Available abstracts/paper summaries for this Workshop's talks
Bernard J. Baars
Do Brain Rhythms Underlie Conscious and Unconscious Cognition?
Necessity and Sufficiency in the Emergence of Consciousness:
When is a Complex Brain Complex Enough?
The LIDA model’s hypotheses on the cognitive cycle,
high-level cognitive processes, and theta/alpha and gamma rhythms.
Alpha and theta oscillations:
Conscious control of information processing in the human brain?
Long-distance Synchronization of Neural Activity
across Cortical Areas Correlates with Conscious Perception
Paul L. Nunez
Brain Rhythms, Anatomy and the Emergence of Consciousness: Why Hearts Don't Love and Brains Don't Pump.
Brain Rhythms and the Global Workspace Theory
Lawrence M. Ward
Neural Synchrony in Attention and Consciousness
Do Brain Rhythms Underlie Conscious and Unconscious Cognition?
Bernard J. Baars
Necessity and Sufficiency in the Emergence of Consciousness:
When is a Complex Brain Complex Enough?
Over the past decade, there has been a growing interest in animal consciousness,
particularly in regard to the possibility of conscious states in non-mammalian
lineages. Yet a comprehensive review of animal cognitive neuroscience relevant
to consciousness has not been forthcoming; nor has there been a serious
experimental program undertaken to investigate animal consciousness
substantively and systematically. Clearly, investigations of human consciousness
can provide benchmarks in the design of such a program; specifically, certain properties of,
and criteria for, consciousness can be extrapolated from the human case to those of non-human animals.
Moreover, there is some neuroanatomical, neurophysiological, and behavioral evidence that at least suggests
the possibility of awareness in some non-human species. But a fundamental question arises when considering the
issue of consciousness in non-human species: precisely what kind of a brain is required for conscious experience?
Specifically, how much central nervous system (CNS) (i.e., number of neural and non-neural cells), regional and cellular
specialization, and inter- and intra-areal connectivity actually underlie the process of consciousness? At its heart,
this is, of course, a question of neural complexity, the properties and measurement of which have recently become prominent
points of departure in the scientific investigation of consciousness. So, to rephrase the question: what degree of neural
complexity is both necessary and sufficient to yield conscious experience? We don’t really know, of course. However, recent
computer simulations based on actual neurophysiological and neuroanatomical data do suggest that, in terms of numbers of
neurons and the degree of connectivity between them, nervous systems with far fewer cells and less connectivity—by at least
several orders of magnitude—than the typical mammalian CNS could exhibit spontaneously a number of functional properties
previously associated with very large brains containing hyper-dense connectivity.
I will discuss the problem of animal consciousness in the context of findings in a variety of species,
as well as with regard to recent computer simulations of aspects of the mammalian brain undertaken at the
Neurosciences Institute. Data from the work I will summarize here suggest that, in the quest to identify
consciousness in non-human species, we may have to cast our net a bit wider than previously imagined.
The LIDA model’s hypotheses on the cognitive cycle,
high-level cognitive processes, and theta/alpha and gamma rhythms.
The LIDA conceptual and partially computational model of cognition is based on
Global Workspace Theory and other theories from cognitive science and neuroscience.
It hypothesizes that high-level cognitive processes such as deliberation and
volitional decision making are composed of cascading sequences of cognitive
cycles corresponding to action-perception cycles. Occurring at a rate of ~10 hz
these cognitive sense-process-act cycles can be thought of as cognitive atoms
or cognitive moments. The LIDA model further hypothesizes that theta/alpha
rhythms are generated by passing cognitive cycles while the superimposed gamma
rhythms reflect the internal activity of individual cognitive cycles.
As the LIDA model is a broad, integrated model of cognition itself, it must be
founded on the underlying neuroscience. We finally hypothesize that representations
in LIDA correspond to basins of attraction in the non-linear dynamics of
trajectories of activation patterns of cell assemblies. In this talk we will
briefly describe the LIDA model, and expand upon these several hypotheses.
Alpha and theta oscillations: Conscious control of information
processing in the human brain?
