diff --git a/figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py b/figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py
index db8902d1c97efe8104e7dee0c028ec92461a6e5c..c6ccb9e287784f3dcbfe27adb93a5a2b2bea64e0 100644
--- a/figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py
+++ b/figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py
@@ -45,7 +45,8 @@ def set_boxplot_props(d):
     pl.setp(d['means'], marker='x', color='k',
             markerfacecolor='k', markeredgecolor='k', markersize=3.)
 
-def plot_resting_state(A, label_spikes, data_path):        
+def plot_resting_state(A, label_spikes, data_path, sim_params):
+    t_sim = sim_params[]
     """
     Figure layout
     """
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 49223a6dc54ef50723925f46e5edd0a4f5f77827..3a3e8ecccf0ad81b629fc3fa75d422730debae18 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -239,7 +239,7 @@
     "# Scaling factor for cortico-cortical connections (chi) \n",
     "# Value range/options: [1., 2.5]\n",
     "# Value assgined: 1.0\n",
-    "cc_weights_factor = 1.9\n",
+    "cc_weights_factor = 2.5\n",
     "\n",
     "# Cortical areas included in the simulation\n",
     "# Value range/options: any sublist of complete_ares_list\n",
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "id": "6e4bed8d",
    "metadata": {},
    "outputs": [],
@@ -355,7 +355,7 @@
      "output_type": "stream",
      "text": [
       "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 5398\n"
+      "RAND_DATA_LABEL 1039\n"
      ]
     },
     {
@@ -383,13 +383,13 @@
       "                       'fac_nu_ext_5E': 1.125,\n",
       "                       'fac_nu_ext_6E': 1.41666667,\n",
       "                       'fac_nu_ext_TH': 1.2,\n",
-      "                       'g': -16.0,\n",
+      "                       'g': -11.0,\n",
       "                       'replace_non_simulated_areas': 'het_poisson_stat'},\n",
       " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
       " 'input_params': {'rate_ext': 10.0},\n",
       " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
       "========================================\n",
-      "Simulation label: bda0e9cd1fe12d802c8759a6bf5d72db\n",
+      "Simulation label: 27d81076e6d6e9e591684be053078477\n",
       "Copied files.\n",
       "Initialized simulation class.\n"
      ]
@@ -422,7 +422,7 @@
      "output_type": "stream",
      "text": [
       "Iteration: 0\n",
-      "Mean-field theory predicts an average firing rate of 0.000 spikes/s across all populations.\n"
+      "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
      ]
     }
    ],
@@ -492,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
    "outputs": [
@@ -502,35 +502,35 @@
      "text": [
       "Prepared simulation in 0.00 seconds.\n",
       "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1911.43 MB\n",
+      "Memory after V1 : 1911.49 MB\n",
       "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1938.14 MB\n",
+      "Memory after V2 : 1938.06 MB\n",
       "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1967.33 MB\n",
+      "Memory after VP : 1967.25 MB\n",
       "Rank 0: created area V3 with 0 local nodes\n",
       "Memory after V3 : 1995.62 MB\n",
       "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 2015.41 MB\n",
+      "Memory after V3A : 2015.45 MB\n",
       "Rank 0: created area MT with 0 local nodes\n",
       "Memory after MT : 2041.07 MB\n",
       "Rank 0: created area V4t with 0 local nodes\n",
       "Memory after V4t : 2066.02 MB\n",
       "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 2093.08 MB\n",
+      "Memory after V4 : 2092.96 MB\n",
       "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 2118.23 MB\n",
+      "Memory after VOT : 2118.27 MB\n",
       "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 2139.70 MB\n",
+      "Memory after MSTd : 2139.74 MB\n",
       "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 2161.17 MB\n",
