Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integration. However, propagation delays are obligatory. Delays vary with the lengths and conduction velocities of the axons carrying the information, causing phase dispersion. In order to determine how synchrony is achieved despite dispersion, we recorded EEG signals from multiple electrode arrays on five cortical areas in cats and rabbits, that had been trained to discriminate visual or auditory conditioned stimuli. Analysis by time-lagged correlation, multiple correlation and PCA, showed that maximal correlation was at zero lag and averaged.7, indicating that 50 % of the power in the gamma range among the five areas was at zero lag irrespective of phase or frequency. There were no stimulus-related episodes of transiently increased phase locking among the areas, nor EEG "bursts " of transiently increased amplitude above the sustained level of synchrony. Three operations were identified to account for the sustained correlation. Cortices broadcast their outputs over divergent-convergent axonal

prediction is made for clinical testing that wave packets are precursor to states of awareness. They are not by themselves accessible to experience, as may be the macroscopic states initiated by global state transitions. Keywords: EEG; meaning; mesoscopic neurodynamics; phase cone; state transition; wave packet. 1. Introduction Brain systems operate on many levels of organization, each with its own scales of time and space. Dynamics applies to every level from the atomic to the molecular, and from macromolecular organelles to the neurons that incorporate them. In turn neurons form populations, these form the subassemblies in brains, and so on to embodied brains interacting intentionally with material, interpersonal, and social environments. Each level is macroscopic to that below it and microscopic to that above it. Among the most di#cult tasks scientists face are those of conceiving and describing the exchanges between levels, seeing that the measures of time 3 and distance ar

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suggesting separate functional roles of low- and highfrequency synchronization. Key words: # oscillation; synchronization; primary visual cortex; awake behaving cat; area 18; local field potential; multiunit activity; tracking task Introduction Much attention has been paid to the temporal structure of neuronal activity. A notable contribution was a result of the idea that neuronal synchronization serves as a temporal code in cortical information processing (Abeles, 1982; Singer, 1999). Numerous studies have demonstrated high-frequency oscillatory synchronizations of neuronal activity in various species, cortical systems, and experimental paradigms (Eckhorn, 1994; Konig and Engel, 1995; Singer and Gray, 1995; Steriade, 1999; Hatsopoulos et al., 2001; Varela et al., 2001). Because most studies fall back on the frequency classification traditionally used in EEG analysis, the described high-frequency synchronizations are often called #-synchronizations. However, several problems are as

Exemplar theories of categorization depend on similarity for explaining subjects’ ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction mechanisms and thus, seemingly, of generalization ability. Here, we use insights from machine learning to demonstrate that exemplar models can actually generalize very well. Kernel methods in machine learning are akin to exemplar models and are very successful in real-world applications. Their generalization performance depends crucially on the chosen similarity measure. Although similarity plays an important role in describing generalization behavior, it is not the only factor that controls generalization performance. In machine learning, kernel methods are often combined with regularization techniques in order to ensure good generalization. These same techniques are easily incorporated in exemplar models. We show that the generalized context model (Nosofsky, 1986) and ALCOVE (Kruschke, 1992) are closely related to a statistical model called kernel logistic regression. We argue that generalization is central to the enterprise of understanding categorization behavior, and we suggest some ways in which insights from machine learning can offer guidance.

Key words: beta activity β; brain-computer interface BCI; electrocorticogram ECoG; epsilon activity ε; gamma activity γ; Hilbert transform; local field potential LFP; multiple spike activity MSA; stationarity 30 July 2006 22 Pages 8,460 words 6 Figures 1 AppendixState variables for BCI, Part 2 2 Walter J Freeman 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

Neural computations are modelled in various ways, but still there is no clear understanding of how the brain performs its computational tasks. This paper presents new results about analysis of neural processes in terms of activity pattern computations. It is shown that it is possible to extract from high resolution EEG data a first order Markov approximation of a neural communication system employing pattern computations, which is significantly different from similar purely random systems. In our view this result shows that it is likely that neural activity patterns measurable at the macro-level by EEG are correlated with underlying neural computations.

The aim of this study was to measure and classify spatial patterns in sensory cortical EEGs relating to conditioned stimuli (CSs) in order to test the hypothesis, based on clinical reports, that cortical dynamics is not continuous but operates in steps that resemble frames in a cinema. Recent advances in the application of the Hilbert Transform to intracranial recordings of the EEG in animals have revealed markers for repetitive phase transitions in neocortex at frame rates in the theta band. The frames were sought in multichannel EEGs that had been recorded from 8x8 highdensity arrays that were fixed on primary sensory cortices of rabbits trained to discriminate visual, auditory or somatic conditioned stimuli with reinforcement (CS+) or without (CS−). Localization of frames in EEGs was by use of a new index, He(t), called “pragmatic information”. Each spatial pattern was represented by a feature vector from the 64 analytic amplitudes at a maximal value of He(t) from the Hilbert transform and expressed as a 64 ×1 feature vector specifying a point in 64-space. Classification with respect to CS+/ − was by calculation of Euclidean distances of points from centers of gravity of clusters after preprocessing by nonlinear mapping. Stable spatial patterns were found in the form of amplitude modulation (AM) of aperiodic waveforms that included all channels. The impact of a CS on a sensory neocortex reorganized background EEG into two types of sequential patterns of coordinated activity, initially local and modality-specific, later global. The initial stage of phase transitions required 3–7 ms. Large-scale cortical activity then reorganized itself repeatedly and reliably over relatively immense cortical distances within the cycle duration of the center frequency of oscillation. The size, texture, timing, and duration of the AM patterns support the hypothesis that these

The history of brain theory is described in terms of three kinds of theory of perception. The most widely used kind sees perception as dependent on passive inflow from the environment of information that is used to make and process representations of objects and events. A second kind views perception as an active search for information that is inherent in the environment and is extracted by tuned resonances in brain circuits. A third kind holds that perception works by the creation of information through chaotic dynamics by forming hypotheses about the environment, through which learning takes place. Experimental evidence for creative dynamics in brains is briefly sketched. The explanation is offered that brains, being finite systems, work this way in order to cope with the infinite complexity of the world. All that brains can know is the hypotheses they construct and the results of testing them by acting into the environment, and learning by assimilation from the sensory consequences of their actions. The process is described as intentionality. It works through the action-perception-assimilation cycle. The cost of this solution to the problem of infinite complexity by hypothesis testing is the progressive isolation of individuals, as they accumulate their unique experiences through which their personalities form. Socialization and the acquisition of shared knowledge requires the emergence of new personality structure by self-organization through chaotic dissolution of existing structure, as prelude to creation of new traits, habits and values. Dissolution works in a crisis situation by regression to earlier stages of development, from which a fresh start can be made. A state of malleability emerges in the depth of crisis, in which compassionate companions through lovin...

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 signif...