What are Computational and Systems Neuroscience?

One of the fundamental questions motivating neuroscientists is to understand the relationship between brain activity and lived experience: how the different parts of the brain work together to produce the key ingredients for behavior: memory, feeling, thinking and imagination. These motivating issues have been pretty much inaccessible for most of the history of neuroscience, because we could not observe very much of the brain in action in enough detail to identify individual circuits or on the time scale on which they work. That is starting to change.

I’ve just come back from the Computational and Systems Neuroscience (CoSyNe) conference and workshops in Salt Lake City. I’ll be discussing in up-coming posts several of the interesting issues raised at this conference, but for now I want to try to introduce what these terms “computational neuroscience” and “systems neuroscience” mean.

The term “Systems neuroscience” describes investigations of how the different components of the central nervous system interact to produce experience and to generate behavior. For example, scientists may record from many cells in an animal’s brain, in different regions, while the animal is making a decision. In order to study systems neuroscience we need to measure brain activity in many different cells at once. You may have heard of the BRAIN Initiative , which aims to develop new tools to do this.

Why do we need such detailed (and technically hard) measurements to understand the brain? We understand the function of most other organs, such as liver, kidney or muscles, by measuring in bulk their responses to situations. The key difference is that cells of the same type mostly play similar roles in the functions of those organs, whereas cells in the brain, even those right next to each other, usually play very different, often even opposed, roles in behavior. Therefore we need to monitor very many cells individually to understand how they interact during experience or behavior.

The term “computational neuroscience” describes investigations that use computers extensively to understand brain function. There are two major aspects to this computational work. One aspect is the sophisticated analysis and interpretation of the complex ‘big data’ recorded from living brains while an animal or person behaves, in support of the goals of systems neuroscience. The second aspect is building artificial networks that simulate some properties of real nervous systems. Some researchers build these model networks in order to try to simulate aspects of actual brain activity; this is a prime goal of the EU Human Brain Project. Other researchers try to develop new modes of artificial intelligence (AI). You may hear about ‘Deep Neural Networks’, which are the engines behind the AlphaGo AI that finally beat a human at the strategic game of Go.

Where these two aspects of computational neuroscience come together is comparing the patterns of activity produced by models to the patterns actually observed in many cells or brain regions, while a person or animal is behaving. I’ll be saying more about these issues in coming posts.

While these topics will be my main focus in these blog posts, a secondary theme will be the roles of genes in the brain, and psychiatric or behavioral genetics. And some quirky observations, such as the structure of bird and octopus brains, may appear here as well.

I work in the Neuroscience Program at Michigan State University in East Lansing, MI. I teach courses in both aspects (data analysis and modeling) of computational neuroscience and also in brain genomics and genetics. My research work is primarily to develop new analysis methods for the ‘big data’ now being generated in neuroscience, in order to improve our understanding of brain function.

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Mark Reimers

About Mark Reimers

Dr. Mark Reimers uses advanced data analysis and computational modeling to study brain function. His research work focuses on analyzing and interpreting the very large data sets now being generated in neuroscience especially using the high-throughput technologies developed by the BRAIN Initiative. He obtained his MSc in scientific computing, and his PhD in probability theory from the University of British Columbia in Canada. He has worked at several start-up companies, at Memorial University in Canada, the Karolinska Institute in Stockholm, the National Institutes of Health in Maryland, the Virginia Institute for Psychiatric and Behavioral Genetics in Richmond, and since January 2015 in the Neuroscience Program at MSU. He supervised the data analysis for the BrainSpan paper on the development of gene expression in the human brain in Nature in 2011, and assisted in data analysis for a paper analyzing cortical dynamics on the surface of mouse brain in Nature Neuroscience in 2013.

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