diff --git a/notebooks/readouts_tutorial.ipynb b/notebooks/readouts_tutorial.ipynb
index 46683cf8..b4667052 100644
--- a/notebooks/readouts_tutorial.ipynb
+++ b/notebooks/readouts_tutorial.ipynb
@@ -40,16 +40,11 @@
    "outputs": [],
    "source": [
     "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
-    "\n",
+    "import random\n",
     "import torch\n",
-    "import torch.nn as nn\n",
     "import numpy as np\n",
-    "from nnfabrik.builder import get_data\n",
-    "from nnfabrik.utility.nn_helpers import set_random_seed, get_dims_for_loader_dict\n",
-    "from neuralpredictors.utils import get_module_output\n",
     "\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
     "random_seed = 42"
    ]
@@ -86,17 +81,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# in_shapes_dict = {\n",
-    "#     k: get_module_output(core, v[in_name])[1:]\n",
-    "#     for k, v in session_shape_dict.items()\n",
-    "# }\n",
-    "\n",
     "in_shapes_dict = {\n",
     "    '21067-10-18': torch.Size([64, 144, 256]),\n",
     "    '22846-10-16': torch.Size([64, 144, 256])\n",
     "}\n",
     "\n",
-    "# n_neurons_dict = {k: v[out_name][1] for k, v in session_shape_dict.items()}\n",
     "n_neurons_dict = {'21067-10-18': 8372, '22846-10-16': 7344}"
    ]
   },
@@ -276,7 +265,7 @@
     "Let us import some data and define a sample core from which we will readout.\n",
     "You can download the data [here](https://gin.g-node.org/cajal/Sensorium2022/src/master/static22846-10-16-GrayImageNet-94c6ff995dac583098847cfecd43e7b6.zip) and [here](https://gin.g-node.org/cajal/Sensorium2022/src/master/static21067-10-18-GrayImageNet-94c6ff995dac583098847cfecd43e7b6.zip).\n",
     "\n",
-    "We will also need some utility functions from `nnfabrik` and `sensorium`, so you can run the next cell to install them if you have not done so already."
+    "We will also need some utility functions from `sensorium`, so you can run the next cell to install them if you have not done so already."
    ]
   },
   {
@@ -286,8 +275,14 @@
    "outputs": [],
    "source": [
     "%%capture \n",
-    "!pip install git+https://github.com/sinzlab/sensorium.git\n",
-    "!pip install nnfabrik"
+    "!pip install git+https://github.com/sinzlab/sensorium.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will also need some helper function to load the data, which we define in the next cell:"
    ]
   },
   {
@@ -295,14 +290,63 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# The following are minimal adaptations of three utility functions found in nnfabrik that we need to initialise\n",
+    "# the core and readouts later on.\n",
+    "\n",
+    "def get_data(dataset_fn, dataset_config):\n",
+    "    \"\"\"\n",
+    "    See https://github.com/sinzlab/nnfabrik/blob/5b6e7379cb5724a787cdd482ee987b8bc0dfacf3/nnfabrik/builder.py#L87\n",
+    "    for the original implementation and documentation if you are interested.\n",
+    "    \"\"\"\n",
+    "    return dataset_fn(**dataset_config)\n",
+    "\n",
+    "def get_dims_for_loader_dict(dataloaders):\n",
+    "    \"\"\"\n",
+    "    See https://github.com/sinzlab/nnfabrik/blob/5b6e7379cb5724a787cdd482ee987b8bc0dfacf3/nnfabrik/utility/nn_helpers.py#L39\n",
+    "    for the original implementation and docstring if you are interested.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    def get_io_dims(data_loader):\n",
+    "        items = next(iter(data_loader))\n",
+    "        if hasattr(items, \"_asdict\"):  # if it's a named tuple\n",
+    "            items = items._asdict()\n",
+    "\n",
+    "        if hasattr(items, \"items\"):  # if dict like\n",
+    "            return {k: v.shape for k, v in items.items()}\n",
+    "        else:\n",
+    "            return (v.shape for v in items)\n",
+    "\n",
+    "    return {k: get_io_dims(v) for k, v in dataloaders.items()}\n",
+    "\n",
+    "\n",
+    "def set_random_seed(seed: int, deterministic: bool = True):\n",
+    "    \"\"\"\n",
+    "    See https://github.com/sinzlab/nnfabrik/blob/5b6e7379cb5724a787cdd482ee987b8bc0dfacf3/nnfabrik/utility/nn_helpers.py#L53\n",
+    "    for the original implementation and docstring if you are intereseted.\n",
+    "    \"\"\"\n",
+    "    random.seed(seed)\n",
+    "    np.random.seed(seed)\n",
+    "    if deterministic:\n",
+    "        torch.backends.cudnn.benchmark = False\n",
+    "        torch.backends.cudnn.deterministic = True\n",
+    "    torch.manual_seed(seed)  # this sets both CPU and CUDA seeds for PyTorch\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "## Load the data: you can modify this if you have stored it in another location\n",
+    "from sensorium.datasets import static_loaders\n",
+    "\n",
     "filenames = [\n",
     "    '../../data/static21067-10-18-GrayImageNet-94c6ff995dac583098847cfecd43e7b6.zip', \n",
     "    '../../data/static22846-10-16-GrayImageNet-94c6ff995dac583098847cfecd43e7b6.zip'\n",
     "    ]\n",
     "\n",
-    "dataset_fn = 'sensorium.datasets.static_loaders'\n",
     "dataset_config = {'paths': filenames,\n",
     "                 'normalize': True,\n",
     "                 'include_behavior': False,\n",
@@ -312,12 +356,12 @@
     "                 'cuda': True if device == 'cuda' else False,\n",
     "                 }\n",
     "\n",
-    "dataloaders = get_data(dataset_fn, dataset_config)"
+    "dataloaders = get_data(dataset_fn=static_loaders, dataset_config=dataset_config)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -342,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -443,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -520,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -550,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -578,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {