Open, Closed, and Non-Degenerate Embedding Dimensions of Neural Codes

Abstract

AbstractWe study the open, closed, and non-degenerate embedding dimensions of neural codes, which are the smallest respective dimensions in which one can find a realization of a code consisting of convex sets that are open, closed, or non-degenerate in a sense defined by Cruz, Giusti, Itskov, and Kronholm. For a given code $$\mathcal {C}$$ C we define the embedding dimension vector to be the triple (a, b, c) consisting of these embedding dimensions. Existing results guarantee that $$\max {\{a,b\}}\le c$$ max { a , b } ≤ c , and we show that when any of these dimensions is at least 2 this is the only restriction on such vectors. Specifically, for every triple (a, b, c) with $$2\le \min {\{a,b\}}$$ 2 ≤ min { a , b } and $$\max {\{a,b\}}\le c\le \infty $$ max { a , b } ≤ c ≤ ∞ we construct a code $$\mathcal {C}_{(a,b,c)}$$ C ( a , b , c ) whose embedding dimension vector is exactly (a, b, c) (where an embedding dimension is $$\infty $$ ∞ if there is no realization of the corresponding type). Our constructions combine two existing tools in the convex neural codes literature: sunflowers of convex open sets, and rigid structures, the latter of which was recently defined in work of Chan, Johnston, Lent, Ruys de Perez, and Shiu. Our constructions provide the first examples of codes whose closed embedding dimension is larger than their open embedding dimension, but still finite.