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IMAGINE the scene. A roving robot is trundling over the Martian landscape
when static electricity zaps one of its sensors, blinding it. It鈥檚 millions of
miles from the repair shop back home, so what can it do to recover?
Simple鈥攊t rewires its nervous system so other sensors can take over from
the dead one, then carries on.

That鈥檚 the idea behind a robot controller being developed at the department
of electronics and computer science at the University of Southampton. It is
inspired by the ability of animals to adapt to injury and unfamiliar
environments by rearranging the connections in their brains.

The brain鈥檚 ability to change the connections between its neurons is known as
鈥減lasticity鈥�, and is crucial to the way animals learn how to respond to sensory
inputs. The Southampton team has built plasticity into the software that
controls a pair of robots.

Terry Elliot and Nigel Shadbolt built their biologically inspired plasticity
algorithm into a simple two-wheeled lab robot. The droid was equipped with eight
infrared sensors, and its 鈥渘ervous system鈥� consisted of a neural-network
program. The program learns from experience how to interpret signals from the
sensors, and controls the motors driving the two wheels so it can avoid bumping
into obstacles.

The algorithm is based on a biological model in which 鈥済rowth factor鈥�
chemicals strengthen the influence of the most active synapses鈥攖he
connections between neurons. The growth factor is in short supply, so neurons
are forced to compete for it. 鈥淚f a neuron takes up that growth factor then that
means there鈥檚 less left for other synapses,鈥� explains Elliot. But if a neuron
stops taking up growth factors, that leaves more for its near neighbours.

In the robot, the growth factor is represented by a fixed numerical value
that has to be shared between the connections to its sensors. When one sensor is
damaged, its neighbours get a bigger share鈥攕trengthening their influence
over the robot鈥檚 movement. 鈥淭he robot is recovering performance because it is
allowing its nervous system to be plastic when part of its sensors have been
knocked out,鈥� say the researchers.

To see how well the robot adapts to damage, the team turned off some of their
sensors. 鈥淲ithout the plasticity algorithm the robots obviously crash more
because some of its sensors have gone,鈥� says Elliot. But when running the
algorithm, the number of crashes halved as the robots rewired their brains to
make the best possible use of the remaining sensors.

This could be very important for robots placed in unpredictable environments,
says Phil Husbands at the University of Sussex. For example, on distant planets
it might be useful to navigate visually during the day and with infrared sensors
at night. 鈥淚t is very hard to do with conventional algorithms,鈥� says Husbands.
鈥淵ou soon get in a mess.鈥�

While there are other means of getting software to repair itself, they are
nowhere near as good as the ways nature deals with the unpredictable. 鈥淏iology
has had about 700 million years for evolving organisms that have had to adapt to
their environments,鈥� says Elliot. 鈥淪o it has some useful tricks for doing that.鈥