Functional and spatial rewiring principles jointly regulate context-sensitive computation.

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.

Planning to revisit: Neural activity in refixation precursors.

Eye tracking studies suggest that refixations-fixations to locations previously visited-serve to recover information lost or missed during earlier exploration of a visual scene. These studies have largely ignored the role of precursor fixations-previous fixations on locations the eyes return to later. We consider the possibility that preparations to return later are already made during precursor fixations. This process would mark precursor fixations as a special category of fixations, that is, distinct in neural activity from other fixation categories such as refixations and fixations to locations visited only once. To capture the neural signals associated with fixation categories, we analyzed electroencephalograms (EEGs) and eye movements recorded simultaneously in a free-viewing contour search task. We developed a methodological pipeline involving regression-based deconvolution modeling, allowing our analyses to account for overlapping EEG responses owing to the saccade sequence and other oculomotor covariates. We found that precursor fixations were preceded by the largest saccades among the fixation categories. Independent of the effect of saccade length, EEG amplitude was enhanced in precursor fixations compared with the other fixation categories 200 to 400 ms after fixation onsets, most noticeably over the occipital areas. We concluded that precursor fixations play a pivotal role in visual perception, marking the continuous occurrence of transitions between exploratory and exploitative modes of eye movement in natural viewing behavior.

Effects of Temporal Expectations on the Perception of Motion Gestalts.

Gestalt psychology has traditionally ignored the role of attention in perception, leading to the view that autonomous processes create perceptual configurations that are then attended. More recent research, however, has shown that spatial attention influences a form of Gestalt perception: the coherence of random-dot kinematograms (RDKs). Using ERPs, we investigated whether temporal expectations exert analogous attentional effects on the perception of coherence level in RDKs. Participants were presented fixed-length sequences of RDKs and reported the coherence level of a target RDK. The target was indicated immediately after its appearance by a postcue. Target expectancy increased as the sequence progressed until target presentation; afterward, remaining RDKs were perceived without target expectancy. Expectancy influenced the amplitudes of ERP components P1 and N2. Crucially, expectancy interacted with coherence level at N2, but not at P1. Specifically, P1 amplitudes decreased linearly as a function of RDK coherence irrespective of expectancy, whereas N2 exhibited a quadratic dependence on coherence: larger amplitudes for RDKs with intermediate coherence levels, and only when they were expected. These results suggest that expectancy at early processing stages is an unspecific, general readiness for perception. At later stages, expectancy becomes stimulus specific and nonlinearly related to Gestalt coherence.

Large-scale cortical travelling waves predict localized future cortical signals.

Predicting future brain signal is highly sought-after, yet difficult to achieve. To predict the future phase of cortical activity at localized ECoG and MEG recording sites, we exploit its predominant, large-scale, spatiotemporal dynamics. The dynamics are extracted from the brain signal through Fourier analysis and principal components analysis (PCA) only, and cast in a data model that predicts future signal at each site and frequency of interest. The dominant eigenvectors of the PCA that map the large-scale patterns of past cortical phase to future ones take the form of smoothly propagating waves over the entire measurement array. In ECoG data from 3 subjects and MEG data from 20 subjects collected during a self-initiated motor task, mean phase prediction errors were as low as 0.5 radians at local sites, surpassing state-of-the-art methods of within-time-series or event-related models. Prediction accuracy was highest in delta to beta bands, depending on the subject, was more accurate during episodes of high global power, but was not strongly dependent on the time-course of the task. Prediction results did not require past data from the to-be-predicted site. Rather, best accuracy depended on the availability in the model of long wavelength information. The utility of large-scale, low spatial frequency traveling waves in predicting future phase activity at local sites allows estimation of the error introduced by failing to account for irreducible trajectories in the activity dynamics.

Interhemispheric Synchrony of Spontaneous Cortical States at the Cortical Column Level.

