Alexander/P-unit_model
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add dendritic low pass filter

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This commit is contained in:
A. Ott 2020-04-11 13:56:16 +02:00
parent 7fda8d65f1
commit b7da40ffd3
1 changed files with 8 additions and 2 deletions
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10
models/LIFACnoise.py
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@ -23,7 +23,8 @@ class LifacNoiseModel(AbstractModel):
"tau_a": 0.1, "tau_a": 0.1,
"a_zero": 2, "a_zero": 2,
"noise_strength": 0.05, "noise_strength": 0.05,
"step_size": 0.00005} "step_size": 0.00005,
"dend_tau": 0.001}
def __init__(self, params: dict = None): def __init__(self, params: dict = None):
super().__init__(params) super().__init__(params)
@ -103,9 +104,10 @@ class LifacNoiseModel(AbstractModel):
mem_tau = self.parameters["mem_tau"] mem_tau = self.parameters["mem_tau"]
noise_strength = self.parameters["noise_strength"] noise_strength = self.parameters["noise_strength"]
input_scaling = self.parameters["input_scaling"] input_scaling = self.parameters["input_scaling"]
dend_tau = self.parameters["dend_tau"]
rectified_stimulus = rectify_stimulus_array(stimulus.as_array(time_start, total_time_s, step_size)) rectified_stimulus = rectify_stimulus_array(stimulus.as_array(time_start, total_time_s, step_size))
parameters = np.array([v_zero, a_zero, step_size, threshold, v_base, delta_a, tau_a, v_offset, mem_tau, noise_strength, time_start, input_scaling]) parameters = np.array([v_zero, a_zero, step_size, threshold, v_base, delta_a, tau_a, v_offset, mem_tau, noise_strength, time_start, input_scaling, dend_tau])
voltage_trace, adaption, spiketimes = simulate_fast(rectified_stimulus, total_time_s, parameters) voltage_trace, adaption, spiketimes = simulate_fast(rectified_stimulus, total_time_s, parameters)
@ -333,21 +335,25 @@ def simulate_fast(rectified_stimulus_array, total_time_s, parameters: np.ndarray
noise_strength = parameters[9] noise_strength = parameters[9]
time_start = parameters[10] time_start = parameters[10]
input_scaling = parameters[11] input_scaling = parameters[11]
dend_tau = parameters[12]
time = np.arange(time_start, total_time_s, step_size) time = np.arange(time_start, total_time_s, step_size)
length = len(time) length = len(time)
output_voltage = np.zeros(length) output_voltage = np.zeros(length)
adaption = np.zeros(length) adaption = np.zeros(length)
input_voltage = np.zeros(length)
spiketimes = [] spiketimes = []
output_voltage[0] = v_zero output_voltage[0] = v_zero
adaption[0] = a_zero adaption[0] = a_zero
input_voltage[0] = rectified_stimulus_array[0]
for i in range(1, len(time), 1): for i in range(1, len(time), 1):
noise_value = np.random.normal() noise_value = np.random.normal()
noise = noise_strength * noise_value / np.sqrt(step_size) noise = noise_strength * noise_value / np.sqrt(step_size)
input_voltage[i] = input_voltage[i - 1] + (-input_voltage[i - 1] + rectified_stimulus_array[i] * input_scaling) / dend_tau
output_voltage[i] = output_voltage[i-1] + ((v_base - output_voltage[i-1] + v_offset + (rectified_stimulus_array[i] * input_scaling) - adaption[i-1] + noise) / mem_tau) * step_size output_voltage[i] = output_voltage[i-1] + ((v_base - output_voltage[i-1] + v_offset + (rectified_stimulus_array[i] * input_scaling) - adaption[i-1] + noise) / mem_tau) * step_size
adaption[i] = adaption[i-1] + ((-adaption[i-1]) / tau_a) * step_size adaption[i] = adaption[i-1] + ((-adaption[i-1]) / tau_a) * step_size