Among the most impressive transportation networks on the planet are the complex trails that ants create around their nests. These networks arise through the ants’ exploration of their environment and end up channelling the distribution of food for the colony and the daily movements hundreds of thousands of individuals.
What’s more, these networks aren’t just a random criss-crossing of space. Instead, they are a highly efficient solutions to the problem of searching and transporting food. Various groups have created ant-like foraging algorithms to do other types of virtual exploration.
One question that has fascinated biologists is how ants build these networks. They’ve known for some time that ants leave small deposits of pheromones as they travel and that other ants follow these trails, leaving their own deposits. This increases the concentration of the pheromone, strengthening the trail.
But the precise algorithm that governs the way ants respond to pheromones has been harder to pin down. Many experiments show that a trail can only be reinforced if ants have a disproportionately higher probability to follow a trail with higher pheromone concentration.
Biologists have always assumed that this disproportionate response means ants must have a non-linear response to the chemical. In other words, an ant’s tendency to turn towards a pheromone deposit is related in a non-linear fashion to the concentration.
But that seems to conflict with one of the great triumphs of experimental biology–Weber’s Law, which relates the perceived intensity of a stimulus to its physical magnitude. Biologists know this holds for the human perception of many stimuli, such as the intensity of sound, and have also verified it in many insects. So why not in ants?
Today, Andrea Perna at the Complex Systems Institute of Paris Ile de France and a few pals, resolve the issue. These guys have developed an entirely new way to image pheromone trails which allows them to study ant response to pheromones in more detail than ever before.
They say the structure of ant trails can be entirely explained if the ants’s response to a pheromone droplet concentration is linear. “One ant will turn to the left in proportion to the diﬀerence between the pheromone it has on its left side and the pheromone on its right,” say Perna and co.
They also point out that this is exactly what Weber’s law predicts.
So where does the non-linearity required to create trails come from? Perna and co say that ant behaviour is inevitably noisy. “We show that the required non-linearity does not reside in the perceptual response of the ants, but in the noise associated with their movement,” they say.
That’s a fascinating result because it reveals how complexity in nature forms with the simplest of inputs.
And it clearly has implications for the study of other complex structures that ants create, such as their nests. Just how ants create these huge vibrant structures has long puzzled biologists.
Perna and friends hint at an answer in their conclusion. “We can imagine that other collective phenomena, such as group decision-making, could also be founded on coupling between Weber’s Law and simple feedback mechanisms.”
In the case of nests, this mechanism would have to operate in three dimensions rather than two. But that shouldn’t be too much of a challenge. Perhaps a problem that a relatively simple computer model could help solve.
Ref: arxiv.org/abs/1201.5827 :Individual Rules For Trail Pattern Formation In Argentine Ants (Linepithema Humile)
Capitalizing on machine learning with collaborative, structured enterprise tooling teams
Machine learning advances require an evolution of processes, tooling, and operations.
The Download: how to fight pandemics, and a top scientist turned-advisor
Plus: Humane's Ai Pin has been unveiled
The race to destroy PFAS, the forever chemicals
Scientists are showing these damaging compounds can be beat.
How scientists are being squeezed to take sides in the conflict between Israel and Palestine
Tensions over the war are flaring on social media—with real-life ramifications.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.