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The authors propose a new approach for solving constraint satisfaction problems (CSPs) in a neural architecture. While previous attempts in this direction have largely relied on stochastic neuron models to prevent a neural network from getting stuck in local optima (e.g. through the use of Boltzmann machines), the proposed architecture is capable of finding optimal solutions to problems using purely deterministic network dynamics. This is achieved by coupling oscillator modules such that the strength of their mutual interaction depends on their phase difference. Although the paper does not provide a rigorous theoretical explanation for it, these phase differences are observed to vary irregularly in simulations, thus providing sufficient exploratory drive to escape local optima. In small simulations of a CSP with ten binary variables and nine tertiary constraints the network is observed to find either of the two correct solutions (which satisfy all constraints) in each trial.