Added link to teaching material

paper/sections/meta_science.md

@@ -20,14 +20,14 @@ Our starting notebook used a combination of NumPy [@harris2020array], Matplotlib
 Note that we didn't require participants to use our starting notebook, and indeed in [](#inhib-model), De Santis and Antonietti implemented a very different sound localization model from scratch.
 
 ### GitHub
-Like many open-source efforts, [our public GitHub repository](https://github.com/comob-project/snn-sound-localization) was the heart of our project. This provided us with three main benefits. First, it made joining the project as simple as cloning and committing to the repository. Second, it allowed us to collaborate asynchronously. That is, we could easily complete work in our own time, and then share it with the group later. Third, it allowed us to track contributions to the project. Measured in this way, 28 individuals contributed to the project. However, interpreting this number is challenging, as these contributions vary significantly in size, and participants who worked in pairs or small groups, often contributed under a single username. We return to this point in the [](#discussion).
+Like many open-source efforts, our public [GitHub repository](https://github.com/comob-project/snn-sound-localization) was the heart of our project. This provided us with three main benefits. First, it made joining the project as simple as cloning and committing to the repository. Second, it allowed us to collaborate asynchronously. That is, we could easily complete work in our own time, and then share it with the group later. Third, it allowed us to track contributions to the project. Measured in this way, 28 individuals contributed to the project. However, interpreting this number is challenging, as these contributions vary significantly in size, and participants who worked in pairs or small groups, often contributed under a single username. We return to this point in the [](#discussion).
 
 ### Website via MyST Markdown
 For those interested in pursuing a similar project our repository can easily be used as a template. It consists of a collection of documents written in Markdown and executable [Jupyter Notebooks](https://jupyter.org/) {cite:p}`Kluyver2016jupyter` containing all the code for the project. Each time the repository is updated, GitHub automatically builds these documents and notebooks into a website so that the current state of the project can be seen by simply navigating to the [project website](https://comob-project.github.io/snn-sound-localization). We used [MyST Markdown](https://mystmd.org/) to automate this process with minimal effort. This paper itself was written using these tools and was publicly visible throughout the project write-up.
 
 (teaching-section)=
 ## Teaching with this framework
-This project emerged from a tutorial, and the code remains well suited for teaching several concepts from across neuroscience. We integrated our project into a Physics MSc course on Biophysics and Neural Circuits. Working individually or in pairs, students actively engaged by adjusting network parameters and modifying the provided code to test their own hypotheses. Later, brief progress report presentations stimulated dynamic discussions in class, as all students, while working on the same project and code, pursued different hypotheses. We found that this setup naturally piqued interest in their peers’ presentations, enhanced their understanding of various project applications, and facilitated collaborative learning. It allowed for engagement from students at a range of skill levels and with diverse interests, and helped bridge the gap between teaching and research.
+This project emerged from a tutorial, and the code remains well suited for teaching several concepts from across neuroscience. We integrated our project into a Physics MSc course on Biophysics and Neural Circuits. Working individually or in pairs, students actively engaged by adjusting network parameters and modifying the provided code to test their own hypotheses. Later, brief progress report presentations stimulated dynamic discussions in class, as all students, while working on the same project and code, pursued different hypotheses. We found that this setup naturally piqued interest in their peers’ presentations, enhanced their understanding of various project applications, and facilitated collaborative learning. Moreover, it allowed for engagement from students at a range of skill levels and with diverse interests, and helped bridge the gap between teaching and research. For those interested in teaching with this framework, slides and a dedicated Python notebook are available on our [GitHub repository](https://github.com/comob-project/snn-sound-localization).
 
 % The project’s stochastic outcomes necessitated substantial statistical analysis, adding an experimental dimension that made the project outcome less deterministic and, thus, more engaging than standard step-wise exercises. However, the project does not demand complex programming nor deep mathematical understandings of neural networks, and so allows practical exploration of neural network applications appropriate for various student levels. This adaptability allowed students of varying skill levels to progress at their own pace. Moreover, the open-ended nature of the project allowed the use of generative AI tools, enabling students to overcome coding challenges and deepen their understanding of the provided code and underlying machine learning concepts, thereby enhancing their learning curve and engagement.