Communications of the ACM

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 honeybees navigate.

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.”

Further Reading

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.

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 machine increases.

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.