p&mJRJz VOL.30 I4 JULY 1983       COMMENTARY                                     III



The function of dream sleep

Francis Crick* & Graemi Mitchison*

We propose that the function of dream sleep (more proper& rapid-&ye movement or REM sleep) LT to
remove certain undesirable modes of interaction in networks of cells in the cerebral cortex. We
postulate that this is done in REMsleep by a revem learning mechanism (see also p. 158), so that the
trace in the brain of the unconscipus dream is weakened, rather than strengthened, by the dream.

MANKIND has always been fascinated by
dreams. As might be expected, there have
heen many attempts to assign a purpose Or
significance to them. Although we dream
for one or two hours every night, we do not
remember most of our dreams. Earlier
thinkers, such as Freud, did not know this.
Modern theories (not reviewed here in
detail) have usually proposed that sleep
and dreams save energy or have various
restorative functions, either to replenish
the brain biochemically in some way, or to
reclassify or reorder the information stored
in it.

Sleep is of several kinds. Dream sleep, or
rapid eye movement (REM) sleep, is
predominantly found in viviparous mam-
mals and birds. It seems to be associated
with homeothermy (a constant internal
temperature) and the possession of an
appreciable neocortex or its equivalent.
It is not unimportant because of the ap-
preciable amount of time we spend in this
Peculiar state.

We propose here a new explanation for
the function of REM sleep. The basis of
our theory is the assumption that in
viviparous mammals the cortical system
(the cerebral cortex and some of its'
associated subcortical structures) can be
regarded as a network of interconnected
Cells which can support a great variety of
modes of mutual excitation. Such a system
is likely to be subject to unwanted or
`Parasitic' modes of behaviour, which arise
as it is disturbed either by the growth of the
brain or by the modifications produced by
experience. We propose that such modes
are detected and suppressed by a special
mechanism which operates during REM
sleep and has the character of an active pro-
cess which is, loosely speaking, the op-
posite of learning. We call this `reverse
learning* or `unlearning'. This mechanism,
which is not the same as normal forgetting,

*TheSalk Institute 10010NorthTorrey PinesRoad, La
JOlkX California 92037. USA. Present address (GM.):
MRC Laboratory of Molecular Biology and the
KmWh Craik Laboratory, Cambridge CBZ 3EG. UK.

is explained in more detail below. Without
it we believe that the mammalian cortex
could not perform so well.

We first describe our ideas about the cor-
tex followed by a brief account of neural
networks. Next we outline what is known
about REM sleep. (For general accounts,
see refs 1,2.) We then describe our
postulated mechanism and how it might be
tested. Finally.we discuss various implica-
tions of our ideas.    .

The cortex

The cortex consists of two separate sheets
of neural tissue, one on each side of the
head. The neocortex, which has a
characteristic layered structure, is found
only in mammals (see ref. 3 for recent
survey), although a somewhat analogous
structure, the wulst, is found in birds. If
allowance is made for body weight, it is
larger in primates than in most other mam-
mals and larger in man than in other
primates. It makes up a substantial fraction
of the human brain.

Different areas of the cortex perform
different functions, some being mainly
associated with vision, touch and so on,
while others appear to process more com-
plex information not associated with a
single sensory mode. The exact function of
the neocortex is unknown but it appears to
be closely associated with higher mental ac-
tivities. It seems likely that it has evolved to
perform in a rather special way.

In examining the neuroanatomy of the
neocortex one is struck by the very large
number of axon collaterals (this is not true,
for instance, of the thalamus). In any area
of the cortex the great majority of synapses
come from axons originating locally and
running within it. There is also evidence
that the majority of the synapses in the cor-
tex are excitatory in their action. This sug-
gests a capacity for self-excitatory modes
of behaviour in the cortex. And indeed, in
various conditions, such as epilepsy,
migraine and certain kinds of drug-induced
hallucination*, parts of the cortex appear

to go into large-amplitude instabilities5 .

