  We 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 $\mathcal C$ we define the embedding dimension vector to be the
triple $(a,b,c)$ consisting of these embedding dimensions. Existing results
guarantee that $\max\{a,b\} \le c$, 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 $2\le \min \{a,b\}$ and
$\max\{a,b\}\le c$ we construct a code $\mathcal C_{(a,b,c)}$ whose embedding
dimension vector is exactly $(a,b,c)$. 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.
