173 lines
4.4 KiB
Python
173 lines
4.4 KiB
Python
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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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time = 30
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adaption_time = 1
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step = 0.00005
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stimulus = np.zeros(int((time + adaption_time)/step))
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v1, spikes = pif_simulation(stimulus, step)
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spikes = np.array(spikes)
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count = len(spikes[spikes > adaption_time])
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print("Baseline freq PIF: {:.2f}".format(count / time))
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v1, spikes = lif_simulation(stimulus, step)
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spikes = np.array(spikes)
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count = len(spikes[spikes > adaption_time])
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print("Baseline freq LIF: {:.2f}".format(count / time))
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v1, spikes = lifac_simulation(stimulus, step)
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spikes = np.array(spikes)
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count = len(spikes[spikes > adaption_time])
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print("Baseline freq LIFAC: {:.2f}".format(count / time))
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v1, spikes = lifac_ref_simulation(stimulus, step)
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spikes = np.array(spikes)
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count = len(spikes[spikes > adaption_time])
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print("Baseline freq LIFAC+ref: {:.2f}".format(count / time))
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def pif_simulation(stimulus, step_size):
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v_0 = 0
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v_base = 0
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threshold = 1
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v_offset = 0.15
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mem_tau = 0.015
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v_1 = np.zeros(len(stimulus))
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v_1[0] = v_0
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spikes = []
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for i in range(1, len(v_1), 1):
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# dvdt = (v_offset + stimulus[i]) / mem_tau
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v_1[i] = v_1[i - 1] + ((v_offset + stimulus[i]) / mem_tau) * step_size
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if v_1[i] > threshold:
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v_1[i] = v_base
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spikes.append(i*step_size)
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return v_1, spikes
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def lif_simulation(stimulus, step_size):
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v_0 = 0
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v_base = 0
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threshold = 1
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v_offset = 1.001255
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mem_tau = 0.015
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v_1 = np.zeros(len(stimulus))
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v_1[0] = v_0
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spikes = []
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for i in range(1, len(v_1), 1):
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v_1[i] = v_1[i - 1] + ((v_offset - v_1[i-1] + stimulus[i]) / mem_tau) * step_size
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if v_1[i] > threshold:
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v_1[i] = v_base
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spikes.append(i * step_size)
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return v_1, spikes
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def lifac_simulation(stimulus, step_size):
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v_0 = 0
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v_base = 0
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threshold = 1
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v_offset = 1.3445
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mem_tau = 0.015
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adaption_tau = 0.1
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adaption_step = 0.05
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adaption = np.zeros(len(stimulus))
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adaption[0] = 0.5
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v_1 = np.zeros(len(stimulus))
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v_1[0] = v_0
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spikes = []
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for i in range(1, len(v_1), 1):
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v_1[i] = v_1[i - 1] + ((v_offset - v_1[i-1] - adaption[i-1] + stimulus[i]) / mem_tau) * step_size
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adaption[i] = adaption[i-1] + (-adaption[i-1] / adaption_tau) * step_size
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if v_1[i] > threshold:
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v_1[i] = v_base
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spikes.append(i * step_size)
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adaption[i] += adaption_step / adaption_tau
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return v_1, spikes
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def lifac_ref_simulation(stimulus, step_size):
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v_0 = 0
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v_base = 0
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threshold = 1
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v_offset = 1.3445
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mem_tau = 0.015
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adaption_tau = 0.1
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adaption_step = 0.05
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ref_time = 0.005
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adaption = np.zeros(len(stimulus))
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adaption[0] = 0.5
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v_1 = np.zeros(len(stimulus))
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v_1[0] = v_0
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spikes = []
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for i in range(1, len(v_1), 1):
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if len(spikes) > 0 and i*step_size < spikes[-1] + ref_time:
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v_1[i] = v_base
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else:
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v_1[i] = v_1[i - 1] + ((v_offset - v_1[i-1] - adaption[i-1] + stimulus[i]) / mem_tau) * step_size
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adaption[i] = adaption[i-1] + (-adaption[i-1] / adaption_tau) * step_size
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if v_1[i] > threshold:
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v_1[i] = v_base
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spikes.append(i * step_size)
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adaption[i] += adaption_step / adaption_tau
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return v_1, spikes
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def lifac_ref_noise_simulation(stimulus, step_size):
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v_0 = 0
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v_base = 0
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threshold = 1
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v_offset = 1.32
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mem_tau = 0.015
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adaption_tau = 0.1
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adaption_step = 0.05
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ref_time = 0.005
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noise_strength = 0.05
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adaption = np.zeros(len(stimulus))
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adaption[0] = 1
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v_1 = np.zeros(len(stimulus))
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v_1[0] = v_0
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spikes = []
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for i in range(1, len(v_1), 1):
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noise_value = np.random.normal()
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noise = noise_strength * noise_value / np.sqrt(step_size)
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if len(spikes) > 0 and i*step_size < spikes[-1] + ref_time:
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v_1[i] = v_base
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else:
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v_1[i] = v_1[i - 1] + ((v_offset - v_1[i-1] - adaption[i-1] + stimulus[i] + noise) / mem_tau) * step_size
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adaption[i] = adaption[i-1] + (-adaption[i-1] / adaption_tau) * step_size
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if v_1[i] > threshold:
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v_1[i] = v_base
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spikes.append(i * step_size)
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adaption[i] += adaption_step / adaption_tau
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return v_1, spikes
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if __name__ == '__main__':
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main()
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