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Cortical dynamics and oscillations: What controls what we see?

Cees van Leeuwen Laboratory for Perceptual Dynamics

University of Leuven

To appear in:

Oxford Handbook of Perceptual Organization

Oxford University Press

Edited by Johan Wagemans

Acknowledgments

The author is supported by an Odysseus research grant from the Flemish Organization for Science

(FWO) and wishes to thank Lee de-Wit, Michael Herzog, and Naoki Kogo for useful comments.

1. The visual system as distributed and parallel

In the previous chapter, I sketched the visual system as a complex network in which lateral and large-

scale within-area as well as between-areas feedback loops connect brain regions and circuits. The

system reaches integral representation through recurrent activation cycles operating at multiple

scales within this network. These cycles work in parallel (for instance between ventral and dorsal

stream), but where the onset of their evoked activity differs, they may operate as cascaded stages. In

all these stages, activity spreading within and between regions makes visual representations

dynamically dependent on their context; from contour patterns in early visual perception, to episodic

events in the later stages. In perceptual organization, it is clearly in evidence that these different

processes jointly contribute to what we perceive.

For instance, which part of an image we see as figure and which as ground, depends on traditional

Gestalt factors such as good continuation, parallelism, convexity, and symmetry (Rubin, 1921). These

are likely to belong to the “what” system in perceptual organization, in other words: the ventral

stream. But contrary to the notion that visual object information is exclusively processed in the

ventral stream, object representations exist in parallel in both streams (Konen & Kastner, 2008).

Foreground depends also on the dorsal stream, or the “where system”: perceivers tend to assign the

role of figure to surfaces in the lower part of the visual field (Vecera, Vogel, & Woodman, 2002) and

to surfaces with a wide base and a narrow top (Hulleman & Humphreys, 2004). Also semantic or

episodic factors come into play; a silhouette of familiar shape is more likely to be considered figure

than the same shape upside-down (Peterson & Skow-Grant, 2003). We may conclude that

representation in the visual system is distributed; different parts of the system represent visual

information in different, and potentially contradictory respects.

Classical recurrent neural networks can represent visual information in a distributed manner. But

they can process only one distributed pattern at a time, since pattern components are identified

based on simultaneous activity (von der Malsburg, 1981). Perceptual representations are distributed

in a more radical sense than this; visual input is intrinsically ambiguous and, because of this, it would

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be important for perceptual organization not to settle on one single representation, but offer a range

of options. Partially occluded objects illustrate this. Any such object can be completed in an indefinite

number of ways, in principle, and the task for the visual system is to consider a range of plausible

ones (Buffart, Leeuwenberg, & Restle 1983; van Lier, van der Helm, & Leeuwenberg, 1995). We

maintain such alternative representations, at least for the time needed for the visual system to settle

on one of these alternatives. Among the possibilities, there is likely to be a representation as of the

pattern without occlusion, i.e. as a mosaic. For instance, consider Figure 1.

Figure 1. Four occluded figures (right side of each panel) and their possible local, global

and mosaic interpretations (Figures in the top left panel after van Lier et al., 1995; the

others after Plomp, Nakatani, Bonnardel et al., 2004).

In line with the hierarchical account of perception described in the previous chapter, Sekuler and

Palmer (1992) proposed that the mosaic interpretation is actually computed first. In behavioral

studies, priming with short stimulus onset asynchrony (SOAs; the latency between the onset of the

prime and the target stimulus) facilitated the mosaic figure, whereas long SOAs facilitate the

occlusion interpretation. More recent studies of facilitation by the prime using MEG measurement

showed no such processing order. Indeed, in the period of 50-300 ms after stimulus onset, priming

facilitated both mosaic and different occluded interpretations. This effect was found in

occipitotemporal areas, in particular in the right fusiform cortex, which therefore acts as a hub for

different occluded figure interpretations in this stage of perception (Liu, Plomp, van Leeuwen et al.,

2006). Thus, for at least this time period, this part of the visual system keeps active multiple

alternative representations of a pattern, including the mosaic, and thus leaves the choice between

several alternative options open. Surrounding (Bruno et al., 1997; Dinnerstein & Wertheimer, 1957;

Rauschenberger et al., 2004) or preceding context, including primes (Plomp et al., 2006; Plomp & van

Leeuwen, 2006) can bias the choice between these interpretations during this interval. Occlusion,

therefore, provides a key example of the visual system keeping multiple representations of the same

object active at the same time.

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Since the visual system compiles and maintains different representations in parallel, even of the

same pattern, neural networks, which allow one pattern to be processed at a time, will not do. Since

each of these representations is determined, to various extents, by shared information from “what”

and “where” visual functions, as well as by episodic and semantic memory, a study of isolated areas,

regions or activity sources alone, will not do. We need to consider the co-existence of these

representations, their interaction, and the mechanisms with which these interactions are

effectuated.

