International collaboration is the best way to understand the complexities of the brain

On a small computer screen in a cage, a black bar appears. In response, a trained mouse, hooked up to sensors that track the activity of its neurons, turns a Lego steering wheel, pushing it to the left or the right depending on the bar’s orientation.

Then, something incredible happens. Instead of poring over the neuronal recordings on their own, the scientists in charge of the mouse and her miniature wheel upload their data into a shared archive. Across the United States and Europe, more than 20 labs carry out exactly the same experiment, diligently recording the behaviors of thousands of neurons while hundreds of mice turn small steering wheels in response to black bars. At the same time, theoreticians and mathematicians digest the tangles of data, coming up with creative new ways to process the unprecedented trove of neural activity.

This will be the daily work of a brand new collaborative neuroscience project: the International Brain Laboratory (IBL). Its goal is to uncover how our brains process complex decisions, and it's part of a necessary movement in science to reorganize large projects into more collaborative, effective, and transparent programs.

For over a century, neuroscientists have worked tirelessly to solve the puzzles of how brains work. It’s an overwhelming task (your brain boasts 100 billion neurons and 100 trillion neuronal connections), and modern progress is hindered by the professional structure in which scientists operate, a system that is often competitive, and insular.

The problem lies not in the expertise or work ethic of scientists, but in how conducive their findings are to meaningful and broad interpretation. Most of biology is performed in solitary labs within universities, led by professors who spend an enormous amount of time writing grants, which they need for research funding and their students' salaries. Driven by a hunger for knowledge and the need for financial support, these labs tinker with their experiments, creating finicky techniques, and hoping that their specific research will unveil a stunning new truth about the brain. They share their data sparingly, and come up with increasingly idiosyncratic methodologies. Often, interpreting a new publication is so confounded by the differences between labs' methods that it’s challenging to have conversations about what results actually mean.

This inefficiency becomes glaringly problematic in efforts to study the brain. To reveal how the biological computers in your cranium make decisions, assess risks, and feel emotions, we need to tap not only biologists but computer scientists, cognitive scientists, and mathematicians. We’ll need more lab space, time, and equipment than any single research group has available. The truth has become crystal clear: we won’t be able to understand the brain by working alone.

The International Brain Laboratory, and other large, multi-institutional projects like it, hope to make progress in some of the thorniest areas of research by allowing large and diverse groups of scientists to share and compare work on a single project, in real time. No longer will teams come up with ways of approaching an experiment that are just different enough to make results ineligible for comparison. Labs participating in the IBL will be required to use identical experimental methods, and troubleshoot procedures in exactly the same way. 

They’ll also share data as soon as they collect it with dedicated theoreticians, opening up a data set of unprecedented size to computational teams, which can fast-track their analyses. The IBL will concentrate on detangling the neural underpinnings of simple decision making using their combined resources to expedite the massive data collection. 

Much of the structure of IBL was inspired by projects in the physical sciences, like CERN or LIGO. 

"We were impressed by what the physicists had done," explained Alexandre Pouget, a computational neuroscientist at the Université de Genève, and one of the founding organizers of IBL. These projects bring together scientists from hundreds of labs, and have been successful in making fundamental advances in clearly defined, ambitious research programs. Like the ATLAS collaboration at CERN, the IBL will shy away from a centralized top-down organization, instead opting for collective decision making, with input from all involved groups. Pouget said that he hopes this will drive imaginative and impactful science, as it has at ATLAS, and inspire organizational schemes for future projects. 

Collaborations in science aren’t uncommon, but collaborations of this scope are, in part due to the expense and organizational challenge such a massive undertaking. Given lean federal funding environments, scientists increasingly rely on private funding for big projects. IBL is made possible by funding from the Wellcome Trust, Simons Foundation, and the INCF, three private philanthropies dedicated to supporting biological research.

The private model is not dissimilar to startup investing, but instead of venture capitalists giving to a small company, private groups award teams of inspiring scientists. Compared to the government-only model, this allows for more flexibility in redefining research goals as projects evolve. Integrating private philanthropic support with federal sources of funding will allow higher-risk projects to get the traction they deserve, especially in times of increasing competition.

Pressing scientific mysteries are more complex and interdisciplinary than ever, and scientists have to adapt their research to match them. We must forge avenues for communication, and standardize and share our data. 

It is no longer efficient to incentivize intense competition between isolated labs. Instead, we must throw the collective weight of our skills and intellect toward lofty goals that would be unattainable by any one person or team alone.