Umar Faruk Abubacar

Neuroscientists are increasingly initiating large-scale collaborations which bring together tensto hundreds of researchers. However, while these projects represent a step-change in scale, theyretain a traditional structure with centralised funding, participating laboratories and data shar-ing on publication. Inspired by an open-source project in pure mathematics, we set out to testthe feasibility of an alternative structure by running a grassroots, massively collaborative pro-ject in computational neuroscience. To do so, we launched a public Git repository, with codefor training spiking neural networks to solve a sound localisation task via surrogate gradientdescent. We then invited anyone, anywhere to use this code as a springboard for exploringquestions of interest to them, and encouraged participants to share their work both asynchro-nously through Git and synchronously at monthly online workshops. At a scientific level, ourwork investigated how a range of biologically-relevant parameters, from time delays to mem-brane time constants and levels of inhibition, could impact sound localisation in networks ofspiking units. At a more macro-level, our project brought together 31 researchers from multiplecountries, provided hands-on research experience to early career participants, and opportunitiesfor supervision and teaching to later career participants. Looking ahead, our project provides aglimpse of what open, collaborative science could look like and provides a necessary, tentativestep 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 asynchro-nously 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 mem-brane 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. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it.