Abstract: A dynamically stable associative learning neural system includes a plurality of neural network architectural units. A neural network architectural unit has as input both condition stimuli and unconditioned stimulus, an output neuron for accepting the input, and patch elements interposed between each input and the output neuron. The patches in the architectural unit can be modified and added. A neural network can be formed from a single unit, a layer of units, or multiple layers of units.

Abstract: A dynamically stable associative learning neural network system includes, in its basic architectural unit, at least one each of a conditioned signal input, an unconditioned signal input and an output. Interposed between input and output elements are "patches," or storage areas of dynamic interaction between conditioned and unconditioned signals which process information to achieve associative learning locally under rules designed for application-related goals of the system. Patches may be fixed or variable in size. Adjustments to a patch radius may be by "pruning" or "budding." The neural network is taught by successive application of training sets of input signals to the input terminals until a dynamic equilibrium is reached. Enhancements and expansions of the basic unit result in multilayered (multi-subnetworked) systems having increased capabilities for complex pattern classification and feature recognition.