2. Distributed systems and connectivity issues

This section will be concerned with the question: what kind of architecture permits the visual system

to consist of different subsystems, and yet have such a rich connectivity that enables them to share

their information? We need to consider the connectivity to link circuits within brain areas, as well as

large-scale brain networks. These are collections of interconnected brain areas at distances larger

than 2 cm, which involve cortical areas, subcortical structures and the neurons that control muscles

and glands (Bressler & Menon, 2010). The brain has a complex network structure that is known as a

“modular small-world” structure (He et al., 2007; Iturria-Medina et al., 2007; Sporns & Zwi, 2004).

“Small world” means that the network consists of a number of densely interconnected clusters (like

regular networks) with sparse connections in-between, which connect units in an optimally efficient

ways (like random networks) between clusters. “Modular” means that the clusters are connected via

hubs.

Processing in domain-specific subsystems (local processes) and processing with access to widely

distributed domains of semantic and episodic information (global processes) might seem to require

two vastly different kinds of network architectures. However, small-world networks do enable us to

combine both types of processes, as their architecture is both locally clustered and globally

connected (Watts & Strogatz, 1998). In fact, small-world structure is demonstrably the best way to

organize how large arrays of dynamical units interact (Latora & Marchiori, 2001). The architecture is

efficient enough to enable global processing, without the need for the output of local processes to

converge on a single area. Areas in which information from different local processes converge could

therefore have a different function than previously considered. Rather than the seat of higher, global

processing, they are the hubs, the relay stations that globally shared information passes through.

How did the brain become such an optimal structure? It cannot possibly be prescribed by the genes,

which simply do not contain enough information to determine the layout of all possible connections

between a billion neurons. This suggests that part of the problem is solved by self-organization.

Brain structure evolves through gradual rewiring of synaptic connections, in which, along with

processes such as maturation, the activity patterns within the network play a constitutive role. Early

on in the visual system and throughout the immature brain, large-scale burst and wave-like pattern

dynamics (Nakatani, Khalilov, Gong et al., 2003) dominate spontaneous activity. In a series of papers,

Gong & van Leeuwen, 2003; 2004; Kwok et al., 2007; and Rubinov et al., 2009a; van den Berg et al.,

2012; van den Berg & van Leeuwen, 2004) have shown in a simplified, theoretical model, how such

spontaneous activity shapes and maintains, in principle, the essential properties of a global brain

network’s optimal state (see Figure 2 for illustration).

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Figure 2. From van den Berg et al., 2012. Adaptive rewiring leads from an initial random

network (left), to modular small-world structure (right) in small iterative steps. Coupled

chaotic oscillators at the nodes synchronize and desynchronize their activity

spontaneously. Over time, pairs of synchronized units that are not connected receive a

connection, and where connected units are not synchronized, connections are removed.

During this process, a modular, small-world structure emerges from an initially random

configuration.

Evolution of small-world structure may be disrupted when connections become too sparse. This may

be what we are observing in initial stages of schizophrenia. Failure to maintain small-world structure

with increasing sparseness means that the network tends, to some degree, to resemble a random

structure (van den Berg et al, 2012). In the real brain, this may have dramatic consequences. Because

of the randomness, the system will have difficulties tracing the origin of signals in the brain, which

means that the observer cannot distinguish perception from hallucination. In random networks,

global connections are relatively predominant (Rubinov et al., 2009b; van den Berg et al. 2012). The

consequence is that patients who suffer connectivity loss, e.g. beginning schizophrenics, will have

difficulty in directing their attention towards local structures (Bellgrove, Vance, & Bradshaw, 2003;

Coleman, Cestnick, Krastoshevsky, et al., 2009).

Sleep deprivation is another way in which excess randomness is introduced to the network. Our

wakeful experiences continually modifies brain connectivity, in a manner that can be considered

random as far large-scale structure is concerned. One of the functions of sleep, therefore, is to

restore the small-world network structure (Koenis et al., 2011). Indeed, whereas in (REM)sleep

deprivation selectively only affects basic visual discrimination tasks (Karni, Tanne, Rubinstein, et al.,

1994), general sleep deprivation (but not, for instance, physical exercise) leads to weakened

perceptual organization performance on the hidden figures task (Lybrand, Andrew, & Ross, 1954). In

non-REM sleep, we observe wave-like activity similar to the immature brain, and we may speculate

on its role in restoring the network connectivity structure.

