edits

paper/organising.md

@@ -87,6 +87,16 @@ For papers, include the paper in ``paper.bib`` using standard BiBTeX notation, a
 ```{note} On this page, not all links to sections of the paper work, [follow this link for the working version](./paper.md#contributors).
 ```
 
+```{attention}
+Hello contributors! I have inserted a table of names, websites and GitHub usernames of contributors from the repository below. There is a lot of information missing. Please can you submit pull requests modifying the file [paper/sections/contributor_table.md](https://github.com/comob-project/snn-sound-localization/blob/main/paper/sections/contributor_table.md) to update your information, and also get in touch with us if you would like to be listed as an author. Either email [Dan Goodman](mailto:[email protected]) or join the [SNUFA discord channel](https://discord.gg/aYvgGakrVK) ``#sound-localisation-paper``.
+```
+
+If you add a contribution, please use one of the following templates (see examples below):
+
+* Wrote the paper (plus which section if you would like to specify)
+* Conducted research (please give a link to your notebook formatted like this ``[](../research/3-Starting-Notebook.ipynb)``, or specify another sort of contribution)
+* Supervised research (please give the name of your supervisee)
+
 ```{include} sections/contributor_table.md
 ```
 

paper/paper.md

@@ -42,6 +42,7 @@ authors:
   - name: Gabriel Béna
     affiliations:
       - Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom
+    orcid: 0009-0009-2802-4702
 
   - name: Mingxuan Hong
     affiliations: 
@@ -112,7 +113,7 @@ downloads:
 
 
 +++ {"part": "abstract"}
-Neuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. However, while these projects represent a step-change in scale, they retain a traditional structure with centralised funding, participating laboratories and data sharing on publication. Inspired by an open-source project in pure mathematics, we set out to test the feasibility of an alternative structure by running a grassroots, massively collaborative project in computational neuroscience. To do so, we launched a public Git repository, with code for training spiking neural networks to solve a sound localisation task via surrogate gradient descent. We then invited anyone, anywhere to use this code as a springboard for exploring questions of interest to them, and encouraged participants to share their work both asynchronously through Git and synchronously at monthly online workshops. At a scientific level, our work demonstrates how a range of biologically-relevant parameters, from time delays to membrane decays and levels of inhibition, impact sound localisation in networks of spiking units. At a more macro-level, our project brought together 35 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it.  
+Neuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. However, while these projects represent a step-change in scale, they retain a traditional structure with centralised funding, participating laboratories and data sharing on publication. Inspired by an open-source project in pure mathematics, we set out to test the feasibility of an alternative structure by running a grassroots, massively collaborative project in computational neuroscience. To do so, we launched a public Git repository, with code for training spiking neural networks to solve a sound localisation task via surrogate gradient descent. We then invited anyone, anywhere to use this code as a springboard for exploring questions of interest to them, and encouraged participants to share their work both asynchronously through Git and synchronously at monthly online workshops. At a scientific level, our work investigated how a range of biologically-relevant parameters, from time delays to membrane time constants and levels of inhibition, could impact sound localisation in networks of spiking units. At a more macro-level, our project brought together 35 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it.  
 +++
 
