Neural codes, decidability, and a new local obstruction to convexity

by   Aaron Chen, et al.

Given an intersection pattern of arbitrary sets in Euclidean space, is there an arrangement of convex open sets in Euclidean space that exhibits the same intersections? This question is combinatorial and topological in nature, but is motivated by neuroscience. Specifically, we are interested in a type of neuron called a place cell, which fires precisely when an organism is in a certain region, usually convex, called a place field. The earlier question, therefore, can be rephrased as follows: Which neural codes, that is, patterns of neural activity, can arise from a collection of convex open sets? To address this question, Giusti and Itskov proved that convex neural codes have no "local obstructions," which are defined via the topology of a code's simplicial complex. Codes without local obstructions are called locally good, because the obstruction precludes the code from arising from open sets that form a good cover. In other words, every good-cover code is locally good. Here we prove the converse: Every locally good code is a good-cover code. We also prove that the good-cover decision problem is undecidable. Finally, we reveal a stronger type of local obstruction that prevents a code from being convex, and prove that the corresponding decision problem is NP-hard. Our proofs use combinatorial and topological methods.


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