From: Brian Atkins (firstname.lastname@example.org)
Date: Thu Dec 04 2003 - 10:17:05 MST
New Scientist vol 150 issue 2032 - 01 June 96, page Page 28
WHEN Samuel Morse tapped out his first long-distance telegraph message
back in 1844, one of his biggest problems was how to detect the signal
above the noise caused by static electricity. Today, engineers still do
their best to strip such uninvited pops and crackles from their
communications. Their ideal is a clean, clear signal as free as possible
Like Morse's telegraph, most of life operates in a sea of background
noise. In nature, for example, crayfish and crickets have to pick out
the sound of approaching predators from a background clatter of
irrelevant sounds. In the military, where electronic devices strain to
detect the telltale signs of enemy submarines, signal engineers struggle
to filter out enough of the background cacophony for the faint signal
they are looking for to come through.
But the suspicion is taking hold that those engineers are on a fool's
errand, and that they should be leaving some crackles in. Over the past
few years, researchers have discovered that background noise—any
unwanted signal interference, from radio buzz to television snow—can
actually make it easier to pick up faint signals on the very verge of
This extraordinary idea emerged in 1981 in the equally unlikely context
of an attempt to explain what was causing the ice ages that cover much
of the northern hemisphere with ice every 100 000 years or so.
Researchers had noticed that the distance between the Earth and Sun
changes on roughly the same timescale—part of a natural wobble in the
eccentricity of the Earth's orbit. The obvious explanation is that
different amounts of solar radiation reaching the Earth at different
times are periodically plunging the Earth into an ice age, and then
warming it back up again. But there is a problem: the difference in
solar radiation reaching the Earth at opposite ends of the cycle seems
much too small to account for the climate changes that occurred.
So Italian physicists Roberto Benzi, Alfonso Sutera and Angelo Vulpiani
wondered whether natural noise might be helping the signal to get
through. They suggested that the effects of the Earth's small wobble are
boosted by other, shorter-term changes—annual swings in the Earth's
retained heat, for instance, or the general variability of climate. The
extra noise, the researchers argued, could be enough to boost the weak
signal from the wobble, and trigger the ice ages. They called this
"stochastic resonance"—stochastic because the noise is essentially
random, and resonance because the noise resonates, or works with, the
signal to maximise it and push the Earth into a frozen state.
Neat as this idea seemed, it was never proved, and the concept of
stochastic resonance more or less disappeared from view until 1988, when
physicists Bruce McNamara, Rajarshi Roy and Kurt Wiesenfeld at the
Georgia Institute of Technology demonstrated in a laser experiment that
noise really can boost an inherently weak effect. Their experiment used
a ring laser, a system in which angled mirrors can direct laser light to
travel either clockwise or anticlockwise in a closed loop. The
researchers can switch the light's direction using a crystal in the path
of the light beam. Most of the laser light goes straight through this
crystal, but a small portion is diffracted and veers off the main path.
By generating sound waves within the crystal, the researchers were able
to change the ratio of diffracted to unaffected light. Eventually, when
the sound waves are intense enough and have just the right frequency,
the diffraction becomes so strong that the laser light changes direction.
The researchers then created a faint signal, in the form of a regular
variation in the frequency of the sound waves in the crystal. At first
they made sure that the signal was too weak to affect the direction of
the laser light. Then the team began to add background noise to the
signal. As the noise level grew, they found that the light began to
switch direction, in step with the signal. At some point the noise
became too loud, and the correspondence between the signal and the laser
So what exactly is happening? After all, adding noise to a system should
decrease the chances of detecting the signal you were looking for. Well,
a crucial feature of both the ring laser and the ice caps is that they
are nonlinear systems. In a linear system, a change in input produces a
proportional change in output. Suspend two identical weights from a
spring, and it will stretch twice as far as with one. Double the voltage
across an electrical resistor and you double the current.
