Nondegenerate Neural Codes and Obstructions to Closed-Convexity-Reference-Cited by-同舟云学术

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Nondegenerate Neural Codes and Obstructions to Closed-Convexity

Published:2023-01-20 Issue:1 Volume:37 Page:114-145
ISSN:0895-4801
Container-title:SIAM Journal on Discrete Mathematics
language:en
Short-container-title:SIAM J. Discrete Math.

Author:

Chan Patrick¹,Johnston Katherine²,Lent Joseph³,de Perez Alexander Ruys⁴,Shiu Anne⁴^ORCID

Affiliation:

1. Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL 60660 USA.

2. Department of Mathematics, Lafayette College, Easton, PA 18042 USA.

3. Department of Mathematics, Pennsylvania State University, State College, PA 16802 USA.

4. Department of Mathematics, Texas A&M University, College Station, TX 77843 USA.

Funder

National Science Foundation

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

General Mathematics

Reference21 articles.

1. Neural Codes, Decidability, and a New Local Obstruction to Convexity

2. What Makes a Neural Code Convex?

3. Algebraic signatures of convex and non-convex codes

4. The Neural Ring: An Algebraic Tool for Analyzing the Intrinsic Structure of Neural Codes

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Wheels: A new criterion for non-convexity of neural codes;Advances in Applied Mathematics;2023-09

2. Open, Closed, and Non-Degenerate Embedding Dimensions of Neural Codes;Discrete & Computational Geometry;2023-06-05

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