I mentioned the importance of brain connectivity and its pathologies. But the structural connectivity

is only relevant, insofar it leads to co-activation of brain circuits and regions. Studies using fMRI have

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shown large-scale, distributed patterns of spontaneous activity in the brain (Cordes et al., 2000;

Lowe, Mock, & Sorenson, 1998). These patterns reflect brain connectivity structure (Achard et al.,

2006; Bassett et al., 2006; Stam, 2004). Correlated patterns in spontaneous fMRI activity predict

which brain regions are likely to respond together during a task (Fox & Raichle, 2007). Pre-stimulus

activity could therefore be a way of anticipating the incoming sensory information by dynamically

established coordination of active circuits (Hesselmann et al., 2008). These authors briefly presented

Rubin’s ambiguous face/vase stimuli and observed that when pre-stimulus activity in the fusiform

area, a cortical region preferentially responding to faces, was high, observers were likely to

subsequently perceive the stimulus as a face instead of a vase. Correlated activity in brain circuits

and regions should enable transient coalitions of distributed brain regions, which jointly represent

the information available to the system.

It is possible, therefore, to extract a “functional network” from the activity patterns (for reviews, see

Basset & Bullmore, 2006; Bullmore & Sporns, 2009). In addition to small worlds, functional networks

extracted from fMRI (Eguiluz et al., 2005) and EEG (Linkenkaer-Hansen, Nikouline, Palva et al., 2001

for amplitude; Gong, Nikolaev, & van Leeuwen, 2003 for coherence interval durations) have the

property of scale invariance. This means that their characteristics are preserved if the measurement

scale is increased or decreased. Scale invariance is a necessary condition for criticality, and hence for

dynamically assembled complexity and long-term memory in brain activity (Linkenkaer-Hansen et al.,

2001). Networks that have both scale-invariance and modular small world properties can arise as a

product of network rewiring to spontaneous activity, if we assume that new units are recruited at

random into the network (Gong & van Leeuwen, 2003). Thus, the properties of functional

connectivity networks may be the product of adaptation of the system to its own spontaneous

activity patterns.

3. Oscillatory activity

Coordination of brain regions across a range of scales should be flexible, in a manner that hardwired

connectivity alone could not provide. One way in which this could be achieved is through control of

excitability. Simultaneous activity between neurons, or regions, is an effective means of enhancing

signal effectivity (Fries, 2005).

Let us therefore consider which properties of brain activity are useful in this respect. Activity that is

bounded and cyclical is called oscillatory or (in the continuous case) as wave activity. Periodic and a-

periodic oscillators have a natural tendency to synchronize, either complete (Yamada & Fujisaka,

1983; Pecora & Caroll, 1990) or phase only (Rosenblum, Pikovsky & Kurths, 1996).

In 1929, Hans Berger first observed the oscillatory properties of the EEG. Tallon-Baudry & Bertrand

(1999) argued that synchrony is always the result of a mixture of internal states and external events.

The effects of spontaneous activity on perception can be explained by the fact that it continues

during task performance: evoked activity shows a similar neuroanatomical distribution to that

observed at rest (Arieli, Sterkin, Grinvald, & Aertsen, 1996). This property of brain activity may have

become recruited for coordinating activity, and for enabling multiple patterns of activity

simultaneously (evidence reviewed in Thut et al., 2012). According to an influential point of view,

synchronization of oscillatory activity binds together distributed representations (Milner, 1974; von

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der Malsburg, 1981). Unlike in classical neural networks, synchronous oscillations allow multiple

distributed patterns to be processed in parallel, as they can be separated in phase.

Episodes of oscillatory brain activity are typically decomposed into an array of band-passed signals.

We distinguish delta , theta, alpha, beta and gamma frequency bands. Distinct cognitive and

perceptual functions have traditionally been associated with each of these bands. EEG and MEG

signals provide us with a picture of how phase and amplitude evolve in time over within bands at

different locations of the scalp. We can study couplings between amplitudes and/or phases at

different locations within frequency bands or between phases and amplitudes of different frequency

bands. This includes, for instance, the coupling of phase (phase synchrony) at two different locations

at the scalp or the coupling between theta phase and gamma amplitude at a certain location (phase-

amplitude coupling).

3.1. Alpha activity

Generally, large-scale wave patterns in activity, below 8 Hz, are uncommon in healthy adults when

awake. Without stimulation and when the observer is relaxed spontaneous activity is dominated by

8-12 Hz, i.e. alpha activity. Alpha activity is a “far from unitary phenomenon” (Foxe & Snyder, 2011,

p. 10). It arises from cortico-thalamic or cortico-cortical loops. Alpha frequency increases during

execution of difficult tasks compared with more simple ones (complex addition and mental rotation

vs. simple addition and visual imagery). The increase is largest in the hemisphere that is dominant for

the task, i.e. arithmetical tasks for the left, and visuo-spatial tasks for the right hemisphere (Osaka,

1984). Peak alpha frequency correlates positively with specific verbal and non-verbal abilities

(Anokhin and Vogel, 1996; Jausovec and Jausovec, 2000; Shaw, 2004) and memory performance

(Klimesch et al. 1990) and are a reliable individual characteristic.

