DREAMING AND THE SELF-ORGANIZING BRAIN
The Ionasphere, ©2001
THE SELF-ORGANIZING BRAIN
David Kahn, Stanley Krippner, and Allan Combs
Abstract: We argue the REM dream experiences owe their structure
and meaning to inherent self-organizing properties of the brain itself.
Thus, we offer a common meeting ground for brain based studies of dreaming
and traditional psychological dream theory. Our view is that the
dreaming brain is a self-organizing system highly sensitive to internally
generated influences. Several lines of evidence support a process
view of the brain as a system near the edge of chaos, one that is highly
sensitive to internal influences. Such sensitivity is due to several
factors. First, the dreaming brain normally gates our external input
and thus operates without the stabilizing influences of external feedback.
Second, the pre-frontal cortex is only minimally activated during REM sleep,
and hence the brain operates with weakened volition, reduced logic, and
diminished self-reflection. Third, because the neuromodulatory inhibition
mechanism is turned off during REM, the brain responds spontaneously to
the least provocation. In addition, the dreaming brain is also subject
to powerful intermittent cholinergic stimulation which may stimulate creative
patterns of dream activity.
Over the past three decades numerous empirical and theoretical investigations
have made it apparent that self-organizing dynamics are fundamental to
processes at many levels of the organic as well as the physical world (e.g.,
Kaufmann, 1993; Laszlo, 1987; Maturana, Varela, & Uribe, 1974, Prigogine
& Stengers, 1984). Recent work shows this to be no less true
for the brain (e.g., Freeman, 1991) Kahn & Hobson, 1993; Pribram, 1995;
Varela, Thompson, & Rosch, 1991), and indeed for the process structure
of human experience itself (e.g., Combs, 1996; Combs & Krippner, 1998).
The present paper examines such self-organizing dynamics in the brain with
the aim of understanding the REM dream experience and how it differs from
waking consciousness. We begin with the brain.
The Self-Organizing Brain
Many lines of evidence argue for the idea that the brain is a self-organizing
system comprised of self-organizing subsystems. To begin with, how
could it be otherwise? Though the brain is commonly conceptualized
in terms of neural networks and circuitry, there seems little doubt that
this circuitry is not rigid, but is significantly influenced by neurological
development, day to day learning experiences, and many types of neuromodulation.
Thus, the apparent neuroanatomical stability of the brain hides beneath
itself many dynamic processes of change. Moreover, the widespread and continuous
presence of both single unit firing and mass activity suggests that process
itself is an essential feature of the brain, as important as anatomy.
While machines and passive electrical circuits can spend indefinite periods
of time in inactivity, self-organization and self-creating (autopoietic)
systems such as ecologies and living organisms are constantly in motion,
as indeed is the living brain.
Many of the activity patterns exhibited by the brain are indicative of
complex underlying self-organizing processes. The EEG rhythm, for
example, tends to be roughly cyclic, but is not precisely so. It's
global form is easily recognized, but the exact shape of its waves differs
from cycle to cycle, defying precise prediction. Moreover, it is
unlikely that it ever exactly repeats itself. This situation of global
familiarity combined with non-predictability, in a pattern that never precisely
repeats itself, is exactly what defines a chaotic process, one whose action
describes a strange, or "chaotic" attractor (Kellert, 1993).
An attractor is a pattern of behavior toward which all nearby patterns
(or trajectories) converge. If they converge to a perfectly cyclic
pattern we have a cyclic attractor and in a physical system we are
dealing with something like a clock, that always settles into a regular
rhythm. When mathematicians discovered equations for attractors that
never settle down in this fashion they humorously called them "strange,"
and these have continued to be known as strange or chaotic attractors.
Such attractors appear to be a common if not universal feature of complex
self-organizing systems such as living cells, ecologies, and evidently
brains as well (e.g., Abraham & Gilgen, 1994; Basar, 1990; Freeman,
1995; Pribram, 1995; Robertson & Combs, 1995; but also see Mandell
& Selz, 1997).
Additionally, the human EEG exhibits significant fractal structure (e.g.,
Basar, 1990; Screenivason, Pradhan, and Rapp, 1999), further suggesting
that it is the result of complex self-organizing processes (Anderson &
Mandell, 1996). With regard to REM sleep, at least one investigation
(Babloyantz, 1990) found rapid eye movement (REM) sleep EEG to exhibit
higher dimensionality than slow wave sleep, suggesting the play of a larger
number of underlying influences, as one might expect if EEG activity in
any way reflects the complexity of accompanying dream experiences.
