A plastic self-adaptive learning machine for pattern recognition

A family of neural networks and learning algorithms is introduced: the plastic self-adaptive learning machines (PSALM), together with a new interpretation of these neural networks as hyperpolyhedra in the N -dimensional Euclidean space. These networks self-adapt to a continually changing environment by properly changing the orientation of the faces of a hyperpolyhedron as well as its volume. The current structure of the hyperpolyhedron reflects the structure of the current outside world. The network optimally classifies its noise-distorted excitations into categories, after a competition between all possible categories. New categories can be created, and the old ones can be changed, or be forgotten if they are not used for a long time