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

TL;DR

This paper characterizes the possible triples of open, closed, and non-degenerate embedding dimensions of neural codes, providing new constructions that demonstrate the range of these dimensions and the first examples where closed embedding exceeds open embedding.

Contribution

It establishes that any triple of embedding dimensions satisfying certain inequalities can be realized by a neural code, and introduces new construction methods using convex set sunflowers and rigid structures.

Findings

Constructed codes for all valid embedding dimension triples.

First examples of codes with larger closed than open embedding dimension.

Provided a complete characterization of feasible embedding dimension triples.

Abstract

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.