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.

Intelligent behavior is characterized by flexible and creative pursuit of endogenously defined goals. Intentionality is a key concept by which to link neuron and brain to goal-directed behavior through brain dynamics. An archetypal form of intentional behavior is an act of observation in space-time, by which information is sought for the guidance of future action to explore unpredictable and ever-changing environments. These acts are based in the brain dynamics that creates spatiotemporal patterns of neural activity, serving as images of goals, of command sequences by which to act to reach goals, and of expected changes in sensory input resulting from intended actions. Prediction of the sensory consequences of intended action and evaluation of performance is by reafference. An intentional act is completed upon modification of the system by itself through learning. These principles are well known among psychologists and philosophers. What is new is the development of nonlinear mesoscopic brain dynamics, by which the theory of chaos can be used to understand and simulate the constructions of meaningful patterns of neural activity that implement the process of observation. The design of neurobiological experiments, analysis of the resulting data, and synthesis of explanatory models require an understanding of the hierarchical nature of brain organization, here conceived as single neurons and neural networks at the microscopic level; clinically defined cortical and subcortical systems studied by brain imaging (for example, fMRI) at the macroscopic level, and self-organizing neural populations at an intermediate mesoscopic level, at which synaptic interactions create novel activity patterns through nonlinear state transitions. The constructive neurodynamics of sensory cortic...

: 177, Introduction: 500, Discussion: 1100 Acknowledgment: Supported by NINDS Award NS29414, Israeli Ministry of Health Grant No. 01350931, and by an Alon Fellowship to Dr. Ruppin. Abstract When an ischemic cerebral infarction occurs, surrounding the core of dying tissue there is usually an ischemic penumbra of non-functional but still viable tissue. One current but controversial hypothesis is that this penumbra tissue often eventually dies due to the metabolic stress imposed by multiple cortical spreading depression (CSD) waves, i.e., by ischemic depolarizations. We describe here a computational model of CSD developed to study the implications of this hypothesis. Following simulated infarction, the model displays the linear relationship between final infarct size and the number of CSD waves traversing the penumbra that has been reported experimentally, even though damage with each individual wave progresses non-linearly with time. It successfully reproduces the experimental dependen...

This paper presents a computational study of the pathogenesis of ischemic tissue damage during acute stroke. Two prime pathogenic mechanisms, cortical spreading depression (CSD) waves and glutamate excitotoxicity (GE), are investigated. Testable predictions describing the patterns of damage that arise if damage is caused by either mechanism are generated. These damaged tissue patterns are inherently different, arising from the distinct propagation characteristics underlying the CSD versus GE mechanisms. More specifically, when a V-shaped lesion is induced in a simulated area of decreased blood flow, the damage is almost circular in the CSD case, while in the GE case it follows more faithfully the shape of the initial lesion. When the center of the ischemic area is surrounded by ring-shaped lesion, tissue damage spreads outwards beyond the ring if the underlying mechanism is GE, but is completely blocked by the ring in the CSD case. The experimental testing of these predictions may help...

The topic of this presentation is the investigation of the epileptic human brain as a nonlinear system that undergoes a phase transition (epileptic seizure). The estimated values of the largest Lyapunov exponent L over time indicated a more chaotic state postictally than ictally or preictally. The start of a seizure corresponds to a simultaneous drop in the values of L at the focal electrode sites. The observed slow cyclic variations in the temporal Lyapunov profiles imply attempts of the system to undergo a phase transition minutes before the seizure's onset. The analysis of the maximum rate of entropy production over space revealed an initial phase difference of minutes preictally at the sites overlying the seizure focus, which progressed to phase locking with a slow entrainment of the rest of the cortical sites shortly before the onset of a seizure. It is also conjectured that the abnormal spiking electrical activity of the brain plays a major role in the unfolding of the phenomeno...

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.

Arrays of 64 electrodes (8x8, 7x7 mm) were implanted epidurally on the surface of the visual, auditory or somatosensory cortex of rabbits trained to discriminate conditioned stimuli in the corresponding modality. The 64 EEG traces at all times displayed a high degree of spatial coherence in wave form, averaging >90% of the variance in the largest PCA component. The EEGs were decomposed with the FFT to give the spatial distributions of amplitude and phase modulation (AM and PM) in segments 128 ms in duration. Spatial (2-D) and temporal (1-D) filters were designed to optimize classification of the spatial AM patterns in the gamma range (20-80 Hz) with respect to discriminative conditioned stimuli. No evidence was found for stimulus-dependent classification of the spatial PM patterns. Instead, some spatial PM distributions conformed to the pattern of a cone. The location and sign (maximal lead or lag) of the conic apex varied randomly with each recurrence. The slope of the phase gradient varied in a range corresponding to that of the conduction velocities of axons reported to extend parallel to the cortical surfaces. The durations and times of recurrence of the phase cones corresponded to those of the optimally classified spatial AM patterns. The interpretation is advanced that the phase cones are manifestations of state transitions in the mesoscopic dynamics of sensory cortices, by which the intermittent AM patterns are formed. The phase cones show that the gamma EEG spatial coherence is not due to volume conduction from a single deep-lying dipole generator, nor to activity at the site of the reference lead on monopolar recording. The random variation of the apical sign shows that gamma AM patterns are self-organized and are not imposed by thalamic pacemakers. The half-po...