A local interaction heuristic for adaptive networks

The standard heuristic for optimization of network parameters is gradient descent. This heuristic can lead to nonoptimal terminal parameter configurations in multilayer networks. By adding a heuristic that coordinates the development of nearby parameter values, this 'local minimum' problem can be reduced. After motivating the use of local interactions during learning, the authors present simulation results that demonstrate improved learning under the heuristic.<<ETX>>