Division of Physiological Psychology, University of Salzburg
Understanding the emergence of spatially and temporally organized firing
patterns in neural networks may provide important insights into
mechanisms reflecting conscious control on human information processing.
It is suggested that theta, and alpha oscillations in particular, play
an important role for the temporal organization of neural activity
during top-down control in two large processing systems. One system,
associated with theta activity, is related to the processing of new
information. Another system, associated with alpha activity, enables
controlled access to already stored information, thereby providing us
with the very basic ability to be ‘semantically’ oriented in
continuously changing environments.
The functional-physiological significance of oscillations for the
controlled timing of neural activity is seen in the fact that they
reflect rhythmic changes in the (relative) level of depolarization in
the (dendritic and somatic) membrane potentials of masses of neurons.
Consequently, different phases of a single oscillatory cycle are related
to phases of enhanced or suppressed neural firing. Rhythmic
synchronization between neurons will have a strong impact on common
target cells, because they will receive neural activity not only
synchronously but also in predictable time windows.
A variety of empirical findings will be discussed that document the
role of theta and alpha oscillations for the timing of cognitive
processes in working memory, semantic memory and perceptual tasks (cf.
the review by Klimesch et al. 2007a). As an example, it will be shown
that alpha and theta phase coherence increases between task relevant
sites and that phase lag lies within a time range that is consistent
with neuronal transmission speed (cf. e.g., Nunez, 2000). Another
important aspect is that phase reset will be a powerful mechanism for
the event-related timing of cortical processes (Klimesch et al. 2007b)
and empirical evidence suggests that the extent of phase locking is a
functionally sensitive measure that is related to cognitive performance
(for a review cf. Klimesch et al. 2007c).
For the functional-cognitive understanding of EEG-oscillations, alpha
is remarkable in several ways: It usually represents the dominant
oscillation, and - in contrast to delta, theta and gamma which show
event-related increases in amplitude in response to cognitive task
demands - alpha typically exhibits decreases in amplitudes. This
peculiar functional reactivity raises the question, whether alpha is
associated with a special and possibly unique type of cognitive process.
With respect to this question there is an interesting functional
similarity with studies on brain metabolism, which typically have
focussed on increases in activity in task relevant regions. Most
interestingly, however, as a variety of findings meanwhile document,
certain brain regions (comprising posterior medial/lateral and
ventral/dorsal medial prefrontal cortices) are more active during rest
and show a decrease in activity in a large variety of tasks (cf. e.g.,
Gusnard & Raichle, 2001). These findings have led to the hypothesis that
during the resting state the brain is active in a specifically organized
way which was termed ‘default mode’ (for a more recent review cf., Fox &
Raichle, 2007) and the corresponding brain regions were termed ‘default
(mode) network’. Many regions of this network are known to play an
important role for consciousness. The functional similarities between
alpha and the default network (both are active during rest and decrease
their activity in many types of cognitive tasks; in both systems
task-related reactivity depends on the resting state) raise the
question, whether alpha reflects a subsystem of the default network, one
that is located in posterior parts of the default network and is
functionally associated with ‘representing or monitoring the world
around us‘ (Gusnard & Raichle, 2001).
Fox, M.D. & Raichle M.E., (2007) Spontaneous fluctuations in brain
activity observed with functional magnetic resonance imaging.
Nature Review Neuroscience, 8, 700-711.
Gusnard, D. A. & Raichle, M. E. (2001). Searching for a baseline:
Functional imaging and the resting human brain. Nature Reviews
Neuroscience, 2, 685-694.
Klimesch, W., Sauseng, P., & Hanslmayr, S., 2007a. EEG alpha
oscillations: The inhibition–timing hypothesis. Brain Res. Brain
Res. Rev. 53, 63-88.
Klimesch, W., Hanslmayr, S., Sauseng, P., Gruber, W., &
Doppelmayr, M., 2007b. The P1 and traveling alpha waves: Evidence
for evoked oscillations. J. Neurophysiol. 97, 1311-1318.
