diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_time_ave_pop_rates-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_time_ave_pop_rates-checkpoint.py
index 1c7820f083d07035367b26a90414c916f99ae8ab..349a3180b2e166608ece8c245fced5519abc0c46 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_time_ave_pop_rates-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_time_ave_pop_rates-checkpoint.py
@@ -64,7 +64,7 @@ def plot_time_averaged_population_rates(M):
     # print(M.network.structure['V1'])
     ax.set_xticks(x_index)
     # ax.set_xticklabels(x_ticks)
-    ax.set_xticklabels(x_ticks) = area_list
+    ax.set_xticklabels(area_list)
     ax.set_yticks(y_index)
     # ax.set_yticklabels(M.network.structure_reversed['V1'])
     ax.set_yticklabels(M.network.structure['V1'])
diff --git a/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py b/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py
index 1c7820f083d07035367b26a90414c916f99ae8ab..349a3180b2e166608ece8c245fced5519abc0c46 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py
@@ -64,7 +64,7 @@ def plot_time_averaged_population_rates(M):
     # print(M.network.structure['V1'])
     ax.set_xticks(x_index)
     # ax.set_xticklabels(x_ticks)
-    ax.set_xticklabels(x_ticks) = area_list
+    ax.set_xticklabels(area_list)
     ax.set_yticks(y_index)
     # ax.set_yticklabels(M.network.structure_reversed['V1'])
     ax.set_yticklabels(M.network.structure['V1'])
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 9a438eaee517ac5fd09e146d12a4614ded84ebe2..be303890997023d282355861d683647776b37d51 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -118,6 +118,17 @@
       " Type 'nest.help()' to find out more about NEST.\n",
       "\n"
      ]
+    },
+    {
+     "ename": "SyntaxError",
+     "evalue": "cannot assign to function call (M2E_visualize_time_ave_pop_rates.py, line 67)",
+     "output_type": "error",
+     "traceback": [
+      "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
+      "\u001b[0m  File \u001b[1;32m/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3378\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\u001b[0m\n",
+      "\u001b[0;36m  Cell \u001b[0;32mIn [2], line 17\u001b[0;36m\n\u001b[0;31m    from M2E_visualize_time_ave_pop_rates import plot_time_averaged_population_rates\u001b[0;36m\n",
+      "\u001b[0;36m  File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py:67\u001b[0;36m\u001b[0m\n\u001b[0;31m    ax.set_xticklabels(x_ticks) = area_list\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to function call\n"
+     ]
     }
    ],
    "source": [
@@ -143,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "7e07b0d0",
    "metadata": {
     "tags": []
@@ -156,28 +167,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "table {float:left}\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Jupyter notebook display format setting\n",
     "style = \"\"\"\n",
@@ -248,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "60265d52",
    "metadata": {},
    "outputs": [],
@@ -285,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6e4bed8d",
    "metadata": {},
    "outputs": [],
@@ -368,55 +363,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ab25f9f8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 1696\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",
-      "{'K_scaling': 0.005,\n",
-      " 'N_scaling': 0.005,\n",
-      " 'connection_params': {'K_stable': 'K_stable.npy',\n",
-      "                       'av_indegree_V1': 3950.0,\n",
-      "                       'fac_nu_ext_5E': 1.125,\n",
-      "                       'fac_nu_ext_6E': 1.41666667,\n",
-      "                       'fac_nu_ext_TH': 1.2,\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: 27d81076e6d6e9e591684be053078477\n",
-      "Copied files.\n",
-      "Initialized simulation class.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %%capture captured\n",
     "M = MultiAreaModel(network_params, \n",
@@ -436,19 +386,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6a7ddf0e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 0\n",
-      "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "p, r = M.theory.integrate_siegert()\n",
     "print(\"Mean-field theory predicts an average \"\n",
@@ -473,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6316ac24",
    "metadata": {},
    "outputs": [],
@@ -489,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
    "metadata": {},
    "outputs": [],
@@ -506,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
    "outputs": [],
@@ -523,53 +464,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 2145\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"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x476.769 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "visualize_interareal_connectivity(M)"
    ]
@@ -604,85 +502,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prepared simulation in 0.01 seconds.\n",
-      "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1516.09 MB\n",
-      "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1542.80 MB\n",
-      "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1571.98 MB\n",
-      "Rank 0: created area V3 with 0 local nodes\n",
-      "Memory after V3 : 1600.25 MB\n",
-      "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 1620.23 MB\n",
-      "Rank 0: created area MT with 0 local nodes\n",
-      "Memory after MT : 1645.77 MB\n",
-      "Rank 0: created area V4t with 0 local nodes\n",
-      "Memory after V4t : 1670.65 MB\n",
-      "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 1697.71 MB\n",
-      "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 1722.30 MB\n",
-      "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 1742.23 MB\n",
-      "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 1763.69 MB\n",
-      "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 1785.07 MB\n",
-      "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 1805.29 MB\n",
-      "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 1826.85 MB\n",
-      "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 1848.36 MB\n",
-      "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 1870.32 MB\n",
-      "Rank 0: created area LIP with 0 local nodes\n",
-      "Memory after LIP : 1894.22 MB\n",
-      "Rank 0: created area PITv with 0 local nodes\n",
-      "Memory after PITv : 1919.54 MB\n",
-      "Rank 0: created area PITd with 0 local nodes\n",
-      "Memory after PITd : 1944.75 MB\n",
-      "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 1965.99 MB\n",
-      "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 1984.80 MB\n",
-      "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2004.12 MB\n",
-      "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2025.57 MB\n",
-      "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2041.21 MB\n",
-      "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2063.92 MB\n",
-      "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2080.52 MB\n",
-      "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2101.80 MB\n",
-      "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2120.55 MB\n",
-      "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2139.68 MB\n",
-      "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2155.04 MB\n",
-      "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2177.59 MB\n",
-      "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2190.29 MB\n",
-      "Created areas and internal connections in 2.20 seconds.\n",
-      "Created cortico-cortical connections in 22.04 seconds.\n",
-      "Simulated network in 76.77 seconds.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %%capture captured\n",
     "# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\n",
@@ -719,23 +542,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "bea30fc8",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'nrows' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot_instan_mean_firing_rate(M)\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_instantaneous_and_mean_firing_rates.py:14\u001b[0m, in \u001b[0;36mplot_instan_mean_firing_rate\u001b[0;34m(M)\u001b[0m\n\u001b[1;32m     11\u001b[0m width \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m     12\u001b[0m panel_wh_ratio \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.7\u001b[39m \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39msqrt(\u001b[38;5;241m5\u001b[39m)) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2.\u001b[39m  \u001b[38;5;66;03m# golden ratio\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m height \u001b[38;5;241m=\u001b[39m width \u001b[38;5;241m/\u001b[39m panel_wh_ratio \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[43mnrows\u001b[49m) \u001b[38;5;241m/\u001b[39m ncols\n\u001b[1;32m     15\u001b[0m pl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure.figsize\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m (width, height)\n\u001b[1;32m     17\u001b[0m fig \u001b[38;5;241m=\u001b[39m pl\u001b[38;5;241m.\u001b[39mfigure()\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'nrows' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_instan_mean_firing_rate(M)"
    ]