More on my papers

In the last few posts, we have always been following a system from the outside, watching how it responds to an environment that is changing around it. With our two dust particles, our two friends, our cortical neurons, we were trying to figure out what one part of the system knew about the other, and the environment was the thing that made it hard by giving rise to shared information that was not actually about the other part of the system. This was the story of disentangling and interference, a story about two parts of a system, that kept me busy for a good part of my PhD. But then, we realized we could do something more: we could flip it around.

The puzzle we have been following in the last posts is not only about dust particles. Whenever we measure the activity of two neurons, the expression of two genes, or two species moving together, we face the same ambiguity between a genuine connection and a shared hidden influence. Yet, we have only considered simple physical models so far. This time, we bring the question to a real system: the brain. In doing so, we will also discuss one of the most debated hypotheses in neuroscience: that the brain works the way it does because it sits at the edge of a phase transition.

Let’s get back to where we left off and keep in mind our two examples: two particles in a room, each being pushed around by the same environment, but also connected to each other; or two people, each hearing the same news cycle, but also talking to each other. The question was whether you could tell the difference between the two sources of correlation: the environment and the connection. And the answer was yes, if the connection is simple enough. But what makes a connection “simple enough”? And what happens when it isn’t?

Around five years ago, I submitted the first of what would become a long series of papers at the intersection between information theory, stochastic processes, and statistical physics. It all started with a simple idea and what we thought would be a simple calculation (turns out, it wasn’t): what can we learn about the interactions between two systems if all we can observe is how they move together?