'Mechanical' neural learning and InfoMax orthonormal independent component analysis
Simone Fiori, Pietro Burrascano · 2003
We present a new class of learning models for linear as well as nonlinear neural learners, deriving from the study of the dynamics of an abstract rigid mechanical system. The set of equations describing the motion of this system may be readily interpreted as a learning rule for orthogonal networks. As a simple example of how to use the learning theory, a case of the orthonormal independent component analysis based on the Bell-Sejlunoski's InfoMax principle is discussed through simulations.