diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index f802368..f15e5a2 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -16,7 +16,7 @@ jobs:
         id: set-matrix
         run: |
           FULL='["3.8", "3.9", "3.10", "3.11", "3.12", "3.13", "3.14"]'
-          MINIMAL='["3.11"]'
+          MINIMAL='["3.12"]'
           if [[ "${{ github.event_name }}" == "push" ]]; then
             # Push to main: full matrix
             echo "python-versions=$FULL" >> "$GITHUB_OUTPUT"
diff --git a/AUTHORS.md b/AUTHORS.md
new file mode 100644
index 0000000..58025dd
--- /dev/null
+++ b/AUTHORS.md
@@ -0,0 +1,9 @@
+# Authors
+
+## v0.1.0 Development
+
+🐟<b>i</b><span style="color: #3C92ED;">Tuna</span> was initially developed and is being maintained by Tobias Schmidt ([&#64;TobiasSchmidtDE](https://github.com/TobiasSchmidtDE)) and Steffen Schneider ([&#64;stes](https://github.com/stes)) in the [Dynamical Inference Lab](https://dynamical-inference.ai/) at Helmholtz Munich. 
+
+Lilly May ([&#64;Lilly-May](https://github.com/Lilly-May)) contributed consistency metrics for sparse autoencoders (to be released soon).
+
+Paul Pommer ([&#64;ppommer](https://github.com/ppommer)) contributed demos for [piVAE](https://github.com/zhd96/pi-vae) and [FastSAE](https://github.com/dynamical-inference/fastsae) (to be released soon) and alpha-tested the package.
\ No newline at end of file
diff --git a/ituna/utils.py b/ituna/utils.py
index d6eff38..7d627eb 100644
--- a/ituna/utils.py
+++ b/ituna/utils.py
@@ -1,7 +1,6 @@
 from typing import Tuple
 
 import numpy as np
-import torch
 import typeguard
 
 
@@ -83,21 +82,3 @@ def dense_to_sparse(dense_array: np.ndarray, symmetric: bool = False) -> Tuple[n
 
     values = dense_array[indices[:, 0], indices[:, 1]]
     return indices, values
-
-
-def l2_normalize_columns(x: torch.Tensor, y: torch.Tensor, eps: float = 1e-8) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Normalize each column of x and y to unit L2 norm.
-    """
-    x_norm = x / (x.norm(dim=0, keepdim=True) + eps)
-    y_norm = y / (y.norm(dim=0, keepdim=True) + eps)
-    return x_norm, y_norm
-
-
-def mean_center_columns(x: torch.Tensor, y: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Subtract the column-wise mean from x and y.
-    """
-    x_centered = x - x.mean(dim=0, keepdim=True)
-    y_centered = y - y.mean(dim=0, keepdim=True)
-    return x_centered, y_centered
diff --git a/pyproject.toml b/pyproject.toml
index 88aa198..e5d173a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,12 +30,9 @@ classifiers = [
 requires-python = ">=3.8"
 dependencies = [
     "numpy",
-    "torch",
     "scipy",
     "scikit-learn",
-    "matplotlib",
     "pandas",
-    "igraph",
     "tqdm",
     "python-dotenv",
     "filelock>=3.0.0",
@@ -47,13 +44,19 @@ datajoint = ["datajoint<2"]
 dev = [
     "pytest>=6.0",
     "pytest-cov>=2.0",
-    "black>=21.0",
-    "flake8>=3.8",
     "pre-commit>=3.0",
-    "cebra>=0.5.0",
-    "streamlit>=1.49.1",
+    "cebra>=0.4.0",
     "ruff"
 ]
+docs = [
+    "matplotlib",
+    "cebra[datasets,demos]>=0.4.0",
+    "jupyter-book<2",
+    "ghp-import",
+]
+dashboard = [
+    "streamlit>=1.49.1",
+]
 
 
 [tool.setuptools.dynamic]