Emerson Harkin

Hi! I'm Emerson. 👋

I'm a Ph.D. candidate in neuroscience at the University of Ottawa with Richard Naud. My scientific dream is to find a one-sentence explanation of what serotonin does in the brain. My approach revolves around merging top-down ideas about what the serotonin system should do with bottom-up constraints about what it could do.

I also work on creating and applying new approaches to single neuron modelling that are simple and interpretable to physiologists. I'm interested in reinforcement learning, computational statistics, writing data analysis code that people will actually use, and about a hundred other things.

To learn more about my research, check out my CV. You can find some of my programming projects on GitHub.

What's the deal with brain soup ?

Unlike most of my colleagues in computational neuroscience, my academic background is in biology rather than physics or computer science. As a result, my first conception of the brain wasn't as a dynamical system, a collection of algorithms, or an electrical circuit (in fact, I remember sitting in a physics lab in the first year of my undergrad, then in biopharmaceutical sciences, thinking "I'll never use Ohm's law to understand how drugs affect the brain."), but more as a kind of neurotransmitter soup.

I made my way into computational neuroscience during my M.Sc. with Jean-Claude Béïque when, after completing an undergraduate honours thesis on the effects of lithium salts on serotonin receptor gene expression, I was finding it increasingly difficult to understand mental disorders as imbalances of seasoning.

These days, I think the most valuable thing I gained from my training in biology is the ability to work closely with people with completely different areas of expertise. Unfortunately, cross-disciplinary communication is something of an invisible skill (in the sense that it goes unnoticed if done well), so when I find myself not immediately grasping the connection between control theory and reinforcement learning, or struggling to give an intuitive explanation of PCA to someone with no math background, I try to remember where I started.

Call it chicken soup for the scientific soul.

(It also happens that I have strong opinions about whether soup can be a meal on its own.)