Intelligent artificial olfactory nervous system with sensory adaptation capabilities

Accurate gas identification across diverse environments is crucial for the advancement of intelligent artificial olfaction, but remains challenging for conventional data-driven learning approaches. Here, we report an artificial olfactory nervous system capable of reliably identifying novel gas stimuli regardless of environmental variations by leveraging sensory adaptation mechanisms inspired by biological olfactory systems. Inspired by biological olfactory systems, our system effectively integrates artificial olfactory receptor neurons, cortical neurons, and intermediate olfactory neurons that facilitate signal transmission. Receptor neurons can dynamically adapt their sensitivity to olfactory stimuli, enabling rapid responses to familiar gases and selective filtering of background interference, thus generating distinctive signal patterns for novel gases. Signals from receptor neurons are modulated in olfactory neurons, then fed into the cortical neurons and translated into olfactory perception. We show that, despite being trained in limited settings, the system can accurately identify gases in various environments. The authors report an artificial olfactory nervous system that uses sensory adaptation to distinguish gas stimuli across changing environments, enabling reliable gas identification with limited training.