Update README.md

README.md

@@ -23,13 +23,31 @@ We recommend starting out with the demo notebook ```notebooks/mcorr_cnmf.ipynb``
 Documentation is available at: https://mesmerize-core.readthedocs.io/ \
 User guide: https://mesmerize-core.readthedocs.io/en/latest/user_guide.html
 
+# Overview
+
+![batch_management](https://user-images.githubusercontent.com/9403332/179145962-82317da6-0340-44e4-83ba-7dace0300f55.png)
+
 # Visualization
 
-For visualization we recommend [`mesmerize-viz`](https://github.com/kushalkolar/mesmerize-viz) which contains a standard set of visualizations (a WIP), or [`fastplotlib`](https://github.com/kushalkolar/fastplotlib). You can also use the [`mesmerize-napari`](https://github.com/nel-lab/mesmerize-napari) plugin for small datasets.
+For visualization we recommend [`mesmerize-viz`](https://github.com/kushalkolar/mesmerize-viz) which contains a standard set of visualizations (a WIP), or [`fastplotlib`](https://github.com/kushalkolar/fastplotlib). Here are some examples of visualizations using `fastplotlib`, these visualizations are all performed within jupyter notebooks therefore they will also work on cloud computing intrastructure!
 
-# Overview
+View raw and motion corrected movie side by side:
 
-![batch_management](https://user-images.githubusercontent.com/9403332/179145962-82317da6-0340-44e4-83ba-7dace0300f55.png)
+https://user-images.githubusercontent.com/9403332/191207398-39a027d7-079e-475b-baec-381f2d271652.mp4
+
+Contours from CNMF, good components in cyan and bad components in magenta:
+
+https://user-images.githubusercontent.com/9403332/191207461-9c5c4cad-867b-413a-b30b-ea61f010eed6.mp4
+
+Input movie, constructed movie `(A * C)`, residuals `(Y - A * C - b * f)`, and reconstructed background (b * f):
+
+https://user-images.githubusercontent.com/9403332/191207782-566e24bc-7f0d-40a3-9442-37c86d0ebe48.mp4
+
+Interactive Component evaluation after CNMF:
+
+https://user-images.githubusercontent.com/9403332/191207883-2393664d-b5e1-49a5-84d1-8ed7eadcf7a0.mp4
+
+This is all possible within jupyter notebooks using `fastplotlib`!
 
 # Installation