# Fruit fly inspired algorithm solves electronic nose learning and memory challenges

- URL: https://talkfuse.com/story/just-like-a-fruit-fly-a-new-algorithm-never-forgets-old-scents/
- Published: 2026-09-03T18:22:52+00:00
- Updated: 2026-09-03T20:22:14+00:00
- Original publisher: Ars Technica
- Original article: https://arstechnica.com/science/2026/09/just-like-a-fruit-fly-a-new-algorithm-never-forgets-old-scents/
- Topics: Software

## Key points

- New algorithm Spi-Fly mimics fruit fly smell-processing to fix electronic noses
- Uses "sparse coding" barcodes instead of dense, forgetting-prone neural networks
- Could enable cheaper, more adaptable odour sensors without retraining from scratch

## Summary

Researchers at the Okinawa Institute of Science and Technology have developed an algorithm called Spi-Fly that mimics how fruit flies process and remember smells, aiming to overcome two persistent weaknesses in existing "electronic nose" technology: the need for huge amounts of labelled training data and "catastrophic forgetting," where learning a new odour erases memory of previously learned ones. The work matters because commercial odour-sensing devices, used in food safety, environmental monitoring and security screening, remain expensive, narrow in scope and difficult to retrain for new tasks, whereas fruit flies achieve rapid, durable smell recognition with a brain smaller than a poppy seed.

The approach draws on "sparse coding," the technique flies use to assign each odour a unique neural "barcode": signals from odour receptors are randomly and sparsely relayed to around 2,000 Kenyon cells, which are then sharply suppressed by inhibitory neurons so that only a small, distinctive subset stays active. Spi-Fly, developed by Kevin Max and Yang Shen and detailed in a paper in Neuromorphic Computing and Engineering, replicates this in simulation using pre-recorded sensor data, converting readings into spikes projected onto a sparse hidden layer with mutual neuron inhibition, before passing signals to an output layer for classification.

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