plot cell reaction to SAM stimulus

sam_experiments.py

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+
+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
+
+
+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
+
+    model = LifacNoiseModel(parameters)
+
+    # __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 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()