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 d6da1ef94e4a74805a035f295948b82230a456b4..4a212b6462a78647e70fda5f209562d181dcb106 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
@@ -7,6 +7,7 @@ sys.path.append('./figures/Schmidt2018_dyn')
 
 from helpers import original_data_path, population_labels
 from multiarea_model import MultiAreaModel
+from multiarea_model import Analysis
 from plotcolors import myred, myblue
 
 import matplotlib.pyplot as pl
diff --git a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
index d6da1ef94e4a74805a035f295948b82230a456b4..4a212b6462a78647e70fda5f209562d181dcb106 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
@@ -7,6 +7,7 @@ sys.path.append('./figures/Schmidt2018_dyn')
 
 from helpers import original_data_path, population_labels
 from multiarea_model import MultiAreaModel
+from multiarea_model import Analysis
 from plotcolors import myred, myblue
 
 import matplotlib.pyplot as pl
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index ba9073d59d189e9ff3458ef0496241a4fe4cb079..c0c1f27fbaaa41589fc16ad26f84f235b6999f7b 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -570,37 +570,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "id": "ae19bcc3",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "Object arrays cannot be loaded when allow_pickle=False",
+     "ename": "NameError",
+     "evalue": "name 'Analysis' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [18], 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"
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [15], 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:59\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(M, data_path)\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;124;03mAnalysis class.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;124;03mAn instance of the analysis class for the given network and simulation.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;124;03m    simulated areas.\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;66;03m# Instantiate an analysis class and load spike data\u001b[39;00m\n\u001b[0;32m---> 59\u001b[0m A \u001b[38;5;241m=\u001b[39m \u001b[43mAnalysis\u001b[49m(network\u001b[38;5;241m=\u001b[39mM, \n\u001b[1;32m     60\u001b[0m              simulation\u001b[38;5;241m=\u001b[39mM\u001b[38;5;241m.\u001b[39msimulation, \n\u001b[1;32m     61\u001b[0m              data_list\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspikes\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m     62\u001b[0m              load_areas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m     64\u001b[0m \u001b[38;5;66;03m# load data\u001b[39;00m\n\u001b[1;32m     65\u001b[0m load_and_create_data(M, A)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'Analysis' is not defined"
      ]
-    },
-    {
-     "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": [
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index d0edab6777c4ce47ace3c7292067ca6ed837b181..96553e875bfe0bdf749b0ae380efa31031141bef 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
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