+      "Memory after PIP : 2161.09 MB\n",
       "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 2182.64 MB\n",
+      "Memory after PO : 2182.60 MB\n",
       "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 2202.79 MB\n",
+      "Memory after DP : 2202.87 MB\n",
       "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 2224.41 MB\n",
+      "Memory after MIP : 2224.37 MB\n",
       "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 2245.88 MB\n",
+      "Memory after MDP : 2245.80 MB\n",
       "Rank 0: created area VIP with 0 local nodes\n",
       "Memory after VIP : 2267.86 MB\n",
       "Rank 0: created area LIP with 0 local nodes\n",
@@ -538,36 +538,34 @@
       "Rank 0: created area PITv with 0 local nodes\n",
       "Memory after PITv : 2317.14 MB\n",
       "Rank 0: created area PITd with 0 local nodes\n",
-      "Memory after PITd : 2342.35 MB\n",
+      "Memory after PITd : 2342.23 MB\n",
       "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 2363.70 MB\n",
+      "Memory after MSTl : 2363.73 MB\n",
       "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 2383.00 MB\n",
+      "Memory after CITv : 2382.88 MB\n",
       "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2402.33 MB\n",
+      "Memory after CITd : 2402.21 MB\n",
       "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2423.71 MB\n",
+      "Memory after FEF : 2423.68 MB\n",
       "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2439.36 MB\n",
+      "Memory after TF : 2439.32 MB\n",
       "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2462.07 MB\n",
+      "Memory after AITv : 2462.04 MB\n",
       "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2478.69 MB\n",
+      "Memory after FST : 2478.80 MB\n",
       "Rank 0: created area 7a with 0 local nodes\n",
       "Memory after 7a : 2499.98 MB\n",
       "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2518.73 MB\n",
+      "Memory after STPp : 2518.74 MB\n",
       "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2537.76 MB\n",
+      "Memory after STPa : 2537.80 MB\n",
       "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2553.25 MB\n",
+      "Memory after 46 : 2553.13 MB\n",
       "Rank 0: created area AITd with 0 local nodes\n",
       "Memory after AITd : 2575.80 MB\n",
       "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2588.48 MB\n",
-      "Created areas and internal connections in 2.31 seconds.\n",
-      "Created cortico-cortical connections in 23.09 seconds.\n",
-      "Simulated network in 48.36 seconds.\n"
+      "Memory after TH : 2588.52 MB\n",
+      "Created areas and internal connections in 2.28 seconds.\n"
      ]
     }
    ],
@@ -605,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "e7eb052e",
    "metadata": {},
    "outputs": [],
@@ -628,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "902f2800",
    "metadata": {},
    "outputs": [],
@@ -662,285 +660,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "f5b58845-4d1a-430f-83f4-402fdf918aef",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loading spikes\n",
-      "V1 23E\n",
-      "V1 23I\n",
-      "V1 4E\n",
-      "V1 4I\n",
-      "V1 5E\n",
-      "V1 5I\n",
-      "V1 6E\n",
-      "V1 6I\n",
-      "V2 23E\n",
-      "V2 23I\n",
-      "V2 4E\n",
-      "V2 4I\n",
-      "V2 5E\n",
-      "V2 5I\n",
-      "V2 6E\n",
-      "V2 6I\n",
-      "VP 23E\n",
-      "VP 23I\n",
-      "VP 4E\n",
-      "VP 4I\n",
-      "VP 5E\n",
-      "VP 5I\n",
-      "VP 6E\n",
-      "VP 6I\n",
-      "V3 23E\n",
-      "V3 23I\n",
-      "V3 4E\n",
-      "V3 4I\n",
-      "V3 5E\n",
-      "V3 5I\n",
-      "V3 6E\n",
-      "V3 6I\n",
-      "V3A 23E\n",
-      "V3A 23I\n",
-      "V3A 4E\n",
-      "V3A 4I\n",
-      "V3A 5E\n",
-      "V3A 5I\n",
-      "V3A 6E\n",
-      "V3A 6I\n",
-      "MT 23E\n",
-      "MT 23I\n",
-      "MT 4E\n",
-      "MT 4I\n",
-      "MT 5E\n",
-      "MT 5I\n",
-      "MT 6E\n",
-      "MT 6I\n",
-      "V4t 23E\n",
-      "V4t 23I\n",
-      "V4t 4E\n",