Anderson and Mandell (1996), who have made detailed studies of the temporal
structure of REM state electrical activity in fetal rats, believe that
such activity reflects self-organizing hierarchical integrative processes
in the developing nervous system. Interestingly, preliminary evidence
indicates that this integrative process may follow an abnormal developmental
course in the case of autistic individuals (Tanguay, Ornitz, Forsythe,
& Ritvo, 1976).
The fractal constituency of the EEG also suggest the possibility that the
brain resides in a state of self-organized criticality (Bak, 1996).
A system is said to be in a critical state if a small stimulation can set
it into fluctuation on all length or temporal scales--in other words, if
the response distribution is fractal. The classic example of a critically
poised system is a sand pile ready to cascade into an avalanche when a
single grain of sand is dropped onto it. Bak points out that the
brain must also be critically poised. Otherwise it would not, for
instance, respond globally to the appearance of a single visual image which
carries but a minute amount of actual physical energy. Unlike the
sand pile, however, the brain is not a randomly organized static structure,
but an enormously complex ongoing dynamical process system, a product
of its own self-organizing tendencies, and thus can rightly be said to
exhibit self-organized criticality. With regard to the importance
of self-organized criticality in biological systems, Stewart Kauffman (1993)
observed that "selection achieves and maintains complex systems poised
on the boundary or edge of between order and chaos. These systems
are best able to coordinate complex tasks and evolve in a complex environment"
All this is simply another way of understanding the notion that even small
influences can exert sizable or even dramatic effects on ongoing patterns
of brain activity. The best known example of this is the butterfly
effect, which refers to the idea that no matter how small an external
influence (such as sensory stimulation) might be, its influence, when compounded
through many recurrent cycles of system activity, can grow to virtually
unlimited proportions (Kellert, 1993; Peak, 1994). The effect was
originally discovered by meteorologist Edward Lorenz (1963) in models of
fluid convection. It came to be known technically as sensitive
dependence on initial conditions and is a distinguishing feature of
chaotic behavior. In the popular literature, as most present readers
will know, the "butterfly effect" refers to the notion that the stroke
of the butterfly's wing, say, in Brazil, might cascade a few days later
into a hurricane in the Bahamas--or alternatively quell a potential hurricane
More important than the butterfly effect, however, is the seemingly paradoxical
effect known as stochastic resonance, that has been demonstrated
in electronic circuits as well as in nerve cells (Moss and Wiesenfeld,
1995). It refers to the fact that the presence of vibration or noise
keeps the system in motion and tracking an overall course of least resistance,
rather than getting stuck in small groves or "minima." For instance,
an object on a vibrating tabletop will sometimes "walk" about, especially
if the table is not level, following the overall line of least resistance
down the slope of the surface. Stochastic resonance can actually
improve the effective signal to noise ratio in a communication situation.
In the brain it may allow ongoing processes to "relax" into inherently
natural patterns of activity, an important point to which we will return
First, let us consider the possibility that the brain's activity, like
that of other extremely complex systems such as the weather, can be understood
as an exquisitely intricate strange attractor, one exhibiting an intricate
array of "wings" or "compartments" (Goertzel, 1994). During wakefulness
the shape of this attractor, especially in the sensory cortices, is powerfully
constrained by sensory input, which itself is often highly patterned (e.g.,
Gibson, 1966, 1979). Freeman and his colleagues (Freeman, 1991, 1995;
Freeman & Barrie, 1994) have mapped such attractors in a variety of
different sensory cortices. They found that the sensory regions of
the brain are critically poised to respond robustly and in an ordered fashion
to even the smallest stimulation. In the REM state, however, such
attractors are not constrained by sensory input. In this state the
self-organizing dynamics of the brain are set into motion not by external
stimulation but by its own internal situation. Interestingly, it
is possible to find such self-organizational dynamics at work in the waking
state as well. Freeman, for instance, discovered that new learning
experiences actually modify previously established cortical activity patterns.
For example, a rabbit's original cortical response to an odor is altered
when the odor is experienced in a new context, such as a classical conditioning
situation. Freeman interprets such changes to signify that the meaning
of the stimulus is as important in the production of the brain's response
as the physical structure of the stimulus itself. Speaking informally,
Freeman (1997) once observed that if one sees Hamlet, then sees
and Guildenstern are Dead, returning to Hamlet finds it to be
a different play.
