimulus_strengths = np.arange(20, 32, 1)
find_relation()
def find_fitting_boltzmann(): line_vars, boltzmann_vars = find_fitting_boltzmann(lifac_model, stimulus_strengths)
lifac = LIFACModel({"delta_a": 0}) find_relation(line_vars, boltzmann_vars)
def find_fitting_boltzmann(lifac_model, stimulus_strengths):
# Requieres a lifac model with adaption delta_a = 0, so just the base is fit
frequencies = [] frequencies = []
stim_strengths = np.arange(20, 32, 0.5)
duration = 0.5
for stim_strength in stim_strengths:
lifac.simulate(StepStimulus(0, duration, stim_strength), duration)
spiketimes = lifac.get_spiketimes() duration = 0.2
for stim_strength in stimulus_strengths:
lifac_model.simulate(StepStimulus(0, duration, stim_strength), duration)
spiketimes = lifac_model.get_spiketimes()
if len(spiketimes) == 0: if len(spiketimes) == 0:
frequencies.append(0) frequencies.append(0)
continue continue
time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac.get_sampling_interval()/1000) time, freq = hf.calculate_isi_frequency(spiketimes, 0, lifac_model.get_sampling_interval() / 1000)
frequencies.append(freq[-1]) frequencies.append(freq[-1])
popt, pcov = curve_fit(fu.line, stim_strengths, frequencies) popt, pcov = curve_fit(fu.line, stimulus_strengths, frequencies)
popt2, pcov = curve_fit(fu.full_boltzmann, stim_strengths, frequencies, p0=[700, 0, 5, 25], bounds=([0, 0, -np.inf, -np.inf], [3000, 0.001, np.inf, np.inf])) 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) print("boltzmann:", popt2)
# plt.plot(stim_strengths, frequencies) # plt.plot(stimulus_strengths, frequencies)
# plt.plot(stim_strengths, [fu.line(x, popt[0], popt[1]) for x in stim_strengths], '.') # plt.plot(stimulus_strengths, [fu.line(x, popt[0], popt[1]) for x in stimulus_strengths], '.')
# plt.plot(stim_strengths, [fu.full_boltzmann(x, popt2[0], popt2[1], popt2[2], popt2[3]) for x in stim_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 popt2 return popt, popt2
def test_firerate_model(boltzmann_vars): def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0}) # boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
# line_vars = [5.10369405, -29.79774806]
# example values for base lifac (15.1.20) and stimulus 20-32
frequencies = [] stimulus_step_size = 2
stim_strengths = np.arange(0, 50, 0.5) stimulus_range = np.arange(20, 32, stimulus_step_size)
duration = 0.5
for stim_strength in stim_strengths:
fr_model.simulate(StepStimulus(0, duration, stim_strength), duration)
frequencies.append(fr_model.get_frequency()[-1])
plt.plot(stim_strengths, frequencies)
plt.plot(np.arange(20, 32, 1), [fu.line(x, 5.10369, -29.7977481) for x in np.arange(20, 32, 1)], 'o')
plt.show()
def find_relation(): duration = 0.2
stimulus_step_size = 1
stimulus_range = np.arange(20, 32, stimulus_step_size)
boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
line_vars = [5.10369405, -29.79774806]
duration = 0.5
lifac_adaption_strength_range = np.arange(0, 3, 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:
lifac = LIFACModel({"delta_a": lifac_adaption_strength, "tau_a": 20}, "") print(lifac_adaption_strength)
lifac = LIFACModel({"delta_a": lifac_adaption_strength, "tau_a": 20})
adapted_frequencies = [] adapted_frequencies = []
for stim in stimulus_range: for stim in stimulus_range:
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()
@ -111,12 +103,28 @@ def find_relation():
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.png") plt.savefig("figures/adaption_relation_stimulus_strength_stepsize_0-0001.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)
def test_firerate_model( boltzmann_vars):
fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0})
frequencies = []
stim_strengths = np.arange(0, 50, 0.5)
duration = 0.5
for stim_strength in stim_strengths:
fr_model.simulate(StepStimulus(0, duration, stim_strength), duration)
frequencies.append(fr_model.get_frequency()[-1])
plt.plot(stim_strengths, frequencies)
plt.plot(np.arange(20, 32, 1), [fu.line(x, 5.10369, -29.7977481) for x in np.arange(20, 32, 1)], 'o')
plt.show()
if __name__ == '__main__': if __name__ == '__main__':
main() main()