Comparison of Gaussian ARTMAP and the EM Algorithm

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 outperforms an EM classification algorithm on a classification benchmark, thereby demonstrating the advantage of the ART match criterion for regulating learning, and the ARTMAP match tracking operation for incorporate environmental feedback in supervised learning situations.Office of Naval Research (N00014-95-1-0409

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Neural Network for Dynamic Binding with Graph Representation: Form, Linking, and Depth-From-Occlusion

A neural network is presented which explicity 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 ares to link. With this representation, the network selectivly groups and segments in depth objects based on line junction information, producing results consistent with those of several recent visual search eperiments. In addiction to depth-from-occlusion, the network provides a sufficient framework for local line-labelling processes to recover other 3-D variables, such as edge/surface contiguity, edge, slant, and edge convexity.Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI-90-24877, IRI-90-00530); Office of Naval Research (N0014-91-J-4100, N00014-92-J-4015

Self-Organization of Topographic Mixture Networks Using Attentional Feedback

This paper proposes a biologically-motivated neural network model of supervised 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. Possible relationships are discussed between the network's learning properties and those of biological neural networks. Possible future extensions of the network are also discussed.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409

Gaussian Artmap: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps

A new neural network architecture for incremental supervised learning of analalog multidimensional maps is introduced. The architecture, called Gaussian ARTMAP, is a synthesis of a Gaussian classifier and an Adaptive Resonance Theory (ART) neural network, achieved by defining the ART choice function as the discriminant function of a Gaussian classifer with separable distributions, and the ART match function as the same, but with the a priori probabilities of the distributions discounted. While Gaussian ARTMAP retains the attractive parallel computing and fast learning properties of fuzzy ARTMAP, it learns a more efficient internal representation of a mapping while being more resistant to noise than fuzzy ARTMAP on a number of benchmark databases. Several simulations are presented which demonstrate that Gaussian ARTMAP consistently obtains a better trade-off of classification rate to number of categories than fuzzy ARTMAP. Results on a vowel classiflcation problem are also presented which demonstrate that Gaussian ARTMAP outperforms many other classifiers.National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-92-J-4015, 40014-91-J-4100

ARTEX: A Self-Organizing Architecture for Classifying Image Regions

A self-organizing architect is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to diflicult real-world image classification problems, including classification of synthetic aperture radar and natural textural images, and outperforms a recent state-of-the-art system at classifying natural textures.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100, N00014-95-1-0409); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-4015, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877). an

Bleomycin increases neutrophil adhesion to human vascular endothelial cells independently of upregulation of ICAM-1 and E-selectin

© 2016 Taylor & Francis. Aim of the Study: Bleomycin-induced lung disease is a serious complication of therapy characterized by alveolar injury, cytokine release, inflammatory cell recruitment, and eventually pulmonary fibrosis. The mechanisms underlying bleomycin-induced pulmonary fibrosis may be relevant to other progressive scarring diseases of the lungs. Pulmonary vascular endothelial cells are critically involved in immune cell extravasation at sites of injury through adhesion molecule expression and cytokine release. We sought to determine the effects of bleomycin on adhesion molecule expression and cytokine release by pulmonary vascular endothelial cells, and their functional relevance to inflammatory cell recruitment. Materials and Methods: The effects of pharmacologically relevant concentrations of bleomycin on adhesion molecule expression and cytokine release by human vascular endothelial cells in vitro were studied by flow cytometry, quantitative polymerase chain reaction, and enzyme-linked immunosorbent assay. A flow chamber model was used to assess the functional consequences on adhesion of flowing human neutrophils to endothelial cell monolayers. Results: Bleomycin increased intercellular adhesion molecule 1 (ICAM-1; CD54), vascular cell adhesion molecule (VCAM-1; CD106), and E-selectin (CD62E) expression, and increased monocyte chemoattractant protein (MCP-1) and interleukin (IL-8) release by endothelial cells. Increases in protein expression were accompanied by increased mRNA transcription. In contrast, there was no direct effect of bleomycin on the profibrotic cytokines transforming growth factor-beta (TGF-β), platelet-derived growth factor-BB (PDGF-BB), or endothelin-1. Under flow conditions, endothelial cells exposed to bleomycin supported increased neutrophil adhesion which was independent of ICAM-1 or E-selectin. Conclusion: Our findings demonstrate that bleomycin promotes endothelial-mediated inflammation and neutrophil adhesion. These mechanisms may contribute to the development of pulmonary fibrosis by supporting immune cell recruitment in the lungs