Testing the Cortical Column Conjecture

Name: Testing the Cortical Column Conjecture
Start: 2014-10-01T16:15:00-04:00
End: 2014-10-01T18:45:00-04:00
Location: Gerald D. Fischbach Auditorium

Wednesday October 1, 2014

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Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite those interested in the topic to join us for this weekly lecture series.

Many contemporary theories of neural information processing suggest that the neocortex employs algorithms composed of repeated instances of a limited set of computing primitives. There is a recognized need for tools that interrogate the structure of the cortical microcircuits believed to embody these primitives. The cortical column conjecture suggests that neurons in the neocortex are connected in a graph that exhibits motifs representing repeated processing modules. Carey Priebe and his collaborators will present a notional demonstration of how statistical inference on graphs can inform our understanding of cortical computing.

By modeling the cortical graph as a hierarchical stochastic block model (HSBM), with induced subgraphs, which are themselves independent stochastic block models, a natural question is to estimate the extent to which identified subgraphs share common structure. This will require addressing the problem of identifying candidate subgraphs, and of determining the impact of imperfect subgraph identification on subsequent inference. The application of this connectomics theory and the associated methods will be demonstrated via a bio-inspired, large-scale simulation study.

If this lecture is videotaped, it will be posted here after production.

About the Speaker

Carey E. Priebe received a B.S. in mathematics from Purdue University in Indiana, an M.S. in computer science from San Diego State University, and a Ph.D. in information technology (computational statistics) from George Mason University in Washington, D.C. He worked as a mathematician and scientist in the U.S. Navy research and development laboratory system until he became a professor in the department of applied mathematics and statistics at the Whiting School of Engineering at Johns Hopkins University. At Johns Hopkins, Priebe holds joint appointments in the department of computer science, the department of electrical and computer engineering, the Center for Imaging Science, the Human Language Technology Center of Excellence and the Whitaker Biomedical Engineering Institute.

His research interests include computational statistics, kernel and mixture estimates, statistical pattern recognition, statistical image analysis, dimensionality reduction, model selection and statistical inference for high-dimensional and graph data. Priebe is a senior member of the Institute of Electrical and Electronics Engineers, a lifetime member of the Institute of Mathematical Statistics, an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He received an Office of Naval Research Young Investigator Award in 1995, the 2010 American Statistical Association Distinguished Achievement Award, the 2011 McDonald Award for Excellence in Mentoring and Advising, and in 2008 was named one of six inaugural National Security Science and Engineering Faculty Fellows.