The Dreaming Brain
During REM sleep the brain is as active as it is during the waking state.
(This paper does not pursue the knotty debate over the meaning or even
existence of non-REM dreaming, but for an excellent critical review of
this question see the recent paper by Hobson, Pace-Schott, and Stickgold
(2000). However, information processing is inner-oriented as distinct
from the outer sensory orientation of waking. In this state a number
of factors combine to make the brain acutely reactive to internally generated
influences. To begin with, the stabilizing effects of external sensory
input are actively inhibited. Also, there is a shift away from widespread
aminergic neuromodulatory inhibition which dominates the waking brain,
toward cholinergic modulation that predisposes the sleeping brain to easy
activation (Hobson, 1994, 1988).
In terms of activation patterns in the REM sleeping brain, recent investigations
using PET scans (Braun, et al, 1997, 1998, Maquet, et al, 1996, 1997) show
notable arousal of the extrastriate visual cortex, especially in the ventral
processing stream. Notable activation is also seen in limbic and
para-limbic structures, most significantly in the anterior cingulate and
the amygdaloid complexes. Meanwhile, activity in the dorsolateral
prefrontal cortex is markedly reduced. Taken together, these finding
point toward emotional arousal during dreaming, while at the same time
suggest a reduction o memory as well as diminished capacity for logic and
self-reflection. These conclusions are entirely consistent with many
studies of the subjective qualities of REM dreaming (e.g., Hall & Van
de Castle, 1966; Tonay, 1991).
Interestingly, Braun et al (1998) also reports decreased activation of
the primary visual cortex during REM. This observation may seem surprising,
since a deactivated primary visual cortex due, say, to a stroke, results
in the absence of visual awareness. It is, however, consistent with
the suggestion that the conscious experience of vision is more directly
associated with the extrastriate association areas, and their connections
with the frontal cortex, than with the primary visual cortex itself (Crick
& Koch, 1992; Koch, 1998; Revonsuo, 1998). In line with this,
lesion studies show that damage to the extrastriate cortex, as well as
damage to the parietal operculum and to the mediobasal frontal cortex,
result in decreased dreaming (Solma, 1997; Hobson, et al 1998a).
Patients who reported a global cessation of dreaming had damage in the
parietal convexity or suffered disconnection of the mediobasal frontal
cortex from the brainstem and diencephalic limbic regions (Solms, 1997;
Hobson et al 1998b).
PGO Stimulation, the Dream, and the Self-Organizing Brain
Sleep affords the opportunity, within certain limits, for the brain
to act of itself, and dreams are the result.
Edward Clarke, A Study of False Sight, 1878
A prominent feature of REM sleep is the presence of large PGO (pontine-geniculate-occipital)
spikes which originate in the brainstem, travel upward to the lateral geniculate
bodies of the thalamus, and then on to the occipital lobes where they exert
powerful cholinergic stimulation (Callaway et al, 1987). Hobson and
McCarley (1977) proposed in their original activation-synthesis hypothesis
that this PGO activity is interpreted by the visual brain as sensory stimulation.
In this view dreams results from efforts of the visual brain to make sense
out of random PGO bombardment. Taken on face value this idea leaves
relatively little room for dream experiences to be taken seriously as meaningful.
Recently, however, Hobson and one of the present authors took the initial
steps toward exploring the notion that the content of dream consciousness
is the result of self-organizing dynamics in the brain (Kahn & Hobson,
1993). This approach, continued in the present paper, offers the
potential of shedding light on how coherent dream experiences can result
from the influence of seemingly unpatterned PGO stimulation. From
our point of view, PGO activity might have two effects on the dreaming
First, the cortical bombardment by PGO spikes might act as a perturbation
to the dreaming visual cortex, creating stochastic resonance. The
resulting effect would be something like that of tapping a drumhead on
which sand has been sprinkled. In response to this action the sand
forms complex patterns characteristic of the dynamics of the drum-head
itself. These induced vibrations allow the system of the sand on
the drumhead to "relax" into its own unique configuration. In like
fashion, this raising of the cortical "temperature" by PGO stimulation
would allow the ongoing patterns of cortical activity to relax into natural
forms (attractors) shaped by the emotional and cognitive influences present
at each moment (also see Globus, 1989). The origins of these influences
are addressed below, but the point is that the dreaming brain, isolated
from external sensory constraints, is subject to even subtle influences,
which might lead to sizable effects on patterns of neural activity (Combs
& Krippner, 1998). Such effects are felt experientially as the
conscious flow of the dream. This does not mean, for instance, that
dream narratives carry no forward momentum of their own. Indeed,
the creation of stories seems to be virtually obligatory to the human mind
and brain. Rather, the pelting of the cortex by PGO waves "heats
up" the entire process, yielding a stochastic resonance effect that does
not let the system stagnate, but keeps it going in forward motion that
is sensitive to the changing psychphysiological state of the brain--or
in other words keeps the dream narrative in motion. As an interesting
aside, we not that PGO timing becomes progressively more coherent over
the neocortex during periods of REM sleep, suggestive of any underlying
self-organizing stochastic process (Amzica and Steriade, 1996).