Klimesch, W., Sauseng, P., Hanslmayr, S., Gruber, W., &
Freunberger, R., 2007c. Event-related phase reorganization may
explain evoked neural dynamics. Neurosci. Biobehav. Rev. 31 (7),
Nunez, P.L., 2000. Toward a quantitative description of
large-scale neocortical dynamic function and EEG. Behav. Brain
Sci. 23, 371–437.
Long-distance Synchronization of Neural Activity across Cortical Areas
Correlates with Conscious Perception
Brain Imaging Center Frankfurt, Germany
In my talk I will present evidence which suggests that long-distance synchronization
in the gamma frequency range plays a crucial role in conscious perception. I will
present several studies where long–distance synchronization and local gamma synchronization
were measured during the presentation of visible versus invisible stimuli.
We hypothesized that brain states associated with conscious processing should be
characterized by a high degree of synchrony, i.e. temporal coherence of activity between
distant cerebral assemblies, whereas unconscious processing would be characterized by
In a first experiment we studied the sequence of electrophysiological events leading
to conscious perception. We found that the earliest electrophysiological marker that
distinguished visible from invisible stimuli was a brief burst of long-range synchrony
in the gamma frequency range.
Given that visible stimuli are usually associated with more extensive processing,
it could be argued that enhanced long-distance synchrony in the former experiment
is a reflection of differential depths of processing and not of a mechanism related
to conscious perception. To disentangle these two hypotheses we studied the differential
electrophysiological responses of conscious and unconscious processing of information
under conditions of similar depth of processing. In a subliminal semantic priming
paradigm we found that invisible but highly processed words elicited local gamma
oscillations whereas visible and highly processed words elicited both local gamma
oscillations and long distance synchronization. This result is compatible with the
hypothesis that local gamma oscillations correlate with depth of processing and
long-distance synchronization with conscious perception.
In a third experiment we studied how bottom-up information is modulated by top-down
representations. Higher-order representations might serve the crucial role of stabilizing
percepts and bringing them into conscious perception in an environment where stimuli can
be either ambiguous or where constant changes in low-level stimulus parameters occur
(i.e., contrast variations). It is currently unknown how such top-down influence is
reflected in brain activity, and how neuronal activity related to perceptual awareness
is modulated by top-down and bottom-up influences. To investigate this question, we
measured electroencephalographic activity in a visual paradigm where we generated perceptual
hysteresis by gradually increasing and then decreasing the visibility of an initially
hidden stimulus in a stepwise manner. Under this condition, perceptual hypotheses are
built up as soon as the subject perceives the stimuli, which in turn increases the
visibility of subsequent lower contrast stimuli.
Our behavioral results confirmed this effect by demonstrating a shift in the visibility threshold.
In addition, we found that long distance synchronization correlates with conscious perception
(seen vs. unseen stimuli), whereas gamma oscillations correlate with the hysteresis
phenomenon itself, i.e. the presence or absence of a preceding top-down concept.
In summary, our studies suggest that precise synchronization of oscillatory neuronal
responses in the high frequency range plays an important role in gating the access of
sensory signals to the work space of consciousness.
Brain Rhythms, Anatomy and the Emergence of Consciousness: Why Hearts Don’t Love and Brains Don’t Pump.
Paul L Nunez
Tulane University and Brain Physics LLC
Knowledge from several disparate scientific fields is integrated with
plausible conjectures to suggest neural correlates of conscious
experience. I address a fundamental question of brain science: What
physical and biological properties of brain tissue are critical to
consciousness? The earth’s weather system provides a useful analogy.
Weather patterns are determined by solar radiation, fluid frictional
forces, the earth’s spin, energy exchanges between oceans and
atmosphere, and so forth. Knowing these variables is a necessary, but
insufficient, condition for weather prediction.
My tentative answers to the basic question of critical brain properties
are based on the following scientific fields (1) Brain anatomy,
especially the columnar structure of neocortex (grey matter) and its
long range interconnections, the corticocortical fibers (white
matter). (2) Neurophysiology with emphasis on signal transmission by
action potentials, system control with chemical neurotransmitters, and
synaptic action fields Psi(r, t) . (3) Electroencephalography (EEG), the
electric field recorded from the scalp, a large scale measure of
synaptic action fields in neocortex. (4) Synergetics, the so-called
science of cooperation, which describes dynamic processes in complex
physical, biological, and social systems, especially (nested)
hierarchical interactions across spatial scales, both top-down and
bottom-up. The preponderance of long range (non-local) cortico-cortical
connections and the existence of distinct anatomical structures at
multiple spatial scales, columns within columns nested like a Russian
doll, distinguish brains from other tissue, e.g., the heart.
