A two-dimensional shift invariant image classification neural network which overcomes the stability/plasticity dilemma

A neural network for two-dimensional visual pattern learning and classification is outlined. The new architecture combines the important aspects of two previously developed network designs, simultaneously taking advantage of the unique properties of both. The structure of the Neocognitron network is incorporated to allow shift-invariant and partial scale-invariant recognition, while the top-down attentional and matching mechanisms found in the adaptive resonance theory (ART) model are used to solve the stability-plasticity dilemma. The new network is self-organizing, shift invariant and able to switch automatically between its stable and plastic modes. Computer simulation results for a group of edge extracted patterns are detailed. The neural design uses viable neural mechanisms similar to those thought to exist in biological neural systems