-      "V4t 4I\n",
-      "V4t 5E\n",
-      "V4t 5I\n",
-      "V4t 6E\n",
-      "V4t 6I\n",
-      "V4 23E\n",
-      "V4 23I\n",
-      "V4 4E\n",
-      "V4 4I\n",
-      "V4 5E\n",
-      "V4 5I\n",
-      "V4 6E\n",
-      "V4 6I\n",
-      "VOT 23E\n",
-      "VOT 23I\n",
-      "VOT 4E\n",
-      "VOT 4I\n",
-      "VOT 5E\n",
-      "VOT 5I\n",
-      "VOT 6E\n",
-      "VOT 6I\n",
-      "MSTd 23E\n",
-      "MSTd 23I\n",
-      "MSTd 4E\n",
-      "MSTd 4I\n",
-      "MSTd 5E\n",
-      "MSTd 5I\n",
-      "MSTd 6E\n",
-      "MSTd 6I\n",
-      "PIP 23E\n",
-      "PIP 23I\n",
-      "PIP 4E\n",
-      "PIP 4I\n",
-      "PIP 5E\n",
-      "PIP 5I\n",
-      "PIP 6E\n",
-      "PIP 6I\n",
-      "PO 23E\n",
-      "PO 23I\n",
-      "PO 4E\n",
-      "PO 4I\n",
-      "PO 5E\n",
-      "PO 5I\n",
-      "PO 6E\n",
-      "PO 6I\n",
-      "DP 23E\n",
-      "DP 23I\n",
-      "DP 4E\n",
-      "DP 4I\n",
-      "DP 5E\n",
-      "DP 5I\n",
-      "DP 6E\n",
-      "DP 6I\n",
-      "MIP 23E\n",
-      "MIP 23I\n",
-      "MIP 4E\n",
-      "MIP 4I\n",
-      "MIP 5E\n",
-      "MIP 5I\n",
-      "MIP 6E\n",
-      "MIP 6I\n",
-      "MDP 23E\n",
-      "MDP 23I\n",
-      "MDP 4E\n",
-      "MDP 4I\n",
-      "MDP 5E\n",
-      "MDP 5I\n",
-      "MDP 6E\n",
-      "MDP 6I\n",
-      "VIP 23E\n",
-      "VIP 23I\n",
-      "VIP 4E\n",
-      "VIP 4I\n",
-      "VIP 5E\n",
-      "VIP 5I\n",
-      "VIP 6E\n",
-      "VIP 6I\n",
-      "LIP 23E\n",
-      "LIP 23I\n",
-      "LIP 4E\n",
-      "LIP 4I\n",
-      "LIP 5E\n",
-      "LIP 5I\n",
-      "LIP 6E\n",
-      "LIP 6I\n",
-      "PITv 23E\n",
-      "PITv 23I\n",
-      "PITv 4E\n",
-      "PITv 4I\n",
-      "PITv 5E\n",
-      "PITv 5I\n",
-      "PITv 6E\n",
-      "PITv 6I\n",
-      "PITd 23E\n",
-      "PITd 23I\n",
-      "PITd 4E\n",
-      "PITd 4I\n",
-      "PITd 5E\n",
-      "PITd 5I\n",
-      "PITd 6E\n",
-      "PITd 6I\n",
-      "MSTl 23E\n",
-      "MSTl 23I\n",
-      "MSTl 4E\n",
-      "MSTl 4I\n",
-      "MSTl 5E\n",
-      "MSTl 5I\n",
-      "MSTl 6E\n",
-      "MSTl 6I\n",
-      "CITv 23E\n",
-      "CITv 23I\n",
-      "CITv 4E\n",
-      "CITv 4I\n",
-      "CITv 5E\n",
-      "CITv 5I\n",
-      "CITv 6E\n",
-      "CITv 6I\n",
-      "CITd 23E\n",
-      "CITd 23I\n",
-      "CITd 4E\n",
-      "CITd 4I\n",
-      "CITd 5E\n",
-      "CITd 5I\n",
-      "CITd 6E\n",
-      "CITd 6I\n",
-      "FEF 23E\n",
-      "FEF 23I\n",
-      "FEF 4E\n",
-      "FEF 4I\n",
-      "FEF 5E\n",
-      "FEF 5I\n",
-      "FEF 6E\n",
-      "FEF 6I\n",
-      "TF 23E\n",
-      "TF 23I\n",
-      "TF 4E\n",
-      "TF 4I\n",
-      "TF 5E\n",
-      "TF 5I\n",
-      "TF 6E\n",
-      "TF 6I\n",
-      "AITv 23E\n",
-      "AITv 23I\n",
-      "AITv 4E\n",
-      "AITv 4I\n",
-      "AITv 5E\n",
-      "AITv 5I\n",
-      "AITv 6E\n",
-      "AITv 6I\n",
-      "FST 23E\n",
-      "FST 23I\n",
-      "FST 4E\n",
-      "FST 4I\n",
-      "FST 5E\n",
-      "FST 5I\n",
-      "FST 6E\n",
-      "FST 6I\n",
-      "7a 23E\n",
-      "7a 23I\n",
-      "7a 4E\n",
-      "7a 4I\n",
-      "7a 5E\n",
-      "7a 5I\n",
-      "7a 6E\n",
-      "7a 6I\n",
-      "STPp 23E\n",
-      "STPp 23I\n",
-      "STPp 4E\n",
-      "STPp 4I\n",
-      "STPp 5E\n",
-      "STPp 5I\n",
-      "STPp 6E\n",
-      "STPp 6I\n",
-      "STPa 23E\n",
-      "STPa 23I\n",
-      "STPa 4E\n",
-      "STPa 4I\n",
-      "STPa 5E\n",
-      "STPa 5I\n",
-      "STPa 6E\n",
-      "STPa 6I\n",
-      "46 23E\n",
-      "46 23I\n",
-      "46 4E\n",
-      "46 4I\n",
-      "46 5E\n",
-      "46 5I\n",
-      "46 6E\n",
-      "46 6I\n",
-      "AITd 23E\n",
-      "AITd 23I\n",
-      "AITd 4E\n",
-      "AITd 4I\n",
-      "AITd 5E\n",
-      "AITd 5I\n",
-      "AITd 6E\n",
-      "AITd 6I\n",
-      "TH 23E\n",
-      "TH 23I\n",
-      "TH 5E\n",
-      "TH 5I\n",
-      "TH 6E\n",
-      "TH 6I\n",
-      "Computing population rates\n",
-      "Computing population rates done\n",
