Spike latency coding in biologically inspired microelectronic nose

Hung Tat Chen, Kwan Ting Ng, Amine Bermak, Man Kay Law, Dominique Martinez

Research output: Contribution to journalArticle

42 Citations (Scopus)


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×\,5 cm×\,2.2 cm) electronic nose, which is integrated with a tinoxide 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.

Original languageEnglish
Article number5682068
Pages (from-to)160-168
Number of pages9
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number2
Publication statusPublished - Apr 2011
Externally publishedYes



  • Electronic nose
  • gas sensors
  • neuromorphic engineering
  • olfactory system
  • spiking neurons

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Biomedical Engineering

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