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add option to fit without dendTau or ref_period

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a.ott
2020-09-11 17:21:47 +02:00
parent 4c0bde5833
commit 5af4117d4c
3 changed files with 256 additions and 28 deletions
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54
models/LIFACnoise.py
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@@ -128,8 +128,10 @@ class LifacNoiseModel(AbstractModel):
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, ref_period])
voltage_trace, adaption, spiketimes, input_voltage = simulate_fast(rectified_stimulus, total_time_s, parameters)
if dend_tau >= step_size:
voltage_trace, adaption, spiketimes, input_voltage = simulate_fast(rectified_stimulus, total_time_s, parameters)
else:
voltage_trace, adaption, spiketimes, input_voltage = simulate_fast_no_dend_tau(rectified_stimulus, total_time_s, parameters)
self.stimulus = stimulus
self.input_voltage = input_voltage
@@ -304,3 +306,51 @@ def simulate_fast(rectified_stimulus_array, total_time_s, parameters: np.ndarray
adaption[i] += delta_a / tau_a
return output_voltage, adaption, spiketimes, input_voltage
@jit(nopython=True)
def simulate_fast_no_dend_tau(rectified_stimulus_array, total_time_s, parameters: np.ndarray):
v_zero = parameters[0]
a_zero = parameters[1]
step_size = parameters[2]
threshold = parameters[3]
v_base = parameters[4]
delta_a = parameters[5]
tau_a = parameters[6]
v_offset = parameters[7]
mem_tau = parameters[8]
noise_strength = parameters[9]
time_start = parameters[10]
input_scaling = parameters[11]
dend_tau = parameters[12]
ref_period = parameters[13]
time = np.arange(time_start, total_time_s, step_size)
length = len(time)
output_voltage = np.zeros(length)
adaption = np.zeros(length)
input_voltage = rectified_stimulus_array
spiketimes = []
output_voltage[0] = v_zero
adaption[0] = a_zero
for i in range(1, len(time), 1):
noise_value = np.random.normal()
noise = noise_strength * noise_value / np.sqrt(step_size)
output_voltage[i] = output_voltage[i - 1] + ((v_base - output_voltage[i - 1] + v_offset + (
input_voltage[i] * input_scaling) - adaption[i - 1] + noise) / mem_tau) * step_size
adaption[i] = adaption[i - 1] + ((-adaption[i - 1]) / tau_a) * step_size
if len(spiketimes) > 0 and time[i] - spiketimes[-1] < ref_period + step_size/2:
output_voltage[i] = v_base
if output_voltage[i] > threshold:
output_voltage[i] = v_base
spiketimes.append((i * step_size) + time_start)
adaption[i] += delta_a / tau_a
return output_voltage, adaption, spiketimes, input_voltage