Spiking Neural Network Models of Interaural Time Difference Extraction via a Massively Collaborative Process
Bibliographic record
Abstract
Neuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. At this scale, such projects can tackle big challenges and engage diverse participants. Inspired by projects in 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, and 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 localization 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 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 biological parameters, from time delays to membrane time constants and levels of inhibition, could impact sound localization in networks of spiking units. At a more macro-level, our project brought together 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".