Domains of cooperative neural activity called 'wave packets' have been discovered in the visual, auditory, and somatomotor cortices of rabbits that were trained to discriminate conditioned stimuli in these modalities. Each domain forms by a first order state transition, which strongly resembles a phase transition from vapor to liquid. In this view, raw sense data injected into cortex by sensory axons drive cortical action potentials in swarms like water molecules in steam. The increased activity destabilizes the cortex. Within 3 to 7 milliseconds of transition onset, the activity binds together into a state resembling a scintillating rain drop, which lasts ~ 80 to 100 milliseconds, then dissolves. Wave packets form at rates of 2 to 7/second in all sensory areas, overlapping in space and time. Results of sensory information processing are seen in spatial patterns of amplitude modulation (AM) of wave packets with carrier waves in the gamma range (20 to 80 Hz in rabbits). The AM patterns correspond to categories of CSs that the rabbits can discriminate. The patterns are found in electroencephalographic (EEG) potentials generated by dendrites and recorded with high-density electrode arrays. The state transitions by which AM patterns form are manifested in the spatial pattern of phase modulation (PM), which have the radial symmetry of a cone. The apex of a PM cone marks the site of nucleation of an AM pattern. The phase gradient gives a soft boundary condition, where the axonal delay in spread gives sufficient phase dispersion to reach the half-power level. The size of the wave packets (10 to 30 mm in diameter in rabbits) is determined largely by the conduction velocities of intracortical axons through which the neural cooperation is maintained. The findings show that significant cortical activity takes the form of mesoscopic interactions of millions of neurons in broad areas of cortex, which are more clearly detected in graded dendritic potentials than in action potentials. The distinction is analogous to the difference between statistical mechanical and thermodynamic descriptions of particle behavior. Both types of neural activity show spatial and temporal discontinuities but at distinctive scales of microns and msec versus mm and tenths of a second. The aim of measurement here is to establish the wave packet as the information carrier at the mesoscopic level in brain dynamics, comparable to the role of the action potential as the information carrier at the microscopic level in neuron dynamics.

The hypothesis is proposed that the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action–perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain.

Perception is an intentional action through space in time by which the finite brain explores the infinite world. By acting, the brain thrusts its body into the future spacetime of the world while predicting the sensory consequences. Through perceiving its actions and their results, it remembers its predictions, its actions, and their consequences. To perform these operations the brain, through chaotic dynamics, constructs and uses finite perceptual matrices of spacetime and infers causation. Perceived time differs from world time in ways that are determined by the neural mechanisms of intentionality. In particular, perception of the self in action, through the mechanism of preafference, gives structure and content to the concepts of continuity, contiguity, duration, temporal order, cause, and effect. Perceptual scales are expanded beyond kinesthesia by conversion of time into space, such as by clocks and calendars. Remembered time differs from perceived time in being dependent on awareness, which makes it episodic, fragmentary, and subject to large variations in rates of time lapse in the flow of meanings. The attribution of causal agency to objects and events in the world results from anthropomorphization in accordance with the neural mechanisms of the internal perception of intentional action.

When a person glimpses the face of a famous actor, sniffs a favorite food or hears the voice of a friend, recognition is instant. Within a fraction of a second after the eyes, nose, ears, tongue or skin is stimulated, one knows the object is familiar and whether it is desirable or dangerous. How does such recognition, which psychologists call preattentive perception, happen so accurately and quickly, even when the stimuli are complex and the context in which they arise varies?

Much is known about the way the cerebral cortex, the outer rind of the brain, initially analyzes sensory messages. Yet investigations are only now beginning to suggest how the brain moves beyond the mere extraction of features-how it combines sensory messages with past experience and with expectation to identify both the stimulus and its particular meaning to the individual.

My own group's studies, carried out over more than 30 years at the University of California at Berkeley, suggest that perception cannot be understood solely by examining properties of individual neurons, a microscopic approach that currently dominates neuroscience research. We have found that perception depends on the simultaneous, cooperative activity of millions of neurons spread throughout expanses of the cortex. Such global activity can be identified, measured and explained only if one adopts a macroscopic view alongside the microscopic one.

There is an analogy to this approach in music. To grasp the beauty in a choral piece, it is not enough to listen to the individual singers sequentially. One must hear the performers together, as they modulate their voices and timing in response to one another.

Our studies have led us as well to the discovery in the brain of chaos- complex behavior that seems random but actually has some hidden order. The chaos is evident in the tendency of vast collections of neurons to shift abruptly and simultaneously from one complex activity pattern to another in response to the smallest of inputs.

This changeability is a prime characteristic of many chaotic systems. It is not harmful in the brain. In fact, we propose it is the very property that makes perception possible. We also speculate that chaos underlies the ability of the brain to respond flexibly to the outside world and to generate novel activity patterns, including those that are experienced as fresh ideas. 

