Spotlight: Mikkel Lepperød

In this interview he also shares the ways he collaborates with research partners, along with the interesting, and trend-defying technological advancements within neuroscience & AI. Mikkel works in Numerical Analysis and Scientific Computing, part of the Scientific computing research area at Simula.

Can you share about your educational background & professional journey?

I have had a winding path toward research, as I started as an electrician. With the ambition to become an electrical engineer and the prospect of having more time for climbing, I decided to go back to school. The defining moment in this journey was to realize that mathematics & programming are so much fun, and used in the right way can have a profound impact on society. In the end, I obtained a master's in applied mathematics and a PhD in neuroscience. Being introduced to systems neuroscience by my supervisors Torkel Hafting and Marianne Fyhn I was later introduced to causal inference by Konrad Kording during a research stay at UCSD applied to neuroscience & AI. Combining these is now a defining aspect of my current research.

In neuroscience we try to understand how neural activity gives rise to behavior, the classic computational neuroscience approach is typically limited to only looking at the neural activity. For example, we want to know why neurons are activated in a certain way when an animal is solving a task such as navigation. Here, machine learning can be very powerful because neural activity will arise as a consequence of learning a particular behavior. In addition, the architectures in artificial neural networks are inspired by real neural networks, which makes it possible to compare artificial & real systems at multiple levels.

Can you describe your area of expertise within the field?

I work extensively with machine learning and artificial intelligence. My work spans neuroscience, with a particular emphasis on the causal understanding of neural connectivity, the role of grid cells and place cells in spatial navigation and memory, & the application of optogenetic methods. I’ve also spent a lot of time making tools for data management & rigor in neuroscientific software.

To some extent, my expertise is having no expertise; I try to look for overarching questions & pinpoint methodology to solve these questions. I have had difficulty settling for one specific topic because, in neuroscience, the questions fundamentally require multidisciplinarity; so biology, physics, mathematics, computer science, and statistics. 

How does your research contribute to the advancement of your field, and what real-world applications can it benefit?

I mainly conduct basic research to understand the principles underlying computation, learning, & memory in the brain. 

Understanding how mechanisms of learning and memory underlie our ability to perform computations, for example, our ability to navigate may prove instrumental in understanding how humans can learn more efficiently or how diseases such as dementia can be identified at an early stage. 

It can also be beneficial for developing more robust and trustworthy AI. 

There are some fundamental issues with AI today, such as learning unstable solutions to a problem affecting generalizability. Or, when learning a new task, agents typically forget previously learned information. These are issues that are not found to the same extent in animals.

Can you share an example of how you collaborate with industry partners or other researchers in your work?

Collaboration is vital in my work, especially in creating teaching materials and conducting research. A key example that I base much of my current project management on was during my PhD, where I worked with biologists on diverse experiments, combining our expertise in software development, modeling, & data analysis. As a generalist, I find that collaboration accelerates learning and discovery.

Additionally, digital communication enables global partnerships that enhance research with diverse cultural and intellectual insights. Moreover, maximizing my time with family and reducing carbon emissions, I find digital communication particularly effective.

Are there emerging trends or technologies within your field that you find particularly exciting or promising?

In neuroscience, the emerging technologies are mind-boggling. When I started in neuroscience 10 years ago, recording hundreds of neurons was a big accomplishment. Now we can record from tens of thousands of neurons & do precise perturbations using genetic and optic tools.

In AI there is massive excitement with the recent models that are popping up, such as chatGPT, but I’m most excited about how challenges identified in the 80s & 90s are still very prominent, which means there is still much to be done. On the other hand, it is undeniable that these large networks are extremely impressive, pointing to a potential resemblance between biological nervous systems and artificial neural networks.

AI's very foundation is intertwined with our quest to understand the brain. For instance, the development of Convolutional Neural Networks was inspired by the Nobel Prize-winning work of Hubel & Wiesel in 1962. They made significant discoveries about visual processing in the brain, which directly influenced computer vision. More recently, the attention mechanism, used in Transformers and Large Language Models, is based on 1998 research by Itti, Koch, and Niebur.

These trends are very exciting and I am honoured to be a scientist in this field.

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Thanks to Mikkel Lepperød for contributing to this researcher profile.

At Simula, we take pride in our people, with over 150 scientific researchers, fostering a collaborative and innovative environment for science research.

Associated contacts

Mikkel Lepperød

Research Scientist

Email Mikkel