BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
PRODID:-//Virginia Tech//VT Calendar//EN
BEGIN:VEVENT
DTSTAMP:20250323T180000Z
UID:1741355193199@events.msu.edu
CATEGORIES:Conferences / Seminars / Lectures
DTSTART:20250323T180000Z
DTEND:20250323T190000Z
SUMMARY:Tatyana Sharpee - How hyperbolic organization facilitates learning in biology
DESCRIPTION:
 Abstract\n
 \n
 From the speaker:\n
 \n
 "Learning 
 is one way how biological systems change. This 
 presentation will describe emerging evidence 
 showing that biological systems organize 
 them according to hyperbolic surfaces and that 
 these surfaces expand according to similar 
 principles.\n
 \n
 Across different scales of biological 
 organization, biological networks often 
 exhibit hierarchical tree-like organization. 
 For networks with such structure, hyperbolic 
 geometry provides a natural metric because 
 of its exponentially expanding resolution. I 
 will describe how the use of hyperbolic geometry 
 can be helpful for visualizing and analyzing 
 information acquisition and learning process 
 from across biology, from viruses, to plants 
 and animals, including the brain. We find 
 that local noise causes data to exhibit Euclidean 
 geometry on small scales, but that at 
 broader scales hyperbolic geometry becomes visible 
 and pronounced. The hyperbolic maps are 
 typically larger for datasets of more diverse 
 and differentiated cells, e.g. with a range 
 of ages. We find that adding a constraint on 
 large distances according to hyperbolic geometry 
 improves the performance of t-SNE algorithm 
 to a large degree causing it to outperform 
 other leading methods, such as UMAP and standard 
 t-SNE. For neural responses, I will describe 
 data showing that neural responses in the 
 hippocampus have a low-dimensional hyperbolic 
 geometry and that their hyperbolic size is 
 optimized for the number of available neurons. 
 It was also possible to analyze how neural 
 representations change with experience. In 
 particular, neural representations continued 
 to be described by a low-dimensional hyperbolic 
 geometry but the radius increased logarithmically 
 with time. This time dependence matches 
 the maximal rate of information acquisition 
 by a maximum entropy discrete Poisson process, 
 further implying that neural representations 
 continue to perform optimally as they change 
 with experience."\n
 \n
 Bio\n
 \n
 Tatyana 
 Sharpee received her PhD in condensed matter 
 physics from Michigan State University studying 
 under the supervision of Mark Dykman. After 
 her PhD, she started to work in computational 
 neuroscience at the University of California, 
 San Francisco, where she developed statistical 
 methods for analyzing neural responses 
 to natural stimuli, which exhibit strong correlations 
 and non-Gaussian effects. These methods 
 made it possible to reveal new adaptation 
 processes in the brain by comparing neural responses 
 to white noise and natural stimuli. 
 Her independent research program has started 
 at the Salk Institute for Biological Studies 
 where she is currently a professor in the Computational 
 Neurobiology and Integrative Biology 
 Laboratories. Sharpee is a fellow of the American 
 Physical Society.\n
 \n\n
 Price: free\n
 Sponsor: public\n
 Contact name: Bob Patterer\n
 Contact email: events@frib.msu.edu\n
 for more info visit the web at:\n 
 https://msu.zoom.us/webinar/register/WN_X3eDAcCVR_ad7HSy0RvdfQ\n
LOCATION:Virtual
END:VEVENT
END:VCALENDAR