Furthermore, cortico-cortical connections appear to be much more
important in the human brain than in lower mammals.
Neuron sociology as a brain metaphor
In order to facilitate communication with minimal mathematical and
technical detail, I choose a convenient brain metaphor, but emphasize
that metaphor cannot replace genuine theory. My metaphor of choice
is the human global social system, which has the following properties
that serve our purpose. (1) This metaphorical system is quite familiar
to scientists, philosophers, mathematicians and laymen, distinguishing
social systems from several common brain analogs, the brain as a
hologram, so-called “quantum brains” as so forth. For readers lacking
expertise in holography or quantum theory, adoption of these analogs may
defeat the purpose of drawing parallels between well known and poorly
understood systems. (2) Human brains are often considered to be systems
of preeminent complexity. Our metaphorical system not only qualifies as
a genuine complex system (as defined in the physical sciences); it is
more complex than any single brain. (3) For genuine scientific reasons
that are independent of our sociological metaphor, it appears that two
general features of brain tissue are especially important in healthy
brains, nested hierarchical interactions and non-local interactions.
These properties are also important characteristics of the human global
social system. The cooperation and conflict between individuals, cities,
nations and so forth serves as a convenient metaphor for neural
interactions at multiple spatial scales.
Neurophysiology and the time for consciousness to develop
Sensory signals (action potentials) pass through midbrain relay stations
in the thalamus (excepting smell) and enter primary sensory neocortex.
These inputs then spread widely in the brain, especially to other
cortical areas. After integration with endogenous activity (representing
memory, attention, motivation, and so forth), decisions are made that
lead to motor output (physical action). While the input and output parts
of this process are partly understood, very little is known of the
intermediate steps involving conscious decision making. It does appear,
however, that consciousness of an external event takes substantial time
to develop. A visual or auditory signal may reach neocortex in 10 ms,
and it takes perhaps 30 ms for signals to cross the entire brain on
corticocortical fibers. Consciousness of the external signal, on the
other hand, appears to take perhaps 300 to 500 ms, implying that
consciousness requires multiple feedback loops (in corticocortical,
thalamocortical, and other fiber systems) involving widespread
neocortical and other brain regions. The form of the sensory stimulus
is represented in the total activity of distributed cortical networks
rather than being located at any one “node.”
Brain Rhythms and Cognition
Electroencephalography is a very large scale measure of dynamic
electrical activity in neocortex. A single electrode may space average
the activity in 1000 macrocolumns, each column containing 100,000 or so
neurons and perhaps a billion synapses. It might seem unlikely that
useful information about consciousness could be obtained with the severe
space averaging resulting from such blunt instruments. However, it has
long been recognized that EEG provides a genuine window on the mind,
albeit one with many technical limitations. Human brains exhibit complex
dynamic behavior measured by scalp recordings of electric fields (EEG).
These fields typically oscillate at frequencies in the range of 1 to 15
cycles per second and are distributed over the scalp in various ways.
Both oscillation frequencies and spatial patterns are strongly
correlated with conscious experience. Here I cite only a small subset
of these data.
EEG signals reveal the general state of consciousness with high
reliability: large amplitude, low frequency signals near 1 Hz (deep
sleep, coma, epileptic seizure, and moderate to deep anesthesia) and
with very low amplitudes and irregular waveforms (very deep anesthesia,
deep coma, and brain death). Consciousness nearly always occurs with
low to intermediate amplitudes and higher frequencies, with substantial
relative power in the 4 to 15 Hz range when recorded from the scalp.
