James A. Anderson - US grants

This proposal is a request for a multi-user central computer facility for the cognitive science group at Brown University. Broadly defined, cognitive science is the study of the mind and its structural and functional representation. Such study includes the investigation of representational and computational components involved in using and comprehending language, and in perceiving, acting, thinking, and knowing. A central characteristic of cognitive science is its concern with computational problems. There are four broad areas of research represented in Cognitive Science at Brown University in which the computer has served as a necessary tool. These include neural modelling of language and cognitive process, computational vision, speech, and memory and cognitive processes. Although most of us represented in this proposal have our own computer equipment, the need for and the utility of a central cognitive science machine has been proven to us over and over again. The requested equipment and associated software will allow us not only to maintain but increase our current computational resources and will allow us to maintain, and develop a state-of-the-art computer and communications network for research in many areas of cognitive science.

A mini-supercomputer will be acquired to support a variety of research activities in the area of computational biology. These activities include research in the areas of: 1) neural networks, 2) computational vision, 3) visual psychophysics 4) quantum chromodynamics, and 5) computer algebra.

Computers of any kind take time to produce an answer to a problem. Mental operations also take time to produce answers. Psychologists have studied reaction time data for over a century in an effort to infer from the observed patterns of reaction time the details of the mental computation being performed. The past decade has seen a resurgence of interest in "brain-like computation" which attempts to capture in artificial systems some of the computational power of the nervous system. Such "neural network" or "connectionist" models are potentially very powerful and very fast, though it is not yet clear for what problems they are best suited. This project will study the behavior of neural networks with respect to the time they take to perform simple computations, with the primary objective of understanding the operations of the human brain, comparing neural network simulations to human experimental data on the same tasks. Different neural network architectures differ a great deal in the time it takes for a particular operation to be performed. Parallel computers, for instance, generally exhibit reaction time patterns completely different from more traditional von Neumann machines, because they can perform many operations at the same time. For example, the most popular class of neural networks, multilayer feed forward networks, use completely parallel operations operating in synchrony. All computations take essentially the same time, although it is possible to build time dependencies into these models with additional assumptions. However, another class of parallel neural network models, non- linear dynamical system models, show very strong intrinsic time dependencies. Examples of such neural network models are Hopfield networks, the ART models, and the model to be used in this research, the BSB model. Most of the interest in neural networks has been in their computational abilities, i.e., getting interesting answers to problems. But ideally, a model of a human mental operation should do two things, compute the answer and take the same relative time to do it as humans. Preliminary simulations using the BSB model for a simple task have shown a reasonable match to experimental reaction time patterns in the well-studied, simple problem of deciding whether two stimuli are the same or different and responding appropriately. Study of the reaction time patterns produced by neural network models for human cognition will begin with simple tasks and then progress to more complex problems. One example is application of this model to the reaction time required to obtain the answers to problems in simple arithmetic.

IBN-9720320 PI: ANDERSON This project is being funded through the Learning & Intelligent Systems Initiative. This study investigates functional interactions among groups of neurons (nerve cells) in the brain. The cerebral cortex of the brain is a dynamic ensemble of groups of neurons with activities that coalesce and dissolve in the performance of particular tasks. A computational model called 'network of networks' describes the operation of computations based on neurons interacting in intermediate groupings, in the size range between single neurons (only one computing element) and entire brain regions (to hundreds of millions of computing elements). In the intermediate scale groupings, the model makes predictions about the behavior of both its component single neurons and the overall nature of the cortical computation, manifested as behavior and perception. Experimental tests utilize the mammalian visual system because so much is known about cortical processing of visual information at the level of single neurons, and also there is a large body of related experimental results for visual perception. There are three inter-related parts to this project. 1) Computer simulations and mathematical analysis further develop the 'network of networks' model itself. 2) Simultaneous activity of multiple neurons in visual areas are recorded physiologically to examine long-range transfer of information across visual cortex, of the type suggested by the model. 3) Analyses of previously obtained psychophysical behavioral data from human subjects are combined with computer simulations to try to understand the surprising effectiveness of silhouettes in object recognition, and to provide a test system for the network model. Results will have an impact because of the importance of linking cognition with neuroscience to understand mechanisms that underlie learning and perception, and because understanding how the brain handles complex computations will provide insights for the design of artificia l recognition and decision-making systems.

Brown University has received a Major Research Instrumentation Award for the acquisition of a virtual reality display system (a CAVE), a graphics computer to provide synchronized output to the CAVE, and a large shared-memory computer interconnected to the graphics computer via a high speed link. This equipment will form the core of a shared research computing environment at Brown University. Connectivity to resources available at federally funded computing laboratories will be provided through vBNS connection at Brown. A large array of computer and computational science research projects drawn from computer science, chemistry, cognitive and linguistic sciences, geological sciences, mathematics, and physics will be utilizing the proposed equipment. The research projects range over scientific visualization conducted by the Brown Graphics Group in collaboration with the NSF Science and Technology Center for Graphics and Visualization, interactive graphics in computational fluid mechanics at the Center for Fluid Mechanics, geophysical research on earthquake mechanics using numerical modeling, and condensed matter physics. The equipment will be used by graduate students for research and by undergraduate students in over a dozen courses in at least six different science departments, as well for honors thesis projects. In addition, new course units and new interdisciplinary courses in topics such as computational science, neural networks, and medical imaging will be developed to take advantage of this powerful new facility. Ongoing work by undergraduates that could take advantage of this equipment include projects that span departments such as computational steering, scientific visualization, interaction techniques for immersive virtual reality, and the creation of immersive interactive teaching tools.

[unreadable] DESCRIPTION (provided by applicant): Fayetteville State University proposes a strategy for Building Infrastructure Capacity in Social and Behavioral Research. The overall goal of FSU-RIMI project is to upgrade the capacity of the university for research productivity, which permits FSU to establish a research center of excellence in social and behavioral aspects of health disparities. Specifically, our goal is to focus on research, which helps understanding the social and behavioral factors that can be influenced for reducing racial/ethnicity and class differences in health outcomes and behaviors particularly with diseases that disproportionately affect African-Americans. The specific aims of the project are: SPECIFIC AIM 1) To increase the space and facilities for social and behavioral research in health at FSU SPECIFIC AIM 2) To increase the presence and research leadership of faculty with social and behavioral research training and experiences at FSU SPECIFIC AIM 3) To increase the community of researchers and their research productivity, with focus on social and behavioral aspects of health disparities at FSU. [unreadable] [unreadable] The program implementation plan includes four Core areas: Administrative and Evaluation Core Research Facility and Instrumentation Core Faculty and Research Staff Core Community of Researchers Core [unreadable] [unreadable]