improve clarity of project aim in intro

paper/sections/meta_science.md

@@ -1,5 +1,5 @@
 ## Workflow
-Our project grew out of a tutorial at the [Cosyne conference](https://www.cosyne.org/) (2022) for which we provided [video lectures and code online](https://neural-reckoning.github.io/cosyne-tutorial-2022/) {cite:p}`10.5281/zenodo.7044500`. Participants joining the project were encouraged to review this material, and then to work through an introductory Jupyter Notebook {cite:p}`Kluyver2016jupyter` containing Python code, figures and markdown text, which taught them how to train a spiking neural network to perform a sound localisation task. Participants were then directed to our website where we maintained a list of open scientific and technical questions for inspiration. For example, how does the use of different neuron models impact network performance and can we learn input delays with gradient descent? Then, with a proposed or novel question in hand, participants were free to approach their work as they wished. In practice, much like a "typical" research project, most work was conducted individually, shared at monthly online meetings and then iteratively improved upon. For example, several early career researchers tackled questions full-time as their dissertation or thesis work and benefited from external input at monthly workshops.
+Our project was launched at a tutorial at the [Cosyne conference](https://www.cosyne.org/) (2022) for which we provided [video lectures and code online](https://neural-reckoning.github.io/cosyne-tutorial-2022/) {cite:p}`10.5281/zenodo.7044500`. Students in that tutorial were invited to take part in an ongoing research project starting from the material covered in the tutorial, and this was also opened to anyone in the world and advertised via mailing lists and social media. Participants joining the project were encouraged to review the material from the tutorial, and then to work through an introductory Jupyter Notebook {cite:p}`Kluyver2016jupyter` containing Python code, figures and markdown text, which taught them how to train a spiking neural network to perform a sound localisation task. Participants were then directed to our website where we maintained a list of open scientific and technical questions for inspiration. For example, how does the use of different neuron models impact network performance and can we learn input delays with gradient descent? Then, with a proposed or novel question in hand, participants were free to approach their work as they wished. In practice, much like a "typical" research project, most work was conducted individually, shared at monthly online meetings and then iteratively improved upon. For example, several early career researchers tackled questions full-time as their dissertation or thesis work and benefited from external input at monthly workshops.
 
 ```{figure} ./sections/_figures/COMOB_workflow.png
 :label: COMOB_workflow