Unsupervised learning in noise

The structural stability of real-time unsupervised learning in feedback dynamical systems is demonstrated with the Ito-Stratonovich stochastic calculus. Structural stability allows globally stable feedback systems to be perturbed without changing their qualitative equilibrium behavior. This increases the reliability and biological plausibility of large-scale hardware implementations of such networks. These structurally stable dynamical systems are called random adaptive bidirectional associative memory (RABAM) models. RABAM models include several popular nonadaptive and adaptive feedback models, such as the Hopfield circuit and the ART-2 model. A new hybrid unsupervised-learning law, called the differential competitive law, which uses the signal velocity as a local unsupervised reinforcement mechanism, is introduced, and its coding and stability behavior in feedforward and feedback networks is studied.<>