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, élan 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 self-determining.

Brains and computers are both dynamical systems that manipulate symbols, but they differ fundamentally in their architectures and operations. Human brains do mathematics; computers do not. Computers manipulate symbols that humans put into them without grounding them in what they represent. Human brains intentionally direct the body to make symbols, and they use the symbols to represent internal states. The symbols are outside the brain. Inside brains the construction is effected by spatiotemporal patterns of neural activity that are operators, not symbols. The operations include formation of sequences of neural activity patterns that we observe by their electrical signs. The process is by neurodynamics, not by logical rule-driven symbol manipulation. The aim of simulating human natural computing should be to simulate the operators. In its simplest form natural computing serves for communication of meaning. Neural operators implement non-symbolic communication of internal states by all mammals, including humans, through intentional actions. The neural operators that implement symbol formation must differ, but how is unknown, so we cannot yet simulate human natural computing. Here I propose that symbol-making operators evolved from neural mechanisms of intentional action by modification of non-symbolic operators. Both kinds of operators can be investigated by their signs of neuroelectric activity. I propose that the postulated differences should be sought by classification of the spatial textures of the signs in EEG recorded from the scalp overlying those cortical structures unique to humans in the brain that I designate as koniocortex, while the subjects are engaged in elementary arithmetic operations.

The aim of this tutorial is to document a novel approach to brain function, in which the key to understanding is the capacity of brains for self-organization. The property that distinguishes animals from plants is the capacity for directed movement through the environment, which requires an organ capable of organizing information about the environment and predicting the consequences of self-initiated actions. The operations of predicting, planning acting, detecting, and learning comprise the process of intentionality by which brains construct meaning. The currency of brains is primarily meaning and only secondarily information. The information processing metaphor has dominated neurocognitive research for half a century. Brain certainly process information for input and output. They pre-process sensory stimuli before constructing meaning, and they post-process cognitive read-out to control appropriate action and express meaning. Neurobiologists have thoroughly documented sensory information processing bottom-up, and neuropsychologists have analyzed the later stages of cognition top-down, as they are expressed in behavior. However, a grasp of the intervening process of perception in which meaning forms requires detailed analysis and modeling of neural activity that is observed in brains during meaningful behavior of humans and other animals. Unlike computers brains function hierarchically. Sensory and motor information is inferred from pulses of microscopic axons. Meaning is inferred from local mean fields of dendrites in mesoscopic and macroscopic populations. This tutorial is aimed to introduce engineers to an experimental basis for a theory of meaning, in terms of the nonlinear dynamics of the mass actions of large neural populations that construct meaning. The focus is on the higher frequency ranges of cortical oscillations. Part 1 introduces background on information, meaning and oscillatory activity (EEG). Part 2 details the properties of wave packets. Part 3 describes the covariance structure of the oscillations. Part 4 addresses the amplitude modulations, and Part 5 deals with the phase modulations. The significance of a theory of meaning lies in applications using population neurodynamics, to open new approaches for treatment of clinical brain disorders, and to devise new machines with capacities for autonomy and intelligence that might approach those of simpler free-living animals.

multiple spike activity (MSA), mesoscopic using local field potentials (LFP) and electrocorticograms (ECoG), and macroscopic using electroencephalograms (EEG) and brain imaging. Transactions between levels occur in all areas of cortex, upwardly by integration (abstraction, generalization) and downwardly by differentiation (speciation). The levels are joined by circular causality: microscopic activity upwardly creates mesoscopic order parameters, which downwardly constrain the microscopic activity that creates them. Integration dominates in sensory cortices. Microscopic activity evoked by receptor input in sensation induces emergence of mesoscopic activity in perception, followed by integration of perceptual activity into macroscopic activity in concept formation. The reverse process dominates in motor cortices, where the macroscopic activity embodying the concepts supports predictions of future states as goals. These macroscopic states are conceived to order mesoscopic activity in patterns that constitute plans for actions to achieve the goals. These planning patterns are conceived to provide frames in which the microscopic activity evolves in trajectories that adapted to the immediate environmental conditions detected by new stimuli. This circular sequence forms the action-perception cycle. Its upward limb is understood through correlation of sensory cortical activity with behavior. Now brain-machine interfaces (BMI) offer a means to understand the downward sequence through correlation of behavior with motor cortical activity, beginning with macroscopic goal states and concluding with recording of microscopic MSA trajectories that operate neuroprostheses. Part 1 develops a hypothesis that describes qualitatively the neurodynamics that supports the actionperception cycle and derivative reflex arc. Part 2 describes episodic, “cinematographic” spatial pattern formation and predicts some properties of the macroscopic and mesoscopic frames by which the embedded trajectories of the microscopic activity of cortical sensorimotor neurons might be organized and controlled.