In cat early visual cortex, neural activity patterns resembling evoked orientation maps emerge spontaneously under anesthesia. To test if such patterns are synchronized between hemispheres, we performed bilateral imaging in anesthetized cats using a new improved voltage-sensitive dye. We observed map-like activity patterns spanning early visual cortex in both hemispheres simultaneously. Patterns virtually identical to maps associated with the cardinal and oblique orientations emerged as leading principal components of the spontaneous fluctuations, and the strength of transient orientation states was correlated with their duration, providing evidence that these maps are transiently attracting states. A neural mass model we developed reproduced the dynamics of both smooth and abrupt orientation state transitions observed experimentally. The model suggests that map-like activity arises from slow modulations in spontaneous firing in conjunction with interplay between excitation and inhibition. Our results highlight the efficiency and functional precision of interhemispheric connectivity.

Critical dynamics, anesthesia and information integration: Lessons from multi-scale criticality analysis of voltage imaging data.

Critical dynamics are thought to play an important role in neuronal information-processing: near critical networks exhibit neuronal avalanches, cascades of spatiotemporal activity that are scale-free, and are considered to enhance information capacity and transfer. However, the exact relationship between criticality, awareness, and information integration remains unclear. To characterize this relationship, we applied multi-scale avalanche analysis to voltage-sensitive dye imaging data collected from animals of various species under different anesthetics. We found that anesthesia systematically varied the scaling behavior of neural dynamics, a change that was mirrored in reduced neural complexity. These findings were corroborated by applying the same analyses to a biophysically realistic cortical network model, in which multi-scale criticality measures were associated with network properties and the capacity for information integration. Our results imply that multi-scale criticality measures are potential biomarkers for assessing the level of consciousness.

Refixation control in free viewing: a specialized mechanism divulged by eye-movement-related brain activity.

In free viewing, the eyes return to previously visited locations rather frequently, even though the attentional and memory-related processes controlling eye-movement show a strong antirefixation bias. To overcome this bias, a special refixation triggering mechanism may have to be recruited. We probed the neural evidence for such a mechanism by combining eye tracking with EEG recording. A distinctive signal associated with refixation planning was observed in the EEG during the presaccadic interval: the presaccadic potential was reduced in amplitude before a refixation compared with normal fixations. The result offers direct evidence for a special refixation mechanism that operates in the saccade planning stage of eye movement control. Once the eyes have landed on the revisited location, acquisition of visual information proceeds indistinguishably from ordinary fixations. NEW & NOTEWORTHY A substantial proportion of eye fixations in human natural viewing behavior are revisits of recently visited locations, i.e., refixations. Our recently developed methods enabled us to study refixations in a free viewing visual search task, using combined eye movement and EEG recording. We identified in the EEG a distinctive refixation-related signal, signifying a control mechanism specific to refixations as opposed to ordinary eye fixations.

Large-Scale Traveling Waves in EEG Activity Following Eye Movement.

In spontaneous, stimulus-evoked, and eye-movement evoked EEG, the oscillatory signal shows large scale, dynamically organized patterns of phase. We investigated eye-movement evoked patterns in free-viewing conditions. Participants viewed photographs of natural scenes in anticipation of a memory test. From 200 ms intervals following saccades, we estimated the EEG phase gradient over the entire scalp, and the wave activity, i.e. the goodness of fit of a wave model involving a phase gradient assumed to be smooth over the scalp. In frequencies centered at 6.5 Hz, large-scale phase organization occurred, peaking around 70 ms after fixation onset and taking the form of a traveling wave. According to the wave gradient, most of the times the wave spreads from the posterior-inferior to anterior-superior direction. In these directions, the gradients depended on the size and direction of the saccade. Wave propagation velocity decreased in the course of the fixation, particularly in the interval from 50 to 150 ms after fixation onset. This interval corresponds to the fixation-related lambda activity, which reflects early perceptual processes following fixation onset. We conclude that lambda activity has a prominent traveling wave component. This component consists of a short-term whole-head phase pattern of specific direction and velocity, which may reflect feedforward propagation of visual information at fixation.

Task modulates functional connectivity networks in free viewing behavior.

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.

Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains.

