Update README.md

README.md

@@ -19,7 +19,7 @@ Here, you can create a [virtual enviroment](https://docs.conda.io/projects/conda
 First, you should make sure that you have a GPU powerful enough to train deep learning networks. In our paper, we were using a single Nvidia GTX 1080 Ti to train our network. A hardware guide can be found [here](https://timdettmers.com/2018/12/16/deep-learning-hardware-guide/). Once you have your hardware ready, try VAME following the [workflow guide](https://github.com/LINCellularNeuroscience/VAME/wiki/1.-VAME-Workflow).
 
 ## News
-* March 2020: We are happy to release VAME 1.0 with a bunch of improvements and new features! These include the community analysis script, a model allowing generation of unseen datapoints, new visualization functions, as well as the much requested function to generate GIF sequences containing UMAP embeddings and trajectories together with the video of the behaving animal. Big thanks also to @MMathisLab for contributing to OS compatibility and usability of our code.
+* March 2020: We are happy to release VAME 1.0 with a bunch of improvements and new features! These include the community analysis script, a model allowing generation of unseen datapoints, new visualization functions, as well as the much requested function to generate GIF sequences containing UMAP embeddings and trajectories together with the video of the behaving animal. Big thanks also to [@MMathisLab](https://github.com/MMathisLab) for contributing to the OS compatibility and usability of our code.
 * November 2020: We uploaded an egocentric alignment [script](https://github.com/LINCellularNeuroscience/VAME/blob/master/examples/align_demo.py) to allow more researcher to use VAME
 * October 2020: We updated our manuscript on [Biorxiv](https://www.biorxiv.org/content/10.1101/2020.05.14.095430v2)
 * May 2020: Our preprint "Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion" is out! [Read it on Biorxiv!](https://www.biorxiv.org/content/10.1101/2020.05.14.095430v1)