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arXiv 2002.03542math.COmath.ACq-bio.NC

偏序体与组合神经码

Oriented Matroids and Combinatorial Neural Codes

Alexander Kunin, Caitlin Lienkaemper, Zvi Rosen

AI总结:

本文将偏序体理论与组合神经码理论联系起来,证明了判断组合码是否凸是NP难问题。

摘要:

一个组合神经码C⊆2^[n]是凸的,如果它作为实数空间R^d中凸开子集的交集模式出现。我们把新兴的凸神经码理论与已有的偏序体理论在范畴和几何及计算复杂性方面联系起来。在范畴方面,我们证明将一个无环偏序体映射到其顶点的正部分码的映射是一个忠实的函子。我们将Novik、Postnikov和Sturmfels引入的偏序体理想适应为从偏序体范畴到环范畴的函子;然后,我们证明由此得到的环映射自然地映射到偏序体神经码的神经环。在几何和计算复杂性方面,我们证明一个码如果能用凸多面体实现当且仅当它在Jeffs引入的码的偏序中位于可表示偏序体的码之下。我们证明之前发表的非凸码例子不位于任何偏序体之下,并且构造了位于非可表示偏序体之下的非凸码例子。通过这种构造,我们可以应用Mnëv-Sturmfels普遍性来证明判断一个组合码是否凸是NP难的。

英文摘要:

A combinatorial neural code $\mathscr C\subseteq 2^{[n]}$ is convex if it arises as the intersection pattern of convex open subsets of $\mathbb R^d$. We relate the emerging theory of convex neural codes to the established theory of oriented matroids, both categorically and with respect to geometry and computational complexity. On the categorical side, we show that the map taking an acyclic oriented matroid to the code of positive parts of its topes is a faithful functor. We adapt the oriented matroid ideal introduced by Novik, Postnikov, and Sturmfels into a functor from the category of oriented matroids to the category of rings; then, we show that the resulting ring maps naturally to the neural ring of the matroid's neural code. For geometry and computational complexity, we show that a code has a realization with convex polytopes if and only if it lies below the code of a representable oriented matroid in the partial order of codes introduced by Jeffs. We show that previously published examples of non-convex codes do not lie below any oriented matroids, and we construct examples of non-convex codes lying below non-representable oriented matroids. By way of this construction, we can apply Mnëv-Sturmfels universality to show that deciding whether a combinatorial code is convex is NP-hard.

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