Large Structures Seminar: Zvi Rosen
This talk is part of the AScI Thematic program "Challenges in Large Geometric Structures and Big Data" seminar. Check out our upcomning talks at https://aaltoscienceinst.github.io/lsbdseminar/.
| Where: | U250a, Otakaari 1 |
| When: | 09.11.2015 @ 14.15 |
| Speaker: | Zvi Rosen Pennsylvania State University |
| Title: | What makes a neural code convex? |
A neural code is a set of 0-1 vectors (codewords) derived from the non-empty non-covered regions of a collection of sets in Rn. The code is said to be convex if there exists a convex set arrangement generating the code. This definition is motivated by place cells in neuroscience, which won its discoverers the 2014 Nobel Prize in Medicine.
The question we tackle: what makes a neural code convex? We use commutative algebra, topology, and discrete geometry to place some necessary conditions and describe the minimal realizing dimension of a convex neural code. Based on joint work with Carina Curto, Elizabeth Gross, Jack Jeffries, Katie Morrison, Mohamed Omar, Anne Shiu, and Nora Youngs.