diff --git a/examples/quickstart.ipynb b/examples/quickstart.ipynb
index 262004a1..fe1a73da 100644
--- a/examples/quickstart.ipynb
+++ b/examples/quickstart.ipynb
@@ -39,7 +39,7 @@
         "For a comprehensive overview on how SNNs work, and what is going on under the hood, [then you might be interested in the snnTorch tutorial series available here.](https://snntorch.readthedocs.io/en/latest/tutorials/index.html)\n",
         "The snnTorch tutorial series is based on the following paper. If you find these resources or code useful in your work, please consider citing the following source:\n",
         "\n",
-        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". arXiv preprint arXiv:2109.12894, September 2021.](https://arxiv.org/abs/2109.12894) </cite>"
+        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". Proceedings of the IEEE, 111(9) September 2023.](https://ieeexplore.ieee.org/abstract/document/10242251) </cite>"
       ]
     },
     {
@@ -88,7 +88,7 @@
       "outputs": [],
       "source": [
         "batch_size = 128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
       ]
     },
diff --git a/examples/tutorial_5_FCN.ipynb b/examples/tutorial_5_FCN.ipynb
index 6a38c78b..fd0086b1 100644
--- a/examples/tutorial_5_FCN.ipynb
+++ b/examples/tutorial_5_FCN.ipynb
@@ -43,7 +43,7 @@
       "source": [
         "The snnTorch tutorial series is based on the following paper. If you find these resources or code useful in your work, please consider citing the following source:\n",
         "\n",
-        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". arXiv preprint arXiv:2109.12894, September 2021.](https://arxiv.org/abs/2109.12894) </cite>\n",
+        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". Proceedings of the IEEE, 111(9) September 2023.](https://ieeexplore.ieee.org/abstract/document/10242251) </cite>\n",
         "\n"
       ]
     },
diff --git a/examples/tutorial_6_CNN.ipynb b/examples/tutorial_6_CNN.ipynb
index 891e7f98..c14a2f1a 100644
--- a/examples/tutorial_6_CNN.ipynb
+++ b/examples/tutorial_6_CNN.ipynb
@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/jeshraghian/snntorch/blob/docs-update/examples/tutorial_6_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -41,7 +41,7 @@
       "source": [
         "The snnTorch tutorial series is based on the following paper. If you find these resources or code useful in your work, please consider citing the following source:\n",
         "\n",
-        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". arXiv preprint arXiv:2109.12894, September 2021.](https://arxiv.org/abs/2109.12894) </cite>\n"
+        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". Proceedings of the IEEE, 111(9) September 2023.](https://ieeexplore.ieee.org/abstract/document/10242251) </cite>\n"
       ]
     },
     {
@@ -261,7 +261,7 @@
       "source": [
         "# dataloader arguments\n",
         "batch_size = 128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "\n",
         "dtype = torch.float\n",
         "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
@@ -630,6 +630,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "y_VQ9es-gSO3"
+      },
+      "outputs": [],
       "source": [
         "optimizer = torch.optim.Adam(net.parameters(), lr=1e-2, betas=(0.9, 0.999))\n",
         "num_epochs = 1\n",
@@ -671,12 +676,7 @@
         "              test_acc_hist.append(test_acc.item())\n",
         "\n",
         "        counter += 1"
-      ],
-      "metadata": {
-        "id": "y_VQ9es-gSO3"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -816,9 +816,9 @@
   "metadata": {
     "accelerator": "GPU",
     "colab": {
+      "include_colab_link": true,
       "name": "tutorial_6_CNN.ipynb",
-      "provenance": [],
-      "include_colab_link": true
+      "provenance": []
     },
     "kernelspec": {
       "display_name": "Python 3",
diff --git a/examples/tutorial_7_neuromorphic_datasets.ipynb b/examples/tutorial_7_neuromorphic_datasets.ipynb
index 0d0c38fe..05d6dd1d 100644
--- a/examples/tutorial_7_neuromorphic_datasets.ipynb
+++ b/examples/tutorial_7_neuromorphic_datasets.ipynb
@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/jeshraghian/snntorch/blob/docs-update/examples/tutorial_7_neuromorphic_datasets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -41,7 +41,7 @@
       "source": [
         "The snnTorch tutorial series is based on the following paper. If you find these resources or code useful in your work, please consider citing the following source:\n",
