diff --git a/docs/source/notebooks/0-Creating_networks.ipynb b/docs/source/notebooks/0-Creating_networks.ipynb
index af96536b0..74ce45ae8 100644
--- a/docs/source/notebooks/0-Creating_networks.ipynb
+++ b/docs/source/notebooks/0-Creating_networks.ipynb
@@ -18,29 +18,49 @@
   {
    "cell_type": "markdown",
    "id": "e368e07c-2dbb-47df-bf93-f2b8ec3a7e5b",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/0-Creating_networks.ipynb)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "81a63672",
+   "execution_count": null,
+   "id": "93c52124-b1b7-4688-bac6-3886c86b9352",
    "metadata": {
     "editable": true,
     "slideshow": {
      "slide_type": ""
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
     "%%capture\n",
     "import sys\n",
-    "\n",
     "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
+    "    !pip install pyhgf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "81a63672",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
diff --git a/docs/source/notebooks/0-Creating_networks.md b/docs/source/notebooks/0-Creating_networks.md
index 4281c2614..cb433c510 100644
--- a/docs/source/notebooks/0-Creating_networks.md
+++ b/docs/source/notebooks/0-Creating_networks.md
@@ -17,7 +17,7 @@ kernelspec:
 (probabilistic_networks)=
 # Creating and manipulating networks of probabilistic nodes
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/0-Creating_networks.ipynb)
 
@@ -29,10 +29,16 @@ slideshow:
 ---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
diff --git a/docs/source/notebooks/0-Theory.ipynb b/docs/source/notebooks/0-Theory.ipynb
index 52e259e5b..f0d2053ec 100644
--- a/docs/source/notebooks/0-Theory.ipynb
+++ b/docs/source/notebooks/0-Theory.ipynb
@@ -3,7 +3,13 @@
   {
    "cell_type": "markdown",
    "id": "c2dcb78a-a246-4537-82f0-82ea35010cda",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "(theory)=\n",
     "# Introduction to the Hierarchical Gaussian Filter\n",
diff --git a/docs/source/notebooks/0-Theory.md b/docs/source/notebooks/0-Theory.md
index e67e9ba0a..c88b306a2 100644
--- a/docs/source/notebooks/0-Theory.md
+++ b/docs/source/notebooks/0-Theory.md
@@ -12,6 +12,8 @@ kernelspec:
   name: python3
 ---
 
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
 (theory)=
 # Introduction to the Hierarchical Gaussian Filter
 
