9307632 Sandberg Nonlinear transformation and recognition of continuous-time signals or signal sequences is fundamental to a wide range of cognitive processes. This work aims to build a comprehensive understanding of the processing of spatio-temporal signals. It is founded on recent results obtained by the proposer showing that very large classes of continuous functionals and shift-invariant functional maps can be uniformly approximated by certain conceptually simple neural-like structures. These structures are the Function Space Neural Networks (FSNNs) that involve a preprocessing linear operation stage such as convolution with suitable kernel functions, followed by a network of nonlinear cells. The project shall address key issues pertaining to the design and use of FSNNs. These include determination of suitable kernel functions, network size, connectivity patterns and form of nonlinearity for different problems classes, effectiveness of alternate learning algorithms and their convergence rates, and techniques for constructive/destructive network growth. Anew call of FSNNs based on higher order networks that have proved very effective for static classification, will also propose be evaluated. Theoretical studies shall be supplemented by extensive simulations using several suites of spatio-temporal signals ranging form low-dimensional artificial patterns to a set of over 1000 short duration signals representing actual passive sonar returns form underwater biologics. This work shall try to raise the understanding of the capabilities and usability of artificial neural network structures for the processing of spatio-temporal signals to a level comparable to that achieved at present for (static) multilayered feedforward networks. ***
