Large-Scale Computational Modeling in Neural Science
Prof. David W. McLaughlin
Silver Professor of Mathematics and Neural Science, Courant Institute of Mathematical Sciences (NYU)

In this lecture, I will use our work in visual neural science to illustrate the potential that large-scale computational modeling presents to neural science today. Neural science is primarily an experimental science, with major advances following closely upon advances in experimental technology. Similarly, advances in computational technology over the past two decades have positioned computational scientists to contribute to the theoretical understanding of neuronal systems. For some time now, our group at NYU has been developing a large-scale computational representation of an input layer of the primary visual cortex (V1) of Macaque monkey – the “front end” of the monkey’s visual system. Neurons in V1 are “edge detectors” – detecting the orientation of edges within the visual scene. Very recent experiments on mouse V1 from the Scanziani and Tao labs have used opto-genetics techniques to observe for the first time “thalamus to cortical” excitation separately from “cortical to cortical” excitation. Mouse V1 has many differences from Monkey V1. For example, while monkey V1 is tiled by an ordered map of orientation preference, mouse V1 is tiled by a disordered “salt and pepper” map. In recent work, we have adapted our monkey V1 model to mouse V1, and have studied the differences in neuronal response in the presence, and the absence, of an ordered map of orientation preference. Our mouse model reproduces experimental observations, and allows us to analyze the mechanisms by which the model achieves the observed responses – with its disordered map of orientation preference.

About the Speaker

Dr. McLaughlin received a B.S in Physics and Mathematics from Creighton University in 1966, an M.S. in Physics from Indiana University in1969, and a Ph.D. in Physics from Indiana University in 1971. Dr. McLaughlin is Silver Professor of Mathematics and Neural Science at the Courant Institute of Mathematical Sciences (CIMS). He is an applied mathematician, whose recent work in visual neural science has focused upon computational models of the primary visual cortex. He is a member of the National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences, the American Mathematical Society, the American Association for the Advancement of Science, and the Society for Industrial and Applied Mathematics. From 1994-2002, he was Director of NYU's Courant Institute of Mathematical Sciences, and from 2002-1016, he was Provost of New York University.

2018-04-27 3:00 PM
Room: Conference Room I
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