156 lines
6.2 KiB
Python
156 lines
6.2 KiB
Python
|
|
from models.FirerateModel import FirerateModel
|
|
from models.LIFACnoise import LifacNoiseModel
|
|
from stimuli.StepStimulus import StepStimulus
|
|
from my_util import helperFunctions as hf, functions as fu
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from scipy.optimize import curve_fit
|
|
|
|
|
|
def main():
|
|
values = [1] # np.arange(5, 40, 1)
|
|
parameter = "currently not active"
|
|
for value in values:
|
|
lifac_model = LifacNoiseModel({"delta_a": 0})
|
|
# lifac_model.set_variable(parameter, value)
|
|
stimulus_strengths = np.arange(50, 60, 1)
|
|
|
|
line_vars = find_fitting_line(lifac_model, stimulus_strengths)
|
|
relation = find_relation(lifac_model, line_vars, stimulus_strengths, confirm=True)
|
|
|
|
print(parameter, value)
|
|
print(relation)
|
|
|
|
|
|
def find_fitting_line(lifac_model, stimulus_strengths):
|
|
# Requires a lifac model with adaption delta_a = 0, so just the base is fit
|
|
frequencies = []
|
|
|
|
duration = 0.2
|
|
for stim_strength in stimulus_strengths:
|
|
lifac_model.simulate_slow(StepStimulus(0, duration, stim_strength), duration)
|
|
|
|
spiketimes = lifac_model.get_spiketimes()
|
|
if len(spiketimes) == 0:
|
|
frequencies.append(0)
|
|
continue
|
|
time, freq = hf.calculate_time_and_frequency_trace(spiketimes, lifac_model.get_sampling_interval())
|
|
|
|
if len(freq) == 0:
|
|
frequencies.append(0)
|
|
else:
|
|
frequencies.append(freq[-1])
|
|
|
|
popt, pcov = curve_fit(fu.line, stimulus_strengths, frequencies)
|
|
print("line:", popt)
|
|
# 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, [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.show()
|
|
return popt # , popt2
|
|
|
|
|
|
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]
|
|
# line_vars = [5.10369405, -29.79774806]
|
|
# example values for base lifac (15.1.20) and stimulus 20-32
|
|
|
|
duration = 0.4
|
|
|
|
lifac_adaption_strength_range = np.arange(0, 0.31, 0.05)
|
|
firerate_adaption_variables = []
|
|
for lifac_adaption_strength in lifac_adaption_strength_range:
|
|
print(lifac_adaption_strength)
|
|
lifac.set_variable("delta_a", lifac_adaption_strength)
|
|
lifac.set_variable("tau_a", 40)
|
|
|
|
adapted_frequencies = []
|
|
firerate_adaption_strengths = []
|
|
for stim in stimulus_strengths:
|
|
#print("stim:", stim)
|
|
stimulus = StepStimulus(0, duration, stim)
|
|
lifac.simulate_slow(stimulus, duration)
|
|
spiketimes = lifac.get_spiketimes()
|
|
time, freq = hf.calculate_time_and_frequency_trace(spiketimes, lifac.get_sampling_interval())
|
|
|
|
if len(freq) == 0:
|
|
adapted_frequencies.append(0)
|
|
goal_adapted_freq = 0
|
|
else:
|
|
adapted_frequencies.append(freq[-1])
|
|
goal_adapted_freq = freq[-1]
|
|
|
|
# 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])
|
|
|
|
# needed adaption strength
|
|
adaption_strength = stim - stimulus_strength_after_adaption
|
|
# adaption variable in model:
|
|
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_variables.append(firerate_adaption_strengths)
|
|
|
|
# plt.plot(stimulus_range, firerate_adaption_strength, label=str(lifac_adaption_strength))
|
|
# plt.show()
|
|
|
|
for i in range(len(lifac_adaption_strength_range)):
|
|
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.median(strengths) for strengths in firerate_adaption_variables]
|
|
|
|
l_vars, x = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
|
|
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.ylabel("firerate adaption strength: alpha")
|
|
plt.legend()
|
|
if parameter != "":
|
|
plt.savefig("figures/adaption_relation_" + parameter + "_" + str(value) + ".png")
|
|
else:
|
|
plt.savefig("figures/adaption_relation.png")
|
|
plt.close()
|
|
popt, pcov = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
|
|
|
|
# print(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):
|
|
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__':
|
|
main()
|