An Odorant Encoding Machine For Sampling, Reconstruction And Robust Representation Of Odorant Identity

Despite recent advances in the understanding of olfactory signal processing, 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 architectural organization of the fruit fly early olfactory system. The OEM provides event-driven sensing, reconstruction and 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 developed for future in silico implementations.