Nonlinear systems are not like that. A change in the input (a small drop
in the amount of heat reaching the Earth, for example) can produce a
disproportionate response (a great deal more ice)—and it is in systems
like this that stochastic resonance can operate. It is easiest to
picture in terms of a threshold below which the signal cannot be
detected, or has no effect. It's as if the faint signal surfs on a sea
of background noise, hitting random waves that lift it over the system's
Stochastic resonance is an attractive concept because noise pervades
life, notes Peter McClintock, a physicist at the University of
Lancaster. Rather than seek impossible silence, we might learn to live
with, and use, the noise around us. Over the past few years, inspired by
the demonstration that, in the ring laser, the phenomenon really works,
researchers have been hunting for stochastic resonance in all sorts of
systems—and now they are starting to find it.
Neurobiology was an obvious place to start. After all, sensory neurons
routinely pick out sights and sounds from a cacophony of background
noise. Perhaps, researchers reasoned, the neurons have evolved to use
that noise. Sensory neurons encode information in the form of electrical
impulses, but the way they respond to stimuli is highly nonlinear:
gradual changes in sound or light intensity don't result in a gradual
increase in perception. It is more like pulling the trigger of a gun.
Squeeze the trigger gently and nothing happens. Only when the pressure
is firm enough does the gun fire. External noise enters this picture if
you are nervously aiming the gun at an intruder. Your hand probably
shakes, adding random tremors to your weak pressure on the trigger. The
result: BOOM! The added noise has boosted your weak pressure over the
trigger's threshold, and the gun goes off.
In the nervous system, electrical charges collect at a neuron until they
reach a critical threshold. At that point the neuron fires, sending a
message down its body to the next neuron in line. It then resets itself
to its resting electrical state, and waits for charge to build up again.
The question is, can background noise push feeble signals over the
neuron's firing threshold?
The answer, it seems, is yes. In 1993, physicist Frank Moss and
biologist Lon Wilkens from the University of Missouri in St Louis
discovered just this effect at work in crayfish. The crayfish's tail fan
displays tiny hairs that twitch in response to subtle water movements,
like those caused by a predatory fish swimming nearby. When a tail hair
twitches, it generates an electrical impulse along a nerve to a neural
hub called a ganglion, which processes the impulse, and tells the
crayfish to swim away fast.
In their laboratory, the researchers strung a crayfish hair and its
attached nerve to a vibrating post, which was made to vibrate weakly at
a frequency similar to that caused by a swimming fish. They then added
increasing levels of the sort of random, noisy vibrations that a
crayfish might encounter in the surrounding water. Sure enough, as the
noise level increased, the crayfish nerve began to pick up and respond
to the fishy signals. Eventually, as Moss and Wilkens predicted, the
noise level was so high that it began to swamp the signal, and the
benefit to the crayfish was lost.
Though this result showed how noise can help with a nice regular signal,
real-world survival can be more challenging. Rather than a hum at a
single frequency, most sensory stimuli are isolated and infrequent.
Recognising this, researchers are now conducting experiments with
At the spring meeting of the American Physical Society in St Louis,
Missouri, Jacob Levin, a researcher at the University of California in
Berkeley, described one such experiment. His subjects were crickets
rather than crayfish, but the principle is the same. Like crayfish,
crickets use tiny hairs attached to sensory neurons to detect movement
in their surroundings. In his lab, Levin exposed crickets to
low-amplitude air currents—the signal—amid a background of random, noisy
movements. The signal was too weak to trigger the neurons, but the
background noise was occasionally high enough to make them fire.
Using intracellular electrodes, Levin recorded the cricket's neuronal
firings as it sensed the air waves. Correlating the neuron output with
the weak air currents, Levin could find out how well the cricket
detected the faint stimuli. Finally, he measured those responses against
the background noise, checking to see how rising noise improved the
cricket's signal reception.
As with the crayfish, noise boosted the cricket's ability to pick up
weak signals. "The cricket takes advantage of stochastic resonance to
piggyback important small signals on the broadband background noise it
can't avoid," says Levin. But this only worked with noise up to a
certain level; if the level of the background noise level was set too
high, it simply swamped the signal.
This raises the question of whether stochastic resonance could be useful
for boosting signals with a range of magnitudes—not only those that peak
just below the trigger point of the sensor, but also much weaker signals
that require more noise to bring them above the threshold. For this to
work, the background noise level would have to change to suit the
magnitude of the signal, something that is unlikely to happen in the
real world. But neurons can still maximise the benefit they get from
background noise, according to James Collins, a biomedical engineer at
Boston University. As he points out, neurons in living organisms do not
operate in isolation, but work as units in a network, reporting to
neural hubs such as the ganglion. The theory is that individual neurons
have their own intrinsic noise levels, depending on their cellular
makeup and connections to other neurons.
