Gröbner Bases of Convex Neural Code Ideals (Research)
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-42687-3_8
Reference14 articles.
1. E. Babson, S. Onn, and R. Thomas. The Hilbert zonotope and a polynomial time algorithm for universal Gröbner bases. Advances in Applied Mathematics, 30(3):529–544, 2003.
2. J. Cruz, C. Giusti, V. Itskov, and Bill Kronholm. On open and closed convex codes. Discrete & Computational Geometry, 61(2):247–270, 2019.
3. C. Curto, E. Gross, Jack Jeffries, K. Morrison, M. Omar, Z. Rosen, A. Shiu, and N. Youngs. What makes a neural code convex? SIAM Journal on Applied Algebra and Geometry, 1:222–238, 2017.
4. C. Curto, E. Gross, J. Jeffries, K. Morrison, Z. Rosen, A. Shiu, and N. Youngs. Algebraic signatures of convex and non-convex codes. Journal of Pure and Applied Algebra, 223(9):3919–3940, 2019.
5. C. Curto, V. Itskov, A. Veliz-Cuba, and N. Youngs. The neural ring: an algebraic tool for analyzing the intrinsic structure of neural codes. Bulletin of Mathematical Biology, 75:1571–1611, 2013.
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