More sophisticated dynamic measures reveal information about sleep
stage, depth of anesthesia, cognitive state, behavioral state, and so
Various studies show that behavior and mental activity of various kinds
are correlated with EEG oscillations in selective frequency bands,
especially theta (4 to 7 Hz), alpha (8 to 13 Hz) and beta (14 to
20 Hz) in scalp recordings. These bands act in different ways in
different brain states; for example, mental calculations may occur with
increased amplitude or coherence (a frequency domain correlation
coefficient between scalp locations) of 6 Hz theta and 10 Hz alpha
rhythms, at the same time that the 8 Hz alpha amplitude or coherence
decreases. Other subjects exhibit different dynamic behaviors, reminding
us that our brains are unique; however, nearly all subjects appear to
use some combination of alpha and theta bands to accomplish the task.
Animal studies and some human studies suggest that gamma oscillations
(especially near 40 Hz) are also important measures of cortical
function, although these higher frequencies are difficult to study in
scalp recordings for technical reasons. I conjecture that complex tasks
involve multiple frequency bands, but only the lower frequencies are
observed in scalp recordings.
Ramesh Srinivasan and colleagues have carried out studies of binocular
rivalry using steady state visually evoked magnetic fields, suggesting
that consciousness of a single percept (e.g., pattern flicker of one
color) is associated with increases in cross-hemispheric coherence;
frontal and occipital/temporal areas of the brain also appear to
synchronize to the perceived stimulus. In summary, conscious
perception of an image is correlated with a more integrated dynamic
state of the neurons that process the stimulus. More generally we find
that conscious perception and selective attention to stimuli involve
both functional integration of some cortical areas (including occipital
and prefrontal cortex), together with the segregation of other areas,
for example, parietal cortex. This observation that some brain areas
become more functionally connected while other areas tend to disconnect
has often been observed in subjects doing various mental tasks as
suggested by the works of Alan Gevins, Richard Silberstein, Wolfgang
Klimesch, and others.
Global fields and local or regional networks
One way to think about large scale electrical activity in neocortex
involves (generally) extended networks, believed responsible for
behavior and cognition, embedded within global synaptic action fields
Psi(r, t). A synaptic action field is defined as a continuous mathematical
function representing the number of active synapses (excitatory or
inhibitory) per mm^3 of cortical tissue, independent of functional
significance. Neural networks are believed to be embedded within the
synaptic fields in a manner analogous to social networks embedded within
a global culture. We introduce these field variables because their
dynamic modulations dPsi(r, t) are apparently directly responsible for EEG,
thereby providing an important cortical metric of conscious experience.
We conjecture substantial hierarchical interactions between global
fields and networks, both top-down and bottom-up. In this manner, local
networks can influence global fields, and these fields can facilitate
interactions between remote networks, leading to an (apparent) unified
behavior and consciousness. This view differs substantially from the
old idea of thalamic pacemakers driving the cortex (strictly bottom up
interactions) and is more consistent with known dynamic processes in
genuine complex systems.
The idea of neural networks (or cell assemblies) is closely
associated with the mid-20^th century work of Donald Hebb. By contrast,
Gestalt psychology at the time regarded neocortex as governed by an
abstract field theory. In this historical context, I am essentially
proposing a marriage of Hebbian neurophysiology to Gestalt psychology,
except that synaptic fields are neither abstract nor controversial,
rather they are based on conventional neurophysiology and estimated with
Top-down and bottom-up hierarchical interactions across spatial scales
are critical to the operation of human social systems as well as
neocortex. Analogous dynamic phenomena have been studied in physical,
biological, social, and financial systems under the rubric
synergetics, the so-called science of cooperation. Top down/bottom
up interactions in complex dynamic systems, including our postulated
neocortical interactions, have been labeled circular causality by
Herman Haken, the founder of synergetics. In the global social system,
families, neighborhoods, cities, and nations interact with each other,
at both the same scales and across scales. In neocortex, the
metaphorical nested “Russian dolls”, the minicolumns, corticocortical
columns, macrocolumns, Broadman regions, lobes, cortical hemispheres,
and brain may interact similarly. I conjecture that such cross-scale
interactions are essential to brain function.
