minor updates to discussion

paper/sections/discussion.md

@@ -1,8 +1,8 @@
 ## What went well
 
-The decision to start from the code base of the [Cosyne tutorial](https://neural-reckoning.github.io/cosyne-tutorial-2022/) {cite:p}`10.5281/zenodo.7044500` was very helpful. It meant that users had a clear entry path for the project without needing prior expertise, and a code base that was designed to be easy to understand. In addition, the popularity of the tutorial (over 30k views on YouTube at the time of writing) meant that many people heard about this project and were interested in participating. In addition, the GitHub-based infrastructure allowed for asynchronous work our website, that was automatically updated each time anyone made a change to their code or to the text of the paper, allowed for easy sharing of results. 
+The decision to start from the code base of the [Cosyne tutorial](https://neural-reckoning.github.io/cosyne-tutorial-2022/) {cite:p}`10.5281/zenodo.7044500` was very helpful. It meant that users had a clear entry path for the project without needing prior expertise, and a code base that was designed to be easy to understand. In addition, the popularity of the tutorial (over 38k views on YouTube at the time of writing) meant that many people heard about this project and were interested in participating. In addition, the GitHub-based infrastructure allowed for asynchronous work our website, that was automatically updated each time anyone made a change to their code or to the text of the paper, allowed for easy sharing of results. 
 
-By providing models which used spiking neurons to transform sensory inputs into behavioural outputs, participants were free to explore in virtually any direction they wished, much like an open-world or sandbox video game. Indeed over the course of the project we explored the full sensory-motor transformation from manipulating the nature of the input signals to perturbing unit activity and assessing network behaviour. Consequently, our code forms an excellent basis for teaching, as concepts from across neuroscience can be introduced and then implemented in class. In this direction, we integrated our project into two university courses and provide slides and a highly annotated python notebook, for those interested in teaching with these models.  
+By providing models which used spiking neurons to transform sensory inputs into behavioural outputs, participants were free to explore in virtually any direction they wished, much like an open-world or sandbox video game. Indeed over the course of the project we explored the full sensory-motor transformation from manipulating the nature of the input signals to perturbing unit activity and assessing network behaviour. Consequently, in addition to it's role in research, our code forms an excellent basis for teaching, as concepts from across neuroscience can be introduced and then implemented in class. In this direction, we integrated our project into two university courses and provide slides and a highly annotated python notebook, for those interested in teaching with these models.  
 
 Beyond providing teaching and hands-on research experience, the project also offered many opportunities for participants to improve their "soft" scientific skills. For early career researchers (undergraduate and master's students) these included learning how to work with Git, collaborate with researchers from diverse countries and career stages, and contribute to a scientific publication. For later career researchers (PhD, Postdoc) the project provided many supervision and leadership opportunities. For example, during online workshops, later career participants were encouraged to lead small groups focussed on tackling specific questions.