From 04815de85c7b7ad087c90a6a6ee05a100e93f580 Mon Sep 17 00:00:00 2001
From: "a.ott" <a.ott@student.uni-tuebingen.de>
Date: Tue, 3 Mar 2020 09:24:10 +0100
Subject: [PATCH] add actual input scaling

---
 models/LIFACnoise.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/models/LIFACnoise.py b/models/LIFACnoise.py
index 8ba3618..63e1db3 100644
--- a/models/LIFACnoise.py
+++ b/models/LIFACnoise.py
@@ -7,6 +7,7 @@ from numba import jit
 import helperFunctions as hF
 from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus
 from scipy.optimize import curve_fit
+from warnings import warn
 
 
 class LifacNoiseModel(AbstractModel):
@@ -27,6 +28,8 @@ class LifacNoiseModel(AbstractModel):
     def __init__(self, params: dict = None):
         super().__init__(params)
 
+        if self.parameters["step_size"] >= 0.0001:
+            warn("LifacNoiseModel: The step size is quite big simulation could fail.")
         self.voltage_trace = []
         self.adaption_trace = []
         self.spiketimes = []
@@ -49,7 +52,7 @@ class LifacNoiseModel(AbstractModel):
         for i in range(1, len(time), 1):
             time_point = time[i]
             # rectified input:
-            stimulus_strength = fu.rectify(stimulus.value_at_time_in_s(time_point))
+            stimulus_strength = fu.rectify(stimulus.value_at_time_in_s(time_point)) * self.parameters["input_scaling"]
 
             v_next = self._calculate_voltage_step(current_v, stimulus_strength - current_a)
             a_next = self._calculate_adaption_step(current_a)
@@ -99,9 +102,10 @@ class LifacNoiseModel(AbstractModel):
         v_offset = self.parameters["v_offset"]
         mem_tau = self.parameters["mem_tau"]
         noise_strength = self.parameters["noise_strength"]
+        input_scaling = self.parameters["input_scaling"]
 
         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])
+        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])
 
         voltage_trace, adaption, spiketimes = simulate_fast(rectified_stimulus, total_time_s, parameters)
 
@@ -161,6 +165,9 @@ class LifacNoiseModel(AbstractModel):
         :return: baseline_freq, vs, sc
         """
         base_stimulus = SinusAmplitudeModulationStimulus(base_stimulus_freq, 0, 0)
+        import matplotlib.pyplot as plt
+        plt.plot(base_stimulus.as_array(0,30,0.00005))
+        plt.show()
         _, spiketimes = self.simulate_fast(base_stimulus, 30)
         time_x = 5
         baseline_freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, time_x)
@@ -263,6 +270,7 @@ def simulate_fast(rectified_stimulus_array, total_time_s, parameters: np.ndarray
     mem_tau = parameters[8]
     noise_strength = parameters[9]
     time_start = parameters[10]
+    input_scaling = parameters[11]
 
     time = np.arange(time_start, total_time_s, step_size)
     length = len(time)
@@ -279,7 +287,7 @@ def simulate_fast(rectified_stimulus_array, total_time_s, parameters: np.ndarray
         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 + stimulus_values[i] - adaption[i-1] + noise) / mem_tau) * step_size
+        output_voltage[i] = output_voltage[i-1] + ((v_base - output_voltage[i-1] + v_offset + (stimulus_values[i]*input_scaling) - adaption[i-1] + noise) / mem_tau) * step_size
         adaption[i] = adaption[i-1] + ((-adaption[i-1]) / tau_a) * step_size
 
         if output_voltage[i] > threshold: