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Eye movement research has shown that attention shifts from the currently fixated location to the next before a saccade is executed. We investigated whether the cost of the attention shift depends on higher-order processing at the time of fixation, in particular on visual working memory load differences between fixations and refixations on task-relevant items. The attention shift is reflected in EEG activity in the saccade-related potential (SRP). In a free viewing task involving visual search and memorization of multiple targets amongst distractors, we compared the SRP in first fixations versus refixations on targets and distractors. The task-relevance of targets implies that more information will be loaded in memory (e.g. both identity and location) than for distractors (e.g. location only). First fixations will involve greater memory load than refixations, since first fixations involve loading of new items, while refixations involve rehearsal of previously visited items. The SRP in the interval preceding the saccade away from a target or distractor revealed that saccade preparation is affected by task-relevance and refixation behavior. For task-relevant items only, we found longer fixation duration and higher SRP amplitudes for first fixations than for refixations over the occipital region and the opposite effect over the frontal region. Our findings provide first neurophysiological evidence that working memory loading of task-relevant information at fixation affects saccade planning. [ABSTRACT FROM AUTHOR]

Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally- and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. Intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure. [ABSTRACT FROM AUTHOR]

Global workspace access is considered as a critical factor for the ability to report a visual target. A plausible candidate mechanism for global workspace access is coupling of slow and fast brain activity. We studied coupling in EEG data using cross-frequency phase-amplitude modulation measurement between delta/theta phases and beta/gamma amplitudes from two experimental sessions, held on different days, of a typical attentional blink (AB) task, implying conscious access to targets. As the AB effect improved with practice between sessions, theta-gamma and theta- beta coupling increased generically. Most importantly, practice effects observed in delta-gamma and delta-beta couplings were specific to performance on the AB task. In particular, delta-gamma coupling showed the largest increase in cases of correct target detection in the most challenging AB conditions. All these practice effects were observed in the right temporal region. Given that the delta band is the main frequency of the P3 ERP, which is a marker of global workspace activity for conscious access, and because the gamma band is involved in visual object processing, the current results substantiate the role of phase-amplitude modulation in conscious access to visual target representations. [ABSTRACT FROM AUTHOR]

Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation. [ABSTRACT FROM AUTHOR]

In free visual exploration, eye-movement is immediately followed by dynamic reconfiguration of brain functional connectivity. We studied the task-dependency of this process in a combined visual search-change detection experiment. Participants viewed two (nearly) same displays in succession. First time they had to find and remember multiple targets among distractors, so the ongoing task involved memory encoding. Second time they had to determine if a target had changed in orientation, so the ongoing task involved memory retrieval. From multichannel EEG recorded during 200 ms intervals time-locked to fixation onsets, we estimated the functional connectivity using a weighted phase lag index at the frequencies of theta, alpha, and beta bands, and derived global and local measures of the functional connectivity graphs. We found differences between both memory task conditions for several network measures, such as mean path length, radius, diameter, closeness and eccentricity, mainly in the alpha band. Both the local and the global measures indicated that encoding involved a more segregated mode of operation than retrieval. These differences arose immediately after fixation onset and persisted for the entire duration of the lambda complex, an evoked potential commonly associated with early visual perception. We concluded that encoding and retrieval differentially shape network configurations involved in early visual perception, affecting the way the visual input is processed at each fixation. These findings demonstrate that task requirements dynamically control the functional connectivity networks involved in early visual perception. [ABSTRACT FROM AUTHOR]

Individually, visual neurons are each selective for several aspects of stimulation, such as stimulus location, frequency content, and speed. Collectively, the neurons implement the visual system's preferential sensitivity to some stimuli over others, manifested in behavioral sensitivity functions. We ask how the individual neurons are coordinated to optimize visual sensitivity. We model synaptic plasticity in a generic neural circuit and find that stochastic changes in strengths of synaptic connections entail fluctuations in parameters of neural receptive fields. The fluctuations correlate with uncertainty of sensory measurement in individual neurons: The higher the uncertainty the larger the amplitude of fluctuation. We show that this simple relationship is sufficient for the stochastic fluctuations to steer sensitivities of neurons toward a characteristic distribution, from which follows a sensitivity function observed in human psychophysics and which is predicted by a theory of optimal allocation of receptive fields. The optimal allocation arises in our simulations without supervision or feedback about system performance and independently of coupling between neurons, making the system highly adaptive and sensitive to prevailing stimulation. [ABSTRACT FROM AUTHOR]

Abstract: Analyzing single trial brain activity remains a challenging problem in the neurosciences. We gain purchase on this problem by focusing on globally synchronous fields in within-trial evoked brain activity, rather than on localized peaks in the trial-averaged evoked response (ER). We analyzed data from three measurement modalities, each with different spatial resolutions: magnetoencephalogram (MEG), electroencephalogram (EEG) and electrocorticogram (ECoG). We first characterized the ER in terms of summation of phase and amplitude components over trials. Both contributed to the ER, as expected, but the ER topography was dominated by the phase component. This means the observed topography of cross-trial phase will not necessarily reflect the phase topography within trials. To assess the organization of within-trial phase, traveling wave (TW) components were quantified by computing the phase gradient. TWs were intermittent but ubiquitous in the within-trial evoked brain activity. At most task-relevant times and frequencies, the within-trial phase topography was described better by a TW than by the trial-average of phase. The trial-average of the TW components also reproduced the topography of the ER; we suggest that the ER topography arises, in large part, as an average over TW behaviors. These findings were consistent across the three measurement modalities. We conclude that, while phase is critical to understanding the topography of event-related activity, the preliminary step of collating cortical signals across trials can obscure the TW components in brain activity and lead to an underestimation of the coherent motion of cortical fields. [Copyright &y& Elsevier]