Neuronal networks

Now, if one asks what functions such richly
interconnected assemblies of cells could
serve, one attractive possibility is that they
could store associations6-R. To see this,
suppose an `event' is represented by the ac-
tivity of a subset of cells in a cell assembly.
If all the cells involved in that event form
mutual synapses, then when part of that
event is encountered again these synapses
can cause the regeneration of the activity in
the entire subset.

Much exploratory theoretical work has
been done on such networks of cells (for an
introduction see refs 6-8). In these models,
information is stored in the strengths of the
many synapses and sometimes in the firing
thresholds of cells as well. Although the ex-
act behaviour naturally depends on the
details of the particular model, certain
general properties can emerge even from
relatively simple models. The associations
which are stored are not assigned specific
locations for each item, as in a digital com-
puter. Instead the information is: (1)
Distributed: this implies that a particular
piece of information is distributed over
very many synapses. (2) Robust: this im-
plies that the information will not be totally
lost if a few synapses are added or remov-
ed. (3) Superimposed: this implies that one
synapse is involved in storing several
distinct pieces of information.

A properly designed net can be trained
(meaning that the strengths of the synapses
can be adjusted) so that given an input (a
pattern of axonal firings) it can produce the
appropriate output (another pattern of ax-
onal firings). It is found that certain
general properties will often emerge. (1)
Completion: given only part of the input
(as a clue) it can produce fairly exactly the
whole of the appropriate output (examples
aregiven in ref. 7). In computer jargon, the
memory is `content addressable'. (2)
Classification: given an input which is
related toseveral of its associations, it may


112

produce an output which combines many
of the common features of its normal out-
puts.

A major difficulty with all nets of this
general type is that they become overload-
ed if an attempt is made to store
simuhaneously too many. different pat-
terns or associations of patterns, or if the
stored patterns have too large an overlap.
This is because of the superimposed nature
of the storage. How the net will behave
when overloaded depends on the exact
structure of the net, but certain patterns of
behaviour are likely to emerge: (1) The net
may produce many far-fetched or bizarre
associations (`fantasy'). (2) The net may
tend to produce the same state, or one of a
small set of states, whatever the input
(`obsession'). (3) Certain kinds of nets,
particularly those which feed back on
themselves, may respond to inappropriate
input signals which would normally evoke
no response from the net.(`hallucination').

It is against this background of rather
tentative and idealized theory that our pro-
posals must be judged.

If the cortex were hard-wired during em-
bryogenesis to an exactly predetermined
pattern of synaptic connections, the
burden of eliminating parasitic modes in
cortical nets would have to be undertaken
by the genes alone. Although there is con-
siderable evidence for specificity in the cor-
tical wiring, it is likely that many of the
details of the synaptic connections - their
exact locations and their strength - are
made in a semirandom manner and refined
by experience. This is almost a necessity in
an organism which is capable of learning
very large amounts of novel information.
Thus it seems likely that both during cor-
tical growth (when we may say that certain
broadly predetermined `associations' are
layed down), and also in facing the ex-
periences of adult life, such parasitic modes.
will be unavoidably generated.

How would one attempt to eliminate
these modes? We suggest the following.
The major inputs and outputs of the system
should be turned off, so that the system is
largely isolated. It should then be given SUC-
cessive `random' activations, from internal
sources, so that any incipient parasitic
modes would be excited, especially if the
general balance of excitation to inhibition
had been temporarily tilted towards excita-
tion. Some mechanism is then needed to
make changes so that these potentially
parasitic modes are damped down. Such a
rough outline description immediately
reminds one of REM sleep and the
hallucinoid dreams associated with it.

REM sleep

It was discovered in the 1950s that in mam-
mals there are two main types of sleep.
Periods of REM sleep (also called D sleep
or paradoxical sleep) alternate with periods
of non-REM sleep (also called S sleep,
slow-wave sleep, or orthodox sleep) of
which four stages of increasing depth of
sleep are usually distinguished. During

COMMENTARY                       NATURE VOL304 14 JUI Y I%3

REM periods may of the muscles of the
sleeping animal, especially its head and
neck muscles, are more relaxed than in
non-REM sleep. Its cortex, as judged by
the electroencephalogram (EEG) and by
the rapid movement of the eyes beneath
closed lids, appears to be very active and in
a state similar to the waking state. On the
other hand, the monoamine neurones in
the brain stem, especially those in the locus
coeruleus, raphe and peribranchial nuclei,
reduce their firing rates in REM sleep to on-
ly a few per cent of the corresponding rate
in the waking state9.

Another major difference between REM
and non-REM sleep lies in the dreams
associated with them. For most people the
few dreams found in non-REM sleep tend
to -have a rather thought-like character.
During REM sleep, on the other hand,
dreams occur more frequently and usually
have a perceptual vividness and the illogical
episodic character with which we are all
familiar. A human adult usually spends a
total of 1% to 2 hours each night in REM
sleep, spread over several periods. The
evidence suggests that most of the dreams
during these REM periods do not reach
normal consciousness, dreams being
remembered only if the sleeper awakes
whiledreaming. Even then the memory of a
dream is usually very transient, fading
quickly if no effort is made to remember it
by rehearsing its content.

A most remarkable finding is that
newborn humans may have as much as 8
hours of REM sleep per day lo. There is also
evidence to suggest that in the womb,
especially in the third trimester, REM sleep
occurs even more frequently. This large
amount of REM sleep before and after
birth is also found in other mammals.

AI1 viviparous mammals examined, in-
cluding primitive marsupials such as the
opposum, show periods of REM sleep"*`*.
Even an animal like the mole, which can
hardly move its eyes, shows the
characteristic EEG of REM sleep. Birds
have REM sleep, although often only-a
very small amount of it, occupying perhaps
5% of their sleep t3. There are no very
convincing reports of REM sleep (as
judged by the EEG) in reptiles, amphibia
or fish.

If an animal is deprived of REM sleep for
one or more nights (but allowed non-REM
sleep) then it will usually have more REM
sleep in subsequent nights14*t5.

All this evidence suggests that REM
sleep has an important function, at least for
mammals. Since the majority of dreams are
not remembered, that function is more
likely to be associated with the unconscious
dreaming process - that is, with REM
sleep without awakening - rather than
with the few dreams which are recalled.

It has been shown that during REM sleep
the forebrain is periodically and widely
stimulated by the brain stem. This activity
in the brain stem can happen even in the
absence of the cortex. Hobson and Mc-
Carleyr6, following the pioneer work of

Jouvet ", have postulated a `dream state
generator' which lies mainly in the pontine
reticular formation (the question of which
exact cell groups are involved is controver.
sial). It produces the so-called PGO waves.
They propose that the activity of such cells
is the cause of both rapid eye movements
and the periodic intrusion of new subject
matter into hallucinoid dreams. Our pro-
posals are based on this idea.
In summary, the evidence suggests that
in REM sleep the brain is isolated from its
normal input and output channels and that
it is very active, this activity being pro-
moted by rather nonspecific signals from
the brain stem and reflected in the uncon-
scious equivalent of dreaming, which only
reaches normal consciousness if the sleeper
awakes.

The postulated mechanism

We need a mechanism which will tune the
cortical system, in the sense of removing
parasitic modes which arise after the
system has been disturbed either by growth
of the brain (when new connections are
constantly being made) or by the modifica-
tions produced by experience. The
mechanism we propose is based on the
more or less random stimulation of the
forebrain by the brain stem that will tend to
excite the inappropriate modes of brain ac-
tivity referred to earlier, and especially
those which are too prone to be set off by
random noise rather than by highly struc-
tured specific signals. We further postulate
a reverse learning mechanism which will
modify the cortex (for example, by altering
the strengths of individual synapses) in
such a way that this particular activity is
less likely in the future. For example, if a
synapse needs to be strengthened in order
to remember something, then in reverse
learning it would be weakened. Put more
loosely, we suggest that in REM sleep we
unlearn our unconscious dreams. "We
dream in order to forget."

