This grant will support Dr. Williams' theoretical and experimental work in two important areas of artificial neural computation: recursive networks for processing time-varying signals, and multiscale networks for exploiting information at multiple resolutions. Recursive networks will be important for control and signal-processing problems with feedback delays of unknown duration. Multiscale architectures and learning strategies are needed for large-scale problems of practical importance, including those with complex spatial and temporal components. Proposed target applications are in speech recognition and adaptive sensorimotor control.
