diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
index daaa6f70c7f11b57bc3737dff68a2da1a0453e59..d1970f682c8e4fb7047b9f024c37ba18ce27a018 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
@@ -36,7 +36,7 @@ def set_boxplot_props(d):
 
 def plot_resting_state(M, data_path):
     # load data
-    # A = load_and_create_data(M)
+    A = load_and_create_data(M)
     
     label_spikes = M.simulation.label
     label = M.simulation.label
diff --git a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
index daaa6f70c7f11b57bc3737dff68a2da1a0453e59..d1970f682c8e4fb7047b9f024c37ba18ce27a018 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
@@ -36,7 +36,7 @@ def set_boxplot_props(d):
 
 def plot_resting_state(M, data_path):
     # load data
-    # A = load_and_create_data(M)
+    A = load_and_create_data(M)
     
     label_spikes = M.simulation.label
     label = M.simulation.label
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index c9896241ff809670b8cbce4cdc1c06ea5911e155..f9c882e161bcf23f2fd5e3c001ddee158735a87f 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "15778e9c",
    "metadata": {},
    "outputs": [],
@@ -530,10 +530,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "bea30fc8",
    "metadata": {},
-   "outputs": [],
+   "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"
+    }
+   ],
    "source": [
     "from M2E_visualize_instantaneous_and_mean_firing_rates import plot_instan_mean_firing_rate\n",
     "plot_instan_mean_firing_rate(M)"
@@ -557,12 +570,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "ae19bcc3",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Object arrays cannot be loaded when allow_pickle=False",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [16], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mM2E_visualize_resting_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m plot_resting_state(M, data_path)\n",
+      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:161\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(M, data_path)\u001b[0m\n\u001b[1;32m    159\u001b[0m     spike_data[area] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m    160\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m pop \u001b[38;5;129;01min\u001b[39;00m M\u001b[38;5;241m.\u001b[39mstructure[area]:\n\u001b[0;32m--> 161\u001b[0m         spike_data[area][pop] \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    162\u001b[0m \u001b[43m                                                     \u001b[49m\u001b[43mlabel_spikes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    163\u001b[0m \u001b[43m                                                     \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrecordings\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    164\u001b[0m \u001b[43m                                                     \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m-spikes-\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m-\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m.npy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel_spikes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    165\u001b[0m \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\n\u001b[1;32m    166\u001b[0m \u001b[38;5;66;03m# spike_data = A.spike_data\u001b[39;00m\n\u001b[1;32m    167\u001b[0m \n\u001b[1;32m    168\u001b[0m \u001b[38;5;66;03m# stationary firing rates\u001b[39;00m\n\u001b[1;32m    169\u001b[0m fn \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(data_path, label, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAnalysis\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpop_rates.json\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/lib/npyio.py:430\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    428\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m.\u001b[39mopen_memmap(file, mode\u001b[38;5;241m=\u001b[39mmmap_mode)\n\u001b[1;32m    429\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 430\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_pickle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_pickle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mpickle_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpickle_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    433\u001b[0m     \u001b[38;5;66;03m# Try a pickle\u001b[39;00m\n\u001b[1;32m    434\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_pickle:\n",
+      "File \u001b[0;32m/srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.22.4-2oqgru7t5upcffz4fffhepvquuy3hdsh/lib/python3.8/site-packages/numpy/lib/format.py:742\u001b[0m, in \u001b[0;36mread_array\u001b[0;34m(fp, allow_pickle, pickle_kwargs)\u001b[0m\n\u001b[1;32m    739\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mhasobject:\n\u001b[1;32m    740\u001b[0m     \u001b[38;5;66;03m# The array contained Python objects. We need to unpickle the data.\u001b[39;00m\n\u001b[1;32m    741\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_pickle:\n\u001b[0;32m--> 742\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mObject arrays cannot be loaded when \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    743\u001b[0m                          \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_pickle=False\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    744\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m pickle_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    745\u001b[0m         pickle_kwargs \u001b[38;5;241m=\u001b[39m {}\n",
+      "\u001b[0;31mValueError\u001b[0m: Object arrays cannot be loaded when allow_pickle=False"
+     ]
+    },
+    {
+     "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"
+    }
+   ],
    "source": [
     "from M2E_visualize_resting_state import plot_resting_state\n",
     "plot_resting_state(M, data_path)"