Complexity measures and local/global neocortical dynamics
My proposed global picture, with local and regional networks embedded in
synaptic action fields, substantially overlaps views expressed by many
other neuroscientists. For example, a quantitative measure of
complexity has been proposed as a measure of consciousness and brain
binding by Gerald Edelman and Giulio Tononi
…high values of complexity correspond to an optimal synthesis of
functional specialization and functional integration within a system.
This is clearly the case for systems like the brain--different areas and
different neurons do different things (they are differentiated) at
the same time they interact to give rise to a unified conscious scene
and to unify behaviors (they are integrated).
In this view complexity (and by implication, cognition) tends to
maximize between the extremes of isolated networks and global coherence.
I find this to be a compelling working hypothesis. It then follows that
local network dynamics, interactions between networks, and interactions
between networks and global fields are critical for healthy brain function.
Implications for disease states
Richard Silberstein has taken this general local/global dynamic picture
a step further in the physiological and clinical directions by
suggesting several ways in which brainstem neurotransmitter systems
might act to change the coupling strength between global fields and
local or regional networks. He outlines how different
neurotransmitters might alter coupling by selective actions at
different cortical depths. For example, dopamine appears to be well
positioned in the appropriate cortical layers to reduce corticocortical
coupling and increase local network (cortical or thalamocortical)
coupling, effecting a shift from more global to more locally dominated
dynamics, a shift from more hypercoupled to more hypocoupled brain
states. In contrast, the neurotransmitter 5-HT could move the cortex to
a more hypercoupled state. Silberstein further conjectures that several
diseases may be manifestations of hypercoupled or hypocoupled dynamic
states brought on by faulty neurotransmitter action. For example, the
association of (positive) schizophrenia symptoms with hypocoupled states
is consistent with observations of a therapeutic response to dopamine
receptor blockers and prominent beta activity likely due to enhanced
local networks. Again, healthy consciousness is associated with a
proper balance between local, regional and global mechanisms.
Non-local neocortical interactions
Cortical neurons interact locally by means of mm scale intracortical
fibers and the corticocortical fibers. The dynamic significance of the
label “non-local” is that neural activity at some location A can
influence another location B without altering local dynamics in the
intervening tissue. In our sociological metaphor, a croissant baker in
Paris can have (same scale) influence on a colleague in New Orleans with
a simple e-mail message without activating a serial chain of bakers
connecting the two locations. Alternately, a croissant expert can
provide a top-down influence on the global croissant culture using the
mass media, perhaps facilitating correlated croissant activity between
disconnected bakers. In mathematical physics, non-local systems are
governed by integral equations in contrast to local systems governed by
differential equations. Intuitively, we expect that non-local systems
are generally capable of much more complex dynamics.
Brains also provide for non-local dynamics interactions in the time
domain when information is retrieved from long term memory. A system in
which the conditional probability of future states depends only on the
current state is known as a Markovian process in probability theory.
If long term memory (stored chemically) is separate from brain dynamics,
brain dynamics is non-Markovian in this sense. In an analogous manner,
our global social system dynamics may be considered non-Markovian since
the dead easily communicate to us through books, films, and so forth,
thereby altering the future in ways that cannot be predicted from the
current dynamic state of our culture. The dead have much to tell us.
What makes the human brain “human”?
Consider the following paradox: For a variety of reasons, we know that
much of the “humanness” of human brains results from processes in
neocortex. However, an anatomist looking through a microscope at a slice
of cortex will have a difficult time distinguishing one mammalian cortex
from another. Rat, cow, dog, and cat look very much the same. Humans are
often said to be distinguished by their large brains with convoluted
surfaces; however, dolphins, elephants and whales have even larger
brains with extensive cortical folding. To the best of my knowledge none
of these species has contributed papers in the scientific literature.
