Identifying Fixed Points in Attractor Neural Networks using Directional Fibers: Supplemental Material on Theoretical Results and Practical Aspects of Numerical Traversal
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Fixed points of attractor neural networks can represent many things, including stored memories, solutions to optimization problems, and remnants of non-fixed attractor waypoints, which are relevant to a number of neurocomputational phenomena, ranging from low-level motor control and tool use to high-level problem solving and decision making. As such, global solution of the fixed point equations can improve our understanding and engineering of attractor neural networks. While local solvers and statistical characterizations abound, we do not know of any method for efficiently and precisely locating all fixed points of an arbitrary network. To solve this problem we have proposed a novel strategy for global fixed point location, based on numerical traversal of mathematical objects we defined called directional fibers . This report supplements our results in  by presenting the technical aspects of our method in more depth, including the relevant theoretical results and implementation details.