Collins set out to mimic this using a computer model of such a network,
and last year he showed that only a minimum noise level—not a changing
optimal noise range—is necessary to boost the system's ability to detect
a range of signals. Collins and colleagues crafted a network in which
neurons received a common weak input signal, but each had a different
fixed noise level. The researchers recorded this fixed-noise system's
response to a variety of weak input signals. They then compared those
responses with a model system lacking noise. Sure enough, the
fixed-noise system detected faint signals better than its quiet counterpart.
Collins says a little noise goes a long way because networks average the
output of many components. For a signal of any given size, the chances
are that at least some of the neurons in the network will be
experiencing the optimum noise level to boost the signal over the
threshold without swamping it. So the system can handle signals with a
range of magnitudes.
Encouraged by his findings, Collins hopes to try to use neural noise
therapeutically. One target group is people suffering from movement
disorders that cause a loss in proprioception—the "sense" that makes it
possible to know what your arm is doing, for example, even when you
can't see it. In certain people, neurons involved in this process are
not as sensitive as they should be: their firing thresholds are set too
high. If someone with this disability tries to turn, for example, they
may not realise they are twisting an ankle too far—until it breaks.
One solution might be to add noise to the defective neurons to bring the
signals above the neurons' firing threshold. "The challenge is to find a
viable way to introduce noise to the human system," Collins says. One
option being considered is for doctors to apply mechanical vibrations to
tendons in the ankle. If this works, it could help people suffering from
a variety of conditions involving lost sensitivity in touch or balance.
Getting the picture
Boosting faint images is another possible use of stochastic resonance
being studied by Moss, in conjunction with Enrico Simonotto, a physicist
at the University of Genoa in Italy. The researchers have been sitting
volunteers in front of TV screens, where they are shown a series of
videotaped images. At first, all they are shown is a noise-free image
that is too dim to be detected. As the video plays, noise is introduced
in the form of random changes to the shade of individual pixels. As the
noise increases, Simonotto says, the underlying picture becomes clearer,
and most volunteers appear to agree on the range of noise that is most
useful for a given picture.
Simonotto now plans to tinker with the timing and position of noise
added to a picture. He suggests that noise-enhanced images may
eventually improve visual systems for pilots attempting to find their
way in dark or stormy conditions.
Electronic sensing systems may also benefit from some judiciously added
noise. Some physicists hope to use noise in electronic or
superconducting systems. Adi Bulsara, a senior scientist at the US Naval
Command, Control and Ocean Surveillance Center in San Diego, is thinking
of harnessing the noise that affects some types of magnetic detectors
known as superconducting quantum interference devices (SQUIDS). The
Navy's SQUIDS are designed to detect enemy submarines, planes and mines
by picking up the tiny changes in magnetic field that can be caused,
say, by a submarine's power supply.
One problem with SQUIDs is that the magnetic environment they operate in
is full of noise from sources that range from lightning flashes to
interference from the Earth's magnetic field. Bulsara noted that some
SQUIDS operate as nonlinear two-state systems just like ring lasers.
Perhaps, he reasoned, you could use the external noise to push weak
signals over the detection threshold. Another possibility, he says, is
to link SQUIDs to gain an aggregate boost as for the nervous system.
There is clearly a lot more that must be learnt about stochastic
resonance before many of the proposed applications become reality, as
most researchers will readily admit. Some, such as McClintock, fear that
enthusiasts may be overselling the phenomenon's direct applications.
"How many crickets' lives were saved by the fact that they used
stochastic resonance?" Levin asks. "We don't know." In fact, no one
knows whether crayfish and crickets truly use noise, still less whether
researchers will ever succeed in finding useful applications of the
phenomenon. But the enthusiasts remain optimistic. Noise is everywhere,
they argue, so that chances are that nature has found a way to use
it—and surely we will, too.
-- Brian Atkins Singularity Institute for Artificial Intelligence http://www.intelligence.org/
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