In perceptual organization, the peak alpha frequency has implications for whether a perceiver is

likely to integrate the surrounding context (i.e. field dependence) or as isolated from its surrounding

context (field independence) –see van Leeuwen & Smit (2012). This individual difference has

consequences for whether a pattern is perceived as a consistent whole, or as a loose collection of

object features. According to some authors (Peterson & Hochberg, 1983; Peterson & Gibson, 1991),

objects are predominantly perceived in a “piecemeal fashion”. That is, they are seen as a loose

collection of features. This, however, may be a consequence of presenting objects in isolation. When

object are seen in a surrounding context, the objects themselves tend overall to be seen as integral

wholes. However, this happens to different degrees, depending on perceiver’s peak alpha. Alpha

activity, thus, is an important modulator of whether perception is predominantly local or global.

This observation is in accordance with the understanding that alpha activity is involved in suppressing

neurons responsible for processing stimuli outside of the focus of attention (Lopes da Silva, 1991).

Alpha oscillations, represent a certain rhythm “pulsed inhibition” (Mathewson, Lleras, Beck et al.,

2011) on attentional processes. In the previous chapter, we have seen that attention spreads over

time (e.g. Roelfsema, 2006). When the spreading is periodically inhibited, then if this happens

relatively fast, perceptual integration will remain within a restricted region.

Presentation of a stimulus affects the ongoing alpha EEG/MEG. This effect takes the form of an

event-related amplitude decrease (called event-related desynchronization or ERD, based on the

assumption that amplitude is the result of large numbers of neurons firing in unison) and subsequent

re synchronization (ERS). A visual input results in the desynchronization of occipital alpha rhythms

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(Pfurtscheller & Lopes da Silva 1999). The alpha ERD can be understood as a sign that the area is

engaged in processing.

3.2. Pattern Dynamics of Alpha Activity

Pioneering work by Lehmann and colleagues has analyzed the spatial distribution of amplitude of

spontaneous EEG activity in the alpha range (Lehmann, Ozaki, & Pal, 1987). They showed that in the

resting condition, certain spatial patterns of EEG activity across the scalp are systematically

preferred. Distributions of electrical brain potential, consisting of a maximum and minimum, each

surrounded by concentric gradients, remained stationary for certain periods of time, before suddenly

jumping to a new location.

More recently this phenomenon has been studied using phase synchronization of alpha activity over

the entire scalp. The large-scale correlation patterns in spontaneous activity have a small-world

structure with heritable characteristics (Smit et al., 2007).

The patterns themselves take the form of travelling or standing waves (Ito, Nikolaev, & Leeuwen,

2005): one is a gradual phase shift in alpha activity between frontal and occipital regions. The other

pattern involves an abrupt phase shift in the central region. This pattern may correspond to a

standing wave composed of two traveling waves propagating in opposite directions. In-between the

periods where wave activity dominates the brain, there are episodes where the activity appears

more disorganized.

The alternation of irregular and regular episodes is a fundamental property of brain activity (Gong,

Nikolaev, & van Leeuwen, 2007; Kitzbichler, Smith, Christensen, & Bullmore, 2009). These episodes

emerge, hold, and dissipate across a range of temporal scales (Freeman & Baird, 1987; Friston, 2000;

Gong et al., 2003; Leopold & Logothetis, 2003;). Ito, Nikolaev, & van Leeuwen (2007) characterized

the short-and long-term behavior of these patterns. To some patterns visited earlier, the system had

a tendency to dwell in, or return within hundreds of milliseconds; on a time scale of several to ten

seconds. The transitions were irregular in the short-term but showed systematic preferences in the

long-term dynamics. This kind of wandering behavior is called chaotic itinerancy (Kaneko & Tsuda,

2001). Chaotic itinerancy is a mechanism that enables a system to visit a broad variety of

synchronized states, and to dwell near them without becoming trapped in any of them. Chaotic

itinerancy offers a theoretical basis for the transient character of brain dynamics and suggests

flexibility which is essential for effective brain functioning. Thus, the dynamical properties of

spontaneous activity provide the brain with flexibility: an openness to respond to a great variety of

stimuli.