diff --git a/docs/source/notebooks/1.1-Binary_HGF.ipynb b/docs/source/notebooks/1.1-Binary_HGF.ipynb
index 5ef52b029..c1c620d1f 100644
--- a/docs/source/notebooks/1.1-Binary_HGF.ipynb
+++ b/docs/source/notebooks/1.1-Binary_HGF.ipynb
@@ -17,23 +17,40 @@
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/1.1-Binary_HGF.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d805e368-99d3-41b2-a6d9-59a5d7876e82",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install pyhgf"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "927d8f75-a606-4af9-a68b-a182df2ed970",
    "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": [
      "hide-cell"
     ]
    },
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "import sys\n",
-    "\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
     "import arviz as az\n",
     "import jax.numpy as jnp\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/notebooks/1.1-Binary_HGF.md b/docs/source/notebooks/1.1-Binary_HGF.md
index 4565352fe..68afef0ab 100644
--- a/docs/source/notebooks/1.1-Binary_HGF.md
+++ b/docs/source/notebooks/1.1-Binary_HGF.md
@@ -20,14 +20,24 @@ kernelspec:
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/1.1-Binary_HGF.ipynb)
 
 ```{code-cell} ipython3
-:tags: [hide-cell]
-
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+tags: [hide-cell]
+---
 import arviz as az
 import jax.numpy as jnp
 import matplotlib.pyplot as plt
diff --git a/docs/source/notebooks/1.2-Categorical_HGF.ipynb b/docs/source/notebooks/1.2-Categorical_HGF.ipynb
index c0e1f2bcf..b9285edf4 100644
--- a/docs/source/notebooks/1.2-Categorical_HGF.ipynb
+++ b/docs/source/notebooks/1.2-Categorical_HGF.ipynb
@@ -21,23 +21,40 @@
     "```"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9143ab5-131e-4a1c-9130-326c3538f888",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install pyhgf"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "d2c1e257-91ab-455d-a3ea-0ac0136797ed",
    "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": [
      "hide-cell"
     ]
    },
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "import sys\n",
-    "\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
     "import arviz as az\n",
     "import jax.numpy as jnp\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/notebooks/1.3-Continuous_HGF.ipynb b/docs/source/notebooks/1.3-Continuous_HGF.ipynb
index 0dba6b920..23a33e40c 100644
--- a/docs/source/notebooks/1.3-Continuous_HGF.ipynb
+++ b/docs/source/notebooks/1.3-Continuous_HGF.ipynb
@@ -18,11 +18,36 @@
   {
    "cell_type": "markdown",
    "id": "fb99e4b3-b5f6-4393-b6f0-f867d61b1223",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/1.2-Continuous_HGF.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08c9af0d-6dc1-46ce-a41e-ec694b6f5940",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install pyhgf"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -38,12 +63,6 @@
    },
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "import sys\n",
-    "\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
     "import arviz as az\n",
     "import jax.numpy as jnp\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/notebooks/1.3-Continuous_HGF.md b/docs/source/notebooks/1.3-Continuous_HGF.md
index 9217932bb..d7405eeb9 100644
--- a/docs/source/notebooks/1.3-Continuous_HGF.md
+++ b/docs/source/notebooks/1.3-Continuous_HGF.md
@@ -17,7 +17,7 @@ kernelspec:
 (continuous_hgf)=
 # The continuous Hierarchical Gaussian Filter
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/1.2-Continuous_HGF.ipynb)
 
@@ -26,14 +26,20 @@ kernelspec:
 editable: true
 slideshow:
   slide_type: ''
-tags: [hide-cell]
 ---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+tags: [hide-cell]
+---
 import arviz as az
 import jax.numpy as jnp
 import matplotlib.pyplot as plt
diff --git a/docs/source/notebooks/2-Using_custom_response_functions.ipynb b/docs/source/notebooks/2-Using_custom_response_functions.ipynb
index d891b3fdf..ff310ddd6 100644
--- a/docs/source/notebooks/2-Using_custom_response_functions.ipynb
+++ b/docs/source/notebooks/2-Using_custom_response_functions.ipynb
@@ -3,7 +3,13 @@
   {
    "cell_type": "markdown",
    "id": "3aebaca2-f14e-4b6f-9d68-8ff7ea3c7b71",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "(custom_response_functions)=\n",
     "# Using custom response models"
@@ -12,15 +18,21 @@
   {
    "cell_type": "markdown",
    "id": "1a9a9835-a027-4c14-9649-e7b51545b83d",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/2-Using_custom_response_functions.ipynb)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "96efb544-a8f7-44af-8345-ab6af53aaaf5",
+   "execution_count": null,
+   "id": "6522fb9d-5799-4fe4-848e-a711bd3c2e15",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -32,10 +44,23 @@
    "source": [
     "%%capture\n",
     "import sys\n",
-    "\n",
     "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
+    "    !pip install pyhgf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "96efb544-a8f7-44af-8345-ab6af53aaaf5",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
     "import arviz as az\n",
     "import jax.numpy as jnp\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/notebooks/2-Using_custom_response_functions.md b/docs/source/notebooks/2-Using_custom_response_functions.md
index 1aff8de32..7716129d7 100644
--- a/docs/source/notebooks/2-Using_custom_response_functions.md
+++ b/docs/source/notebooks/2-Using_custom_response_functions.md
@@ -12,10 +12,12 @@ kernelspec:
   name: python3
 ---
 
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
 (custom_response_functions)=
 # Using custom response models
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/2-Using_custom_response_functions.ipynb)
 
@@ -27,10 +29,16 @@ slideshow:
 ---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 import arviz as az
 import jax.numpy as jnp
 import matplotlib.pyplot as plt
diff --git a/docs/source/notebooks/3-Multilevel_HGF.ipynb b/docs/source/notebooks/3-Multilevel_HGF.ipynb
index d4761cf36..c3893fb9a 100644
--- a/docs/source/notebooks/3-Multilevel_HGF.ipynb
+++ b/docs/source/notebooks/3-Multilevel_HGF.ipynb
@@ -3,7 +3,13 @@
   {
    "cell_type": "markdown",
    "id": "3d531da8-f5e7-4979-9c95-a2dd12c8298e",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "(multilevel_hgf)=\n",
     "# Multilevel embeding of Hierarchical Gaussian Filters"
@@ -13,40 +19,62 @@
    "cell_type": "markdown",
    "id": "ab7fa0a5-7fc9-47b8-96da-4ff5820a8f70",
    "metadata": {
+    "editable": true,
     "execution": {
      "iopub.execute_input": "2023-11-06T08:59:25.111871Z",
      "iopub.status.busy": "2023-11-06T08:59:25.110906Z",
      "iopub.status.idle": "2023-11-06T08:59:25.122657Z",
      "shell.execute_reply": "2023-11-06T08:59:25.121477Z"
-    }
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/3-Multilevel_HGF.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba34f2ab-bca8-499d-bfd5-f2c022409b50",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install pyhgf"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "b2718c0d-5a41-4f56-89be-80318f9ab728",
    "metadata": {
+    "editable": true,
     "execution": {
      "iopub.execute_input": "2023-11-06T08:59:25.126516Z",
      "iopub.status.busy": "2023-11-06T08:59:25.126230Z",
      "iopub.status.idle": "2023-11-06T08:59:29.157547Z",
      "shell.execute_reply": "2023-11-06T08:59:29.156092Z"
     },
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": [
      "hide-cell"
     ]
    },
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "import sys\n",
-    "\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
     "import arviz as az\n",
     "import jax.numpy as jnp\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/notebooks/3-Multilevel_HGF.md b/docs/source/notebooks/3-Multilevel_HGF.md
index b4e55d1c9..74f8cd8d9 100644
--- a/docs/source/notebooks/3-Multilevel_HGF.md
+++ b/docs/source/notebooks/3-Multilevel_HGF.md
@@ -12,22 +12,34 @@ kernelspec:
   name: python3
 ---
 
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
 (multilevel_hgf)=
 # Multilevel embeding of Hierarchical Gaussian Filters
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/3-Multilevel_HGF.ipynb)
 