As a candidate mechanism of neural representation, large numbers of synfire chains can efficiently be embedded in a balanced recurrent cortical network model. Here we study a model in which multiple synfire chains of variable strength are randomly coupled together to form a recurrent system. The system can be implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model. The latter has binary-state pools as basic units but is otherwise isomorphic to the large-scale model, and provides an efficient tool for studying its behavior. Both the large-scale system and its reduced counterpart are able to sustain ongoing endogenous activity in the form of synfire waves, the proliferation of which is regulated by negative feedback caused by collateral noise. Within this equilibrium, diverse repertoires of ongoing activity are observed, including meta-stability and multiple steady states. These states arise in concert with an effective connectivity structure (ECS). The ECS admits a family of effective connectivity graphs (ECGs), parametrized by the mean global activity level. Of these graphs, the strongly connected components and their associated out-components account to a large extent for the observed steady states of the system. These results imply a notion of dynamic effective connectivity as governing neural computation with synfire chains, and related forms of cortical circuitry with complex topologies.

Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities.

Co-registration of EEG and eye movement has promise for investigating perceptual processes in free viewing conditions, provided certain methodological challenges can be addressed. Most of these arise from the self-paced character of eye movements in free viewing conditions. Successive eye movements occur within short time intervals. Their evoked activity is likely to distort the EEG signal during fixation. Due to the non-uniform distribution of fixation durations, these distortions are systematic, survive across-trials averaging, and can become a source of confounding. We illustrate this problem with effects of sequential eye movements on the evoked potentials and time-frequency components of EEG and propose a solution based on matching of eye movement characteristics between experimental conditions. The proposal leads to a discussion of which eye movement characteristics are to be matched, depending on the EEG activity of interest. We also compare segmentation of EEG into saccade-related epochs relative to saccade and fixation onsets and discuss the problem of baseline selection and its solution. Further recommendations are given for implementing EEG-eye movement co-registration in free viewing conditions. By resolving some of the methodological problems involved, we aim to facilitate the transition from the traditional stimulus-response paradigm to the study of visual perception in more naturalistic conditions.

A neural mass model of phase-amplitude coupling.

Brain activity shows phase-amplitude coupling between its slow and fast oscillatory components. We study phase-amplitude coupling as recorded at individual sites, using a modified version of the well-known Wendling neural mass model. To the population of fast inhibitory interneurons of this model, we added external modulatory input and dynamic self-feedback. These two modifications together are sufficient to let the inhibitory population serve as a limit-cycle oscillator, with frequency characteristics comparable to the beta and gamma bands. The frequency and power of these oscillations can be tuned through the time constant of the dynamic and modulatory input. Alpha band activity is generated, as is usual in such models, as a result of interactions of pyramidal neurons and a population of slow inhibitory interneurons. The slow inhibitory population activity directly influences the fast oscillations via the synaptic gain between slow and fast inhibitory populations. As a result, the amplitude envelope of the fast oscillation is coupled to the phase of the slow activity; this result is consistent with the notion that phase-amplitude coupling is effectuated by interactions between inhibitory interneurons.

Intermittent regime of brain activity at the early, bias-guided stage of perceptual learning.

Perceptual learning improves visual performance. Among the plausible mechanisms of learning, reduction of perceptual bias has been studied the least. Perceptual bias may compensate for lack of stimulus information, but excessive reliance on bias diminishes visual discriminability. We investigated the time course of bias in a perceptual grouping task and studied the associated cortical dynamics in spontaneous and evoked EEG. Participants reported the perceived orientation of dot groupings in ambiguous dot lattices. Performance improved over a 1-hr period as indicated by the proportion of trials in which participants preferred dot groupings favored by dot proximity. The proximity-based responses were compromised by perceptual bias: Vertical groupings were sometimes preferred to horizontal ones, independent of dot proximity. In the evoked EEG activity, greater amplitude of the N1 component for horizontal than vertical responses indicated that the bias was most prominent in conditions of reduced visual discriminability. The prominence of bias decreased in the course of the experiment. Although the bias was still prominent, prestimulus activity was characterized by an intermittent regime of alternating modes of low and high alpha power. Responses were more biased in the former mode, indicating that perceptual bias was deployed actively to compensate for stimulus uncertainty. Thus, early stages of perceptual learning were characterized by episodes of greater reliance on prior visual preferences, alternating with episodes of receptivity to stimulus information. In the course of learning, the former episodes disappeared, and biases reappeared only infrequently.

