Mark Reimers

Neuroscience Program, Michigan State University

Today’s sophisticated technologies enable researchers to gather data in quantities unimagined ten years ago. These data acquisition technologies are changing the nature of research in neuroscience. At the same time the infrastructure of knowledge is changing: a great deal of relevant information is stored in online databases, which may aid interpretation of experimental and clinical data.

The challenge of analyzing and and interpreting these data increasingly depends on computational methods. We aim to build models that give insight into the data; and we integrate new data with existing data sets. The first challenge is to extract a clear signal from the technologies; there are many confounding factors, such as technical or physiological artifacts, which distort the signals. The second challenge is to test hypotheses about biological organization or mechanisms against the data; usually we are testing hypotheses of a common form for many neurons, genes or brain regions. Then we look for new insights about the relationships of the many measured components, some of which interact. Finally we take advantage of previous efforts, usually in the form of databases, to constrain and aid our analysis.

Recently new technologies such as fMRI, calcium imaging, and voltage-sensitive dyes have enabled collection of broad swathes of neural activity over time. This is the domain of multivariate analysis but only recently have a few statisticians begun to develop multivariate methods specific for such data.

We who analyze such data are like the prisoners in Plato’s Cave: with our measures we perceive only a shadow of the reality, and we must infer the reality from the data using our imagination and logic. In my opinion the best analytic approaches combine statistical subtility with knowledge of the processes under study.


Current Research

Teaching

My Collaborators

Selected Publications

Readings for the 'Enlightened Brain' course

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