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

Real-time assembly of ribonucleoprotein complexes on nascent RNA transcripts.

Cellular protein-RNA complexes assemble on nascent transcripts, but methods to observe transcription and protein binding in real time and at physiological concentrations are not available. Here, we report a single-molecule approach based on zero-mode waveguides that simultaneously tracks transcription progress and the binding of ribosomal protein S15 to nascent RNA transcripts during early ribosome biogenesis. We observe stable binding of S15 to single RNAs immediately after transcription for the majority of the transcripts at 35 °C but for less than half at 20 °C. The remaining transcripts exhibit either rapid and transient binding or are unable to bind S15, likely due to RNA misfolding. Our work establishes the foundation for studying transcription and its coupled co-transcriptional processes, including RNA folding, ligand binding, and enzymatic activity such as in coupling of transcription to splicing, ribosome assembly or translation

American Woodcock Conservation Plan: A Summary of and Recommendations for Woodcock Conservation in North America

Table of Contents Introduction ....................1 Bird Conservation Region Action Plans 11 Prairie Potholes ....................17James Kelley 12 Boreal Hardwood Transition ....................25Dan Dessecker 13 Lower Great Lakes/St. Lawrence Plain ....................32 Tim Post 14 Atlantic Northern Forest.................... 45 Dan McAuley 21 Oaks and Prairies ....................59David Haukos, James Kelley 22 Eastern Tallgrass Prairie ....................67 James Kelley 23 Prairie Hardwood Transition ....................75 James Kelley 24 Central Hardwoods ....................83 David Krementz, Nick Myatt 25 West Gulf Coastal Plain/Ouachita ....................92 David Krementz, Nick Myatt 26 Mississippi Alluvial Valley ....................99 David Krementz, Nick Myatt 27 Southeastern Coastal Plain ....................108 Scot Williamson 28 Appalachian Mountains.................... 116 Mark Banker 29 Piedmont ....................128 William Palmer 30 New England/Mid-Atlantic Coast ....................138 Scot Williamson 31 Peninsular Florida ....................148 Scot Williamson 37 Gulf Coastal Prairie ....................151 James Kelley Appendix I 155 Appendix II 157 Bibliography 15