Speakers

Walter Freeman
Walter Freeman studied physics at Massachusetts Institute of Technology, mathematics at Hamilton College and English and philosophy at the University of Chicago. He received his M.D. from Yale University's School of Medicine. He was an intern at the Yale School of Medicine and Johns Hopkins Hospital and a postdoctoral fellow at the University of California, Los Angeles, before he joined the faculty at UC-Berkeley in 1959. He is currently professor of the graduate school in molecular and cell biology, and a member of the graduate group in biophysics and in bioengineering.

A Neurobiological Theory of Meaning in Perception
The currency of brains is primarily meaning and only secondarily information. Brains select and pre-process information that sensory stimuli carry and then create meanings. The neuroscientific study of information processing is well served by the theory of information. A corresponding theory of meaning is needed, in order to understand the intervening process of perception by which brains create meanings from information.

Neural populations express meanings in spatial patterns of amplitude modulation of electroencephalographic (EEG) waves. These spatial patterns are shaped by inputs representing attention, expectancy and learning such that the patterns manifest not the features of stimuli, but the meaning of the stimuli for an animal. The theory of meaning emerges from dynamical simulations of these spatiotemporal patterns of brain activity which have shown the existence of equilibrium, limit cycle and chaotic attractors. Applications of the theory of meaning will support new treatments for clinical brain disorders, new robots with capacities for autonomy and intelligence that might approach those of simpler free-living animals and, most importantly, new understanding of how we humans create ourselves through our actions.

Jim Hansen
Jim Hansen is an assistant professor of atmospheric science in MIT's department of earth, atmospheric and planetary sciences. He received a B.S. in 1992 and an M.S. in 1993 in aerospace engineering from the University of Colorado while working his way through school as a member of the football team. A Rhodes Scholarship took him to Oxford, England, where he tried forecasting everything - from El Niño and the rainfall in western Africa, to the trajectory of ballistic missiles - before focusing on applying ideas in nonlinear dynamics/chaos to the numerical weather prediction problem. He performed post-doctoral work in the United Kingdom on quantifying the uncertainty in forecasts of recent climate change before returning to the United States and settling at MIT. Dr. Hansen's research aims to "use forecasting as a tool to better understand the underlying system," as he strives to bridge theory and operational applications.

30 Percent Chance of Rain? Chaos and Numerical Weather Prediction
The sensitive dependence of initial conditions - a hallmark of chaotic systems - places a fundamental limit on our ability to produce accurate weather forecasts. Numerical weather prediction (NWP) is an initial value problem and any error in initial conditions will grow, on average, exponentially quickly. The "on average" is an important caveat. Because the average rate of error growth need never be realized over any finite time period, chaotic systems can contain regions of state space where all errors actually shrink over some forecast time. This day-to-day (or state dependent) error growth can provide information about how much confidence should be placed in any forecast. It may not be obvious from your local weather forecast, but NWP takes state-dependent error growth into account by running not one, but many forecasts each day. The forecasts differ in their initial conditions, and the resulting collection of forecasts - called an "ensemble" forecast - can be interpreted probabilistically to provide information about the confidence that should be placed in a particular forecast. Examples of the utility and limitations of such an approach will be given in systems ranging from two-dimensional chaotic maps all the way to output from state-of-the-science NWP models.

Larry Liebovitch
Dr. Larry Liebovitch graduated from City College of New York with a B.S. in physics and later earned a Ph.D. in astronomy from Harvard University. Now a professor at Florida Atlantic University, Dr. Liebovitch has appointments in the Center for Complex Systems and Brain Sciences, the Center for Molecular Biology and Biotechnology and the Department of Psychology. He has used nonlinear methods, including fractals, chaos and neural networks to study molecular, genetic, cellular, physiological and information systems such as motions in proteins, the timing of heart attacks, the swimming of one-cell organisms and the spread of computer viruses. Dr. Liebovitch is a fellow of the American Physical Society through the Division of Biological Physics for advancing the physics of fractals and chaos and using these methods to analyze and understand biological systems. He also has been developing a CD-ROM with curricula materials for a mathematics course for non-science students that uses fractals to show how mathematicians think about mathematics. An author of numerous fractal and chaos scientific articles, Dr. Liebovitch is also the author of Fractals and Chaos Simplified for the Life Sciences , a book designed to illustrate the application of fractals and chaos in biology and medicine.

How Chaos Theory Changes How We See the World Scientists once believed that if they understood the rules of nature, they could understand the past and predict the future. All of that changed about 100 years ago. Scientists realized that they could understand how a system worked - that is, they could understand the mathematical equations that described it - and yet still couldn't tell what the system would do in the long run. Such systems are called "chaotic." Chaotic systems happen when tiny changes in initial conditions become ever larger later on, called the "butterfly effect." For example, if a butterfly beats its wings in China, the small changes in the direction of the wind will drive ever larger and larger changes in the air, causing a thunderstorm in Minneapolis a week later. These ideas will be illustrated with specific examples, such as the beating of heart cells and insights into more effective management strategies.