Update README.md

README.md

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 # MothMotifs
 
-This project builds off of previous work published [here](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010512) and uses pruned neural networks trained to model insect flight (GitHub repo found [here](https://github.com/oliviatessa/MothPruning#mothpruning)). 
+This project builds off of previous work published in [[1]](#1) and uses pruned neural networks trained to model insect flight. All code used to generate sparse neural networks for use in this project can be found in [this Github repository](https://github.com/oliviatessa/MothPruning#mothpruning). 
 
 ## Project Description
 
-Network analysis techniques (such as network motif theory) could be used to further compare the pruned networks and investigate the impacts of neural network structure on a control task. Network motifs are statistically significant substructures in a network and have been shown to be indicative of network functionality in control systems [[1]](#1).
+Network analysis techniques (such as network motif theory) could be used to further compare the pruned networks and investigate the impacts of neural network structure on a control task. Network motifs are statistically significant substructures in a network and have been shown to be indicative of network functionality in control systems [[2]](#2).
 
 Complex networks across many domains (ecological food webs, neural systems, electrical circuits) are made up of statistically significant, subgraphs called network motifs. Network motifs are described by the number of nodes they involve and the nature of the connections in-between the nodes (e.g., directed, or bi-directed). The order of the motif is defined by the number of nodes it involves (i.e. n-order motif involves n+1 nodes). For example, a second-order diverging motif involves 3 nodes: 
 
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 ## References
 <a id="1">[1]</a> 
+Zahn, Olivia, Jorge Bustamante Jr, Callin Switzer, Thomas L. Daniel, and J. Nathan Kutz. “Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight.” PLoS Computational Biology 18.9 (2022): e1010512. https://doi.org/10.1371/journal.pcbi.1010512
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+<a id="2">[2]</a> 
 Hu, Yu, et al. "Feedback through graph motifs relates structure and function in complex networks." Physical Review E 98.6 (2018): 062312. https://journals.aps.org/pre/pdf/10.1103/PhysRevE.98.062312