diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 695572184de83896a2d267ffc61402e3b9702e5c..8d9e415d5397d6eec829f6e3c2529a3a2d68b888 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -116,7 +116,8 @@
     "# Import the MultiAreaModel class\n",
     "from multiarea_model import MultiAreaModel\n",
     "from multiarea_model import Analysis\n",
-    "from config import base_path, data_path"
+    "from config import base_path, data_path\n",
+    "from figures import MAM2EBRAINS as M2E"
    ]
   },
   {
@@ -1112,15 +1113,17 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.plot(tsteps, rate)\n",
-    "ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')\n",
-    "ax.set_title('Instantaneous and mean firing rate across all populations')\n",
-    "ax.set_xlabel('time (ms)')\n",
-    "ax.set_ylabel('firing rate (spikes / s)')\n",
-    "ax.set_xlim(0, sim_params['t_sim'])\n",
-    "ax.set_ylim(0, 50)\n",
-    "ax.legend()"
+    "plot_instan_mean_firing _rate(tsteps, rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e91c436e-db94-4cd7-a531-29c032efeeae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 5.2 Resting state plots <a class=\"anchor\" id=\"section_5_2\"></a>"
    ]
   },
   {
@@ -1178,28 +1181,7 @@
     }
    ],
    "source": [
-    "\"\"\"\n",
-    "Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.\n",
-    "\n",
-    "Parameters\n",
-    "----------\n",
-    "area : string {area}\n",
-    "    Area to be plotted.\n",
-    "frac_neurons : float, [0,1]\n",
-    "    Fraction of cells to be considered.\n",
-    "t_min : float, optional\n",
-    "    Minimal time in ms of spikes to be shown. Defaults to 0 ms.\n",
-    "t_max : float, optional\n",
-    "    Minimal time in ms of spikes to be shown. Defaults to simulation time.\n",
-    "output : {'pdf', 'png', 'eps'}, optional\n",
-    "    If given, the function stores the plot to a file of the given format.\n",
-    "\"\"\"\n",
-    "t_min = 0.\n",
-    "t_max = 500.\n",
-    "areas = ['V1', 'V2', 'FEF']\n",
-    "frac_neurons = 1.\n",
-    "for area in areas:\n",
-    "    A.single_dot_display(area,  frac_neurons, t_min, t_max)"
+    "plot_raster_plot(A)"
    ]
   },
   {
@@ -1817,9 +1799,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "EBRAINS-23.02",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ebrains-23.02"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1831,7 +1813,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py b/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/figures/MAM2EBRAINS.py b/figures/MAM2EBRAINS.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a60aa419efe9e3589baf63111b55603fc551b62
--- /dev/null
+++ b/figures/MAM2EBRAINS.py
@@ -0,0 +1,36 @@
+# Instantaneous and mean firing rate across all populations
+
+def plot_instan_mean_firing _rate(tsteps, rate):
+    fig, ax = plt.subplots()
+    ax.plot(tsteps, rate)
+    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
+    ax.set_title('Instantaneous and mean firing rate across all populations')
+    ax.set_xlabel('time (ms)')
+    ax.set_ylabel('firing rate (spikes / s)')
+    ax.set_xlim(0, sim_params['t_sim'])
+    ax.set_ylim(0, 50)
+    ax.legend()
+    
+def plot_raster_plot(A):
+    """
+    Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.
+
+    Parameters
+    ----------
+    area : string {area}
+        Area to be plotted.
+    frac_neurons : float, [0,1]
+        Fraction of cells to be considered.
+    t_min : float, optional
+        Minimal time in ms of spikes to be shown. Defaults to 0 ms.
+    t_max : float, optional
+        Minimal time in ms of spikes to be shown. Defaults to simulation time.
+    output : {'pdf', 'png', 'eps'}, optional
+        If given, the function stores the plot to a file of the given format.
+    """
+    t_min = 0.
+    t_max = 500.
+    areas = ['V1', 'V2', 'FEF']
+    frac_neurons = 1.
+    for area in areas:
+        A.single_dot_display(area,  frac_neurons, t_min, t_max)
\ No newline at end of file
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 695572184de83896a2d267ffc61402e3b9702e5c..8d9e415d5397d6eec829f6e3c2529a3a2d68b888 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -116,7 +116,8 @@
     "# Import the MultiAreaModel class\n",
     "from multiarea_model import MultiAreaModel\n",
     "from multiarea_model import Analysis\n",
-    "from config import base_path, data_path"
+    "from config import base_path, data_path\n",
+    "from figures import MAM2EBRAINS as M2E"
    ]
   },
   {
@@ -1112,15 +1113,17 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.plot(tsteps, rate)\n",
-    "ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')\n",
-    "ax.set_title('Instantaneous and mean firing rate across all populations')\n",
-    "ax.set_xlabel('time (ms)')\n",
-    "ax.set_ylabel('firing rate (spikes / s)')\n",
-    "ax.set_xlim(0, sim_params['t_sim'])\n",
-    "ax.set_ylim(0, 50)\n",
-    "ax.legend()"
+    "plot_instan_mean_firing _rate(tsteps, rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e91c436e-db94-4cd7-a531-29c032efeeae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 5.2 Resting state plots <a class=\"anchor\" id=\"section_5_2\"></a>"
    ]
   },
   {
@@ -1178,28 +1181,7 @@
     }
    ],
    "source": [
-    "\"\"\"\n",
-    "Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.\n",
-    "\n",
-    "Parameters\n",
-    "----------\n",
-    "area : string {area}\n",
-    "    Area to be plotted.\n",
-    "frac_neurons : float, [0,1]\n",
-    "    Fraction of cells to be considered.\n",
-    "t_min : float, optional\n",
-    "    Minimal time in ms of spikes to be shown. Defaults to 0 ms.\n",
-    "t_max : float, optional\n",
-    "    Minimal time in ms of spikes to be shown. Defaults to simulation time.\n",
-    "output : {'pdf', 'png', 'eps'}, optional\n",
-    "    If given, the function stores the plot to a file of the given format.\n",
-    "\"\"\"\n",
-    "t_min = 0.\n",
-    "t_max = 500.\n",
-    "areas = ['V1', 'V2', 'FEF']\n",
-    "frac_neurons = 1.\n",
-    "for area in areas:\n",
-    "    A.single_dot_display(area,  frac_neurons, t_min, t_max)"
+    "plot_raster_plot(A)"
    ]
   },
   {
@@ -1817,9 +1799,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "EBRAINS-23.02",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ebrains-23.02"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1831,7 +1813,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,