What do you think was your big advantage when it came to winning this competition?

Damien: Compared to the other teams, I think our major advantage was to have a 'naive' approach, focused on effectiveness. Never having ever worked on brain-related issues before, we had no preconceptions before starting the work. We applied the expertise which we had accumulated in other research areas, such as genetic regulation networks and smart grids, to this particular problem.

Antonio: Thanks to the work of the whole team, we could therefore find a relatively simple method which worked well and which we understood. One important aspect for us was to be able to justify each step of our methodology.

Beyond the competition itself, do the organisers have a specific aim?

Damien: The ultimate aim, or the long-term dream, for this kind of research is certainly to be able to one day put a helmet on someone and be able to measure their brain activity and digitally reconstruct the structure of their neural network

Pierre: Practically, it is possible to observe neural activity, but it is impossible to precisely identify the neural network which is behind it. The advantage of using artificial data in the challenge was that the connectome was perfectly known, because it had been defined and the relevant data had been drawn from it.

Damien: Studying in the connectome improves our general understanding of the brain and its learning capabilities. It is also necessary for researching treatments against illnesses which cause alterations to the connectome, such as epilepsy or Alzheimer's disease.

Pierre: Machine learning competitions are relatively new, but they have enabled the research community working in this field to make enormous progress. In this case, the organisers - who are researchers - had developed a tool to identify neural networks based on the data generated and wanted to see if this tool was competitive in comparison with existing techniques, thus motivating other researchers to develop the best tools possible.

How has your participation in this competition influenced your research work, or how will it influence it in the future?

Antonio: Now we have developed this method, we can move towards a more theoretical analysis and to understanding why it worked well. Similarly, we are going to try to theoretically analyse why the method applied to genetics, and which we thought we could use initially in the competition, proved to be ineffective for the connectome. This will enable us to know the limitations of these methods and to identify other systems to which they can be applied.

Damien: Our victory in the competition also opens the way to future collaboration with research teams in neurosciences. Algorithms which have been developed to construct neural networks could certainly be useful for them in terms of applying our method to real data.

Would you be keen to repeat your experience if there were a new edition of this competition?

Damien: For researchers, this type of competition is very interesting. It is interesting from a research point of view, because we find new methods and solutions to problems, but also from a personal point of view because it is fascinating to try to make progress both as a team and as an individual. This also reinforces partnership working within the university and strengthens team spirit. So, the answer is yes, if there was a 2nd edition of this competition, the whole team would be delighted to take part and to accept a new challenge relating to the connectome.