An Odorant Encoding Machine for Sampling, Reconstruction and Robust Representation of Odorant Identity
Aurel A. Lazar, Tingkai Liu, Chung-Heng Yeh
Abstract
Despite recent advances in the understanding of olfactory signal processing [1], [2], [3], [4], [5], robust odorant sensing in complex environments with time-varying odorant identities and concentrations remains an open problem. Particularly, the operational principles of biological and biomimetic olfactory sensors define a new class of sampling problems in which the odorant identity and intensity are multiplicatively coupled into a volatile signal format. We solve the sampling problem by developing the Odorant Encoding Machine (OEM), a biomimetic system based on the latest insights in the architectural organization of the fruit fly early olfactory system. The OEM provides event-driven sensing, reconstruction and robust representation of odorant identity as a combinatorial code of multidimensional spike trains. Like its biological counterpart, OEM 1) decouples odorant identity and concentration encoding via a predictive coding circuit, 2) enables real-time responses to changing odorant input through an on-off circuit, and 3) provides robust representation of odorant identity with a real-time hashing circuit. Furthermore, the OEM is directly applicable for future in silico implementations.
BibTeX
@inproceedings{icassp2020_anodorantencodin,
title = {An Odorant Encoding Machine for Sampling, Reconstruction and Robust Representation of Odorant Identity},
author = {Aurel A. Lazar and Tingkai Liu and Chung-Heng Yeh},
booktitle = {ICASSP 2020},
year = {2020}
}