91av

IN a dark laboratory, a solitary creature shuffles around, searching, lured
by the love song of a male she will never meet. She has spent her entire
existence in these unnatural surroundings and is doomed never to see the outside
world, forced to comply with the whims of researchers. Yet she is unlikely to
excite the wrath of animal rights activists nor provoke much sympathy from
anyone. Indeed, hers is barely an existence at all since she consists of only a
few wheels and a bundle of electronic components. Still, at least it鈥檚 one less
cage to clean out.

The cybercricket鈥檚 unassuming appearance belies what is a landmark in
robotics. It behaves just like a real cricket鈥攁nd not just outwardly. It
simulates the cricket right down to the neurons, and is one of the first
attempts to reproduce the pattern of neural signalling found in a living
creature. It鈥檚 also proving to be a challenge to biologists, leaving them open
to the charge that they read too much into animal behaviour, and see life as
altogether more complex than it is.

Decades of research have allowed biologists to theorise that certain
behaviours are associated with certain patterns of neural activity. But while
they can observe behaviour and record neural signalling, to prove that the two
are causally linked is extremely difficult. This is where robots are starting to
make their mark.

The key to these 鈥渂iobots鈥� is a better understanding of how natural and
artificial neural systems work. Roboticists can now mimic in metal and silicon
the natural pathways that link optical and acoustic sensors鈥攅yes and
ears鈥攖o motors, which act as surrogate muscles. This gives biologists the
chance to simulate particular patterns of neural activity and see whether or not
they prompt the anticipated behaviours.

The first biobots were very simple, designed to test biological theories. For
example, ethologists believe that in order to navigate, rats construct cognitive
maps of their surroundings. One popular way to test this theory was to place a
rat in an open rectangular box and train it to return to a single location where
food had been hidden. Researchers would then place the rat in a different but
identical box to see whether it would go to the same spot in search of food.
Sure enough, it did. The widely held conclusion from this experiment was that
the rat remembered the location of the hidden food by relating it to the
geometry of the box. In other words, it constructed a cognitive map.

But Henrik Lund, a computer scientist at Aarhus University in Denmark, and
Orazio Miglino, a psychologist at the University of Naples, were not convinced
that this conclusion could be drawn from the rat鈥檚 behaviour. They thought
ethologists were reading too much into the findings. They wanted to make a robot
that could mimic the rat鈥檚 behaviour, without a memory. If the robot had no
memory, they reasoned, it could have no map.

Lund and Miglino chose a standard lab robot called a Khepera, a small
three-wheeled device that looks like an electronic biscuit. They added two
infrared sensors that act like radars, four touch sensors and a pair of motors
to power the two drive wheels. The robot鈥檚 behaviour was dictated by an
artificial neural network, a cluster of simple mathematical processors, or
nodes, that are designed to behave like nerve cells in the brain.

But while rats have millions of neurons, the robot had only 10 nodes. Its
sensors connected directly to eight of the nodes, which in turn fed two nodes
that controlled the motors. With these basic resources, the researchers then
tried to fashion a machine capable of finding a particular spot in a box using a
computer program called a genetic algorithm
(see 鈥淕as on the brain鈥�, 91av, 3 October, p 36).

In an artificial neural network, a node adds together the signals arriving at
its inputs, and when the sum reaches a threshold it fires off a signal to other
nodes (or to one of the motors). In addition, every input to a node is given a
weighting which either increases or reduces the incoming signal. A genetic
algorithm treats the nodes鈥� thresholds and weights as though they are genes
being passed from one generation to the next.

It creates dozens of different potential robot designs with randomly chosen
genes鈥攙alues for the thresholds and weights鈥攁nd tests each one in
turn to see how well it finds the selected spot. It then allows the best
performers to 鈥渂reed鈥� by swapping genes with other successful robots. The new
generation of machines is then tested again. Over many generations, this process
allows the best robot for searching out the target to evolve.

Lund and Miglino found that their best robot could locate the food鈥檚 position
as easily as a rat, simply by seeing and feeling its way round the box. 鈥淲e
wanted to . . . look for the minimum conditions to solve this task,鈥� says Lund.
Their success showed that the open box experiment was not enough on its own to
support the theory that rats use cognitive maps. Personally, Lund believes that
rats do construct these maps, but he wanted to show that biologists were reading
too much into their results.

Inside the animal

Such experiments can be carried out by computer models, but Lund argues that
these are no substitute for the real world. 鈥淥ne main advantage of using a real
robot in a real experiment is that one is sure to have the same environmental
stimuli as in the real animal experiment,鈥� he says. Even so, the minimal robot
approach is of only limited value because it reveals nothing about what goes on
inside the animal. The cybercricket, by contrast, is designed to do just
this.

