MODELS WANTED: MUST FIT DIMENSIONS 
OF SLEEP AND DREAMING* 
J. Allan Hobson, Adam N. Mamelak t and Jeffrey P. Sutton  
Laboratory of Neurophysiology and Department of Psychiatry 
Harvard Medical School 
74 Fenwood Road, Boston, MA 02115 
Abstract 
During waking and sleep, the brain and mind undergo a tightly linked and 
precisely specified set of changes in state. At the level of neurons, this 
process has been modeled by variations of Volterra-Lotka equations for 
cyclic fluctuations of brainstem cell populations. However, neural network 
models based upon rapidly developing knowledge of the specific population 
connectivities and their differential responses to drugs have not yet been 
developed. Furthermore, only the most preliminary attempts have been 
made to model across states. Some of our own attempts to link rapid eye 
movement (REM) sleep neurophysiology and dream cognition using neural 
network approaches are summarized in this paper. 
1 INTRODUCTION 
New models are needed to test the closely linked neurophysiological and cognitive 
theories that are emerging from recent scientific studies of sleep and dreaming. This 
section describes four separate but related levels of analysis at which modeling may 
*Based, in part, upon an invited address by J.A.H. at NIPS, Denver, Dec. 2 1991 and, 
in part, upon a review paper by :J.P.S., A.N.M. and :J.A.H. published in the Pzychiatric 
A nnalz. 
tCurrently in the Department of Neurosurgery, University of California, San Francisco, 
CA. 94143 
$Also in the Center for Biological Information Processing, Whitaker College, E25-201, 
Massachusetts Institute of Technology, Cambridge, MA 02139 
4 Hobson, Mamelak, and Sutton 
be applied and outlines some of the desirable features of such models in terms of the 
burgeoning data of sleep and dream science. In the subsequent sections, we review 
our own preliminary efforts to develop models at some of the levels discussed. 
1.1 THE INDIVIDUAL NEURON 
Existing models or "neuromines"faithfully represent membrane properties but ig- 
nore the dynamic biochemical changes that change neural excitability over the long 
term. This is particularly important in the modeling of state control where the 
crucial neurons appear to act more like hormone pumps than like simple electrical 
transducers. Put succinctly, we need models that consider the biochemical or "wet" 
aspects of nerve cells, as well as the "dry" or electrical aspects (of. McKenna et 
in press). 
1.2 NEURAL POPULATION INTERACTIONS 
To mimic the changes in excitability of the modulatory neurons which control sleep 
and dreaming, new models are needed which incorporate both the engineering prin- 
ciples of oscillators and the biological principles of time-keeping. The latter prin- 
ciple is especially relevant in determining the dramatically variable long period 
time-constants that are observed within and across species. For example, we need 
to equip population models borrowed from field biology (McCarley and Hobson, 
1975) with specialized properties of "wet"neurons as mentioned in section 1.1. 
1.3 
COGNITIVE CONSEQUENCES OF MODULATION OF 
NEURAL NETWORKS 
To understand the state-dependent changes in cognition, such as those that distin- 
guish waking and dreaming, a potentially fruitful approach is to mimic the known 
effects of neuromodulation and examine the information processing properties of 
neural networks. For example, if the input-output fidelity of networks can be al- 
tered by changing their mode (see Sutton et al., this volume), we might be better 
able to understand the changes in both instantaneous associative properties and 
long term plasticity alterations that occur in sleep and dreaming. We might thus 
trap the brain-mind into revealing its rules for making moment-to-moment cross- 
correlations of its data and for changing the content and status of its storage in 
memory. 
1.4 STATE-DEPENDENT CHANGES IN COGNITION 
At the highest level of analysis, psychological data, even that obtained from the 
introspection of waking and dreaming subjects, need to be more creatively reduced 
with a view to modeling the dramatic alterations that occur with changes in brain 
state. As an example, consider the instability of orientation of dreaming, where 
times, places, persons and actions change without notice. Short of mastering the 
thorny problem of generating narrative text from a data base, and thus synthesiz- 
ing an artificial dream, we need to formulate rules and measures of categorizing 
constancy and transformations (Sutton and Hobson, 1991). Such an approach is a 
Models Wanted: Must Fit Dimensions of Sleep and Dreaming 5 
means of further refining the algorithms of cognition itself, an effort which is now 
limited to simple activation models that cannot change mode. 
An important characteristic of the set of new models that are proposed is that each 
level informs, and is informed by, the other levels. This nested, intexlocking feature 
is represented in figure 1. It should be noted that any erroneous assumptions made 
at level 1 will have effects at levels 2 and 3 and these will, in turn, impede our 
capacity to integrate levels 3 and 4. Level 4 models can and should thus proceed 
with a degree of independence from levels 1, 2 and 3. Proceeding from level 1 upward 
is the "bottom-up" approach, while proceeding from level 4 downward is the "top- 
down" approach. We like to think it might be possible to take both approaches in 
our work while according equal respect to each. 
