The brain's ability to do this in a self-stabilizing fashion employs several different types of predictive mechanisms. The lack of a single such mechanism is clarified by accumulating theoretical and empirical evidence that brain specialization is governed by computationally complementary processing streams. The present talk will discuss recent progress towards explaining fundamental brain processes such as 3D vision in natural scenes; opticflow based navigation in natural scenes towards goals around obstacles and spatial navigation in the dark; object and scene learning, recognition, and search; cognitiveemotional dynamics that direct motivated attention towards valued goals; adaptive sensory-motor control circuits, such as those that coordinate predictive smooth pursuit and saccadic eye movements; and planning circuits that temporarily represent sequences of events in working memory and learn sequential plans, including repeated events or actions. These competences clarify the global system-level organization as well as the local microcircuit level organization of many brain systems, ranging from form and motion streams in the visual cortex through inferotemporal and parietal cortex, perirhinal and parahippocampal cortex; supplementary and frontal eye fields; orbitofrontal, ventrolateral, and dorsolateral prefrontal cortex; entorhinal and hippocampal cortex; and subcortical areas including basal ganglia, amygdala, superior colliculus, and nucleus reticularis tegmenti pontis. These model systems are being transferred as they become ready to a wide variety of large-scale applications in technology.

Unfortunately, many students stop using these beneficial learning practices as soon Presented by Stephen Grossberg, Department of Cognitive as the metatutoring ceases. Apparently, the metatutors were and Neural Systems, Center for Adaptive Systems, and Center nagging rather than convincing. This talk will present a of Excellence for Learning in Education, Science, and study of Pyrenees, a metatutor that coaches students to focus Technology, Boston University, Boston, MA 02215 on learning domain principles rather than solutions to A deep and rational understanding of the factors that influence examples. It was convincing, in that students who were effective education and learning technologies depends taught probability with Pyrenees used principle-based problem on a corresponding understanding of how the brain in health solving on post-test more so than students taught by Andes, and disease controls learned behaviors. There has been a which did not focus students on principles. Moreover, revolution in discovering new computational paradigms, organizational when all students were transferred to Andes for learning principles, mechanisms, and models of how of physics, those who were metatutored used the principlefocused learning processes enable brains to give rise to minds.

H3C 3A7 CANADA 2Department of Cognitive and Neural Systems, Boston University Boston, MA 02215 USA Abstract The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance ofARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k 1 extension of NEN. 1 Introduction The recognition process involves both identification and familiarity discrimination. Consider, for example, a neural network designed to identify aircraft based on their radar reflections and trained on sample reflections from ten types of aircraft A . . . After training, the network should correctly classify radar reflections belonging to the familiar classes A . Familiarity discrimination is also referred to as "novelty detection," a "reject option," and "recognition in partially exposed environments."

H3C 3A 7 CAN ADA 2Department of Cognitive and Neural Systems, Boston University Boston, MA 02215 USA Abstract The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance of ARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k 1 extension of NEN. 1 Introduction The recognition process involves both identification and familiarity discrimination. Consider, for example, a neural network designed to identify aircraft based on their radar reflections and trained on sample reflections from ten types of aircraft A... J. After training, the network should correctly classify radar reflections belonging to the familiar classes A... J, but it should also abstain from making a meaningless guess when presented with a radar reflection from an object belonging to a different, unfamiliar class. Familiarity discrimination is also referred to as "novelty detection," a "reject option," and "recognition in partially exposed environments."

Automatic processing of visual scenes often begins by detecting regions of an image with common values of simple local features, such as texture, and mapping the pattern offeature activation into a predicted region label. We develop a self-organizing neural architecture, called the ARTEX algorithm, for automatically extracting a novel and effective array of such features and mapping them to output region labels. ARTEX is made up of biologically motivated networks, the Boundary Contour System and Feature Contour System (BCS/FCS) networks for visual feature extraction (Cohen & Grossberg, 1984; Grossberg & Mingolla, 1985a, 1985b; Grossberg & Todorovic, 1988; Grossberg, Mingolla, & Williamson, 1995), and the Gaussian ARTMAP (GAM) network for classification (Williamson, 1996). ARTEX is first evaluated on a difficult real-world task, classifying regions of synthetic aperture radar (SAR) images, where it reliably achieves high resolution (single 874 S. Grossberg and 1. R. Williamson pixel) classification results, and creates accurate probability maps for its class predictions. ARTEX is then evaluated on classification of natural textures, where it outperforms the texture classification system in Greenspan, Goodman, Chellappa, & Anderson (1994) using comparable preprocessing and training conditions. 2 FEATURE EXTRACTION NETWORKS

Automatic processing of visual scenes often begins by detecting regions of an image with common values of simple local features, such as texture, and mapping the pattern offeatureactivation into a predicted region label. We develop a self-organizing neural architecture, called the ARTEX algorithm, for automatically extracting a novel and effective array of such features and mapping them to output region labels. ARTEXis made up of biologically motivated networks, the Boundary Contour System and Feature Contour System (BCS/FCS) networks for visual feature extraction (Cohen& Grossberg, 1984; Grossberg & Mingolla, 1985a, 1985b; Grossberg & Todorovic, 1988; Grossberg, Mingolla, & Williamson, 1995), and the Gaussian ARTMAP (GAM) network for classification (Williamson, 1996). ARTEX is first evaluated on a difficult real-world task, classifying regions of synthetic apertureradar (SAR) images, where it reliably achieves high resolution (single 874 S.Grossberg and 1. R. Williamson pixel) classification results, and creates accurate probability maps for its class predictions. ARTEXis then evaluated on classification of natural textures, where it outperforms the texture classification system in Greenspan, Goodman, Chellappa, & Anderson (1994) using comparable preprocessing and training conditions. 2 FEATURE EXTRACTION NETWORKS

Review of Perceptrons

It is important material, but it by a main text that consists mostly of belongs earlier in the book. I feel the Robert A. Chalmers engaging narratives about how some lack of a strong positive closing, the The authors of The Rise