From 0104b0b8103965b9f1b02c0cca8138eabafdc2f7 Mon Sep 17 00:00:00 2001
From: Dan Saunders <djsaunde@umass.edu>
Date: Mon, 11 Jun 2018 20:37:17 -0400
Subject: [PATCH] Updating citation.

---
 README.md | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 4caf975d..3d2e2bc1 100644
--- a/README.md
+++ b/README.md
@@ -78,8 +78,25 @@ We are interested in applying SNNs to ML and RL problems. We use STDP to modify
 
 We have provided some simple starter scripts for doing unsupervised learning (learning a fully-connected or convolutional representation via STDP), supervised learning (clamping output neurons to desired spiking behavior depending on data labels), and reinforcement learning (converting observations from the Atari game Space Invaders to input to an SNN, and converting network activity back to actions in the game).
 
-## References
- Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma, [BindsNET: A machine learning-oriented spiking neural networks library in Python.](https://arxiv.org/abs/1806.01423) 2018, Arxiv.
+## Citation
+
+If you use BindsNET in your research, please cite the following [article](https://arxiv.org/abs/1806.01423):
+
+```
+@ARTICLE{2018arXiv180601423H,
+   author = {{Hazan}, H. and {Saunders}, D.~J. and {Khan}, H. and {Sanghavi}, D.~T. and 
+	{Siegelmann}, H.~T. and {Kozma}, R.},
+    title = "{BindsNET: A machine learning-oriented spiking neural networks library in Python}",
+  journal = {ArXiv e-prints},
+archivePrefix = "arXiv",
+   eprint = {1806.01423},
+ keywords = {Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition},
+     year = 2018,
+    month = jun,
+   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180601423H},
+  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+```
 
 ## Contributors