Female crickets home in on potential partners by listening to their
song鈥攖he sound we hear as chirping. The sound is made of short bursts, or
syllables, of a single 鈥渃arrier鈥� frequency which is specific to different
species. This behaviour, called phonotaxis, has been studied for decades in the
cricket Gryllus bimaculatus, both by watching its behaviour and by
studying its nervous system. But showing how the two are linked has proved very
difficult.

Immobilised or sedated

鈥淚t鈥檚 not impossible, but it鈥檚 definitely challenging and not many people are
in a position to do it,鈥� says Barbara Webb, a psychologist at the University of
Nottingham and one of the architects of the cybercricket. A living animal must
have electrodes placed in its brain to record activity from selected neurons. It
is then placed in a spherical treadmill so its movements can be tracked without
it going anywhere. 鈥淵ou have to immobilise the animal or sedate it to keep the
electrodes in place,鈥� explains Webb. 鈥淏ut the question is, can it still behave
苍辞谤尘补濒濒测?鈥�

The ideal way to study the insect would be to isolate any neurons thought to
be relevant to phonotaxis from the rest of the brain, play some cricket love
songs and see what happens. But this just isn鈥檛 practical. Webb reasoned,
however, that with a robot it might work. 鈥淚 wanted to find an animal system
that I could build a complete robot model of. Where the neurons would control
the behaviour when implemented in the real world,鈥� she says.

Webb began building the robot in 1991 while at the University of Edinburgh
with John Hallam. Later, they were joined by Lund. As if mimicking the cricket鈥檚
neural system was not tough enough, the team also had to copy its bizarre
auditory system. G. bimaculatus has two ear drums on its forelegs that
are connected to two holes in its body via internal tubes
(see Diagram).
This structure sets up phase differences in front of and behind the ear
drums that allow the cricket to tell which direction a sound is coming from. But
this works only if the sound is at the carrier frequency of the species.

To simulate the auditory system鈥檚 performance, Webb and her colleagues used a
collection of amplifiers and delay lines, with four microphones positioned on a
Khepera robot. A digital signal from these 鈥渆ars鈥� fed into a neural network.
鈥淭here was a particular pair of neurons that I copied,鈥� says Webb.
Neuroscientists had shown that these were crucial to phonotaxis. 鈥淚 actually
tried to tune them so that the model neurons had the same firing patterns as the
cricket ones do,鈥� she says.

Next, the researchers added extra nodes to see how many more the system
needed before it displayed phonotaxis. Biologists had speculated that it would
need 20 or so. 鈥淏ut it turns out you only need to add two,鈥� says Webb. In her
network, the left input node feeds an output node which controls the Khepera鈥檚
left motor鈥攖he same arrangement is repeated on the right side. To complete
the network, each input node inhibits any signals to the opposite output node
(see Diagram).

This surprising simplicity has been a hallmark of the cybercricket project.
The robot was conceived as a way to test the popular theory that phonotaxis
needs two neural control systems鈥攐ne to recognise a male鈥檚 call and the
other to locate it. But when Webb played a call song to the four-node robot,
using the same experimental set-up as that used for live crickets, not only did
it recognise the sound but also moved towards it. 鈥淭he two systems are just
one,鈥� says Webb.

This unexpected bonus raised the question of whether a real cricket鈥檚 neural
system is simpler than anyone had supposed. To investigate this, the team
decided to see if the automaton could replicate other life-like behaviours. They
played the robot two call songs that it could recognise. Live crickets prefer
songs with a faster syllable rate. So did the robot.

For a cricket to choose between two suitors, some biologists argue that
crickets must have their preferences somehow encoded in their brains. But the
robot showed this was unnecessary. The combination of the right auditory system
and simple neural network selects call songs with faster syllable rates
automatically.

This suggested to Webb that her simpler neural system is the same as that of
a living cricket. Biologists, she says, seem to overcomplicate things, partly
because they anthropomorphise. 鈥淧eople look at behaviour and assume that there
are intentions involved like recognition and goal seeking,鈥� she says. 鈥淲hen they
look to the neurons they try to find neurons that correspond to those
intentions.鈥� The problem is that the neurons might not exist, because other
animals do not work like humans.

But not all biologists are convinced of the robot鈥檚 relevance to a real
cricket. 鈥淭here is no problem in studying the behaviour that emerges from simple
systems,鈥� says Gerald Pollack, a neuroethologist at McGill University in
Montreal who has worked on crickets. 鈥淭he problem is [that] most biological
systems are not simple,鈥� says Pollack, who argues that even if the robot cricket
mimicked the animal鈥檚 behaviour perfectly, it still wouldn鈥檛 constitute evidence
that robots and animals are doing it the same way.

