Insect-inspired “sparse coding” does fast learning, avoids catastrophic forgetting. Fruit flies aren’t exactly famous for their brainpower; you’ve probably drowned more than one in a wine glass left too long on the patio table. And yet, working with roughly 140,000 neurons—a brain smaller than a poppy seed—Drosophila can sort through a huge range of smells in a fraction of a second, and then retain the memory of that scent for a long time.

In this, they do much better than current “electronic noses.” Even the most advanced ones on the market tend to be expensive, painfully narrow in what they can detect, and quick to forget an odor the moment they learn a new one. So why not just copy the fly? That’s the question a growing number of researchers have been asking—including Kevin Max and Yang Shen at the Okinawa Institute of Science and Technology, whose new algorithm, Spi-Fly, is described in a paper recently published in the journal Neuromorphic Computing and Engineering. A rather obscure sense Smell is a strange sense, mechanically speaking.

Vision and hearing both reduce to a single physical dimension you can plot on a graph—wavelength—which makes them relatively tidy to study. Odor molecules, by contrast, can’t be reduced to any single physical dimension. Biology had to find a messier solution instead: hundreds of different receptor proteins, each shaped to grab onto specific molecular features, firing in combinations that the brain then has to decode.

It’s a system so combinatorially complex that it took until 1991 for Linda Buck and Richard Axel to even identify the receptor gene family behind it, work that won them a Nobel Prize in 2004. Despite the difficulties in our understanding of smell, “electronic noses” exist on the market. Companies like Alpha MOS, Aryballe, and Odotech sell them for food-quality control, environmental monitoring, and security screening.

What these noses are bad at is generalizing. A device with software that is tuned to sniff out spoiled olive oil isn’t the same as a device that flags a specific explosive at an airport checkpoint. Retooling one for a new task usually means retraining its software almost from scratch.

Two technical bottlenecks sit behind that limitation. First, these systems typically need a mountain of hand-labeled examples before they can reliably tell one smell from another. Second, teaching them a new odor tends to scramble what they already knew, a problem researchers call “catastrophic forgetting”—the electronic equivalent of forgetting how to ride a bike right after learning to swim.

Odor barcodes Fruit flies—and plenty of other insects—don’t have this problem, despite their minuscule brains. How do they tell odors apart and remember them with so little brainpower to work with? The secret, according to the paper’s authors, is something called sparse coding. Think of it as the fly’s brain assigning a barcode to every smell.

Its olfactory system relies on roughly 2,000 specialized cells, called Kenyon cells, that receive sparse, randomly wired signals passed on from the fly’s odor receptors. Those Kenyon cells all report to a single relay point: the anterior paired lateral neuron, or APL (actually a symmetrical pair of them, one per brain hemisphere). The APLs respond by firing strong, global inhibition back at every Kenyon cell at once, silencing nearly all of them.

The cells that remain active after that crackdown are what Max and Shen call the barcode for that particular odor. Spi-Fly picks up the story only after a sensor has already done its job—everything here happens in simulation, using pre-recorded sensor data, and the algorithm itself has nothing to do with capturing the smell in the first place. In Max and Shen’s work, sensor readings become a stream of spikes, projected sparsely and randomly onto a hidden layer that stands in for the Kenyon cells, with neurons inhibiting each other instead of relying on a single APL-like referee.

From there, the hidden layer conne...