Spaciotemporal neural networks for shortest path optimization

This paper describes a new approach for shortest path optimization using a recurrent neural network. Network temporal and spacial properties are independently exploited in a manner which generalizes upon earlier Hopfield net optimization approaches. The new spaciotemporal method encodes constraints as a spacially distributed energy and costs as time delays incurred during network convergence. This approach yields a robust recurrent neural network for solving single-source shortest path problems. The approach is suitable for the determination of unique solutions as well as the case where multiple solutions exist. In addition, the new method exhibits better space and time complexity than a Hopfield network approach to the same problem