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