[info] [tt] NS: Does the brain feature built-in noise?

Fri Jul 11 20:50:43 UTC 2008

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Date: Fri, 11 Jul 2008 13:55:55 -0400 (EDT)
To: Transhuman Tech <tt at postbiota.org>
Subject: [tt] NS: Does the brain feature built-in noise?

Does the brain feature built-in noise?
http://www.newscientist.com/article.ns?id=mg19826611.400&print=true

18 June 2008
Laura Spinney

DURING the second world war, aircrews who had to calculate mission
routes and bomb trajectories found that their instruments -
mechanical computers packed with cogs and gears - performed better
in the air than on the ground. Realising that the plane's vibrations
were helping to make the instruments' sticky moving parts move more
freely, engineers began building small vibrating motors into them to
make them more accurate. This was one of the earliest applications
of dither, or the deliberate addition of noise.

Noise is usually a nuisance, as anyone who lives under a flight path
or has tried to listen to a distant AM radio station can testify.
But to engineers it can be a godsend, and now its benefits are
cropping up in biology, too. More than a decade of research suggests
that under some circumstances, a small injection of noise can
sharpen up the way in which an organism senses its environment. For
example, crayfish are better at detecting the subtle fin movements
of predatory fish when the water is turbulent rather than still.
Humans are better able to recognise a faint image on a screen when a
dash of noise is added to it.

In these cases the noise source is external to the organism, but
they raise an intriguing possibility: could evolution have beaten
the wartime engineers to it and incorporated dither into the brain
itself? A group of neuroscientists is now claiming to have found
just that, in the form of neural circuits that are "noisy by
design". If they're right, it may be that dither is a common feature
in nature.

A working definition of noise is that it is a broadband signal
containing a jumble of frequencies - the hiss of white noise, for
example, is made up of the full range of audible frequencies, from
very low to very high, in equal amounts. In contrast, meaningful
signals concentrate their energy on a comparatively narrow band of
the spectrum.

The phenomenon of noise increasing the detectability of a faint
signal is called stochastic resonance. Stochastic resonance applies
specifically to non-linear systems, where the output is not
proportional to the input. Neurons are a good example of a
non-linear system, firing only when the electrical potential across
their membrane reaches a critical threshold. In such a system, a
weak input which fails to reach the threshold can be lifted above it
by the injection of noise.

Numerous theoretical models suggest that stochastic resonance could
improve how neurons process signals, and there is good experimental
evidence that adding external noise can enhance the brain's
abilities under certain circumstances. Stochastic resonance explains
why water turbulence helps a crayfish's sensory hair cells detect a
distant fin movement, and why noise helps the human eye to pick out
a faint image. External noise has since been harnessed to enhance
human performance, for example, in cochlear implants to help pick up
faint sounds and in vibrating insoles that reduce swaying in people
who have suffered strokes (New Scientist, 2 November 2002, p 22).

Till now, however, no one has found any evidence that the brain
generates its own internal noise to exploit stochastic resonance.
That is where the work of Gero Miesenböck, a neuroscientist at the
University of Oxford, comes in. Miesenböck thinks he has found a
brain circuit, part of the olfactory system of the fruit fly
Drosophila, that exists specifically to generate noise and thus
enhance brain function. He says his discovery has implications for
the human brain because the basic architecture of the Drosophila
olfactory system is common not only to all insects but also to all
vertebrates - including humans.

Miesenböck didn't set out in search of noise. He was trying to solve
a mystery that has troubled olfactory-system researchers for many
years.

The fly olfactory system is a huge piece of neural circuitry (see
Diagram). It starts in the fly's antennae with around 1200 olfactory
receptor neurons (ORNs), each of which carries a single type of
odour-receptor molecule. There are about 60 different receptor
molecules and hence about 60 different types of ORN.

>  From the antennae, these odour-specific ORNs converge on nodes
called glomeruli where they make synaptic connections with cells
called projection neurons. Each glomerulus receives inputs from only
one type of ORN, so for a long time neuroscientists assumed that
each projection neuron would only respond to a single odour.

But a few years ago, neuroscientists discovered that this is not the
case (Science, vol 303, p 366). Electrical recordings from
individual projection neurons show that they sometimes respond to
odours other than those picked up by their ORNs.

But how do they do this, when each glomerulus receives inputs from
only one type of ORN? While at Yale School of Medicine a few years
ago, Miesenböck and his colleague Yuhua Shang managed to solve this
puzzle.

