157 lines
6.2 KiB
Python
157 lines
6.2 KiB
Python
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from models.FirerateModel import FirerateModel
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from models.LIFACnoise import LifacNoiseModel
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from stimuli.StepStimulus import StepStimulus
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import helperFunctions as hf
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import functions as fu
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def main():
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values = [1] # np.arange(5, 40, 1)
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parameter = "currently not active"
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for value in values:
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lifac_model = LifacNoiseModel({"delta_a": 0})
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# lifac_model.set_variable(parameter, value)
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stimulus_strengths = np.arange(50, 60, 1)
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line_vars = find_fitting_line(lifac_model, stimulus_strengths)
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relation = find_relation(lifac_model, line_vars, stimulus_strengths, confirm=True)
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print(parameter, value)
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print(relation)
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def find_fitting_line(lifac_model, stimulus_strengths):
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# Requires a lifac model with adaption delta_a = 0, so just the base is fit
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frequencies = []
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duration = 0.2
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for stim_strength in stimulus_strengths:
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lifac_model.simulate(StepStimulus(0, duration, stim_strength), duration)
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spiketimes = lifac_model.get_spiketimes()
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if len(spiketimes) == 0:
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frequencies.append(0)
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continue
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time, freq = hf.calculate_time_and_frequency_trace(spiketimes, lifac_model.get_sampling_interval())
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if len(freq) == 0:
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frequencies.append(0)
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else:
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frequencies.append(freq[-1])
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popt, pcov = curve_fit(fu.line, stimulus_strengths, frequencies)
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print("line:", popt)
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# 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]))
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# print("boltzmann:", popt2)
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# plt.plot(stimulus_strengths, frequencies)
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# plt.plot(stimulus_strengths, [fu.line(x, popt[0], popt[1]) for x in stimulus_strengths], '.')
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# plt.plot(stimulus_strengths, [fu.full_boltzmann(x, popt2[0], popt2[1], popt2[2], popt2[3]) for x in stimulus_strengths], 'o')
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# plt.show()
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return popt # , popt2
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def find_relation(lifac, line_vars, stimulus_strengths, parameter="", value=0, confirm=False):
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# boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
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# line_vars = [5.10369405, -29.79774806]
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# example values for base lifac (15.1.20) and stimulus 20-32
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duration = 0.4
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lifac_adaption_strength_range = np.arange(0, 0.31, 0.05)
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firerate_adaption_variables = []
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for lifac_adaption_strength in lifac_adaption_strength_range:
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print(lifac_adaption_strength)
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lifac.set_variable("delta_a", lifac_adaption_strength)
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lifac.set_variable("tau_a", 40)
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adapted_frequencies = []
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firerate_adaption_strengths = []
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for stim in stimulus_strengths:
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#print("stim:", stim)
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stimulus = StepStimulus(0, duration, stim)
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lifac.simulate(stimulus, duration)
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spiketimes = lifac.get_spiketimes()
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time, freq = hf.calculate_time_and_frequency_trace(spiketimes, lifac.get_sampling_interval())
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if len(freq) == 0:
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adapted_frequencies.append(0)
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goal_adapted_freq = 0
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else:
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adapted_frequencies.append(freq[-1])
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goal_adapted_freq = freq[-1]
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# assume fitted linear firing rate as basis of the fire-rate model:
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stimulus_strength_after_adaption = fu.inverse_line(goal_adapted_freq, line_vars[0], line_vars[1])
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# needed adaption strength
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adaption_strength = stim - stimulus_strength_after_adaption
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# adaption variable in model:
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firerate_adaption = adaption_strength / goal_adapted_freq
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# test in model if calculated
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if confirm:
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test_adaption_strength_in_firerate_model(line_vars, firerate_adaption, stimulus, goal_adapted_freq)
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firerate_adaption_strengths.append(firerate_adaption)
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firerate_adaption_variables.append(firerate_adaption_strengths)
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# plt.plot(stimulus_range, firerate_adaption_strength, label=str(lifac_adaption_strength))
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# plt.show()
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for i in range(len(lifac_adaption_strength_range)):
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plt.plot([lifac_adaption_strength_range[i]+p*0.001 for p in range(len(stimulus_strengths))], firerate_adaption_variables[i])
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mean_firerate_adaption_value = [np.median(strengths) for strengths in firerate_adaption_variables]
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l_vars, x = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
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plt.plot(lifac_adaption_strength_range, mean_firerate_adaption_value, label="slope:" + str(round(l_vars[0], 5)))
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plt.title("Relation of adaption strength variables:\n Colored points values for different stimulus strengths")
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plt.xlabel("lifac adaption strength: delta_a")
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plt.ylabel("firerate adaption strength: alpha")
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plt.legend()
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if parameter != "":
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plt.savefig("figures/adaption_relation_" + parameter + "_" + str(value) + ".png")
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else:
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plt.savefig("figures/adaption_relation.png")
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plt.close()
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popt, pcov = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
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# print(popt)
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return popt
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def test_adaption_strength_in_firerate_model(line_vars, adaption_strength, stimulus, expected_freq):
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params = {"function_params": line_vars, "adaptation_factor": adaption_strength, "a_tau": 10}
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model = FirerateModel(params)
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model.simulate(stimulus, 0.2)
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freq = model.get_frequency()[-1]
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diff = expected_freq - freq
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if diff > 0.00001 * expected_freq:
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print("expected freq:", expected_freq, "=?=", str(freq), ":", str(diff < 0.00001 * expected_freq))
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def test_firerate_model( boltzmann_vars):
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fr_model = FirerateModel(params={"function_params": boltzmann_vars, "adaptation_factor": 0})
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frequencies = []
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stim_strengths = np.arange(0, 50, 0.5)
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duration = 0.5
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for stim_strength in stim_strengths:
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fr_model.simulate(StepStimulus(0, duration, stim_strength), duration)
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frequencies.append(fr_model.get_frequency()[-1])
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plt.plot(stim_strengths, frequencies)
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plt.plot(np.arange(20, 32, 1), [fu.line(x, 5.10369, -29.7977481) for x in np.arange(20, 32, 1)], 'o')
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plt.show()
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if __name__ == '__main__':
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main()
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