This kind of dynamics may play a role in perceptual organization. First: consider perceptual

organization to be a process that needs to be achieved rapidly. Too much stability of any preceding

state will hamper that. Second: dynamic flexibility is needed, in order not to settle on a given

interpretation. We can observe spontaneous changes of interpretation in ambiguous figures, such as

the Necker cube. The same mechanism may be at work, when it comes to detecting a hidden

perceptual structure. This will never work if the system settles on a given interpretation of an object

and stays there, until perturbed by new incoming stimulation. Some spontaneous wandering should

characterize perceptual organization.

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3.3. Anticipatory activity: beta and gamma

When the observer changes from relaxation to active anticipation, activity changes as well: faster

rhytms gain in prominence. Lopes da Silva et al. (1970) observed this phenomenon in dogs. Cortical

areas that showed alpha rhythms in relaxed animals shifted to beta and gamma activity when a

stimulus associated with a reward was expected.

The beta band activity has traditionally been associated with sensori-motor integration (Murthy &

Fetz, 1992). Tallon-Baudry et al. (2001) observed sustained beta range activity during short-term

memory rehearsal of a stimulus in epilepsy patients with intracranially implanted electrodes. In a

study in which monkeys had to discriminate between vibrotactile stimuli, beta band oscillations were

observed in medial prefrontal and primary motor cortices prior to the motor response. These

oscillations were absent, however, in a control condition, where the motor behavior did not require a

perceptual decision (Hernandez et al., 2010). Beta-activity is also observed in visual object retrieval

from semantic memory (Supp et al., 2005). Von Stein et al. (1999) observed enhanced beta

coherence in temporal and parietal cortex during presentation of semantic information,

independently of the presentation modality.

Beta oscillations arise in model studies of realistic neural circuits consisting of regular-spiking

pyramidal neurons, fast-spiking and low-threshold interneurons. These oscillations peak at high-beta:

23-24 Hz. Normally, when fast-spiking interneurons are selectively activated, this leads to higher-

frequency, gamma activity. But when the low-threshold spiking neurons become involved, their

intermittent recruitment lowers the resonance frequency of the ensemble (Vierling-Claassen, Cardin,

Moore, et al., 2010). Low-threshold interneurons are interesting for communication between areas,

because, unlike the other interneurons, which synapse locally, these ones synapse on distal dendrites

of pyramidal neurons (Markram et al., 2004). Beta oscillations may therefore facilitate information

transfer between areas (Livanov, 1977). Wrobel (2000) showed in cat that during attentive visual

behavior, 300 ms to 1 s long bursts of beta frequency activity operate within the cortico-geniculate

feedback cycle to enhance visual information transmission from the LGN. Beta bursting spread to

other visual centers, including the lateral posterior and pulvinar complex and higher cortical areas.

These bursts coincide in time with gamma oscillations.

Accordingly, Vierling-Claasen’s et al.’s (2010) model produced a lot of gamma along with the beta

activity. Across various cognitive tasks, beta and gamma power show similar scalp distributions

(Fitzgibbon et al., 2004). According to Siegel, Donner, & Engel (2012), whereas gamma activity

reflects the emergence of a percept, it is likely that beta oscillations reflect maintenance of

perceptual information. Combined with the previous observations about the role of beta in

transmission of information, this implies that maintenance of visual stimuli occurs through

interactions between areas (Simione, Raffone, Wolters et al., 2012).

Gross (2004), using MEG, demonstrated a role for beta oscillations in maintenance of information

attentional blink conditions. The attentional blink involves the presentation of several visual stimuli

in rapid succession (at a rate of approx 100 ms); two targets are embedded in the presentation

sequence. Whereas the first one is usually detected easily, the second one is often missed, in

particular if the temporal separation (lag) equals 300 ms. Gross et al. (2004) showed that detection

in these conditions was accompanied by enhanced beta coherence between sources in temporal

cortex DLPFC and PPC. In the same task, Nakatani (et al. 2005) demonstrated the role of gamma

synchrony prior to the onset of the target, which was increased when the target was successfully

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detected, as compared to when the target was missed. Taken together the results of Gross and

Nakatani support Siegel et al. (2012) about the complementary roles of beta and gamma frequencies.

Synchrony in the gamma band, therefore, may be related to the emergence of the percept rather

than to its maintenance. Nakatani et al. (2006) studied the relationship between long-distance

transient phase synchronization in EEG and perceptual switching in the Necker cube. Transient

periods of response related synchrony between parietal and frontal areas were observed. They start

800–600ms prior to the switch response and are sometimes accompanied by transient alpha band

activity in the occipital area. The results indicate that perceptual switching processes involve parietal

and frontal areas; these are the ones that are normally associated with visual attention and decision-

making.

