Merge branch 'main' of https://www.github.com/mouseland/rastermap into main

Author: carsen-stringer

README.md

@@ -13,9 +13,9 @@
 [![GitHub forks](https://img.shields.io/github/forks/MouseLand/rastermap?style=social)](https://github.com/MouseLand/rastermap/)
 
 
-Rastermap is a discovery algorithm for neural data. The algorithm was written by Carsen Stringer and Marius Pachitariu. For support,  please open an [issue](https://github.com/MouseLand/rastermap/issues). Please see install instructions [below](README.md/#Installation). If you use Rastermap or analysis code in this repo in your work, please cite the [paper](https://www.biorxiv.org/content/10.1101/2023.07.25.550571v1):
+Rastermap is a discovery algorithm for neural data. The algorithm was written by Carsen Stringer and Marius Pachitariu. For support,  please open an [issue](https://github.com/MouseLand/rastermap/issues). Please see install instructions [below](README.md/#Installation). Check out the [**paper**](https://www.nature.com/articles/s41593-024-01783-4) and the [**tutorial video**](https://youtu.be/oQHq7yUWn2k) for more info. If you use Rastermap or analysis code in this repo in your work, please cite the paper:
 
-Stringer C., Zhong L., Syeda A., Du F., Kesa M., & Pachitariu M. (2023). Rastermap: a discovery method for neural population recordings. *bioRxiv* 2023.07.25.550571; doi: https://doi.org/10.1101/2023.07.25.550571
+Stringer C., Zhong L., Syeda A., Du F., Kesa M., & Pachitariu M. (2024). Rastermap: a discovery method for neural population recordings. *Nature Neuroscience*. https://doi.org/10.1038/s41593-024-01783-4.
 
 Rastermap runs in python 3.8+ and has a graphical user interface (GUI) for running it easily. Rastermap can also be run in a jupyter notebook locally or on google colab, see these demos:
 * [rastermap_largescale.ipynb](notebooks/rastermap_largescale.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MouseLand/rastermap/blob/main/notebooks/rastermap_largescale.ipynb) shows how to use it with large-scale data from mouse cortex (> 200 neurons)