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