diff --git a/doc/exercises/perceptual-decision-making.rst b/doc/exercises/perceptual-decision-making.rst
index 3dbd9d7..c92cd4e 100644
--- a/doc/exercises/perceptual-decision-making.rst
+++ b/doc/exercises/perceptual-decision-making.rst
@@ -208,7 +208,9 @@ Check the documentation of :func:`.run_multiple_simulations` and set the paramet
 Question: Percent-Correct, Time-to-decision
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Using :func:`.run_multiple_simulations`, run at least 15 simulations for each of the two ``coherence_levels = [+0.15, +0.8]`` and visualize the results. For each of the questions, ignore the simulations with "no decision". If you have sufficient time/computing-power, you could run more repetitions and more levels and you could even try a larger network.
+Using :func:`.run_multiple_simulations`, run at least 15 simulations for each of the two ``coherence_levels = [+0.15, +0.8]`` and visualize the results. For each of the questions, ignore the simulations with "no decision". Optionally, if you have sufficient time/computing-power, you could run more repetitions and more levels.
+
+try a larger network.
 
 * Visualize ``Percent correct`` versus ``coherence level``.
 
diff --git a/neurodynex/competing_populations/decision_making.py b/neurodynex/competing_populations/decision_making.py
index e332609..9e093d2 100644
--- a/neurodynex/competing_populations/decision_making.py
+++ b/neurodynex/competing_populations/decision_making.py
@@ -398,7 +398,8 @@ def run_multiple_simulations(
         avg_window_width (Quantity): window size when smoothing the firing rates. Passed to f_get_decision_time.
         N_excit (int): total number of neurons in the excitatory population
         N_inhib (int): nr of neurons in the inhibitory populations
-        weight_scaling (float): When increasing the number of neurons by 2, the weights should be scaled down by 1/2
+        weight_scaling (float): When increasing the number of neurons by 2, the weights should be scaled
+            down by 1/2
         w_pos (float): Scaling (strengthening) of the recurrent weights within the
             subpopulations "Left" and "Right"
         f_Subpop_size (float): fraction of the neurons in the subpopulations "Left" and "Right".