After this paper had been initially sub-
mitted for publication, we learnt from Dr
John Hopfield that he and his colleagues
had independently arrived at the idea of
reverse learning, though not in connection
with dreams. In a parallel communica-
tion'* they have shown that the behaviout
of their very idealized neural net is indeed
improved by reverse learning. That is, it
equalizes the accessibility of stored
memories and suppresses most of the
spurious ones. We have since repeated their
simulations and confirmed their general
conclusions. It remains to be seen how well
reverse learning acts on other more realistic
neural nets. We have revised our paper iI'
the light of their results.

Note that the amount of reverse learninE
per step in these simulations was very small
(only about 107'0 of the amount needed f@'
complete learning), although several bun-
dred such steps were used. This alerts us t0
the possibility that the changes produced iI1
REM sleep may individually be very small
but cumulative over many PGO spikes and


&uRE VolM 14 JULY Is3             C~MMLNTAR\/                                         II3

$T
gmlly Ilig~ts'+p~ `-, 1' :' L i    :`.
The ObJection mrght be raised that some

.&reriments have shown that REM may
appear to help the retention of memory,
whereas the process of reverse learning
would tend to make the memory fade. The
results of Hopfield et aI.`* show that this
need not be the case. After reverse learning
the recall of their net was less confused and
more uniform. :
If there is indeed a mechanism for
reverse learning, many questions arise
about its character. Does it act via the same
ntechanism as normal learning (whatever
that is) or Is a special, quite separate,
mechanism involved? Is the mechanism
associated with one particular system in the
brain stem? Another possibility is that a
small amount of reverse learning is always
present but is normally overwhelmed by
the positive plasticity produced by one or
more of the diffuse systems from the brain
stem. When their activity is greatly reduc-
ed, as it is in REM sleep9, the residual
reverse learning can then exert its effect
unopposed, at least on recently modified
synapses.

learning process.. The latter is a positive
mechanism which. does not merely fail to
alter synaptic strengths (or other long-
lasting brain parameters) but changes them
so that the dream is not just forgotten but
actively `unlearned'. The result is that the
dream (or some of the elements of it) is less
likely to recur in the future.

nature of REM dreams.

The effects of REM sleep deprivation are
harder to explain. It is well established that
REM deprivation often produces a re-
bound - more REM sleep than usual oc-
curs when the subject is eventually allowed
to sleep without interruption. We would
have expected that REM deprivation, if
severe enough, might cause hallucinations
- that is, structured visual and auditory
responses to `noise' - and perhaps delu-
sions and obsessions. There is a little
evidence for thisz5, but usually the effects
are either small or absentz6. This is partly
because it is extremely difficult to produce
long periods of complete REM deprivation
in humans by selective arousal. After a
week or two it becomes almost impossible
to awaken them promptly at every onset of
REM sleep, so that prolonged experiments
have not been done. One cannot help but
wonder whether similar experiments on
food deprivation might lead to the conclu-
sion (if unsupported by other evidence)
that food also had no essential function.
However, REM deprivation in animals
does appear to lower the threshold for cor-
tical instability produced by electro-
convulsive shock27-30, which is what we
might expect. REM deprivation in humans
sometimes produces irritability and an in-,
ability to concentrate. One might suggest
that these are the effects of the attention
mechanism being forced to subdue sub-
threshold parasitic modes which would
otherwise break into consciousness. REM
deprivation can also allow feelings and
wishes to appear which had previously
been kept out of consciousness3r, or, in cer-
tain subjects, can show changes towards in-
creased internal fantasy during waking32.

In its simplest form our theory postulates
that there is no intelligent supervisor inside
the brain which decides in detail which
potential neural activities should be left un-
touched -and which should be damped
down. This choice is made solely by the
response of the forebrain to the relatively
nonspecific signals from the brain stem. In
very general terms, the brain stem gives the
forebrain a varied pattern of bangs (the
PGO waves). Any resulting activity is then
modified so that it is less likely to occur in
the future.

It would of course be possible to
postulate a more complex mechanism. For
example, in REM sleep, especially in early
development, there could be innate testing
programmes, together with a `supervisor'
to decide what to store and what to erase,
depending on the result of the tests.
Various workers have made proposals
along these linesrPV2". As far as we know,
nobody has previously suggested that the
testing procedure involves the removal of
Potentially parasitic modes.

