been proposed to be a possible mechanism for the visual representation of objects. The present study examined the topography of gamma band spectral power and event-related potentials in human EEG associated with perceptual switching effected by rotating ambiguous (bistable) figures. Eleven healthy human subjects were presented two rotating bistable figures: first, a face figure that allowed perception of a sad or happy face depending on orientation and therefore caused a perceptual switch at defined points in time when rotated, and, second, a modified version of the Rubin vase, allowing perception as a vase or two faces whereby the switch was orientationindependent. Nonrotating figures served as further control stimuli. EEG was recorded using a high-density array with 128 electrodes. We found a negative event-related potential asso-Vision involves the perception of organized wholes in addition to

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Revealing functionally interacting remote brain areas is an active topic in neuroscience today. In particular the transient phase synchronization of the activity of large neural populations is one of the best candidates for an integration process, the mechanisms of which are still largely unknown. Indeed the dynamics of transient dynamics of phase locked neural patterns fits the time scale of our conscious experience. To investigate this neural behavior in human beings, magnetoencephalography (MEG) and electroencephalography (EEG) are the sole non-invasive tools available. They allow the measure of the electromagnetic field produced by electrical brain activity on the scalp surface. Using MEG/EEG inverse methods, it is possible to obtain a rough estimate of cortical currents generating the MEG/EEG data, and estimate phase locked patterns at a large spatial scale with a fine temporal resolution. In this paper we develop both the theoretical cognitive motivation for studying phase synchronization and the experimental methodology required to estimate such phase-locked patterns on real data. As an illustration we apply these methods to a MEG visual experiment.

in hybrid neuronal networks of the hippocampal formation. J Neurophysiol 93: 1197–1208, 2005. First published November 3, 2004; doi:10.1152/jn.00982.2004. Understanding the mechanistic bases of neuronal synchronization is a current challenge in quantitative neuroscience. We studied this problem in two putative cellular pacemakers of the mammalian hippocampal theta rhythm: glutamatergic stellate cells (SCs) of the medial entorhinal cortex and GABAergic oriens-lacunosum-moleculare (O-LM) interneurons of hippocampal region CA1. We used two experimental methods. First, we measured changes in spike timing induced by artificial synaptic inputs applied to individual neurons. We then measured responses of free-running hybrid neuronal networks, consisting of biological neurons coupled (via dynamic clamp) to biological or virtual counterparts. Results from the single-cell experiments predicted network behaviors well and are compatible with previous model-based predictions

The spiking activity of nearby cortical neurons is not independent. Numerous studies have explored the importance of this correlated responsivity for visual coding and perception, often by comparing the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. Pairwise responses typically provide slightly more information so that encoding is weakly synergistic. The simple comparison between pairwise and summed individual responses conflates several forms of correlation, however, making it impossible to judge the relative importance of synchronous spiking, basic tuning properties, and stimulus-independent and stimulus-dependent correlation. We have applied an information theoretic approach to this question, using the responses of pairs of neurons to drifting sinusoidal gratings of different directions and contrasts that have been recorded in the primary visual cortex of anesthetized macaque monkeys. Our approach allows us to break down the information provided by pairs of neurons into a number of components. This analysis reveals that, although synchrony is prevalent and informative, the additional information it provides frequently is offset by the redundancy arising from the similar tuning properties of the two cells. Thus coding is approximately independent with weak synergy or redundancy arising, depending on the similarity in tuning and the temporal precision of the analysis. We suggest that this would allow cortical circuits to enjoy the stability provided by having similarly tuned neurons without suffering the penalty of redundancy, because the associated information transmission deficit is compensated for by stimulus-dependent synchrony. Key words: cerebral cortex; extracellular recording; information theory; neuronal ensembles; redundancy; striate cortex; synchronization; synchrony; synergy

Abstract. Rhythmic, synchronous firing of groups of neurons is associated with behaviorally relevant states, and it is thus of interest to understand the mechanisms by which synchronization may be achieved. In hippocampal slice preparations, networks of excitatory and inhibitory neurons have been seen to synchronize when strong stimulation is applied at separated sites between which any coupling must be subject to a significant axonal delay. We extend previous work on synchronization in a model system based on the network architecture of these hippocampal slices. Our new analysis addresses the effects of heterogeneous populations and noisy inputs on the stability of synchronous solutions in the system. We find that, with experimentally motivated constraints on the coupling strength, sufficiently large heterogeneity in the input currents renders synchrony unstable. The addition of noise, however, restores stable near-synchrony. We analytically reduce the high-dimensional biophysical equations for the full population to a simple three-dimensional map, and show that the map’s stability properties correctly predict both the loss of stability and the restabilizing effect of the noise.

This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (,100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration.

Neuronal populations throughout the brain achieve levels of synchronous electrophysiological activity as a consequence of both normal brain function as well as during pathological states such as in epileptic seizures. Understanding this synchrony and being able to quantitatively assess the dynamics with which neuronal oscillators across the brain couple their activity is a critical component toward decoding such complex behavior. Commonly applied techniques to resolve relationships between oscillators typically make assumptions of linearity and stationarity that are not likely to be valid for complex neural signals. In this study, intracranial electroencephalographic activity was recorded bilaterally in both hippocampi and in anteromedial thalamus of rat under normal conditions and during hypersynchronous seizure activity induced by focal injection of the epileptogenic agent kainic acid. Nonlinear oscillators were first extracted using empirical mode decomposition. The technique of eigenvalue decomposition was used to assess global phase synchrony of the highest energy oscillators. The Hilbert analytical technique was then used to measure instantaneous phase synchrony of these oscillators as they evolved in time. The application of these analytical techniques provides a means for assessing how complex oscillatory behavior in the brain evolves and changes during both normal activity and as a consequence of diseased states without making restrictive and possibly erroneous assumptions of the linearity and stationarity of the underlying oscillatory activity.

journal homepage: www.elsevier.com/locate/clinph EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony

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