They took a mutant fly in which all the ORNs connected to a
particular glomerulus were missing, and looked for other inputs to
the projection neuron. What they found was a previously unknown
network of "interneurons" connecting the glomeruli to each other and
transmitting activity between them (Cell, vol 128, p 601). These
"excitatory local neurons" seem to provide a sort of diffuse,
stimulatory input to the projection neurons whenever an odour is
present.

That solved the immediate problem, but raised another: why add
something to the system that means losing the exquisite one-to-one
mapping of odour receptors to projection neurons? "It seems
counter-intuitive," says Miesenböck. "Why would you take the crisp,
sharply separated input and blur it out, make it noisier?" The
hypothesis he came up with was that the noise was there for a
reason. Perhaps the excitatory local neurons deliberately inject
noise into the system, taking advantage of stochastic resonance to
make faint odours easier to detect.

Fine-tuning

This makes sense in the light of what subsequently happens to the
sensory input signal. Projection neurons send signals to other
neurons called Kenyon cells in a structure called the mushroom body,
a part of the fly's brain involved in learning and memory. Each
Kenyon cell receives inputs from many projection neurons, but they
have extremely high firing thresholds and are only activated when a
large number of their incoming neurons fire simultaneously. Given
that projection cells fire more readily in response to their own
odour than others, each Kenyon cell only fires in response to a
single odour and the system recaptures specificity.

Miesenböck's group also came across a 1983 paper by Alexander Borst
of the Max Planck Institute of Neurobiology in Martinsried, Germany,
describing a network of inhibitory local neurons linking the
glomeruli. Miesenböck thinks these may have the opposite effect to
his excitatory ones, damping down strong signals from ORNs.

So why bother to boost weak signals and tone down strong ones?
Miesenböck suggests this happens to iron out extremes in odour
concentrations. "You need to be able to smell a rose, and identify
it as a rose, at very faint concentrations and in full bloom, if it
is held directly under your nose," he says. "There has to be some
mechanism that eliminates the variation based on odour
concentration. We think that the middle layer of processing does
exactly that."

Miesenböck's group still has some way to go to prove the "noisy by
design" hypothesis, but they're working on it. By tinkering with
local neurons, they hope to learn how to change the volume of the
noise. Miesenböck predicts that turning it down or silencing it
entirely will make faint odours less likely to trigger Kenyon cells.
Another prediction is that the flies will become behaviourally less
responsive to faint smells, which the researchers can test by
looking at their avoidance of bad ones.

Manipulations of this kind are tricky, however, partly because the
researchers have no idea how many local neurons there are in a
Drosophila brain. They need to modify the majority of them if they
are to see the effects they are looking for.

If they succeed they will then attempt to show that something
similar is happening in the mammalian brain. But finding a
noise-generating cell resembling a fly's local neuron in a mammalian
brain will be a huge challenge, according to Thomas Klausberger,
also at Oxford. Klausberger is busy discovering new kinds of
interneuron in the rat hippocampus, a structure that has been
compared to the insect mushroom body because of its role in learning
and memory. He points out that one region alone contains at least 21
different types of interneuron.

Biophysicist Frank Moss of the University of Missouri in St Louis,
who did the crayfish study in 1993, is impressed by Miesenböck's
findings. He has long suspected that animals take advantage of
stochastic resonance to boost their reproductive success and says
that Miesenböck may be about to clinch it.

Moss was one of the authors of a 1999 study (Nature, vol 402, p 291)
which showed for the first time that externally applied noise worked
via stochastic resonance. He was working on paddlefish, which find
food by using electrosensors in their snouts to detect faint
electrical signals given off by plankton, their natural prey. Moss
put a paddlefish in a tank of water containing plankton, along with
two electrodes which generated noise in the form of a randomly
varying electric field. When he measured the effect of the noise, he
found that there was an intermediate amplitude at which the fish's
success rate significantly increased.

Optimal performance when the noise level is intermediate is one of
the characteristics of stochastic resonance: too little noise and
the signal doesn't reach the threshold, too much and the signal will
be swamped by noise. The noise-benefit relationship is therefore
shaped like an inverted U.

More recently, Moss has turned his attention to tiny aquatic
crustaceans called Daphnia or water fleas. He believes they provide
another strand of evidence pointing to internally generated
stochastic resonance.

