Is anarchist science possible? As an experiment, we got together a large group of computational neuroscientists from around the world to work on a single project without top down direction. Read on to find out what happened. 🤖🧠🧪

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.738Z

The project started as a tutorial on a new technique at the @cosynemeeting.bsky.social 2022. We realised that the technique was easy and cheap for anyone to use with a lot of low hanging fruit. Check out the tutorial at: neural-reckoning.github.io/cosyne-tutor...

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.739Z

At the tutorial we announced a 1-2 year open research project that anyone could join, starting from the tutorial, and a few starting questions we found interesting, but with no other constraints. We were inspired by the Polymath Project in mathematics. en.wikipedia.org/wiki/Polymat...

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.740Z

31 people contributed to the project, joining for monthly meetings to discuss progress. All code was publicly available throughout, and when we started writing up the work in progress was also fully public. You can see that version here: comob-project.github.io/snn-sound-lo...

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.741Z

Not everyone who was involved made it to the paper (didn't respond or couldn't contact), and not all are on Bluesky, but authors include: @marcusghosh.bsky.social @krhab.bsky.social , @tfiers.bsky.social , @friedenberger.bsky.social , @solarpunkgabs.bsky.social , @rory.bio , @idoai.bsky.social

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.742Z

We used GitHub and Jupyter notebooks to coordinate development, with a website showing everyone's current code and results to make collaboration easier. We used @mystmd.org and GitHub actions to automate this. comob-project.github.io/snn-sound-lo...

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.743Z

So how did it work out? Well, some things went well and others not so well. We published a paper with our results and reflections on the process. If you're interested in spiking neural networks, sound localisation, or anarchist science, check it out: www.eneuro.org/content/12/7...

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.744Z

Generally, the infrastructure we built worked well, as did the monthly meetings. Starting from the tutorial was a good decision because it gave everyone a common reference and meant they could easily get started.

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.745Z

However, the lack of direction meant that we didn't achieve very coherent results in the end. We don't think this is a catastrophic problem, but when we try again, this is something we'd like to address. If you have thoughts or would like to be involved, get in touch!

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.746Z

Ultimately, we didn't achieve a scientific breakthrough, but we did show that without top down direction or any specific funding, we could organise a large group of scientists to work together and publish their research in a good journal. We think that's a hopeful sign for the future!

— Dan Goodman (@neural-reckoning.org) 2025-09-04T14:59:02.747Z

Spiking neural network models of interaural time difference extraction via a massively collaborative process

Ghosh M, Habashy KG, De Santis F, Fiers T, Erçelik DF, Mészáros B, Friedenberger Z, Béna G, Hong M, Abubacar U, Byrne RT, Riquelme JL, Liu YH, Aizenbud I, Bicknell BA, Bormuth V, Antonietti A, Goodman DFM
eNeuro (2025) 12 (7) ENEURO.0383-24.2025
doi: https://doi.org/10.1523/ENEURO.0383-24.2025
 

Abstract

Neuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. At this scale, such projects can tackle large-scale challenges and engage a wide range of participants. Inspired by projects in pure mathematics, we set out to test the feasibility of widening access to such projects even further, by running a massively collaborative project in computational neuroscience. The key difference, with prior neuroscientific efforts, being that our entire project (code, results, writing) was public from the outset, and that anyone could participate. To achieve this, 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. Our hope was that the resulting range of participants would allow us to make discoveries that a single team would have been unlikely to find. 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 31 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. While our scientific results were not groundbreaking, our project demonstrates the potential for massively collaborative projects to transform neuroscience.

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