  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 encoding the intersections of 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.