Daphnia have a characteristic foraging behaviour that follows the
sequence of a hop, a pause, a turn through an angle and another hop.
The turn angles vary and appear random to the naked eye.

Moss thought otherwise. He and his colleagues videoed five different
species of Daphnia as they foraged for food in a shallow tank, and
measured hundreds of turning angles. When they plotted the frequency
distribution of these angles, they found that it was not completely
random: some turning angles were more frequent than others. Their
overall distribution could be described mathematically using a
parameter called "noise intensity" - a measure of how random, or
noisy, it is.

Next they ran computer simulations of foraging Daphnia using
different noise intensities. They found that the most successful
food-gathering strategy used the noise-intensity level they had
measured in real Daphnia. Lower or higher noise intensities reduced
foraging success according to the classic inverted-U shape of
stochastic resonance (Mathematical Biosciences, vol 207, p 165).
Though they don't yet know how Daphnia generate their distribution
of turning angles, they argue it is an example of stochastic
resonance in action and that it must be produced internally. "It
originates somewhere within the Daphnia, maybe its brain, but we
don't know," says Moss. He adds that the optimal noise intensity
must be the product of natural selection, because Daphnia using it
would find more food and so maximise their fitness.

The idea that biological systems exploit internally generated noise
still has questions hanging over it, however. One big one is whether
what is being generated by the local neurons in the fruit fly is
genuine noise. Bart Kosko, an electrical engineer at the University
of Southern California, Los Angeles, and author of a 2006 book
called Noise, says he is not convinced it is.

Noise has a strict mathematical definition and what looks like noise
in a complex biological system usually turns out to be a signal
leaking from elsewhere. "What needs to be done is to take that
'noise' source and show that it has the statistical footprint of
noise," says Kosko. If it isn't genuine noise then, by definition,
you haven't got stochastic resonance.

Neuroscientist György Buzsáki of Rutgers University in New Jersey
goes one step further, arguing that if something is boosting faint
signals to threshold in the brain, it is unlikely to be noise.
"Generating noise is very expensive," he says. "A good system, such
as we presume the brain is, can't afford it."

Buzsáki agrees with Miesenböck that there is probably a noise-like
signal which modulates brain activity in mammals, but says there is
no need to invoke specialised noise-making circuitry. Instead, he
points to spontaneous neural activity occurring across the brain.

Neurons are capable of two types of activity, spontaneous and
evoked. The first happens independently of an external stimulus,
whereas the second is a response to it. Spontaneous activity is
interesting to neuroscientists because it provides a possible
mechanism for generating higher mental activity in the human brain.
Spontaneous activity can spread over networks of neurons, and
transient periods of synchronised neural firing at a rate of about
40 "spikes" a second. So-called gamma waves have been proposed as a
way that different cognitive processes can be bound together to give
rise to perception, for example.

Buzsáki says that faint incoming signals could piggyback on these
spontaneous waves of activity and thus be lifted above threshold.
This would be a more cost-effective way of enhancing a weak signal,
he says, since spontaneous activity consumes little energy.

There is, of course, one key similarity between these two
possibilities: both involve a signal that pushes another signal over
a threshold. "The principle is the same," says Miesenböck. But the
details matter both from the perspective of understanding the basic
workings of the brain and, potentially, in order for us to exploit
stochastic resonance in future sensory aids such as retinal
implants.

We will have to wait a little longer to find out whether natural
selection created a brain with built-in noise, or simply one which
is able to borrow some other neural signal to use as noise. Either
way it seems that fly brains can't function without a little bit of
dither - and that ours are probably dithering too.

The Human Brain - With one hundred billion nerve cells, the
complexity is mind-boggling. Learn more in our cutting edge special
report.

Related Articles

Eye can see better when it's noisy
http://www.newscientist.com/article.ns?id=mg17823982.500
7 June 2003
Walking on shaky shoes
http://www.newscientist.com/article.ns?id=mg17623672.600
2 November 2002
Glorious noise
http://www.newscientist.com/article.ns?id=mg16121685.100
9 January 1999
Noises on
http://www.newscientist.com/article.ns?id=mg15020324.300
1 June 1996

Weblinks

Gero Miesenböck
https://www.neuroscience.ox.ac.uk/directory/gero-miesenboeck/
Frank Moss
http://www.umsl.edu/~neurodyn/faculty/moss.html
Bart Kosko
http://sipi.usc.edu/~kosko/

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