It has been customary to believe that
during an unconscious dream the content
of the dream is stored in some form of very
short-term `memory' but that the
mechanism for transferring it into longer
term memory is inoperative. We normally
become conscious of our dreams only if we
wake up while dreaming is in progress. If
we then pay attention to our dream, some
of its content can be maintained in very
short-term memory and may eventually be
transferred to longer-term memory as the
transfer mechanism becomes activated.
Otherwise our dream fades. Thus we can
speak of forgetting our dreams, meaning
that we know that we had a dream, but are
somewhat uncertain of its content.

This forgetting of a dream, which has
often been remarked on, does not
necessarily involve our postulated reverse


The terms `reverse learning' or `unlearn-
ing' are not ideal because they rather imply
that one has to learn something first in
order to unlearn it. What does a fetus
`learn' that has to be unlearned? Our
answer is that, during development, the
semirandom process of making synaptic
cdnnections is likely to produce parasitic
modes. it is these which must be `unlearn-
ed' in order to obtain a well-behaved
system.

We need some explanation for recurrent
dreams. We propose the ud hoc hypothesis
that a recurrent dream is one which, for one
reason or another, tends to wake up the
sleeper, perhaps because of the anxiety
often associated with them. This will have
the effect that the learning process changes
sign, passing from reverse learning to
positive learning, so that the underlying
spurious associations remain, and so a
similar dream is likely to occur on some
later occasion. This mechanism does
postulate a supervisor of a kind but its sole
function is to decide whether the sleeper
should wake up or not. Thus for a dream to
become recurrent it must have two proper-
ties. It must be related to a potentially
parasitic mode and it must wake up the
dreamer in such a way that he remembers it
rather vividly.

Our theory, in its present state, says
nothing about the function of non-REM
sleep. These stages of sleep usually have
less of the hallucinoid type of dream which
we associate with our reverse learning
mechanism. Non-REM sleep is likely to
have the restorative function often
postulated for it but it may also have some
informational function. For example, it
might be used for the process of `con-
solidating' memory in some way. It is
worth noting that the first REM period of
the night is normally preceded by a
substantial period of non-REM sleep.

Testing the theory

As far as we can tell, our theory is broadly
compatible with a large amount of ex-
perimental data. Starting from a plausible
hypothesis about cortical function, it ex-
plains in an effortless way both the need for
REM sleep in adult life and the large
amount of it during the development of the
brain. We believe no previous theory ex-
plains this distribution of REM sleep in
such a simple manner. Any purely
psychological theory (such as Freud's) is
hard-pressed to explain the large amount
of REM sleep in the womb, and any purely
developmental theory must account for the
quite appreciable amount of REM sleep in
adult life. Our theory accounts for both. It
is also compatible with the hallucinoid

A further difficulty is that some drugs,
such as certain monoamine oxidase in-
hibitors, appear to prevent REM sleep en-
tirely33 without producing very obvious
psychological deficits. This is a difficulty
for any theory which assumes REM sleep is
important and runs in the face of all the
other evidence about it. We can only plead
that such drugs may have complicated side
`effects which make the observations
misleading.

A direct test of our postulated reverse
learning mechanism seems extremely dif-
ficult. It would be necessary to show that
our unconscious dreams (dreams we do not
remember - a new word for this is really
needed, we suggest `remination') reduce
the probability of such thoughts occurring
in the future. This is far beyond the
methods we have available today. It would
be interesting to know if the threshold for
hallucination, induced by drugs or other
means, is lowered as a result of REM
deprivation. Another approach would be
to look for the structural and chemical cor-
relates of the postulated reverse learning
mechanism, but exactly how to do this is at
the moment unclear. Without further
evidence of this kind our theory must be
regarded as speculative.

It is clear that useful insights can come


NATURE VOL.305 14 JULY 19~2

from neural modelling. This appr6ach has-
its limitations, sin&it is difficult .to pro-
duce realistic models and even more
difficult to simulate them effectively,
especially if the. hypothetical neural nets
approach a realistic size, when the com-
putational time becomes prohibitively
long. However, such theoretical studies
should at least reveal some of the types of
networks which would benefit from our
proposed mechanism. They might also
help to give more life to our otherwise
rather vague characterization of the cor-
tical system.

