Information and Geometry
in the Retina
Thursday, September 26, 2024
12:30 pm – 1:45 pm
Hixson-Lied Science Building, Room G-59
Abstract
The information we gather about the world through our senses is encoded and transmitted through the spiking activity of neurons. Viewed this way, the activity of a large set of neurons can be considered a joint probability distribution over a large number of binary states (namely, the spiking or silence of each neuron). A basic challenge of neuroscience is to find a parsimonious description of this distribution, and information theory offers a way to do so, called the Maximum Entropy Principle. In this talk I will give a light introduction to information theory and (Shannon) entropy, explain the dominance of “low-order” correlations in the information content of the retina (aka why does the Ising model show up?), and offer an explanation by means of high school(ish) geometry. Along the way I will muse about the role of network structure in all of this.
Guest Speaker
Dr. Alex Kunin is an Assistant Professor of Mathematics at Creighton University. He attended Stony Brook University where he received a degree in mathematics with a minor in computer science. He then pursued graduate studies at the University of Alabama Huntsville (M.S. in computer science), the University of Nebraska – Lincoln (M.S. in mathematics) and the Pennsylvania State University (PhD in mathematics). Before joining the faculty at Creighton, he was a postdoctoral fellow of the National Library of Medicine’s Biomedical Informatics and Data Science training program at Baylor College of Medicine. His research lies at the intersection of mathematics and neuroscience, with a focus on structure-function relationships in neural networks and applications of algebra and combinatorics for data analysis.