A new unsupervised neural learning rule for orthonormal signal processing

We derive a new class of neural unsupervised learning rules which arises from the analysis of the dynamics of an abstract mechanical system. The corresponding algorithms can be used to solve several problems in the area of digital signal processing, where orthonormal matrices are involved. We present an application which deals with blind separation of sources, i.e. a new method to perform efficient independent component analysis (ICA) of random signals.