Another approach would be to under-
take comparative studies. There is one
mammal which, although possessing a well
developed neocortex", appears not to
show any signs of REM sleep (at least in
young adults), even though it exhibits nor-
mal non-REM sleetis. This is the Echidna
Tachyglossusaculeatus (the spiny anteater)
found in Australia. The Echidnas and the
duck-billed platypus are primitive egg-
laying mammals (monotremes).

Griffiths" has written that ". . . the
gyrencephalic cerebrums of the
Tachyglossidae have been and are a source
of wonder to neurobiologists". He quotes
Elliott SmithM who in ;1902 wrote "The
most,obtrusive feature of this brain is the
relatively enormous development of the
cerebral hemispheres . . . The meaning of
this large neopallium is quite incomprehen-
sible . . .". Griffiths adds: "Determinants
of modern neurophysiology also fail to ex-
plain how echidnas come by this cortex".
We suggest that Tachyglosncs needs such a
large cortex because it cannot tune it up by
the process of reverse learning. Experience
with idealized neural nets shows that one
can usually avoid overloading a net, and
thus the confusion such an overloading
creates, by making the net bigger.

Tachyglouw can be studied in captivity.
It might be rewarding to examine in more
detail its behaviour, neurophysiology and
neuroanatomy compared to a primitive
placental mammal, such as a hedgehog,
which does show REM sleep. If REM sleep
serves an important function; this should
be reflected in some way in its absence in
the spiny anteater.
Possible implications
If it turns out that our ideas are broadly
correct, they could help us to understand
the evolution of the neocortex which is so
typical of mammals. It seems likely that in
order for a highly tuned system to perform
efficiently at least two requirements are

necessary: :a fairly constant internal
temperature, so that its function is not
disturbed by temperature fluctuations, and
in addition a cleaning-up mechanism, to
remove potentially parasitic modes. In
short, without REM dreams evolution
could not have produced the highly refined
neocortex we have today.

If the reverse learning mechanism we
have postulated exists, one might wonder
what effects its failure might have. A com-
plete failure might lead to such grave
disturbances - a state of almost perpetual
obsession or spurious, hallucinatory
associations - that it would probably be
severely selected against. A partial failure
should produce unwanted responses to
random noise, perhaps as hallucinations,
delusions, and obsessions, and produce a
state not unlike some schizophrenias.
`It has been postulated before that there
might be a relation between REM sleep Andy
schizophrenia, but studies have shown that
there is little oi no connection between the
outward signs of REM sleep and
schizophrenia3'. However, a partial failure
of the reverse learning mechanism would
not necessarily alter the amount of REM
sleep, since the control mechanisms for the
occurrence of REM sleep might be
somewhat distinct from the reverse learn-
ing process itself. Thus the possibility that
some forms of schizophrenia might be

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caused by a defect in the reverse learning
process should not be overlooked.

In this model, attempting to remember
one's dreams should perhaps not be en-
couraged, because such remembering may
help to retain patterns of thought which are
better forgotten. These are the very pat-
terns the organism was attempting to damp
down.

Finally we should remark that even if it
turns out that our ideas are wrong and that
nature does not employ the reverse learning
mechanism we have postulated, the process
may well be useful for artificial intelligence
machines of the future, especially those
having extensive parallel processing, a
learning mechanism and a certain amount
of randomness in their construction.

We thank our resident and visiting co]-
leagues at The Salk Institute for many
useful discussions. We are especially
grateful to Drs Allan Hobson and Jim
Horne who made detailed comments on
our draft manuscript, and to Dr John Hop-
field for communicating his work to us
before publication and for helpful discus-
sions. This work has been supported by the
J. W. Kiechkhefer Foundation, Samuel
Roberts Noble Foundation, US Air Force
Grant number AFOSR-82-0042 and the
System Development Foundation.     0

Received 8 October 1982; accepted I2 May 1983.

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