3.4. Evoked activity: beta and gamma

Consistency of synchrony in evoked activity may result from ongoing activity through a

reorganization of phase (phase resetting). Phase resetting is held responsible for the generation

event-related potentials (ERP) (Makeig et al., 2002). Quasi-stable patterns of synchrony in the beta

and gamma frequency range in the rest condition are demarcated by abrupt phase changes with a

frequency in the theta or alpha range (Freeman, Burke, & Holmes, 2003). Stimulation aligns such

patterns to stimulus onset (Freeman, 2005).

Thus, episodes of regular and irregular activities alternate, not only in spontaneous but also in

evoked activity. These episodes may have a functional role in information processing. Irregular

activity will reflect information processes occurring within regions; at the scalp, these periods will

look desynchronized and unstable.

The episodes of quasi-stable synchronized activity have been called ‘‘coherence intervals’’ (van

Leeuwen, 2007; van Leeuwen & Bakker, 1995). During these intervals, previously processed

information is propagated to other brain areas. The differences in time it takes for such information

to reach their multiple destinations is accommodated by keeping the window open for a while, e.g.,

up to 200 ms (van Wassenhove, Grant, & Poeppel, 2007).

The regular episodes thus provide a mechanism for global broadcasting of results in information

processing that are needed for conscious access to visual information (Baars, 1988; 2002). In the

previous chapter, we have seen how traditionally, conscious access is centered upon convergence

zones; areas where the information from many regions comes together. Rather than convergence,

we see these areas as hubs, or relay stations, in the communication between brain regions, based on

principles of synchrony. As a result, conscious access functions belong to organized brain activity,

rather than specific local regions. The activity is not tied to any region in particular, as it travels along

the cortex; it may, however, visit the hubs regions more consistently then others (see Alexander,

Jurica, Trengove, et al., 2013).

During these intervals, the informational content remains unchanged. As a result, the content of

perceptual experience is fixed in an extended psychological present (cf. Stroud, 1955). The duration

of coherence intervals was estimated at 50–300 ms (Bressler, Coppola, & Nakamura, 1993; Dennett

& Kinsbourne, 1991; Varela, 1995). In the rest condition the durations of the patterns have a power-

law distribution (Gong, Nikolaev, & van Leeuwen, 2003; Kitzbichler et al., 2009) which indicates that

the system is in a state of dynamical criticality (Kitzbichler et al., 2009). When the system is

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perturbed by a stimulus, the scale-free distribution is suppressed and changes into a characteristic

distribution (Nikolaev, Gepshtein, Gong, & van Leeuwen, 2010; Nikolaev, Gong, & van Leeuwen,

2005). The new distribution often turns out to be an extreme-value distribution (Nikolaev et al.,

2010). Indeed, as the interval reflects the propagation of information, this will take place in parallel

across multiple channels. The extreme-value distribution of these intervals then means that the

length of the interval is determined by the slowest channel (cf. Pöppel, 1970). Since the slowest

channel determines the durations of episodes of synchronous activity, their averages may reflect

information-processing demands of the task at hand.

We tested this prediction by studying the patterns of quasi-stable synchrony over small regions on

the human scalp with an electrode spacing of 2 cm (Nikolaev et al., 2005). We selected electrode

chains over the scalp region with maximal ERP activity following presentation of the stimuli. To

obtain the intervals of quasi-stable synchrony we measured the variability of phase synchronization

indices within electrode chains. Then the duration of the intervals in which the variability fell below

the threshold was computed (Fig. 3). The comparison of durations showed that in the beta EEG

frequency range the intervals were longer when observers were engaged in a perceptual task than

when they were stimulated without task. This result was interpreted as evidence that more

information was transferred across brain areas in ‘‘task’’ than ‘‘no-task’’ conditions.

3.5. Coherence intervals reflect stimulus pattern information

In order to quantitatively demonstrate the role of these local synchronization patterns in global

information processes, we adopted a paradigm from psychophysics, in which participants reported

orientation of the perceived groupings of dot lattices. Proximity determines perceived grouping

through aspect ratio (AR) which is the ratio of the two shortest inter-dot distances, b vs. a (Ch 53)

(Kubovy, Holcombe, & Wagemans, 1998). The larger AR the stronger is the preference for grouping

according to proximity; the more AR approaches 1, the more ambiguous is interpretation of

orientation of the dot lattice. Ambiguity equals uncertainty or the inverse of information (van

Leeuwen & van den Hof, 1991). Thus, the larger AR, the more information contained in the stimulus.