 ```{code-cell} ipython3
-:tags: [hide-cell]
-
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+tags: [hide-cell]
+---
 import arviz as az
 import jax.numpy as jnp
 import matplotlib.pyplot as plt
diff --git a/docs/source/notebooks/4-Parameter_recovery.ipynb b/docs/source/notebooks/4-Parameter_recovery.ipynb
index b6c5f6f12..e2f77a075 100644
--- a/docs/source/notebooks/4-Parameter_recovery.ipynb
+++ b/docs/source/notebooks/4-Parameter_recovery.ipynb
@@ -3,7 +3,13 @@
   {
    "cell_type": "markdown",
    "id": "3d531da8-f5e7-4979-9c95-a2dd12c8298e",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "(parameters_recovery)=\n",
     "# Parameters recovery, prior predictive and posterior predictive sampling"
@@ -12,31 +18,55 @@
   {
    "cell_type": "markdown",
    "id": "5d880108-f486-4fcc-8bf3-651c6c8b829c",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/4-Parameter_recovery.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e00c09dd-b0ed-4e32-8bc7-af5868acdcd3",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install pyhgf"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "4bc4fb2a-0224-4374-b046-76ab5904e757",
    "metadata": {
+    "editable": true,
     "execution": {
      "iopub.execute_input": "2023-11-06T09:01:29.680704Z",
      "iopub.status.busy": "2023-11-06T09:01:29.680443Z",
      "iopub.status.idle": "2023-11-06T09:01:33.979724Z",
      "shell.execute_reply": "2023-11-06T09:01:33.978722Z"
-    }
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "import sys\n",
-    "\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
     "import arviz as az\n",
     "import jax.numpy as jnp\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/notebooks/4-Parameter_recovery.md b/docs/source/notebooks/4-Parameter_recovery.md
index 239606c74..28725ed21 100644
--- a/docs/source/notebooks/4-Parameter_recovery.md
+++ b/docs/source/notebooks/4-Parameter_recovery.md
@@ -12,20 +12,33 @@ kernelspec:
   name: python3
 ---
 
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
 (parameters_recovery)=
 # Parameters recovery, prior predictive and posterior predictive sampling
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/4-Parameter_recovery.ipynb)
 
 ```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 import arviz as az
 import jax.numpy as jnp
 import matplotlib.pyplot as plt
diff --git a/docs/source/notebooks/Example_1_Heart_rate_variability.ipynb b/docs/source/notebooks/Example_1_Heart_rate_variability.ipynb
index 75d3e3218..457a3756e 100644
--- a/docs/source/notebooks/Example_1_Heart_rate_variability.ipynb
+++ b/docs/source/notebooks/Example_1_Heart_rate_variability.ipynb
@@ -3,7 +3,13 @@
   {
    "cell_type": "markdown",
    "id": "3d531da8-f5e7-4979-9c95-a2dd12c8298e",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "(example_1)=\n",
     "# Example 1: Bayesian filtering of cardiac volatility"
@@ -12,31 +18,55 @@
   {
    "cell_type": "markdown",
    "id": "c013ea97-4534-48f7-b9c9-930fdafaa3fd",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Example_1_Heart_rate_variability.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11c4c38d-4387-4c13-accf-1736b0451cad",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install pyhgf systole"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "53f8336b-b700-42b6-b037-99607bc18ad9",
    "metadata": {
+    "editable": true,
     "execution": {
      "iopub.execute_input": "2023-11-06T09:02:20.281167Z",
      "iopub.status.busy": "2023-11-06T09:02:20.280313Z",
      "iopub.status.idle": "2023-11-06T09:02:24.918431Z",
      "shell.execute_reply": "2023-11-06T09:02:24.917303Z"
-    }
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "import sys\n",
-    "\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf systole\n",
-    "\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
diff --git a/docs/source/notebooks/Example_1_Heart_rate_variability.md b/docs/source/notebooks/Example_1_Heart_rate_variability.md
index d50a102dd..3f0f92ece 100644
--- a/docs/source/notebooks/Example_1_Heart_rate_variability.md
+++ b/docs/source/notebooks/Example_1_Heart_rate_variability.md
@@ -12,20 +12,33 @@ kernelspec:
   name: python3
 ---
 
++++ {"editable": true, "slideshow": {"slide_type": ""}}
+
 (example_1)=
 # Example 1: Bayesian filtering of cardiac volatility
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Example_1_Heart_rate_variability.ipynb)
 
