Modeling of perceptual invariances in biological sensory processing
Monday, February 19, 2018 - 3:30pm

Statistics and Statistical Learning Seminar

Monday February 19, at 3:30 pm, in Harder House 104

Speaker: Alexander G Dimitrov

Title: "Modeling of perceptual invariances in biological sensory processing"

Abstract: A problem faced by all perceptual systems is natural variability in sensory stimuli associated with the same object. This is a common problem in sensory perception: Interpreting varied optical signals as originating from the same object requires a large degree of tolerance. Understanding speech requires identifying phonemes, such as the consonant /g/, that constitute spoken words. A major goal of an object recognition problem then is the ability to identify individual objects while being invariant to changes stemming from multiple stimulus transformations. In an ongoing project, we are testing the hypothesis that broad perceptual invariance is achieved through specific combinations of what we term locally invariant elements. The main questions we would like to address are: 1. What are the characteristics of locally-invariant units in sensory pathways? 2. How are biological locally-invariant units combined to achieve broadly invariant percepts? 3. What are the appropriate mathematical structures with which to address and model these sensory processes? The mathematical aspects of the research involve an interesting combination of probability theory (a must in the study of biological sensory systems) and group theory, needed to characterize invariants and symmetries.

Bio: Dr. Alexander G. Dimitrov received a BSc in physics from Sofia University, Bulgaria in 1991, an MSc degree in physics and a PhD in Applied Mathematics from the University of Chicago, IL, in 1998, under the guidance of Dr. Jack Cowan. He continued with a postdoctoral position at the Center for Computational Biology, Montana State University, Bozeman, and an Assistant Professor in the Department of Cell Biology and Neuroscience. He is currently an Associate Professor at the Department of Mathematics and Statistics at Washington State University Vancouver, in Vancouver, WA. Dr. Dimitrov’s research interests include information-theoretic and probabilistic approaches to neural computing in biological sensory systems and cognitive processes, mathematical neuroscience, and nonlinear neuronal models. He is a member of the OCNS, SIAM and ARO. He served the Computational Neuroscience community as a member of the Program Committee of the annual CNS meeting between 2006 and 2009, on the Board of Directors of the Organization for Computational Neuroscience between 2009 and 2012, and as organizer of the workshop on Information Theory in Neuroscience since 2006. Dr. Dimitrov has co-edited special issues of the Journal of Computational Neuroscience and IEEE Transactions on Molecular, Biological, and Multi-Scale Communications dedicated to applications of Information Theory in Neuroscience.