One possible clue to this apparent paradox concerns non-local
interactions. Consider the fibers that enter (or leave) the underside of
a patch of neocortex. Such white matter is mostly composed of
thalamocortical fibers (radial input/output from the midbrain) and
corticocortical fibers (tangential interactions from other parts of
cortex). Based on the work of anatomist Valentino Braitenberg, my
colleague Ron Katznelson estimated the ratio of corticocortical to
thalamocortical input/output fibers (per unit of cortical surface) in
several mammals. In rat, for example, only about 60% of white matter
input/output fibers are corticocortical, whereas the corresponding
figure for humans is perhaps 98%. Unfortunately, we do not have similar
estimates for dolphins, elephants, and whales. These ideas are closely
related to the classic work of Donald Hebb, who emphasized that the
ratio of association areas to sensory areas of neocortex progressively
increases from rat to dog to monkey to human. Maybe we are smarter than
our pet dogs partly because human neocortex is very strongly
interconnected by non-local connections with relatively fewer
connections to the more primitive midbrain, thereby allowing for more
complex neocortical dynamic behavior.
Standing and traveling brain waves
Over the past 37 years, I have modeled the excitatory and inhibitory
global fields of synaptic action Psi_e(r,t), Psi_i(r, t) with an integral equation derived
from known (but substantially simplified) anatomy and physiology. The
basic model accounts for the non-local feature of neocortical dynamics,
but does not include hierarchical interactions or embedded networks,
which have been included approximately in detailed studies by perhaps a
dozen scientists, especially Viktor Jirsa and Herman Haken. The
excitatory synaptic action at cortical location r may be expressed in
terms of an inner integral over the cortical surface and outer integral
over distributed axon propagation speeds.
This integral equation is based on the simple, non-controversial idea
that excitatory synaptic action at cortical location r is due to
excitatory sub-cortical input dPsi_0(r,t) plus excitatory action potential density
dTheta(r,t) integrated over the entire neocortex; action potentials produce
synaptic activity at some distant location after a delay that is
proportional to cortical separation distance and inversely proportional
to axon speed. Distances are defined on an equivalent smooth cortex. All
the complications of white matter (cortico-cortical) fiber tracts (after
smoothing) are included in an anatomical distribution function. The
model is able to predict several aspects of the measured spatial
temporal dynamics of large scale EEG, including approximate oscillation
frequencies, propagation velocities of traveling waves, relations
between spatial and temporal properties (dispersion relations), and so
The large-scale dynamic behavior of neocortex measured with EEG is
closely correlated with brain state. I describe brain dynamics in terms
of neural networks embedded within global excitatory and inhibitory
synaptic action fields and the action potential field, given the symbols
Psi_e(r,t), Psi_i(r, t) and Theta(r, t), respectively. The (short time) modulation of these synaptic fields
is recorded as EEG. Embedded network dynamics may or may not be recorded
with scalp EEG depending on various technical issues. By studying the
physiological and anatomical bases for this dynamic behavior, we infer
that hierarchical interactions across spatial scales and non-local
interactions in space and time are essential to a healthy consciousness.
Cognitive processes and even consciousness itself appear to be
correlated with EEG oscillations in selective frequency bands. Eugene
Izhikevich has shown that weakly connected “oscillators” (very broadly
defined) substantially interact only when their characteristic
(resonant) frequencies obey certain resonant relations. In the context
of this paper, such oscillators may be associated with both the networks
and global fields. A simple example is the case in which global synaptic
fields oscillating at frequency f0 can cause strong coupling between two
isolated networks with characteristic frequencies f1 and f2, for example when
f0 = f1 +- f2.
Each of these characteristic frequencies could be selectively altered by
neurotransmitters. This raises the speculation that consciousness
depends critically on resonance phenomena and only properly tuned brains
can orchestrate the beautiful music of sentience.
Abstract: Brain Rhythms and the Global Workspace Theory
Functionally specialized brain regions extract and construct features and feature conjunctions from sensory data.
The neuronal signals representing individual features are transiently integrated into neuronal representations of
the entire perceptual objects because human perception, attention, and short-term memory operate with objects
rather than with constellations of features1,2,3. The question of how the neuronal feature representations are
bound into coherent object representations is called the binding problem4 and is though to be achieved by
oscillatory synchronization of the underlying anatomically distributed neuronal activity5,6. Indeed, many
findings4,5,7,8 are in line with the idea that oscillatory activity in the beta- (14–30 Hz) and, in particular,
in the gamma- (30–80 Hz) frequency bands beget the binding of features into neuronal object representations and
reflect, in general, the cortical states of “active” neuronal processing. How are these sensory object
representations perceived consciously? Functional imaging studies have shown that a circuit of frontal and
parietal regions is engaged during conscious perception9,10 but it is not clear how this circuit evolve and
how it is coordinated.
