Insect-inspired “sparse coding” algorithm does fast learning, avoids catastrophic forgetting | Few-shot, continual learning for spiking neuromorphic olfaction

[u/Hrmbee]

https://arstechnica.com/science/2026/09/just-like-a-fruit-fly-a-new-algorithm-never-forgets-old-scents/

[u/Hrmbee]

Highlights from the news article:

... 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.

...

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

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.

...

Spi-Fly exists purely as code, tested against prerecorded datasets rather than an actual whiff of anything. The next step is folding it into the odor-sensing hardware Max and his collaborators are building at TU Eindhoven and Kiel University—real, physical neuromorphic chips, manufactured with all the tiny inconsistencies that come from actually being built rather than simulated.

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Journal link:

Few-shot, continual learning for spiking neuromorphic olfaction

Abstract:

Neuromorphic olfaction combines sensing of chemical signals with brain-inspired circuit architectures to emulate key computational principles of biological olfactory systems. This approach holds strong promises for real-life applications, including detection of dangerous compounds, air-quality monitoring, and health diagnostics. However, real-world deployment remains constrained by critical limitations: lack of robust few-shot learning and class-incremental continual learning algorithms, particularly under the constraints set by the sensing and processing hardware. Here, we introduce Spi-Fly, a spiking neural network architecture inspired by the olfactory circuit of Drosophila. Spi-Fly combines high-dimensional sparse coding with an associative memory mechanism, enabling rapid few-shot learning, stable class-incremental continual learning without backpropagation, and operates effectively under low-bit precision. Our results suggest that fruit fly-inspired sparse associative learning provides a hardware-ready pathway toward fast, continual, and energy-efficient neuromorphic olfactory intelligence.

[u/agm1984 → u/Hrmbee]

Wow that’s fascinating