98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
|
|
from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus as SAM
|
|
from models.LIFACnoise import LifacNoiseModel
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import helperFunctions as hF
|
|
from CellData import CellData
|
|
|
|
|
|
def main():
|
|
# 2012-12-13_ao fit and eod frequency:
|
|
parameters = {'mem_tau': 0.0133705462739553, 'tau_a': 0.06682759542588587, 'input_scaling': 60.766243690761144,
|
|
'v_base': 0, 'step_size': 5e-05, 'dend_tau': 0.0008667253013050408, 'v_zero': 0, 'v_offset': -6.25,
|
|
'noise_strength': 0.03337309379328535, 'a_zero': 2, 'threshold': 1, 'delta_a': 0.0726267312975076}
|
|
eod_freq = 658
|
|
cell_data = CellData("./data/2012-12-13-ao-invivo-1/")
|
|
model = LifacNoiseModel(parameters)
|
|
mean_duration = np.mean(cell_data.get_sam_durations())
|
|
contrasts = cell_data.get_sam_contrasts()
|
|
spiketimes = cell_data.get_sam_spiketimes()
|
|
for i, m_freq in enumerate(cell_data.get_sam_delta_frequencies()):
|
|
|
|
stimulus = SAM(eod_freq, contrasts[i], m_freq)
|
|
prob_desnity_function_model = generate_pdf(model, stimulus, sim_length=mean_duration)
|
|
for spikes in spiketimes[i]:
|
|
prob_density_cell = spiketimes_calculate_pdf(spikes, cell_data.get_sampling_interval())
|
|
|
|
plt.plot(prob_density_cell)
|
|
plt.plot(prob_desnity_function_model)
|
|
plt.show()
|
|
plt.close()
|
|
|
|
|
|
# __init__(carrier_frequency, contrast, modulation_frequency, start_time=0, duration=np.inf, amplitude=1)
|
|
mod_freqs = np.arange(-60, eod_freq*4 + 61, 10)
|
|
sigma_of_pdfs = []
|
|
for m_freq in mod_freqs:
|
|
print(m_freq, "max: {:.2f}".format(mod_freqs[-1]))
|
|
stimulus = SAM(eod_freq, 0.2, m_freq)
|
|
|
|
prob_density_function = generate_pdf(model, stimulus)
|
|
buffer = 0.25
|
|
buffer_idx = int(buffer / model.get_parameters()["step_size"])
|
|
|
|
sigma_of_pdfs.append(np.std(prob_density_function[buffer_idx:-buffer_idx]))
|
|
|
|
normed_mod_freqs = (mod_freqs + eod_freq) / eod_freq
|
|
plt.plot(normed_mod_freqs, sigma_of_pdfs)
|
|
plt.savefig("./figures/sam/test.png")
|
|
plt.close()
|
|
|
|
pass
|
|
|
|
|
|
def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005):
|
|
|
|
trials_rate_list = []
|
|
step_size = model.get_parameters()["step_size"]
|
|
for _ in range(trials):
|
|
v1, spikes = model.simulate(stimulus, total_time_s=sim_length)
|
|
|
|
binary = np.zeros(int(sim_length/step_size))
|
|
spikes = [int(s / step_size) for s in spikes]
|
|
for s_idx in spikes:
|
|
binary[s_idx] = 1
|
|
|
|
kernel = gaussian_kernel(kernel_width, step_size)
|
|
rate = np.convolve(binary, kernel, mode='same')
|
|
trials_rate_list.append(rate)
|
|
|
|
times = [np.arange(0, sim_length, step_size) for _ in range(trials)]
|
|
t, mean_rate = hF.calculate_mean_of_frequency_traces(times, trials_rate_list, step_size)
|
|
|
|
return mean_rate
|
|
|
|
|
|
def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.005):
|
|
length = int(spikes[-1] / step_size)+1
|
|
binary = np.zeros(length)
|
|
spikes = [int(s / step_size) for s in spikes]
|
|
for s_idx in spikes:
|
|
binary[s_idx] = 1
|
|
|
|
kernel = gaussian_kernel(kernel_width, step_size)
|
|
rate = np.convolve(binary, kernel, mode='same')
|
|
|
|
return rate
|
|
|
|
|
|
def gaussian_kernel(sigma, dt):
|
|
x = np.arange(-4. * sigma, 4. * sigma, dt)
|
|
y = np.exp(-0.5 * (x / sigma) ** 2) / np.sqrt(2. * np.pi) / sigma
|
|
return y
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|