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Delineating Ongoing Computations in the Neocortex: A Conversation with Jason MacLean

Updated: Jun 10

The NSF-Simons National Institute for Theory and Mathematics in Biology is composed of investigators at the forefront of innovative research at the interface of mathematics and biology. Each member of the NITMB brings a unique perspective that is vital for achieving the NITMB’s mission to develop new mathematics and inspire biological discovery. In order to highlight the diversity of expertise present, and the valuable contributions of NITMB members, the NITMB will be sharing insight into one of our members every month.


Jason MacLean, Professor of Neurobiology at the University of Chicago


Jason MacLean is a Professor of Neurobiology at the University of Chicago and member of the University of Chicago Neuroscience Institute. His area of expertise is computational neuroscience. MacLean is also a member of the NITMB, where he is collaborating with fellow NITMB member and University of Chicago Associate Professor of Organismal Biology and Anatomy Stephanie Palmer on the NITMB internal research project ‘Quantifying Natural Movement Variation in the Brain and Behavior.’


We reached out to Jason MacLean to learn more about his work, his inspirations, and how the NITMB supports his efforts to delineate ongoing computations in the neocortex.


Can you share with us a big question you've been asking throughout your research program?

 

“My research program is focused on trying to delineate the ongoing computations in the neocortex. To date, most of our understanding about the brain is based on trial average metrics. A mean is very rarely a good description of most distributions and is particularly poor when you think about a time series. I’m trying to develop behavioral assays. My group is a 50/50 split between experiment wet lab and quantitative approaches to the brain. We’ve developed behaviors where we minimize automaticity despite studying the same behavior. And then we are developing the analytical tools to try to best analyze and understand that, trying to move as close to real time understanding of computation in the brain as possible. It’s ambitious, but that’s what we’re trying to do.”

 

What disciplines does your research integrate?

 

“Neuroscience, obviously, but then my group draws very heavily on statistical physics, whether that’s the formal study of complexity, or network science and graph theory. We also draw on computer science, specifically machine learning. From the beginning my group has been very interdisciplinary.”

 

Where do you find inspiration?

 

“First and foremost, I get inspired by my colleagues, collaborators, and the graduate students in my group. What we’re trying to do is very hard, and so to think that I could come up with all the best questions, let alone the best ways to get the answers to those questions, would be naive. My group right now has graduate students who are trained in applied math, statistics, physics, and computer science. I like to have everyone’s point of view represented, and my collaborators also are broadly distributed in terms of areas of expertise and disciplines. I would say that’s where most of the inspiration comes from. I also read broadly. I’m kind of obsessive about reading across scientific domains. I do not focus on one area.”

 

What aspects of the central question you've been working on could be interesting to mathematicians or applied to biology?

 

“Achieving understanding of dynamics in a complex system should interest everyone who’s even modestly interested in math or mathematical approaches to complexity. Also, if we truly achieve understanding of computations that are occurring in the neocortex, that should be broadly interesting to everyone. In the end, that’s the thing that makes us all who we are. Us being able to meaningfully move through the environment, conduct our day-to-day behaviors and our day-to-day decisions, all these things that are so fundamental to who we are as individuals and as a species, it should be interesting to everyone.”

 

What about the mission of NITMB do you find exciting?

 

“The NITMB is like a formalizing and dramatic scaling up of things I was trying to do anyway. I have some competence with quantitative approaches in general, but there are things I can’t do, and I fully recognize that. Being able to readily interact with and speak with people that can do those things is really exciting to me. Also, the wide variety of expertise of members is similarly exciting and has the potential to change the directions that we’re going. My work requires new math. A lot of graph theory is really about undirected, unweighted graphs. And the graphs we build, because we want to think about computation, have directionality to them. Information flows in a direction and has a weight, meaning reliability, or how strong is this relationship? That consistently is a hurdle we have to repeatedly overcome. Having the potential to collaborate with other members of the NITMB who are facing similar challenges, sharing insights that we’ve gained, all these things are very exciting to me.”

 

What career achievement are you most proud of?

 

“First, I’ve graduated ten PhD students since I’ve started. They’re all happy and gainfully employed, some of them stayed in academia and now have their own groups. In terms of research, when I started, people would record multiple neurons in the brain simultaneously. Rather than think of that as one data set, they would think of it as a data set of 300, meaning they still considered each individual neuron as an individual. Since I started my own group, I have treated them not as an individual, but as a node in a complex network. That was a struggle early on. People did not necessarily understand it. And I think we’ve shown the validity of the approach. While I always thought it made perfect sense, there was a lot of resistance to the idea at the time in neuroscience, and now it’s been widely adopted.”

 

What interests do you have outside of your research?

 

“I have a family, that’s a big deal for me, and it doesn’t leave me with a lot of time. But I do find two things to distract myself with. My wife and I are really interested in food, whether that’s cooking or going out to restaurants. And then if I can’t focus on work, I like to distract myself with the analytics and tactics in sports. I wasn’t really into sports, but I met some friends when I was at Cornell who were all about the numbers. Some of them are now actually doing that professionally. It’s still analytical, but it’s a different set of interacting elements.”

 

What are you hoping to work on in the future?

 

“In the near term, Stephanie Palmer and I, the two of us and our groups are really interested in thinking about the correspondence between the complexity of brain activity and the correspondence with the complexity of behavior. This is thematically consistent with this idea of trying to get as close as we can to real time. In addition to this obsession with trial average metrics I mentioned earlier, there’s also been an emphasis in neuroscience on highly stereotyped and overtrained behaviors. What we and many other people have now realized is the brain is different when behaviors aren’t so overtrained and incredibly stereotyped. I mention this idea of minimizing automaticity, and it’s with this idea in mind that we’re enabling the brain to do the things that we actually evolved to do. This is really the very beginning of these kinds of endeavors for us in terms of moving as close to truly natural behavior, thinking about the correspondence between the brain and behavior. It is very near term but it’s also what I’m hoping to kick off the next big step in my group.”

 

Jason MacLean’s efforts to integrate mathematical disciplines such as statistical physics with neuroscience to enhance our understanding of ongoing computations in the brain demonstrates the necessity of combining mathematics and biology to drive future discovery. The NITMB facilitates connecting Professor MacLean with other researchers whose expertise lies in areas that can move MacLean’s research forward. We look forward to seeing the benefits incorporating mathematical disciplines will continue to have on MacLean’s neuroscience work, and the potentially groundbreaking discoveries that may result from this practice.

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