Rotor neurons are introduced to encode states living on the surface of a sphere in D dimensions. Such rotors can be regarded as continuous generalizations of binary (Ising) neurons. The corresponding mean field equations are derived, and phase transition properties based on linearized dynamics are given. The power of this approach is illustrated with an optimization problem—placing N identical charges on a sphere such that the overall repulsive energy is minimized. The rotor approach appears superior to other methods for this problem both with respect to solution quality and computational effort needed.

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