In a preliminary investigation, we determined which evoked component of the brain signal was

sensitive to AR (Nikolaev, Gepshtein, Kubovy, & van Leeuwen, 2008). At the scalp location of that

component, we measured the durations of synchronized intervals in relation to the aspect ratio of

the dot lattice. We found a simple, linear relation of aspect ratio with coherence interval duration

(Fig. 4). This means that the more information contained in the stimulus, the longer the coherence

intervals in the evoked activity. In individuals, the duration of the coherence intervals was found to

be strongly correlated to grouping sensitivity. Thus, coherence intervals directly reflect the amount

of stimulus information processed rather than available in the physical stimulus.

We concluded that the intervals of synchronized activity may reflect the time needed for

promulgation of the stimulus information from the visual system to the rest of the brain. The

coherence intervals, thus, represent global broadcasting of visual information. Global broadcasting

has been associated with visual conscious awareness and the emergence of visual experience

(Dehaene, Changeux, Naccache, Sackur, & Sergent, 2006).

Global broadcasting takes central stage in global workspace theories and models of visual

information processing. These models are increasingly successful in dealing with a wide range of

phenomena in visual experience, such as the limited capacity of visual working memory, visual

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persistence, and the attentional blink (e.g., Simione et al., 2012). Large-scale dynamics provides a

mechanism for coordinating the information processing which endows these models with greater

neural plausibility.

3.6. Event-related gamma activity

With oscillatory activity, two patterns can be simultaneously active and still be separated in phase.

Singer and others set out to study oscillatory activity in local field potentials, initially of mildly

anaesthetized cat and monkey, and later on in awake animals (Eckhorn et al., 1988; Gray et al.,

1989). They observed synchronization between distinct areas of the visual cortex, depending on

whether these areas were activated by a single, coherent pattern. These synchronizations typically

occurred in the gamma range (40-70 Hz) of oscillation frequency. The dynamic phase synchrony in

the gamma band enables transient association of cortical assemblies (Engel & Singer, 2001). The

authors concluded, somewhat controversially to date, that gamma oscillations were involved in the

representation of distinct features as belonging to a perceptual whole, in other words, in perceptual

integration of visual features.

These kind of invasive studies are impossible in humans. At larger scale, oscillations can be studied by

measuring electrical (EEG) or magnetic (MEG) potential at the scalp (Revonsuo et al. 1997; Rodriguez

et al. 1999; Varela et al. 2001; Engel and Singer 2001). Phase synchrony in the gamma band (30–80

Hz) EEG is a sensitive measure for various phenomena, such as object detection, memory retention,

illusion, attention, readiness, and consciousness (Fell, Fernandez, Klaver, Elger, & Fries, 2003; Lee,

Williams, Breakspear, & Gordon, 2003; Lutz, Lachaux, Martinerie, & Varela, 2002; Rodriguez et al.,

1999; Tallon-Baudry & Bertrand, 1999; Tallon-Baudry, Bertrand, Peronnet, & Pernier, 1998; Tallon-

Baudry, Bertrand, Delpuech, & Permier, 1997). In a random-dot stereogram experiment, gamma

band synchrony appears transiently when a percept becomes organized and it disappears quickly

after the percept has been obtained (Revonsuo et al. 1997).

4. Slow Wave modulations

Transitions for conscious access can be related to delta (< 4 Hz) and theta (4-8 Hz) ranges (Baars &

Franklin, 2003; Gaillard et al., 2009; Sergent et al., 2005; Zylberberg, Dehaene, Roelfsema, & Sigman,

2011). Cortical theta is prominent in young children; In older children and adults, it tends to appear

predominantly during drowsy, meditative, or sleeping states, but not during the deepest stages of

sleep. Theta phase is considered as the carrier for information encoding and read-out, which are the

two most fundamental functions of neural information processing and conscious access (Lisman &

Idiart, 1995). Delta band activity is the frequency of the P3 ERP component, which has been taken to

signal the emergence of global workspace activity (Sergent et al, 2005). Delta activity is observed a

solitary, high amplitude brain wave with an oscillation period between 0–4 hertz. Delta phase has

been related to top-down modulation of sensory signal strength (Lakatos et al., 2005; 2009).

4.1. Coupling of slow and fast waves

Lower frequency oscillations tend to recruit neurons in larger cortical areas but tend to be more

spatially restricted in the case of higher frequencies, for instance beta/gamma rhythms. Thus,

whereas in gamma oscillation, the cortex appears to be functionally organized as a mosaic of

neuronal assemblies, the lower frequencies may be more widespread across the brain. A possible

way in which the brain at large scale can coordinate cortical processes at smaller scale is by

12

modulation of fast by slower waves. Canolty et al. (2006) reported coupling between theta band (4-8

Hz) phase and high-gamma band (80-150 Hz) amplitude in ECoG data in various cognitive tasks.

