Spike Latency Coding in Biologically Inspired Microelectronic Nose

Recent theoretical and experimental findings suggest that biological olfactory systems utilize relative latencies or time-to-first spikes for fast odor recognition. These time-domain encoding methods exhibit reduced computational requirements and improved classification robustness. In this paper, we introduce a microcontroller-based electronic nose system using time-domain encoding schemes to achieve a power-efficient, compact, and robust gas identification system. A compact (4.5 cm x 5 cm x 2.2 cm) electronic nose, which is integrated with a tin-oxide gas-sensor array and capable of wireless communication with computers or mobile phones through Bluetooth, was implemented and characterized by using three different gases (ethanol, carbon monoxide, and hydrogen). During operation, the readout circuit digitizes the gas-sensor resistances into a concentration-independent spike timing pattern, which is unique for each individual gas. Both sensing and recognition operations have been successfully demonstrated in hardware. Two classification algorithms (rank order and spike distance) have been implemented. Both algorithms do not require any explicit knowledge of the gas concentration to achieve simplified training procedures, and exhibit comparable performances with conventional pattern-recognition algorithms while enabling hardware-friendly implementation.