This article proposes a neural network model of supervised learning that employs biologically motivated constraints of using local, on-line, constructive learning. The model possesses two novel learning mechanisms. The first is a network for learning topographic mixtures. The network's internal category nodes are the mixture components, which learn to encode smooth distributions in the input space by taking advantage of topography in the input feature maps. The second mechanism is an attentional biasing feedback circuit. When the network makes an incorrect output prediction, this feedback circuit modulates the learning rates of the category nodes, by amounts based on the sharpness of their tuning, in order to improve the network's prediction accuracy. The network is evaluated on several standard classification benchmarks and shown to perform well in comparison to other classifiers.

Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of sufficient complexity to solve a problem it is trained on. GAM's representation is a gaussian mixture model of the input space, with learned mappings from the mixture components to output classes. We show a close relationship between GAM and the well-known expectation-maximization (EM) approach to mixture modeling. GAM outper forms an EM classification algorithm on three classification benchmarks, thereby demonstrating the advantage of the ART match criterion for regulating learning and the ARTMAP match tracking operation for incorporating environmental feedback in supervised learning situations.

A neural network is presented that explicitly represents form attributes and relations between them, thus solving the binding problem without temporal coding. Rather, the network creates a graph representation by dynamically allocating nodes to code local form attributes and establishing arcs to link them. With this representation, the network selectively groups and segments in depth objects based on line junction information, producing results consistent with those of several recent visual search experiments. In addition to depth-from-occlusion, the network provides a sufficient framework for local line-labeling processes to recover other three-dimensional (3-D) variables, such as edge/surface contiguity, edge slant, and edge convexity.