-      "Computing synchrony\n",
-      "Computing synchrony done\n",
-      "Computing population LvR\n",
-      "Computing population LvR done\n",
-      "Computing rate time series\n",
-      "Computing rate time series done\n",
-      "pop_LvR\n",
-      "pop_rates\n",
-      "synchrony\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "label_spikes = M.simulation.label\n",
     "label = M.simulation.label\n",
@@ -979,23 +704,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "bea30fc8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from MAM2EBRAINS_VISUALIZATION import plot_instan_mean_firing_rate\n",
     "plot_instan_mean_firing_rate(tsteps, firing_rate, sim_params)"
@@ -1019,73 +731,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "ae19bcc3",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 208\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "zero-size array to reduction operation minimum which has no identity",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [16]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mMAM2EBRAINS_VISUALIZATION\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplot_resting_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_spikes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py:252\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(A, label_spikes, data_path)\u001b[0m\n\u001b[1;32m    250\u001b[0m pop_data \u001b[38;5;241m=\u001b[39m spike_data[area][pop][ind]\n\u001b[1;32m    251\u001b[0m pop_neurons \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(pop_data[:, \u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m--> 252\u001b[0m neurons_to_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mspike_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43marea\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m, np\u001b[38;5;241m.\u001b[39mmin(\n\u001b[1;32m    253\u001b[0m     spike_data[area][pop][:, \u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m+\u001b[39m n_to_plot[pop], \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pop\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;241m>\u001b[39m (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m    256\u001b[0m     pcolor \u001b[38;5;241m=\u001b[39m ecolor\n",
-      "File \u001b[0;32m<__array_function__ internals>:5\u001b[0m, in \u001b[0;36mamin\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
-      "File \u001b[0;32m/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/fromnumeric.py:2879\u001b[0m, in \u001b[0;36mamin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m   2763\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_amin_dispatcher)\n\u001b[1;32m   2764\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mamin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m   2765\u001b[0m          where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m   2766\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2767\u001b[0m \u001b[38;5;124;03m    Return the minimum of an array or minimum along an axis.\u001b[39;00m\n\u001b[1;32m   2768\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2877\u001b[0m \u001b[38;5;124;03m    6\u001b[39;00m\n\u001b[1;32m   2878\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2879\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmin\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2880\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/fromnumeric.py:86\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     84\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mufunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation minimum which has no identity"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x635.692 with 7 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from MAM2EBRAINS_VISUALIZATION import plot_resting_state\n",
-    "plot_resting_state(A, label_spikes, data_path)"
+    "plot_resting_state(A, label_spikes, data_path, sim_params)"
    ]
   },
   {
