Anna Levina, member of the Bernstein Center for Computational Neuroscience Tübingen, received the Sofja Kovalevskaja Award 2017, one of the most prestigious and most highly endowed research awards in Germany.
/BN, C. Duppé/
Hello Anna, congratulations! You have recently received the Sofja Kovalevskaja Award. What does this award mean to you personally and professionally?
Thank you. For me, it is a great chance to really focus on long-term research topics, or to put it simply “to get something done”. It also means that I can stay in science.
Apart from these personal reasons, I feel honored to receive this particular award as the namesake, Sofja Kovalevskaja, a Russian mathematician like me, was a fascinating person and a role model for women scientists. I just recently read the short story about her, written by Nobel Prize winner Alice Munro. I could not stop thinking, how much easier the life of a female scientist has become. And how much luckier I am than Kovalevskaja had been.
How would you describe the grand goal of your research?
I want to understand the principles that guide brain (self-) organization. On the one hand, I am striving to understand how complex behavior arises from the simple interaction of simple units, like synapses; on the other hand, I am trying to answer why neuronal activity exhibits particular patterns. To this end, I generate my hypotheses from studying simple, analytically treatable models and then try to verify my model results with recorded data sets.
But the brain is not organized like a computer, is it? How is it possible to translate the complexity of the human organ into mathematics?
Yes and no, as scientist we must try to understand it. The laws of physics also looked like a mystery before we could formulate them. Clearly, the laws that govern brain development and functioning are different from simple physical laws, but I do believe that many animal brains have been optimized through evolution since only those organisms which adapted best could survive.
Could you tell us a little more about your research?
I am fascinated by neuronal avalanches that can be seen as a signature of criticality in the brain. To put it simply, each arriving signal often elicits only a tiny response, but sometimes it can lead to a large avalanche of responses throughout the whole system. We speak of “critical avalanches” when the distribution of responses follows a power-law. This in turn indicates that a system is on the border between over-excitation and quietness. Being on the border was shown in many different models to bring about optimal computational properties, such as optimal information storage and transmission, optimal dynamic range and so on. Thus, we hypothesize that being close to the critical state will be beneficial for the brain.
There are many problems related to the definition of criticality and inferring it from the data. On the one hand, there is currently no unified mathematically established way to define this criticality for neuronal avalanches, which is why I am planning to bring different definitions together in the following years. On the other hand, our assessment of criticality is based on the observation of very small parts of the system. In our investigations we work in the order of hundreds of neurons, only a fraction of the billions that are in the brain. Therefore, it’s important to find out which effects such subsampling can cause and how to account for them systematically. It’s a task I undertake together with my colleague Viola Priesemann at the Max Planck Institute for Dynamics and Self-Organization and the BBCN Göttingen.
Which challenges or impediments lie ahead? Or asked in a more positive way, which developments do you consider promising for your research?
Actually, I think that the availability of data covers all the three issues you mention. It’s chance and challenge at the same time. Recently, there has been huge progress in the recording of neuronal activity, so the need for theoretical understanding is as high as ever. At the same time, to keep the models understandable we need to make them simple, and it is very hard to invent something that will fit all the things we can record now.
It is a bit like the dilemma Jorge Luis Borges describes in his short short story about cartographers in “Exactitude in Science”.
Where do you see the overlap to other disciplines?
I hope, that understanding the functioning of the brain will have an impact on modern machine learning methods. I can easily envision, that some of the principles, which lead to the optimal information processing in the brain, could help with understanding and improving deep neural networks. On the other hand, we always hope that a better understanding of the functionality of the brain will help in curing neural diseases, but right now I am looking more into the computational aspect of it and the mathematical challenges, of course.
So mathematics is the soil on which your research grows, would you subscribe to that?
I am not sure it is mathematics, maybe physics? Science is great whichever discipline we’re talking about, simply because you are not ceasing to learn and develop. That’s why I hope to transcend limits when branching out into other disciplines. I guess it’s actually one of the reasons why I am happy with computational neuroscience and the diversity of this research field, and why I enjoy working with Matthias Bethge at the Bernstein Center Tübingen. But you’re right, mathematics is the discipline I studied and I still enjoy the beauty of mathematical models when things come together.
Would you mind telling us a little about the challenge of balancing family management and cutting-edge science?
I always knew that I wanted to have children. I got my first son relatively early, thinking that it might stop my scientific career, but it couldn’t stop my scientific passion. I believe the situation in Germany has changed even during the past eight years: daycare centers now accept toddlers; the awareness of family needs in general is increasing and there are many new initiatives for women. I am optimistic that my female students will have an easier time than I had. Balancing private and professional life is still a challenge, though and it will always be.
My husband is also a scientist and he is very supportive. We share the duties (and the fun) evenly and my husband’s parents support us whenever they can.
Which paths will your research take in the future? What will you do with the prize money and how will the award shape the coming years for you as a scientist?
I will be hiring PhD students, searching for postdocs, expanding my network of international collaboration; After all, the money is not for me personally, but for my research project on unifying views of criticality and investigating whether and how they are applicable to the brain. I have five years of independence ahead of me; there is a pressure of not “mucking up the chance”, but also a lot of positive anticipation.
How would you have tweeted about winning the award?
I am actually a shy person; I would let somebody else tweet about it, like the Bernstein Network. You tweeted, didn’t you?
Yes, we did indeed (@BernsteinNeuro). Thanks Anna for having taken the time for this interview.
Published in the Bernstein Feature. 2018. Next Gen/d/eration Computational Neuroscience, pp 8-11.
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