diff --git a/models/LIFACnoise.py b/models/LIFACnoise.py
index e0b0c6c..cab44a5 100644
--- a/models/LIFACnoise.py
+++ b/models/LIFACnoise.py
@@ -244,9 +244,9 @@ class LifacNoiseModel(AbstractModel):
         test_model = self.get_model_copy()
         simulation_length = 5
 
-        v_search_step_size = 1000
+        v_search_step_size = 100
 
-        current_v_offset = 0
+        current_v_offset = -400
 
         current_freq = test_v_offset(test_model, current_v_offset, base_stimulus, simulation_length)
 
@@ -256,9 +256,6 @@ class LifacNoiseModel(AbstractModel):
             current_v_offset += v_search_step_size
             current_freq = test_v_offset(test_model, current_v_offset, base_stimulus, simulation_length)
 
-        # if current_v_offset == 0:
-        #     return -1000
-
         lower_bound = current_v_offset - v_search_step_size
         upper_bound = current_v_offset
 
@@ -275,26 +272,33 @@ def binary_search_base_freq(model: LifacNoiseModel, base_stimulus, goal_frequenc
         frequency = test_v_offset(model, middle, base_stimulus, simulation_length)
 
         # print('{:.1f}, {:.1f}, {:.1f}, {:.1f} vs {:.1f} '.format(lower_bound, middle, upper_bound, frequency, goal_frequency))
+
         if abs(frequency - goal_frequency) < threshold:
             return middle
         elif frequency < goal_frequency:
             lower_bound = middle
         elif frequency > goal_frequency:
             upper_bound = middle
-        elif abs(upper_bound-lower_bound) < 0.001:
-            warn("Search was stopped no value was found!")
-            return middle
         else:
             print('lower bound: {:.1f}, middle: {:.1f}, upper_bound: {:.1f}, frequency: {:.1f} vs goal: {:.1f} '.format(lower_bound, middle, upper_bound, frequency, goal_frequency))
             raise ValueError("binary_search_base_freq() - LifacNoiseModel: Goal frequency might be nan?")
 
+        if abs(upper_bound-lower_bound) < 0.0001:
+            warn("Search was stopped no value was found!")
+            return middle
+
 
 def test_v_offset(model: LifacNoiseModel, v_offset, base_stimulus, simulation_length):
     model.set_variable("v_offset", v_offset)
-    _, spiketimes = model.simulate_fast(base_stimulus, simulation_length)
+    v, spiketimes = model.simulate_fast(base_stimulus, simulation_length)
 
     freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, simulation_length / 3)
 
+    # if freq > 10000:
+    #     from IPython import embed
+    #     import matplotlib.pyplot as plt
+    #     embed()
+
     return freq