Is it really search or just matching? The influence of Goodness, number of stimuli and presentation sequence in same-different tasks.

The Goodness of Garner dot patterns has been shown to influence same-different response times in a specific way, which has led to the formulation of a memory search model of pattern comparison. In this model, the space of possible variations of each pattern is searched separately for each pattern in the comparison, resulting in faster response times for patterns that have fewer alternatives. Compared to an alternative explanation based on stimulus encoding plus mental rotation, however, the existing data strongly favor this explanation. To obtain a more constraining set of data to distinguish between the two possible accounts, we extended the original paradigm to a situation in which participants needed to compare three, rather than two patterns and varied the way the stimuli were presented (simultaneously or sequentially). Our findings suggest that neither the memory search nor the encoding plus mental rotation model provides a complete description of the data, and that the effects of Goodness must be understood in a combination of both mechanisms, or in terms of cascades processing.

Orientation perception anisotropies indicate functional segregation within the color system.

When stimuli are luminance-defined, the visual system is known to prefer those that are radially oriented with respect to the point of fixation over tangentially oriented ones (the radial bias effect). In two contrast detection experiments and an orientation discrimination experiment, we investigated whether the radial bias effect also exists for chromatic stimuli. The contrast detection experiments revealed the radial bias effect to be color-specific; the effect was present for isoluminant red-green stimuli but absent or in the opposite direction for blue-yellow stimuli with, respectively, low (0.4 c/°) and medium (1 c/°) spatial frequencies. In agreement with previous results, we also found distinct sensitivity distributions for red-green and blue-yellow signals as a function of eccentricity. The results, thus, demonstrate a functional segregation between red-green and blue-yellow signals not only in local but also in nonlocal signal processing.

Donders is dead: cortical traveling waves and the limits of mental chronometry in cognitive neuroscience.

An assumption nearly all researchers in cognitive neuroscience tacitly adhere to is that of space-time separability. Historically, it forms the basis of Donders' difference method, and to date, it underwrites all difference imaging and trial-averaging of cortical activity, including the customary techniques for analyzing fMRI and EEG/MEG data. We describe the assumption and how it licenses common methods in cognitive neuroscience; in particular, we show how it plays out in signal differencing and averaging, and how it misleads us into seeing the brain as a set of static activity sources. In fact, rather than being static, the domains of cortical activity change from moment to moment: Recent research has suggested the importance of traveling waves of activation in the cortex. Traveling waves have been described at a range of different spatial scales in the cortex; they explain a large proportion of the variance in phase measurements of EEG, MEG and ECoG, and are important for understanding cortical function. Critically, traveling waves are not space-time separable. Their prominence suggests that the correct frame of reference for analyzing cortical activity is the dynamical trajectory of the system, rather than the time and space coordinates of measurements. We illustrate what the failure of space-time separability implies for cortical activation, and what consequences this should have for cognitive neuroscience.

Efficiency of conscious access improves with coupling of slow and fast neural oscillations.

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.

Refixation behavior in naturalistic viewing: Methods, mechanisms, and neural correlates.

When freely viewing a scene, the eyes often return to previously visited locations. By tracking eye movements and coregistering eye movements and EEG, such refixations are shown to have multiple roles: repairing insufficient encoding from precursor fixations, supporting ongoing viewing by resampling relevant locations prioritized by precursor fixations, and aiding the construction of memory representations. All these functions of refixation behavior are understood to be underpinned by three oculomotor and cognitive systems and their associated brain structures. First, immediate saccade planning prior to refixations involves attentional selection of candidate locations to revisit. This process is likely supported by the dorsal attentional network. Second, visual working memory, involved in maintaining task-related information, is likely supported by the visual cortex. Third, higher-order relevance of scene locations, which depends on general knowledge and understanding of scene meaning, is likely supported by the hippocampal memory system. Working together, these structures bring about viewing behavior that balances exploring previously unvisited areas of a scene with exploiting visited areas through refixations.

Traveling waves and trial averaging: the nature of single-trial and averaged brain responses in large-scale cortical signals.

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.

High-capacity embedding of synfire chains in a cortical network model.

Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.