LEVEL SCHEMA FEATURES 
IV COGNITIVE 
STATES A-- B ( C -- D 
(eg. dream plot E-- F 
sequences) 
variable associative 
and learning states 
III MODULATION 
OF NETWORKS 
(eg. hippocampus, 
cortex) 
modulation of 
I-O processing 
POPULATIONS 
(eg. pontine 
brainstem) 
variable time- 
constant oscillator 
I SINGLE 
NEURONS 
(eg. NE, 5HT, 
ACh neurons) 
... (;. wet hormonal 
aspects 
Figure 1: Four levels at which modeling innovations are needed to provide more 
realistic simulations of brain-mind states such as waking and dreaming. See text 
for discussion. 
6 Hobson, Mamelak, and Sutton 
2 STATES OF WAKING AND SLEEPING 
The states of waking and sleeping, including REM and non-REM (NREM) sleep, 
have characteristic behavioral, neuronal, polygraphic and psychological features 
that span all four levels. These properties are summarized in figures 2 and 3. 
Changes occurring within and between different levels are affected by the sleep- 
wake or circadian cycle and by the relative shifts in brain chemistry. 
A 
Behavior 
Polygraph 
EM 
EOG 
Sensat/bn and 
Percept/on 
Thought 
WAKE NREM SLEEP REM SLEEP 
Ctis E Commaed 
Voluntary Inlunlory t Inhibit 
Movement 
B 
c 
I I I I I I 
Time (hours) 
. _ ,,'1 
I I I I 
Time (hours) 
Figure 2: (a) States of waking and NREM and REM sleeping in humans. Charac- 
teristic behavioral, polygraphic and psychological features are shown for each state. 
(b) Ultradian sleep cycle of NREM and REM sleep shown in detailed sleep-stage 
graphs of 3 subjects. (c) REM sleep periodograms of 15 subjects. From Hobson 
and Steriade (1986), with permission. 
Models Wanted: Must Fit Dimensions of Sleep and Dreaming 7 
.1 CIRCADIAN RHYTHMS 
The circadian cycle has been studied mathematically using oscillator and other 
non-linear dynamical models to capture features of sleep-wake rhythms (Moore-Ede 
and Czeisler, 1984; figure 2). Shorter (infradian) and longer (ultradian) rhythms, 
relative to the circadian rhythm, have also been examined. In general, oscillators 
are used to couple neural, endocrine and other pathways important in controlling 
a variety of functions, such as periods of rest and activity, energy conservation and 
thermoregulation. The oscillators can be sensitive to external cues or zeitgebers, 
such as light and daily routines, and there is a stong linkage between the circadian 
clock and the NREM-REM sleep oscillator. 
2.2 RECIPROCAL INTERACTION MODEL 
In the 1970s, a brainstem oscillator became identified that was central to regulating 
sleeping and waking. Discrete cell populations in the pons that were most active 
during waking, less active in NREM sleep and silent during REM sleep were found 
to contain the monoamines norepinephrine (NE) and serotonin (5HT). Among the 
many cell populations that became active during REM sleep, but were generally 
quiescent otherwise, were cells associated with acetylcholine (ACh) release. 
ICholinergicl 
 
o 
o o o o o 
B 
D 
5 D W 
-4 
.3 
-2 
-1 
Figure 3: (a) Reciprocal interaction model of REM sleep generation showing the 
structural interaction between cholinergic and monoaminergic cell populations. 
Plus sign implies excitatory influences; minus sign implies inhibitory influences. 
(b) Model output of the cholinergic unit derived from Lotka-Volterra equations. 
(c) Histogram of the discharge rate from a cholinergic related pontinc cell recorded 
over 12 normalized sleep-wake cycles. Model cholinergic (solid line) and monoamin- 
ergic (dotted line) outputs. (d) Noradrenergic discharge rates before (S), during 
(D) and following (W) a REM sleep episode. From Hobson and Steriade (198), 
with permission. 
8 Hobson, Mamelak, and Sutton 
By making a variety of simplifying assumptions, McCarley and Hobson (1975) 
were able to structurally and mathematically model the oscillations between these 
monoaminergic and cholinergic cell populations (figure 3). This level 2 model 
consists of two compartments, one being monoaminergic-inhibitory and the other 
cholinergic-excitatory. It is based pupon the assumptions of field biology (Volterra- 
Lotka) and of dry neuromines (level 3). The excitation (inhibition) originating from 
each compartment influences the other and also feeds back on itself. Numerous pre- 
dictions generated by the model have been verified experimentally (Hobson and 
Steriade, 1986). 