Conceptual models of consciousness provide frameworks that can guide experimental work on, e.g., how sensory
objects become consciously perceived. In the global workspace (GW) theory, consciousness is understood to
consist of unconscious “specialized processors or sensory modules” that i.e. represent sensory information
and of a global workspace that integrate these competing and co-operative networks11,12,13. The global workspace
is thought to involve a fronto-parietal network with which sensory information should interact in order to enter
Alpha-frequency band (8-14 Hz) oscillations may be phase synchronized between widely separated cortical
structures14,15,16,17,18 and hence could mediate the integration of large-scale networks required e.g. by
global workspace. This possibility, however, has not been widely embraced because the amplitude dynamics of
oscillations in the alpha (8–14 Hz) frequency band have been interpreted to indicate that these oscillations
have a role in inhibition and inactivate the task-irrelevant cortical regions19. Nevertheless, many recent studies
on the phase dynamics of alpha oscillations, imply a direct role for alpha oscillations in attention16,18,20,
consciousness14,21, and STM15,17. I show that alpha-band phase-locking in human sensorimotor, as well as in
frontoparietal regions is correlated selectively with the conscious perception of weak somatosensory stimuli14.
These data show no other clear correlates of conscious perception in other frequency bands and underline the
putative role of alpha oscillations in sensory awareness. I also present a model in which alpha-band synchronized
network of frontal and parietal regions define the global workspace22. We suggest that this global workspace
mediates, e.g., attentional and central executive functions and is also essential for working memory. In line with
this hypothesis, human perception23,24, action25,26,27 and eye movements28 are associated with alpha frequency band
In addition, I present data on cross-frequency phase synchrony between oscillations in distinct frequency bands15.
I show that the phase-synchrony between alpha and gamma oscillations is load dependently strengthened by working
memory. Taken the proposed representational5,8 and attentional16,18,20,22 roles of these oscillations these results
suggest that cross-frequency phase synchrony between alpha and gamma oscillations may mediate content-to context
binding during working memory.
We thus suggest that local beta- and gamma-band oscillatory assemblies could be coordinated by the alpha-band
synchronized frontoparietal network through CF-phase synchrony. Such CF synchronized network of oscillatory activity
could underlie conscious perception and integrate “local processors” to “global workspace”.
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Neural synchrony in attention and consciousness
Lawrence M. Ward
University of British Columbia
Complex cognitive processes such as attention, memory, and consciousness
require the recruitment and coordination of task-relevant neural populations
that are widely distributed throughout the brain. Modern neuroimaging
techniques have been able to identify specific regions of cortex that
comprise a functional network for some cognitive tasks, including attention,
memory, and consciousness, but the precise nature of the processing involved
remains elusive. Other techniques, more sensitive to temporal progression,
such as EEG and MEG, have revealed that neural synchronization is deeply
involved in bringing brain activity to consciousness. In particular, increased
synchronization of neural activity in widespread brain areas has been directly
related to the awareness of binocularly-rivaling stimuli. I will present our
recent data on the time course of neural synchronization (phase locking) in
particular frequency bands within and among specific brain areas involved in
endogenous attention orienting and in consciousness changes during binocular
rivalry. These data support the idea that local and long-distance synchrony
play different but complementary roles in the brain's dynamic functional
organization, and that such neural synchrony is an important neural correlate
of consciousness awareness, creating a dynamic core of neural activity that
in turn is responsible for primary conscious awareness. I will also present
arguments that the dynamic core is realized in both cortical and thalamic
brain regions, mediated by reciprocal connections between them. In my theory,
also supported by lesion, direct stimulation, anesthetic, and psychological
data as well as neural modeling and neuroanatomy (which I will briefly review),
the cortex computes the contents of consciousness whereas the thalamus (more
specifically the dendritic trees of higher-order thalamic "relay" neurons) is
the locus of synchronized neural activity that directly reflects/creates