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index 5e2512acd2c491a2a7e720e28a081e4b5bc9488b..629c9eaa35bcd89f441917c0320dae0d976329f6 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
-{"cocomac_completed": {"V1": {"7a": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "IT": {"source_pattern": ["0", "0", 2, "0", "0", "3"], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "LIP": {"source_pattern": ["0", "0", "0", "0", "0", "2"], "target_pattern": null}, "MT": {"source_pattern": ["0", "0", 2, "0", 2, 2], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "PIP": {"source_pattern": null, "target_pattern": null}, "PO": {"source_pattern": null, "target_pattern": null}, "STP": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "STPp": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "TF": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "TH": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": null}, "V1": {"source_pattern": null, "target_pattern": [2.0, 2.0, 2.0, "0", "3", "3"]}, "V2": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": ["3", 2.0, 2.0, "0", 2.0, 2.0]}, "V3": {"source_pattern": ["0", 2, 2, "0", 2, "3"], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "V3A": {"source_pattern": ["0", "0", "0", "0", 2, 2], "target_pattern": null}, "V4": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "V4t": {"source_pattern": ["0", 2, 2, "0", "3", "3"], "target_pattern": null}, "VIP": {"source_pattern": null, "target_pattern": null}, "MSTd": {"target_pattern": null, "source_pattern": null}, "FST": {"target_pattern": null, "source_pattern": null}, "PITd": {"target_pattern": null, "source_pattern": null}, "DP": {"target_pattern": null, "source_pattern": null}, "FEF": {"target_pattern": null, "source_pattern": null}, "VOT": {"target_pattern": null, "source_pattern": null}, "STPa": {"target_pattern": null, "source_pattern": null}, "CITd": {"target_pattern": null, "source_pattern": null}, "AITd": {"target_pattern": null, "source_pattern": null}, "CITv": {"target_pattern": null, "source_pattern": null}, "MSTl": {"target_pattern": null, "source_pattern": null}, "AITv": {"target_pattern": null, "source_pattern": null}, "PITv": {"target_pattern": null, "source_pattern": null}, "46": {"target_pattern": null, "source_pattern": null}, "VP": {"target_pattern": null, "source_pattern": null}}, "V2": {"5": {"source_pattern": null, "target_pattern": null}, "6": {"source_pattern": null, "target_pattern": null}, "7a": {"source_pattern": [2, 2, "0", 2, 2, -1], "target_pattern": ["3", "2", "2", 2.0, 2.0, 2.0]}, "7b": {"source_pattern": null, "target_pattern": null}, "35": {"source_pattern": [2, 2, 2, 0, -1, -1], "target_pattern": null}, "36": {"source_pattern": [2, 2, 2, 0, -1, -1], "target_pattern": null}, "46": {"source_pattern": [2, 2, "0", 2, 2, -1], "target_pattern": ["3", 2.0, "3", "3", 2.0, 2.0]}, "CIT": {"source_pattern": null, "target_pattern": null}, "CITv": {"source_pattern": null, "target_pattern": null}, "DP": {"source_pattern": [2, 2, "0", "0", "0", -1], "target_pattern": null}, "FEF": {"source_pattern": [2, 2, "0", 2, 2, -1], "target_pattern": null}, "FST": {"source_pattern": [2, 2, 2, 2, "0", -1], "target_pattern": null}, "IT": {"source_pattern": [2, 2, 2, 2, 2, "0"], "target_pattern": ["3", "3", 2.0, 2.0, "3", "3"]}, "LIP": {"source_pattern": [2, 2, "0", 2, 2, "0"], "target_pattern": null}, "MIP": {"source_pattern": null, "target_pattern": null}, "MST": {"source_pattern": [2, 2, 2, 2, "0", -1], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "MSTd": {"source_pattern": [2, 2, -1, 0, -1, -1], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "MSTl": {"source_pattern": [2, 2, -1, 0, -1, -1], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "MT": {"source_pattern": [2, 2, 2, 2, "0", "0"], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "PIP": {"source_pattern": null, "target_pattern": null}, "PIT": {"source_pattern": [2, 2, 2, 2, 2, -1], "target_pattern": null}, "PITd": {"source_pattern": [2, 2, 2, 2, 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\ No newline at end of file