diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index ec106194f030bf332a9f6ac5cf920b30c6bbfdbe..d05a844633f2a4b85ad21e4ad20e1ad4bd1d2401 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -189,7 +189,7 @@
    "source": [
     "1. `scale_down_to` is the downscaling factor that defines the ratio by which the full-scale multi-area model is reduced to a model with fewer neurons and indegrees. This reduction is essential to enable simulation on machines with limited computational power, ensuring that simulation results can be obtained in a relatively shorter timeframe. <br> If the value is `scale_down_to = 1.`, the full-scale network will be simulated. <br> In the pre-set downscaled version, `scale_down_to = 0.006`. This setting reduces the number of neurons and indegrees to 0.6 % of their full-scale counterparts, facilitating simulation on a typical local machine. <br> **Warning**: This may not yield reasonable results for the network dynamics and is only meant to demonstrate the simulation workflow! <br> \n",
     "\n",
-    "2. `cc_weights_factor` is the scaling factor that controls the cortico-cortical synaptic strength that targets excitatory neurons. <br> By default it is set to `1.9`, keeping the same value for producing the metastable state as in the original paper. <br> **Important**: This factor plays a crucial role in transitioning the network activity from the ground to the metastable state.  In the full-scale network, the ground state and metastable state activities are achieved when this parameter is set to `1.0` and `1.9`, respectively. In the downscaled multi-area model, a similar metastable state may not be achieved or achieved with a different value. <br>\n",
+    "2. `cc_weights_factor` is the scaling factor that controls the cortico-cortical synaptic strength. <br> By default it is set to `1.9`, keeping the same value for producing the metastable state as in the original paper. <br> **Important**: This factor plays a crucial role in transitioning the network activity from the ground to the metastable state.  In the full-scale network, the ground state and metastable state activities are achieved when this parameter is set to `1.0` and `1.9`, respectively. In the downscaled multi-area model, a similar metastable state may not be achieved or achieved with a different value. <br>\n",
     "\n",
     "3. `areas_simulated` specifies the cortical areas to be included in the simulation process. Its default value is `complete_area_list` meaning all the areas in the complete_area_list will be simulated.\n",
     "```python\n",
@@ -297,7 +297,12 @@
     "        raise Exception(\"'hom_poisson_stat', 'het_poisson_stat', or 'het_current_nonstat' should be assigned to replace_non_simulated_areas when not all areas are simulated!\")\n",
     "\n",
     "# Determine cc_weights_I_factor from cc_weights_factor\n",
-    "# cc_weights_I_factor is the scaling factor that controls the cortico-cortical synaptic strength that targets inhibitory neurons.\n",
+    "# This additional factor scales the cortico-cortical weights targeting inhibitory populations.\n",
+    "# In this case, the cc_weights_factor is multiplied by the cc_weights_I_factor.\n",
+    "# For example:\n",
+    "# - If cc_weights_I_factor is 1.0, cortico-cortical connections have the same synaptic weight\n",
+    "#   for both excitatory and inhibitory targets.\n",
+    "# - If cc_weights_I_factor > 1.0, cortico-cortical connections targeting inhibitory neurons are stronger.\n",
     "# The conditions below are based on the results in Schmidt et al. (2018).\n",
     "if cc_weights_factor == 1.0:                                                  # For ground state with cc_weights_factor as 1., \n",
     "    cc_weights_I_factor = 1.0                                                 # cc_weights_I_factor is set to 1.\n",