Still, as the cybercricket displays more real-life behaviours, Webb feels
that her case improves. 鈥淵ou might say one behaviour is not very hard to
reproduce,鈥� says Webb. 鈥淏ut if you carry out a whole series of different
experiments and you replicate all of them, then it starts to become a stronger
and stronger argument.鈥�

But she recognises that studying behaviour alone can take her only so far.
The robot allows her to make predictions about what neuroscientists should find,
and ideally she wants someone to find out if her predictions are correct. If the
firing patterns and behaviours are identical in creature and Khepera, can there
still be doubt that they work in the same way?

Whatever the outcome of this debate, many scientists agree that, at the very
least, robots are valuable research tools. This is the attitude taken at the
University of Z眉rich in Switzerland, by a team made up of roboticists and
zoologists. Their target is ant navigation.

Ant bearings

The desert ant Cataglyphis forages up to a couple of hundred metres
from its nest and returns home almost unerringly in a straight line. An
essential part of this ability, say biologists, stems from the insect鈥檚 skill in
using polarised light as a compass. As light from the Sun is scattered by
molecules in the atmosphere, it is partially polarised. This effect creates a
pattern in the sky that is symmetrical about the solar meridian, a line that
cuts through the Sun and its zenith point (see Diagram).

Cataglyphis has receptors in its compound eye that are sensitive to
different orientations of polarised light, and researchers believe that these
feed three neurons鈥攃alled POL neurons. Each neuron gives a peak output for
a different orientation, measured as the angle between the direction in which
the light is polarised and the axis of the ant鈥檚 body. Yet precisely how the ant
makes use of these signals is still unknown, says Dimitrios Lambrinos, one of
the team鈥檚 roboticists.

One theory to explain the ant鈥檚 abilities, called the scanning model, is
based on just one POL neuron. It suggests that when an insect goes foraging, it
first rotates through 360 degrees until it locates the meridian. The effect is
the same as looking through two polarised lenses and rotating one until the
light coming through both is brightest. Once the ant has this fix, it heads off
in its chosen direction.

To test such theories, the team built Sahabot 2, a six-wheeled 鈥渁nt鈥� with
three sets of polarised light sensors, to simulate those in Cataglyphis.
For the POL neurons, the researchers took a 鈥渂lack box鈥� approach. They didn鈥檛
want to simulate the actual firing of neurons, as Webb had done, they merely
wanted to reproduce the same inputs and outputs to and from the neural system.
This job fell to three amplifiers, each one tuned鈥攋ust as in real
ants鈥攖o give a maximum output at different angles between the direction of
polarisation and the robot鈥檚 axis.

This year, Lambrinos and his colleagues let the robot loose in Tunisia, where
Cataglyphis lives. In all the experiments, the cyberant behaved very
similarly to real ants, travelling more than 100 metres and returning to its
start point with an error of less than 60 centimetres.

But the robot performed worst when programmed to follow the scanning model.
In the noisy environment of the desert, each amplifier generated only a
flattened peak, which made finding the maximum point鈥攖he ant鈥檚 reference
direction鈥攁 relatively tough task. But what, asked the researchers, if
they combined the outputs of all three amplifiers? By subtracting the output of
two amplifiers from the third, the team created a sharply defined peak that the
robot sensed with greater accuracy. When the robot trundled off with this new
program onboard, its error rate fell.

It was a revelation, says Lambrinos. Previously, no one understood why there
were three POL neurons, since the scanning model needed only one. It took a test
with a robot in the same environment as living ants to provide a better
model.

To go further, the team wants to follow Webb鈥檚 lead down to the level of
individual neurons鈥攖o study the firing patterns in the tiny ant brain and
to recreate them in silicon. This may be easier for the Swiss team than for Webb
because it is multidisciplinary. 鈥淥ne of the reasons this is so successful is
that we are working together,鈥� says Lambrinos. 鈥淚f we don鈥檛 agree with each
other then we know other biologists won鈥檛 agree with us either.鈥�

Robot modelling is fast becoming a valuable tool for biologists to test
theories and create new ones. And it will continue to grow in popularity,
according to Webb, not least because of the time savings. 鈥淚t takes a lot more
time to find out what one neuron does than to build one,鈥� she says.

And copying nature may hold lessons not just for biologists, says Webb. The
cricket project suggests that computer scientists could get far more out of
their neural networks if they tried. 鈥淪omeone making a standard neural network
would never bother making a network with only four neurons because they would
assume that it simply wouldn鈥檛 be enough,鈥� she says. But as the lovelorn
cybercricket found, sometimes four is all you need.