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.