image links/numpydoc

.github/workflows/publish-book.yml

@@ -21,7 +21,7 @@ jobs:
         python -m pip install --upgrade pip
         python -m pip install -e .
         python -m pip install .[docs]
-        pip install jupyter-book sphinxcontrib-mermaid
+        pip install jupyter-book sphinxcontrib-mermaid numpydoc
 
     - name: Build the book
       run: |

README.md

@@ -1,8 +1,8 @@
 # CEBRA-Lens
 
-## A python library for mechanistic interpretability of CEBRA models
+<img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/c443da50-49cd-4fcb-a2f3-a5cae2915f51/zebra.png?format=1500w" title="cebra-lens" alt="cebra-lens" width="150" align="right" style="margin-top:80px;" />
 
- <img src="_static/zebra.png" title="cebra-lens" alt="cebra-lens" width="150" align="right" vspace = "80"/>
+A python library for mechanistic interpretability of CEBRA models
 
 **CEBRA-Lens** is a Python library for analyzing and interpreting neural representations learned by models trained with [CEBRA](https://github.com/AdaptiveMotorControlLab/cebra). It provides tools for mechanistic interpretability, allowing users to probe, visualize, and understand the structure of learned embeddings. The library is designed to support in-depth analysis of representational geometry, feature selectivity, and latent space dynamics in neuroscience and beyond. 👋 We welcome contributions and will continue to expand the library in the coming years.
 
@@ -18,13 +18,13 @@ conda create -n CEBRAlens python=3.12
 conda activate CEBRAlens
 conda install -c conda-forge pytables==3.8.0
 
-# install PyTorch with your desired CUDA version (or for CPU only)- check their website: https://pytorch.org/get-started/locally/
+# install PyTorch with your desired CUDA version - check their website: https://pytorch.org/get-started/locally/
 # example: GPU version of pytorch for CUDA 11.3
 conda install pytorch cudatoolkit=11.3 -c pytorch
 
 # install CEBRA and CEBRA-lens
 pip install --pre 'cebra[datasets,demos]'
-pip install -- cebra_lens
+pip install --pre cebra_lens
 ```
 
 ## 🦓🔍 Analysis Methods
@@ -65,7 +65,9 @@ These analyses quantify the change in the distance calculated per layer in a mod
 - inter-class distance
 - inter-repetition distance (only relevant if the model was trained on a dataset where there is repeating stimuli)
 
-<img src="_static/abstractfig.png" alt="analysis">
+<img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/4e8eaca2-e14a-4a46-aba5-702775ad2797/abstractfig.png?format=1500w" alt="analysis">
+
+
 
 # Demo