Second, the bombardment of the visual cortex with PGO waves might also
have the effect of frequently derailing ongoing patterns of activity, or
in other words producing "catastrophic bifurcations" in the attractor patterns
there. (A transformation in the form of an attractor is termed a
if it occurs abruptly it is called a catastrophic bifurcation.)
One might imagine abrupt alterations in dream experiences at those times.
Consistent with this idea, Mamelak and Hobson (1989) have suggested that
PGO stimulation is tied to the high rate of narrative or plot shifts experienced
during REM dreaming. Such shifts are significantly more frequent
in REM dreaming than during dreaming reported from slow wave sleep (Cavallero,
Cicogna, Natalie, Occhionero & Zito, 1992). Indeed, they seem
essential to the "bizarreness" of REM dreams (Porte & Hobson, 1986).
Abrupt transitions in dream content are made all the more effortless during
REM sleep by a diminished short term memory and the loss of a continuous
objective sense of self (e.g.s see Purcell, Mullington, Moffitt, Hoffman,
& Pigeau, 1986), both perhaps related to the fact that the prefrontal
lobes are essentially taken off-line in the REM state.
Turning to other influences that mold the content of dreams, the observation
of high activation in certain limbic structures during REM sleep is consistent
with the hypothesis that emotional factors play a significant role in dreams.
The brain clearly does not receive emotional influences passively, however,
but incorporates them into complex self-organized patterns that play themselves
our as dream narratives (Combs & Krippner, 1998). Other influences
on dream content include long-term episodic and semantic memories, "relaxed"
into the dream narrative, as well as recent experiences whose emotional
residues remain written on the mind and the brain for as long as a few
hours to a few days (Globus, 1989). Freud (1900/1955), for instance,
pointed out rightly that much dream content is often directly related to
experiences of the prior day, a view that has found general support ever
sense (Hall & Van de Castle, 1966).
Recalling the importance of the butterfly effect in systems governed by
chaotic dynamics suggests that even subtler influences might also be operative
in the dreaming brain. These could include, for example, narratives
and symbols laid down as Hebbian network early in the development of the
brain, perhaps through personal experience or even by genetic patterning
(e.g., Edelman, 1992, 200). If such networks exist they could do
much to give the interpretative views of dynamic psychology a grounding
in the study of the brain.
The actual details of how the brain transforms each night's panoply of
emotional and cognitive influences into the rick and flowing experiences
of dream life remains a deep mystery. These presentations, however,
in which reality is essentially preserved, but stretched, tuned about,
and parceled out into fragments, "look" a lot more like the outcome of
dynamical processes than of computational ones. Gordon Globus (1995),
who has expressed similar ideas about the dreaming brain, observes of dreams
that offer solutions to personal problems.
There is no unconsciousness intelligence, no "wisdom of the species" personified
in the archetype of the Wise Old Man, that is sending me a message of how
to deal with this problem, as Jung thought. Instead the networks
spontaneously move toward harmonious self-consistency; the "wisdom" is
akin to that of a rubber band that spontaneously relaxes after it has been
stretched, but of course the neural system is much more complex.
The spontaneous movement under the harmony principle provides the dream.
The best solution to my problem is spontaneously generated by, this
self-organizing process, (p. 10).
Dreams are still a mystery. But now they are a mystery of the brain
as well as a mystery of the mind, or more succinctly, of the brain-mind,
and as such may yield to continuing scientific efforts.
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