Slow oscillatory activity can bias input selection, connect populations of neurons into assemblies, and

facilitate synaptic plasticity (reviewed in Buzsaki & Draguhn, 2004). Large-scale networks are

recruited during oscillations of low frequency (Steriade, 2001). Slow rhythms synchronize large

spatial domains and connect local neuronal assemblies by orchestrating the timing of high frequency

oscillations (Buzsaki & Draguhn, 2004).

Fast oscillatory activities, in particular, gamma (> 30 Hz) and beta (12-30Hz) oscillations which were

considered important for, respectively, emergence and maintenance of perceptual representation,

both in models (Dehaene et al., 2006; Raffone & Wolters, 2001) and empirical studies (Gross et al.,

2004; Kranczioch, Debener, Maye, & Engel, 2007; Nakatani, Ito, Nikolaev, Gong, & van Leeuwen,

2005) can thus be coupled to slow oscillations. The coupling may therefore support the interaction

between access control processors and sensory information processing and maintenance in posterior

areas.

Such cross-frequency coupling may play a key role for conscious access. Several models of

consciousness agree that conscious access involves large-scale cooperative and competitive

interactions in the brain, beyond specialized processing in segregated modules (e.g. Baars, 1988,

1997; Block, 2001; Dehaene, Kerszberg, & Changeux, 1998, Deheane et al., 2006; Maia &

Cleeremans, 2005; Tononi & Edelman, 1998). The principles for such global processing architecture

were proposed in the Global Workspace Theory (Baars, 1988, 2002); the conditions for the

neurocomputational implementation of such principles were further specified (Dehaene, et al., 2006;

Gaillard, et al., 2009). These views have led to development of computational models with multi-

modular and neurally-inspired characteristics, Global Workspace (GW) models (Dehaene et al., 2006;

Simione et al., 2012).

For instance, Simione’s model accounts for a set of perceptual phenomena in which conscious access

is involved, which includes the effect of partial report (Sperling, 1960), the limited capacity of visual

working memory (Luck & Vogel, 1997), and the Attentional Blink effect e.g., Raymond, Shapiro, &

Arnell, 1992). The Attentional Blink effect arises in the model because the second target is processed

only at a first parallel (perceptual) stage, and therefore does not give rise to the global self-sustained

activity pattern of the GW supporting conscious access, as long as the GW is still occupied. This is the

result of interactive gating between lower perceptual processing modules and higher access control

modules. The access control modules consist of GW and visuo-spatial working memory (VSWM)

modules, with maintenance of target information being largely distributed and also involving

perceptual processing modules.

The model suggests that coupling between theta phase and amplitude of fast oscillations, between

beta and lower-gamma, support the interaction between the GW and distributed codes in posterior

cortex for processing and maintenance of target information. Nakatani et al. (subm) investigated

phase-amplitude coupling in AB conditions. They found coupling between the phase of access

control-related slow oscillatory activity and the amplitude of fast oscillations encoding perceptual

contents for conscious access in a cognitive task. This coupling increased in strength during practice

of the task, corresponding with increase of correct target recognition under AB conditions.

13

5. Conclusions and open issues

Oscillations control in a coordinated fashion the excitability of neurons. Different frequency bands of

oscillations appear to have different roles in information processing: alpha has predominantly been

associated with relaxation and inhibition. Its effect on processing is indirect, insofar peak alpha

frequency provides “pulsed inhibition”, thereby establishing a time window for perceptual

integration. Beta activity reflects the maintenance of visual information and the communication of

the percept between areas, thus establishing a virtual global workspace, a unified theatre of

consciousness. Gamma arise when the percept emerges, and may reflect initial feature binding and

integration, albeit with somewhat shorter loops than beta. The lower frequencies offer a mechanism

for orchestrating the higher frequency ones.

One way in which the organized activity manifests itself, is in the coupling between activity in

different frequency ranges. In characterizing brain function, therefore, the precise timing of activity

plays an essential role. With existing methods for analyzing brain activity, it has been possible to

track the flow of activity with high temporal resolution (Liu & Ioannides, 1996). Doing so in single

trials reveals that results are not described well by the average. There is a great deal of trial-to-trial

variability in the spatiotemporal organization of brain activity. This suggests that signal averaging can

be misleading. Indeed, it was recently shown that trial-averaging techniques lead to false positives in

identifying static sources of brain activity, and to an underestimation of moving, i.e. spreading

components in brain activity, i.e. traveling waves (Alexander et al., 2013). It is this type of activity

that we have emphasized here, as having a role in brain function in general, and in conscious access

in particular. In this account, consciousness does not belong to any specific region, but to the

spatiotemporal organization of brain activity.

14

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