Obstructions to convexity in neural codes

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• 作者：Caitlin Lienkaemper^a ; ^{clienkaemper@g.hmc.edu} ; Anne Shiu^b ; ^{annejls@math.tamu.edu} ; Zev Woodstock^c ; ¹ ; ^{zwoodst@ncsu.edu}
• 关键词：05E45 ; 55U10 ; 92C20
• 刊名：Advances in Applied Mathematics
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：85
• 期：Complete
• 页码：31-59
• 全文大小：531 K
• 卷排序：85

文摘

How does the brain encode spatial structure? One way is through hippocampal neurons called place cells, which become associated to convex regions of space known as their receptive fields: each place cell fires at a high rate precisely when the animal is in the receptive field. The firing patterns of multiple place cells form what is known as a convex neural code. How can we tell when a neural code is convex? To address this question, Giusti and Itskov identified a local obstruction, defined via the topology of a code's simplicial complex, and proved that convex neural codes have no local obstructions. Curto et al. proved the converse for all neural codes on at most four neurons. Via a counterexample on five neurons, we show that this converse is false in general. Additionally, we classify all codes on five neurons with no local obstructions. This classification is enabled by our enumeration of connected simplicial complexes on 5 vertices up to isomorphism. Finally, we examine how local obstructions are related to maximal codewords (maximal sets of neurons that co-fire). Curto et al. proved that a code has no local obstructions if and only if it contains certain “mandatory” intersections of maximal codewords. We give a new criterion for an intersection of maximal codewords to be non-mandatory, and prove that it classifies all such non-mandatory codewords for codes on up to five neurons.