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 connects to an output layer, one neuron per labeled odor, waiting to learn which barcode belongs to which smell.

A simple rule

What actually gets learned is the connection between a barcode and its label, following a simple, decades-old neural network rule: Every time a hidden neuron fires alongside the correct answer, that link gets a little stronger.

Nothing more. There’s no need for backpropagation, the technique co-invented by 2024 physics Nobel laureate Geoffrey Hinton that trains most modern neural networks by working backward through every layer to calculate exactly who’s to blame for a mistake.

In their tests, the simplified system works. On a set of odors picked up by common gas sensors, Spi-Fly peaks after just three exposures to each one, while backpropagation needs roughly 70 to get there.

Feed the network new odors a couple at a time—a stress test for catastrophic forgetting—and Spi-Fly barely blinks, holding onto old smells with almost no accuracy loss, while backpropagation crashes down into near random-guessing territory.

There’s another practical hurdle: memory. The whole point of designing a network this simple is to eventually run it on neuromorphic chips—a fast-developing technology that builds hardware that mimics the brain directly by processing spikes instead of running conventional software. Those chips typically don’t have much memory to work with. Any algorithm running on one has to make do with a fraction of what a regular computer takes for granted. Spi-Fly degrades far less than backpropagation does under those constraints.

There’s a ceiling, though, as Max himself admits. “If the sparse code layer contains 100 neurons, and each odor is represented by 5 neurons, the theoretical upper limit of odor capacity is ‘100, choose 5,’” he explained in an email—roughly 75 million possible “barcodes.” But real-world noise erases nearly all of that headroom. “The same odor is almost never represented by the same sparse code in two exposures, due to noise, airflow conditions, etc.,” Max wrote. “Given the parameters of our model, it should be able to represent a couple hundred odors, but it also depends on what type of odors, and the similarities between them.”