         "\n",
-        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". arXiv preprint arXiv:2109.12894, September 2021.](https://arxiv.org/abs/2109.12894) </cite>"
+        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". Proceedings of the IEEE, 111(9) September 2023.](https://ieeexplore.ieee.org/abstract/document/10242251) </cite>"
       ]
     },
     {
@@ -170,8 +170,8 @@
         "                                                         time_window=1000)\n",
         "                                     ])\n",
         "\n",
-        "trainset = tonic.datasets.NMNIST(save_to='./data', transform=frame_transform, train=True)\n",
-        "testset = tonic.datasets.NMNIST(save_to='./data', transform=frame_transform, train=False)"
+        "trainset = tonic.datasets.NMNIST(save_to='./tmp/data', transform=frame_transform, train=True)\n",
+        "testset = tonic.datasets.NMNIST(save_to='./tmp/data', transform=frame_transform, train=False)"
       ]
     },
     {
@@ -641,10 +641,11 @@
   "metadata": {
     "accelerator": "GPU",
     "colab": {
+      "include_colab_link": true,
       "name": "Copy of tutorial_5_neuromorphic_datasets.ipynb",
-      "provenance": [],
-      "include_colab_link": true
+      "provenance": []
     },
+    "gpuClass": "standard",
     "kernelspec": {
       "display_name": "Python 3 (ipykernel)",
       "language": "python",
@@ -661,8 +662,7 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.8.11"
-    },
-    "gpuClass": "standard"
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 5
diff --git a/examples/tutorial_regression_1.ipynb b/examples/tutorial_regression_1.ipynb
index 960256a3..455841cb 100644
--- a/examples/tutorial_regression_1.ipynb
+++ b/examples/tutorial_regression_1.ipynb
@@ -40,7 +40,7 @@
     "\n",
     "> <cite> [Alexander Henkes, Jason K. Eshraghian, and Henning Wessels. “Spiking neural networks for nonlinear regression\", arXiv preprint arXiv:2210.03515, October 2022.](https://arxiv.org/abs/2210.03515) </cite>\n",
     "\n",
-    "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". arXiv preprint arXiv:2109.12894, September 2021.](https://arxiv.org/abs/2109.12894) </cite>"
+    "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". Proceedings of the IEEE, 111(9) September 2023.](https://ieeexplore.ieee.org/abstract/document/10242251) </cite>"
    ]
   },
   {
diff --git a/examples/tutorial_regression_2.ipynb b/examples/tutorial_regression_2.ipynb
index fa84e58d..02f84e0b 100644
--- a/examples/tutorial_regression_2.ipynb
+++ b/examples/tutorial_regression_2.ipynb
@@ -30,7 +30,7 @@
         "\n",
         "> <cite> [Alexander Henkes, Jason K. Eshraghian, and Henning Wessels. “Spiking neural networks for nonlinear regression\", arXiv preprint arXiv:2210.03515, October 2022.](https://arxiv.org/abs/2210.03515) </cite>\n",
         "\n",
-        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". arXiv preprint arXiv:2109.12894, September 2021.](https://arxiv.org/abs/2109.12894) </cite>"
+        "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". Proceedings of the IEEE, 111(9) September 2023.](https://ieeexplore.ieee.org/abstract/document/10242251) </cite>"
       ]
     },
     {
@@ -455,7 +455,7 @@
       "outputs": [],
       "source": [
         "batch_size = 128\n",
-        "data_path='data/mnist'\n",
+        "data_path='tmp/data/mnist'\n",
         "\n",
         "# Define a transform\n",
         "transform = transforms.Compose([\n",
diff --git a/examples/tutorial_sae.ipynb b/examples/tutorial_sae.ipynb
index b8ed467d..ea71fa0d 100755
--- a/examples/tutorial_sae.ipynb
+++ b/examples/tutorial_sae.ipynb
@@ -26,8 +26,9 @@
    "metadata": {},
    "source": [
     "For a comprehensive overview on how SNNs work, and what is going on under the hood, [then you might be interested in the snnTorch tutorial series available here.](https://snntorch.readthedocs.io/en/latest/tutorials/index.html)\n",
+    "\n",
     "The snnTorch tutorial series is based on the following paper. If you find these resources or code useful in your work, please consider citing the following source:\n",
-    "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". arXiv preprint arXiv:2109.12894, September 2021.](https://arxiv.org/abs/2109.12894) </cite>"
+    "> <cite> [Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. \"Training Spiking Neural Networks Using Lessons From Deep Learning\". Proceedings of the IEEE, 111(9) September 2023.](https://ieeexplore.ieee.org/abstract/document/10242251) </cite>"
    ]
   },
   {