Walter J. Freeman

Bio: Walter J. Freeman is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Electroencephalography & Chaotic. The author has an hindex of 76, co-authored 404 publications receiving 25357 citations. Previous affiliations of Walter J. Freeman include University of Salerno & University of California, Los Angeles.

How brains make chaos in order to make sense of the world

Abstract: Recent “connectionist” models provide a new explanatory alternative to the digital computer as a model for brain function. Evidence from our EEG research on the olfactory bulb suggests that the brain may indeed use computational mechanisms like those found in connectionist models. In the present paper we discuss our data and develop a model to describe the neural dynamics responsible for odor recognition and discrimination. The results indicate the existence of sensory- and motor-specific information in the spatial dimension of EEG activity and call for new physiological metaphors and techniques of analysis. Special emphasis is placed in our model on chaotic neural activity. We hypothesize that chaotic behavior serves as the essential ground state for the neural perceptual apparatus, and we propose a mechanism for acquiring new forms of patterned activity corresponding to new learned odors. Finally, some of the implications of our neural model for behavioral theories are briefly discussed. Our research, in concert with the connectionist work, encourages a reevaluation of explanatory models that are based only on the digital computer metaphor.

The Physiology of Perception

Abstract: The brain transforms sensory messages into conscious perceptions almost instantly Chaotic, collective activity involving millions of neurons seems essential for such rapid recognition.

Simulation of chaotic EEG patterns with a dynamic model of the olfactory system

Abstract: The main parts of the central olfactory system are the bulb (OB), anterior nucleus (AON), and prepyriform cortex (PC). Each part consists of a mass of excitatory or inhibitory neurons that is modelled in its noninteractive state by a 2nd order ordinary differential equation (ODE) having a static nonlinearity. The model is called a KOe or a KOt set respectively; it is evaluated in the “open loop” state under deep anesthesia. Interactions in waking states are represented by coupled KO sets, respectivelyKI e (mutual excitation) andKI i (mutual inhibition). The coupledKI e andKI i sets form aKII set, which suffices to represent the dynamics of theOB, AON, andPC separately. The coupling of these three structures by both excitatory and inhibitory feedback loops forms aKIII set. The solutions to this high-dimensional system ofODEs suffice to simulate the chaotic patterns of the EEG, including the normal low-level background activity, the high-level relatively coherent “bursts” of oscillation that accompany reception of input to the bulb, and a degenerate state of an epileptic seizure determined by a toroidal chaotic attractor. An example is given of the Ruelle-Takens-Newhouse route to chaos in the olfactory system. Due to the simplicity and generality of the elements of the model and their interconnections, the model can serve as the starting point for other neural systems that generate deterministic chaotic activity.

How Brains Make Up Their Minds

Abstract: How do we exercise our will? The erosion of Descartes' concept of the soul in the machine by recent developments in neuroscience leaves us with the challenge of understanding how we control our behaviour and make sense of the world around us. Do our genes and environments determine all that goes on in our brains, or do we create ourselves through what we believe and how we behave? In How Brains Make Up Their Minds, the distinguished US neuroscientist Walter J. Freeman charts the brain's mind, progressing from single nerve cells, through cooperative assemblies of these cells, to the emergence of complex patterns of brain activity. By drawing on new developments in brain imaging and theories of chaos and nonlinear dynamics, he shows how brains create intentions and meanings. The result is an original and stimulating synthesis of neuroscience and philosophy that argues that the power to choose is an essential and inalienable property of brains, and, moreover, the foundation for the development and flourishing of individuals and societies.

Synchronization in chaotic systems

Abstract: Certain subsystems of nonlinear, chaotic systems can be made to synchronize by linking them with common signals. The criterion for this is the sign of the sub-Lyapunov exponents. We apply these ideas to a real set of synchronizing chaotic circuits.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.