To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear causal chains from receptors after the first cortical synapse, so we use circular causality to explain neural pattern formation by self-organizing dynamics. But an aspect of intentional action is causality, which we extrapolate to material objects in the world. Thus causality is a property of mind, not matter.

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this article. However, certain properties of chaotic systems can be described qualitatively. For example, chaotic systems exhibit strong dependence on initial conditions. In the can of the logistic equation, small differences in the initial value x 1 , will result in big differences in the subsequent values x n over time. This strong dependence on initial conditions means that predicting the long-term behavior of chaotic systems is difficult. Another important property of chaotic systems is the ability to show self-organization - to evolve toward ordered temporal and spatial patterns (11). The transition from chaotic to ordered behavior, or the reverse, can occur as an abrupt phase transition with a minute change in the control parameters. As we shall see subsequently, abrupt phase transitions and self-organizing behavior have been demonstrated in electroencephalographs (EEGs) from the epileptogenic foci in humans.

Recent interest in consciousness and the mind-brain problem has been fueled by technological advances in brain imaging and computer modeling in artificial intelligence: Can machines be conscious? The machine metaphor originated in Cartesian "reflections" and culminated in 19th century reflexology modeled on Newtonian optics. It replaced the Aquinian view of mind, which was focused on the emergence of intentionality within the body, with control of output by input through brain dynamics. The state variables for neural activity were identified successively with animal spirits, lan vital, electricity, energy, information, and, most recently, Heisenbergian potentia. The source of dynamic structure in brains was conceived to lie outside brains in genetic and environmental determinism. An alternative view has grown in the 20th century from roots in American Pragmatists, particularly John Dewey, and European philosophers, particularly Heidegger and Piaget, by which brains are intrinsically unstable and continually create themselves. This view has new support from neurobiological studies in properties of self-organizing nonlinear dynamic systems. Intentional behavior can only be understood in relation to the chaotic patterns of neural activity that produce it. The machine metaphor remains, but the machine is seen as selfdetermining. 1.

and the transmission of complex firing patterns between neurons. J

Brain systems operate on many levels of organization, each with its own scales of time and space. Dynamics is applicable to every level, from the atomic to the molecular, and from macromolecular organelles to the neurons into which they are incorporated. In turn the neurons form populations; they form systems, and so on to an embodied brain interacting intentionally with its environment. Each level is "macroscopic" to the one below it and "microscopic" to the one above it. Among the most difficult tasks are those of conceiving and describing the exchanges between levels, seeing that the scales of time and distance are incommensurate, and that causal inference is far more ambiguous between than within levels. That holds for the relation of action potentials from microelectrodes to whole brain activity seen with new techniques for brain imaging: fMRI and PET. A new recourse is to conceive, identify and model an intervening "mesoscopic" level, which is a local selforganizing neural population. Its characteristic activities consist of 'spontaneous' action potentials and EEG dendritic activity. Mesoscopic neurodynamics gives a clear understanding of self-organized chaotic patterns of neural activity in primary sensory areas when significant stimuli arrive. These patterns are created with each sniff, glance, or movement of the head and hands. They are triggered by sensory input, but they are not the result of information processing, and they are not representations of stimuli. They are manifestations of the way in which brains make and test hypotheses. The patterns show that brains do not take information into themselves. They formulate expectations as hypotheses and test them by taking action into the environment. They are not data-driven; they are hypothesisdriven, and all ...

In a passive information processing system a stimulus input gives information, which is transduced by receptors into trains of impulses that signify the features of an object. The symbols are processed according to rules for learning and association and are then bound into a representation, which is stored, retrieved and matched with new incoming representations. In active systems perception begins with the emergence of a goal that is implemented by the search for information. The only input accepted is that which is consistent with the goal and anticipated as a consequence of the searching actions. The key component to be modeled in brains provides the dynamics that constructs goals and the adaptive actions by which they are achieved.

Neurodynamics of intentionality in the behavioral act of observation Intelligent behavior is characterized by flexible and creative pursuit of endogenously defined goals. It has emerged in humans through the stages of evolution that are manifested in the brains and behaviors of other animals. Intentionality is a key concept by which to link brain dynamics to goaldirected behavior. The archetypal form of intentional behavior is an act of observation through time and space, by which information is sought for the guidance of future action. Sequences of such acts constitute the key desired property of freeroving, semi-autonomous devices capable of exploring remote environments that are inhospitable for humans. Intentionality consists of (a) the neurodynamics by which images are created of future states as goals; (b) command sequences by which to act in pursuit of goals; (c) prediction of changes in sensory input resulting from intended actions (reafference); (d) evaluation of performance; and (e) modification of the device by itself in learning from the consequences of its intended actions. These principles are well known among psychologists and philosophers. What is new is the development of nonlinear mesoscopic brain dynamics, by which to apply chaos theory in order to understand and simulate the construction of meaningful patterns of endogenous activity that implement the perceptual process of observation.

Fields of neural activity are seen in synchronized oscillations that are detected at mesoscopic scales in syntheses of multicellular recordings of action potentials and electroencephalograms (EEGs) over broad areas of cerebral cortex. The waves often have large-scale, highly textured spatial patterns of cortical activity that form in the context of associative learning under classical and operant conditioning in rabbits. The patterns show spatial amplitude modulation of shared oscillations of carrier waves in the beta and gamma ranges of the EEG, with recurrence at frame rates in the alpha and theta ranges. The frames also show spatial phase modulation that is inconsistent with driving of the oscillations by focal pacemakers. The hypothesis is developed that the synchronization manifests continuous distributions of activity in cortical neuropil that modulate firings of selected neural networks embedded in the neuropil. Five interactive agencies have been postulated to explain the mechanism for the field synchrony: electric fields; magnetic fields; electromagnetic fields (radio waves); diffusion chemical gradients; and order parameters that control self-organization of large populations of neurons by widespread synaptic interaction constituting negative and positive feedback. Only the last fits the data. The points are emphasized that these field patterns in frames require interactive neural dynamics that is modulated in respect to

The human data were collected in the EEG & Clinical Neurophysiology Laboratory, Harborview Medical Center, Seattle WA. The 8x8 electrode array was constructed by Ad-Tech Medical Instrument Corp. Racine WI 53404 in accordance with a Berkeley design. Programming was by Brian C. Burke in the Division of Neurobiology at Berkeley. Partial support was provided through grants NCC 2-1244 from NASA and EIA-0130352 from NSF to Robert Kozma. We are grateful for support from Dr. Scott Barnhart, Medical Director, Harborview Medical Center, Seattle WA.Stability and flexibility of human neocortex 2 Freeman, Holmes, West, Vanhatalo The electroencephalogram (EEG) in normal resting subjects is robustly stable. In epileptic subjects it reveals instability. We investigated EEGs in states of a neurosurgical patient awake and at rest, in sleep, and in intractable partial complex seizures. We used a microgrid array of 64 electrodes in a 1x1 cm window fixed on the right inferior temporal gyrus. EEG signals were recorded during a week of neurosurgical evaluation for treatment. Comparisons with normal intracranial EEG were perforce made with data from animal studies. Analytic phase and amplitude were calculated with the Hilbert transform to get the temporal resolution