SLAM is one of a number of systems
that use the competition between
groups of simulated neurons to move
activity to the most appropriate location. In these attractor networks,
neurons excite those close to them
and inhibit those further away. However, sometimes new sensor information causes activity to rise elsewhere
until that group of neurons takes over
and, in turn, inhibits its competitors.
Clusters of neurons that seem to
operate as attractor networks have
now been found in the navigation
centers of insects that help with path
integration and steering. Insects lack
the rich collections of cells that mammals use for navigation, but Lund
University biology researcher Stanley
Heinze is impressed by the way insects can recall complex routes that
are sometimes miles long, making it
possible to find their way home easily. Working with Webb’s team from
the University of Edinburgh and colleagues at Lund in Sweden, Heinze
developed a robot to test ideas of how
Webb says ants, bees, and other insects appear to use a combination of
path integration and visual memory
to store routes. She points out that if
you move an ant away from one of the
routes it has memorized and drop it in
a new location, it will adopt a search
pattern; as soon as it encounters a
point on one of its known paths, it will
orient itself and find its way home.
In the cluttered environments
through which they fly, bees appear
to rely more on direction and speed
than the local landmarks that guide
ants. The species chosen for study
by Heinze and Webb has receptors
in its eyes that respond to polarized
light, and tend to forage at times
when this polarization is most ap-
parent. Tests with grid patterns
demonstrated how bees can use
these cells to sense speed accurate-
ly even when a strong wind forces
them to one side.
Heinze and colleagues built versions of the path-integration and
speed-sensor cells into a ring-shaped attractor network to reduce
noisy inputs from multiple sources
into a single packet of activity that
could shift around the ring. Sent
out on random routes, the network
helped the machine find its way
back to the starting point, demonstrating the viability of the concept.
Through such simple models, researchers hope to continue the long
journey towards understanding how
intelligence works and how it can be
emulated in computers and robots.
Milford says, “I always regard spatial
intelligence as a gateway to understanding higher-level intelligence.
It’s the mechanism by which we can
build on our understanding of how
the brain works.”
Milford, M., Jacobsen, A., Chen, Z.,
and Wyeth, G.
RatSLAM: Using Models of the Rodent
Hippocampus for Robot Navigation and
Beyond. Robotics Research: The 16th
International Symposium (2013).
Galluppi, F., Davies, S., Furber, S.,
Stewart, T., and Eliasmith, C.
Real Time On-Chip Implementation
of Dynamical Systems with Spiking
Neurons. IEEE World Congress on
Computational Intelligence (WCCI)
2012, Brisbane, Australia.
Hu, J., Tang, H., Tan, K.C., and Li, H.
How the Brain Formulates Memory:
A Spatial-Temporal Model. IEEE
Computational Intelligence, (2016)
Volume 11, Issue 2.
Stone, T., Webb, B., Adden, A., Weddig, N.B.,
Honkanen, A., Templin, R., Wcislo, W.,
Scimeca, L., Warrant, E., and Heinze, S.
An Anatomically Constrained Model for
Path Integration in the Bee Brain. Current
Biology (2017), Volume 27, Issue 20.
Chris Edwards is a Surrey, U.K.-based writer who reports
on electronics, IT, and synthetic biology.
© 2018 ACM 0001-0782/18/8 $15.00
detail that we know takes incredible
amounts of computational power. We
didn’t want to do that, as we wanted to
create something useful in the short
term. As we became very familiar with
the navigation problem and mapping
problem, we couldn’t find a compelling reason to go to a higher level of fidelity,” Milford says.
Milford and colleagues developed
what they call “pose cells,” which
shared some characteristics with the
place cells found by O’Keefe decades
earlier, but which added information
on the direction in which the robot
faced, and the distance of travel recorded by internal sensors. Such pose
cells can represent multiple physical locations; the robot determines
the difference by adding information
from cells that record the visual scene
at each location.
The pose cells turned out to share
characteristics with a class of neurons called “grid cells” discovered
several years later by neuroscientists
Edvard and May-Britt Moser, then
working at the Norwegian University
of Science and Technology. The Mosers shared the 2014 Nobel Prize in
Physiology or Medicine with O’Keefe
for their study of the multiple types of
navigational cells of mammals.
“Grid cells display strikingly regular
firing responses to the animal’s locations in 2D (two-dimensional) space.
Existing studies suggest place-cell
responses may be generated from a
subset of grid-cell inputs,” says Tang,
pointing to projects conducted by his
team in which simulations of place and
grid cells helped improve robot navigation. Grid cells appear to become more
important as the area covered by the
A key facet of grid cell behavior for
large-scale navigation is its ability to
store information about multiple locations. “The assumption is that this
is a very clever way to map data into
a very compact storage representation. The data so far suggest you can
do immense amounts of data compression,” Milford says, pointing to
work his group is doing for the U.S. Air
Force in this subject area.
As well as the functions of individual types of neurons, a common link
between robot design and biology lies
in the way they are structured. Rat-
In addition to
of individual types
of neurons, a common
link between robot
design and biology
lies in the way they