Because the neural population model shown in figure 3 uses the limited passive 
membrane type of neuromine discussed in the introduction, the resulting oscillator 
has a time-constant in the millisecond range, not even close to the real range of min- 
utes to hours that characterize the sleep-dream cycle (figure 2). As such, the model 
is clearly incapable of realistically representing the long-term dynamic properties 
that characterize interacting neuromodulatory populations. To surmount this limi- 
tation, two modifications are possible: one is to remodel the individual neuromines 
equipping them with mathematics describing up and down regulation of receptors 
and intracellular biochemistry that results in long-term changes in synaptic efficacy 
(cf. McKenna et al., in press); another is to model the longer time constants of 
the sleep cycle in terms of protein transport times between the two populations in 
brainstems of realistically varying width (cf. Hobson and Steriade, 1986). 
3 
NEUROCOGNITIVE ASPECTS OF WAKING, 
SLEEPING AND DREAMING 
Since the discovery that REM sleep is correlated with dreaming, significant ad- 
vances have been made in understanding both the neural and cognitive processes 
occurring in different states of the sleep-wake cycle. During waking, wherein the 
brain is in a state of relative aminergic dominance, thought content and cognition 
display consistency and continuity. NREM sleep mentation is typically character- 
ized by ruminative thoughts void of perceptual vividness or emotional tone. Within 
this state, the aminergic and cholinergic systems are more evenly balanced than in 
either the wake or REM sleep states. As previously noted, REM sleep is a state 
associated with relative cholinergic activation. Its mental status manifestations in- 
clude graphic, emotionally charged and formally bizarre images encompassing visual 
hallucinations and delusions. 
3.1 ACTIVATION-SYNTHESIS MODEL 
The activation-synthesis hypothesis (Hobson and McCarley, 1977) was the first 
account of dream mentation based on the neurophysiological state of REM sleep. 
It considered factors present at levels 3 and 4, according to the scheme in section 1, 
and attempted to bridge these two levels. In the model, cholinergic activation and 
reciprocal monoaminergic disinhibition of neural networks in REM sleep generated 
the source of dream formation. However, the details of how neural networks might 
actually synthesize information in the REM sleep state was not specified. 
Models Wanted: Must Fit Dimensions of Sleep and Dreaming 9 
3.2 NEURAL NETWORK MODELS 
Several neural network models have subsequently been proposed that also attempt 
to bridge levels 3 and 4 (for example, Crick and Mitchison, 1983). Recently, Mame- 
lak and Hobson (1989) have suggested a neurocognitive model of dream bizarreheSS 
that extends the activation-synthesis hypothesis. In the model, the monoaminer- 
gic withdrawal in sleep relative to waking leads to a decrease in the signal-to-noise 
ratio in neural networks (figure 4). When this is coupled with phasic choliner- 
gic excitation of the cortex, via brainstem ponto-geniculo-occipital (PGO) cell fir- 
ing (figure 5), cognitive information becomes altered and discontinuous. A central 
premise of the model is that the monoamines and acetylcholine function as neuro- 
modulators, which modify ongoing activity in networks, without actually supplying 
afferent input information. 
Implementation of the Mamelak and Hobson model as a temporal sequencing net- 
work is described by Sutton et al. in this volume. Computer simulations demon- 
strate how changes in modulation similar to some monoaminergic and cholinergic 
effects can completely alter the way information is collectively sequenced within the 
same network. This occurs even in the absence of plastic changes in the weights 
connecting the artificial neurons. Incorporating plasticity, which generally involves 
neuromodulators such as the monoamines, is a logical next step. This would build 
important level i features into a level 3-4 model and potentially provide useful 
insight into some state-dependent learning operations. 
I TTS 
Pl *10aw. 
Inpu! 
Oupul(s) 
 ,,oo,o 
O == OOe 
 
Figure 4: (a) Monoaminergic innerration of the brain is widespread. (b) Plot of 
the neuron firing probability as a function of the relative membrane potential for 
various values of monoaminergic modulation (parameterized by a). Higher (lower) 
modulation is correlated with smaller (larger) a values. (c) Neuron firing when sub- 
jected to supra- and sub-threshold inputs of 9-10 my and -10 my, respectively, for 
a - 2 and a - 10. (d) For a given input, the repertoire of network outputs generally 
increases as a increases. From Mamelak and Hobson (1989), with permission. 
10 Hobson, Mamelak, and Sutton 
Figure 5: (a) Cholinergic input fom the brainstem to the thalamus and cortex is 
widespread. (b) Unit recordings fom PGO burst cells in the pons are correlated 
with PGO waves recorded in the lateral geniculate bodies (LGB) of the thalamus. 
4 CONCLUSION 
After discussing four levels at which new models are needed, we have outlined some 
preliminary efforts at modeling states of waking and sleeping. We suggest that this 
area of research is ripe for the development of integratire models of brain and mind. 
Acknowledgement s 
Supported by NIH grant MH 13,923, the HMS/MMHC Research & Education Fund, 
the Livingston, Dupont-Warren and McDonnell-Pew Foundations, DARPA under 
ONR contract N00014-85-K-0124, the Sloan Foundation and Whitaker College. 
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