 # Introduction

paper/sections/contributor_table.md

@@ -1,13 +1,3 @@
-```{attention}
-Hello contributors! I have inserted a table of names, websites and GitHub usernames of contributors from the repository below. There is a lot of information missing. Please can you submit pull requests modifying the file [paper/sections/contributor_table.md](https://github.com/comob-project/snn-sound-localization/blob/main/paper/sections/contributor_table.md) to update your information, and also get in touch with us if you would like to be listed as an author. Either email [Dan Goodman](mailto:[email protected]) or join the [SNUFA discord channel](https://discord.gg/aYvgGakrVK) ``#sound-localisation-paper``.
-```
-
-If you add a contribution, please use one of the following templates (see examples below):
-
-* Wrote the paper (plus which section if you would like to specify)
-* Conducted research (please give a link to your notebook formatted like this ``[](../research/3-Starting-Notebook.ipynb)``, or specify another sort of contribution)
-* Supervised research (please give the name of your supervisee)
-
 (contributor-table)=
 ```{list-table} Contributors, ordered by GitHub commits.
 :header-rows: 1
@@ -49,9 +39,9 @@ If you add a contribution, please use one of the following templates (see exampl
 * - Jose Gomes (Portugal, PhD)
   - [\@JoseGomesJPG](https://github.com/JoseGomesJPG)
   - Conducted research ([](../research/Dales_law.ipynb)).
-* - ???
+* - (Unknown)
   - [\@a-dtk](https://github.com/a-dtk)
-  - (TODO)
+  - Conducted research ([](../research/Noise_robustness.ipynb)).
 * - Sara Evers
   - [\@saraevers](https://github.com/saraevers)
   - Conducted research ([](../research/IE-neuron-distribution.ipynb)).
@@ -63,7 +53,7 @@ If you add a contribution, please use one of the following templates (see exampl
   - Wrote the paper (DCLS based delay learning in the appendix). Conducted research ([](../research/Quick_Start_random.ipynb), [](../research/Quick_Start_Delay_DCLS.ipynb)).
 * - Sebastian Schmitt
   - [\@schmitts](https://github.com/schmitts)
-  - (TODO)
+  - Conducted research (background on neuromorphic hardware in [](../research/Background.md)).
 * - [Rowan Cockett](http://row1.ca/)
   - [\@rowanc1](https://github.com/rowanc1)
   - MyST technical support

paper/sections/intro.md

@@ -2,6 +2,6 @@ Inspired by the success of endeavours like the [Human Genome Project](https://ww
 
 One alternative is a bench marking contest, in which participants compete to obtain the best score on a specific task. Such contests have driven progress in fields from machine learning {cite:p}`10.1109/CVPR.2009.5206848` to [protein folding](https://predictioncenter.org/), and have begun to enter neuroscience. For example, in [Brain-Score](https://www.brain-score.org/) [@10.1101/407007;@10.1016/j.neuron.2020.07.040] participants submit models, capable of completing a visual processing task, which are then ranked according to a quantitative metric. As participants can compete both remotely and independently, these contests offer a significantly lower barrier to entry. Though, they emphasise competition over collaboration, and critically they require a well defined, quantifiable endpoint. In [Brain-Score](https://www.brain-score.org/), this endpoint is a composite metric which describes the model's similarity to experimental data in terms of both behaviour and unit activity. However, this metric's relevance is debatable {cite:p}`doi:10.1017/S0140525X22002813` and more broadly, defining clear endpoints for neuroscientific questions remains challenging.    
 
-Another alternative is massively collaborative projects in which participants work together to solve a common goal. For example, in the [Polymath Project](https://polymathprojects.org/) unsolved mathematical problems are posed, and then participants share comments, ideas and equations online as they collectively work towards solutions. Inspired by this approach, we founded [COMOB (Collaborative Modelling of the Brain)](https://comob-project.github.io/) - an open-source movement, which aims to tackle neuroscientific questions. Here, we share our experiences and results from our first project, in which we explored spiking neural network models of sound localization.
+Another alternative is massively collaborative projects in which participants work together to solve a common goal. For example, in the [Polymath Project](https://polymathprojects.org/) unsolved mathematical problems are posed, and then participants share comments, ideas and equations online as they collectively work towards solutions. The [Busy Beaver Challenge](https://bbchallenge.org/) recently [announced](https://discuss.bbchallenge.org/t/july-2nd-2024-we-have-proved-bb-5-47-176-870/237) a formal proof of a conjecture that was open for decades, [based mainly on contributions from amateur mathematicians, organised purely online](https://www.quantamagazine.org/amateur-mathematicians-find-fifth-busy-beaver-turing-machine-20240702/). Inspired by this approach, we founded [COMOB (Collaborative Modelling of the Brain)](https://comob-project.github.io/) - an open-source movement, which aims to tackle neuroscientific questions. Here, we share our experiences and results from our first project, in which we explored spiking neural network models of sound localization.
 
-We start by detailing how we ran the project both in terms of infrastructure and organisationally in [](#metascience). We then briefly summarise the scientific results in [](#science). We conclude the main text with a [](#discussion) of what went well, what went wrong, and how we think future projects of this sort could learn from our experiences. Finally, in the [](#appendices) we give longer more detailed write-ups of some of the more detailed scientific results.
+We start by detailing how we ran the project both in terms of infrastructure and organisationally in [](#metascience). We then briefly summarise the scientific results in [](#science). We conclude the main text with a [](#discussion) of what went well, what went wrong, and how we think future projects of this sort could learn from our experiences. Finally, in the [](#appendices) we give longer write-ups of some of the more detailed scientific results.

paper/sections/meta_science.md

@@ -26,10 +26,11 @@ For those interested in pursuing a similar project our repository can easily be
 
 (teaching-section)=
 ## Teaching with this framework
-As our code uses spiking neurons to transform sensory inputs into behavioural outputs, it forms an excellent basis for teaching, as concepts from across neuroscience can be introduced and then implemented in class. With this in mind we integrated our project into physics M1 and M2 undergraduate lectures 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. This setup naturally piqued interest in their peers’ presentations, enhanced their understanding of various project applications, and facilitated collaborative learning.
+As our code uses spiking neurons to transform sensory inputs into behavioural outputs, it forms an excellent basis for teaching, as concepts from across neuroscience can be introduced and then implemented in class. With this in mind 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. This setup naturally piqued interest in their peers’ presentations, enhanced their understanding of various project applications, and facilitated collaborative learning.
 
 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.
 
 Working on a real research project not only sustained interest and demonstrated real-world impact but also provided additional inspiration through the accessible contributions of all project participants. This educational initiative thus successfully bridged the gap between teaching and research, with student feedback highlighting its effectiveness in enhancing both theoretical and practical knowledge. The desire for more time to delve deeper into the projects indicated its strength in engaging students and sparking their interest.
 
-In sum, this framework's multidisciplinary nature makes it versatile in various teaching contexts, and suited to discussing both machine learning concepts and open challenges in neuroscience, such as how to decipher brain circuits with recording tools and experimental manipulations like optogenetics. For those interested in teaching with this framework, we have provided slides and a highly annotated introductory Python notebook [here](). 
+In sum, this framework's multidisciplinary nature makes it versatile in various teaching contexts, and suited to discussing both machine learning concepts and open challenges in neuroscience, such as how to decipher brain circuits with recording tools and experimental manipulations like optogenetics.
+% For those interested in teaching with this framework, we have provided slides and a highly annotated introductory Python notebook [here]().