 ```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf systole
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
diff --git a/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb
index ae8860b60..309ef65c3 100644
--- a/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb
+++ b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb
@@ -18,15 +18,21 @@
   {
    "cell_type": "markdown",
    "id": "a433e73e-e20e-459e-9744-6193ae31898c",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "edfee021-57f2-434a-9841-990ddeb4ee00",
+   "execution_count": null,
+   "id": "bc2f0d67-9f24-4e44-ad7b-c04c22cff638",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -38,10 +44,23 @@
    "source": [
     "%%capture\n",
     "import sys\n",
-    "\n",
     "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
+    "    !pip install pyhgf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "edfee021-57f2-434a-9841-990ddeb4ee00",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
diff --git a/docs/source/notebooks/Example_2_Input_node_volatility_coupling.md b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.md
index 24fcdffea..cf384cfb9 100644
--- a/docs/source/notebooks/Example_2_Input_node_volatility_coupling.md
+++ b/docs/source/notebooks/Example_2_Input_node_volatility_coupling.md
@@ -17,7 +17,7 @@ kernelspec:
 (example_2)=
 # Example 2: Estimating the mean and precision of an input node
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Example_2_Input_node_volatility_coupling.ipynb)
 
@@ -29,10 +29,16 @@ slideshow:
 ---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
diff --git a/docs/source/notebooks/Exercise_1_Using_the_HGF.ipynb b/docs/source/notebooks/Exercise_1_Using_the_HGF.ipynb
index 63caca603..7b922ec5d 100644
--- a/docs/source/notebooks/Exercise_1_Using_the_HGF.ipynb
+++ b/docs/source/notebooks/Exercise_1_Using_the_HGF.ipynb
@@ -18,34 +18,57 @@
   {
    "cell_type": "markdown",
    "id": "44cc9869-d0be-4cd4-8309-dfc37a1d0a01",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Exercise_1_Using_the_HGF.ipynb)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56fadf0b-a1b3-4aeb-9d81-efd17975448d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install pyhgf"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "48931dea-0be4-4f9e-8989-209d5d3d30b5",
    "metadata": {
+    "editable": true,
     "execution": {
      "iopub.execute_input": "2023-11-06T09:02:59.772473Z",
      "iopub.status.busy": "2023-11-06T09:02:59.772193Z",
      "iopub.status.idle": "2023-11-06T09:03:03.632771Z",
      "shell.execute_reply": "2023-11-06T09:03:03.630725Z"
     },
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": [
      "hide-cell"
     ]
    },
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "import sys\n",
-    "\n",
-    "if 'google.colab' in sys.modules:\n",
-    "    !pip install pyhgf\n",
-    "\n",
     "import arviz as az\n",
     "import jax.numpy as jnp\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/notebooks/Exercise_1_Using_the_HGF.md b/docs/source/notebooks/Exercise_1_Using_the_HGF.md
index a99454cff..8ba5d2178 100644
--- a/docs/source/notebooks/Exercise_1_Using_the_HGF.md
+++ b/docs/source/notebooks/Exercise_1_Using_the_HGF.md
@@ -17,19 +17,29 @@ kernelspec:
 (hgf_exercises)=
 # An introduction to Hierarchical Gaussian Filters through practical exercises
 
-+++
++++ {"editable": true, "slideshow": {"slide_type": ""}}
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ilabcode/pyhgf/blob/master/docs/source/notebooks/Exercise_1_Using_the_HGF.ipynb)
 
 ```{code-cell} ipython3
-:tags: [hide-cell]
-
+---
+editable: true
+slideshow:
+  slide_type: ''
+---
 %%capture
 import sys
-
 if 'google.colab' in sys.modules:
     !pip install pyhgf
+```
 
+```{code-cell} ipython3
+---
+editable: true
+slideshow:
+  slide_type: ''
+tags: [hide-cell]
+---
 import arviz as az
 import jax.numpy as jnp
 import matplotlib.pyplot as plt