We study image segmentation on the basis of locally excitatory globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a potential for each oscillator so that only those oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions, without affecting those corresponding to major regions. We show analytically that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and illustrate network properties by computer simulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real graylevel images. A number of issues regarding biological plausibility and perceptual organization are discussed. We argue that LEGION provides a novel and effective framework for image segmentation and figure-ground segregation. DeLiang Wang and David Terman Image Segmentation 1.

inst log frequency, revealed 1/f spectra in both pre- and post-stimulus segments for CS- and CS+ stimuli. The y-intercepts and slopes for average PSDs were significantly different between pre- and post-stimulus segments, owing to the evoked potentials, but not between CS- and CS+ stimulus segments. 6.##### Spatiotemporal patterns were invariant over all frequency bins from 20-100 Hz in the 1/ f domain. Spatiotemporal patterns in the 2-20 Hz domain progressively differed from the invariant patterns with decreasing frequency. 7.##### In the spatial frequency domain, the logarithm of the average spatial FFT power spectra from pre- and post-stimulus neocortical EEG segments, when plotted against the log spatial frequency, fell monotonically from the maximum at the lowest spatial frequency, concavely curving to a linear 1/f spectral domain. This curve in the 1/f spectral domain extended from 0.133 - 0.880 cycles/mm in the PPC and from 0.095 - 0.624 cycles/mm in the neocortices. 8.#####

In this, the first of three papers, the nature of, and motivation for, neuronal transients is described in relation to characterizing brain dynamics. This paper deals with some basic aspects of neuronal dynamics, interactions, coupling and implicit neuronal codes. The second paper develops neuronal transients and nonlinear coupling in the context of dynamic instability and complexity, and suggests that instability or lability is necessary for adaptive self-organization. The final paper addresses the role of neuronal transients through information theory and the emergence of spatio-temporal receptive fields and functional specialization. By considering the brain as an ensemble of connected dynamic systems one can show that a sufficient description of neuronal dynamics comprises neuronal activity at a particular time and its recent history. This history constitutes a neuronal transient. As such, transients represent a fundamental metric of neuronal interactions and, implicitly, a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in distinct neuronal populations, re£ects the underlying coupling among populations. This coupling may be synchronous (and possibly oscillatory) or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays a role in functional integration that may be as important as synchronous interactions. The distinction between linear and nonlinear coupling has fundamental implications for the analysis and characterization of neuronal interactions, most of which are predicated on linear (synchronous) coupling (e.g. crosscorrelograms and coherence). Using neuromagnetic data it is shown that nonlinear (asynchronous) coupling is, in fact, more abundant and can be more significant than synchronous coupling.

Most cognitive functions are based on highly parallel and distributed information processing by the brain. A paradigmatic example is provided by the vertebrate visual system where numerous cortical areas have been described which analyse different types of visual information. At present, it is unclear how information can be integrated and how coherent representational states can be established in such distributed systems. We suggest that this so-called ‘binding problem ’ may be solved in the temporal domain. The hypothesis is that synchronization of neuronal discharges can serve for the integration of distributed neurons into cell assemblies and that this process may underlie the selection of perceptually and behaviourally relevant information. We review experimental results, mainly obtained in the visual system, which support this temporal binding hypothesis.

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

Objectives: The time series of single trial cortical evoked potentials typically have a random appearance, and their trial-to-trial variability is commonly explained by a model in which random ongoing background noise activity is linearly combined with a stereotyped evoked response. In this paper, we demonstrate that more realistic models, incorporating amplitude and latency variability of the evoked response itself, can explain statistical properties of cortical potentials that have often been attributed to stimulus-related changes in functional connectivity or other intrinsic neural parameters.

The electrical activity of neurons in brains fluctuates erratically both in terms of pulse trains of single neurons and the dendritic currents of populations of neurons. Obviously the neurons interact with one another in the production of intelligent behavior, so it is reasonable to expect to find evidence for varying degrees of synchronization of their pulse trains and dendritic currents in relation to behavior. However, synaptic communication between neurons depends on propagation of action potentials between neurons, often with appreciable distances between them, and the transmission delays are not compatible with synchronization in any simple way. Evidence is on hand showing that the principal form of synchrony is by establishment of a low degree of covariance among very large numbers of otherwise autonomous neurons, which allows for rapid state transitions of neural populations between successive chaotic basins of attraction along itinerant trajectories. The small fraction of covariant activity is extracted by spatial integration upon axonal transmission over divergent-convergent pathways, through which a remarkable improvement in signal:noise ratio is achieved. The raw traces of local activity show little evidence for synchrony, other than zero-lag correlation, which appears to be largely a statistical artifact. Brains rely less on tight phase-locking of small numbers of repetitively firing neurons and more on low degrees of cooperativity achieved by order parameters influencing very large numbers of neurons. Brains appear to be indifferent to and undisturbed by widely varying time and phase relations between individual neurons and even large semi-autonomous areas of cortex comprising their cooperative neural masses.

This paper presents the conjecture that functional integration may be mediated by the mutual induction and maintenance of stereotyped spatiotemporal patterns of activity (i.e., transients) in different neuronal populations. In contradistinction to temporal and rate coding models of neuronal interactions, transient coding considers that transactions among neuronal systems use transient dynamics that are distributed in a structured way over both space and time. In contrast to synchronization models, transient coding does not depend on interactions at the same frequencies, in different parts of the brain, but involves covariations among different frequencies and can therefore be considered a more general form of coding. Using an analysis of the correlations among the spectral density of neuromagnetic signals, measured at different cortical regions, this hypothesis was confirmed. For example high (gamma)-frequency oscillations in the prefrontal cortex are associated with low (20 Hz)-frequency oscillations in the parietal cortex. The results are consistent with transient coding and suggest that transient dynamics endure for at least 40–200 ms. Transient coding means that correlations (rate coding) and coherence (synchrony) are neither complete nor sufficient characterizations of neuronal interactions. Although temporal coding, rate coding, and synchrony are important aspects of neuronal interactions, the results speak to further integrative neuronal mechanisms of a more general nature.

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Zero--lag synchrony arises between two points on the cerebral cortex when these receive concurrent independent inputs and has generally been ascribed to nonlinear mechanisms. We report results obtained by Principal Component Analysis (PCA) applied to simulations of cerebral cortex which exhibit zero--lag synchrony and realistic spectral content, and show that synchrony can arise by distinct and separable linear and nonlinear mechanisms. For lower levels of cortical activation synchrony between the sites of input can be accounted for by the eigenmodes associated with the wave activity generated by the inputs. The first spatial eigenmode arises from even 2 Clare L. Chapman et al. components in the independent input signals and the second spatial eigenmode arises from odd components in the inputs. Together these account for most of the signal variance, while the predominance of the first mode over the second within the near--field of the inputs accounts for zero--lag synchrony in the ne...