Bio-inspired encoding for a real-time and stable single component odor detection with a highly-redundant optical artificial olfactory system

The standard model of artificial olfaction includes arrays of partially selective sensors and pattern recognition algorithms. Despite the brilliant results, such simplified platforms fail to achieve most of the natural olfaction features even if current sensors may rival olfactory receptor performances. Conversely, the olfaction architecture is specific to processing signals of millions of neurons, consisting of copious copies of a limited variety of receptors. The actual gas sensor arrays are still far from the abundance of natural olfaction receptors, so pattern recognition algorithms mainly differ from the processing in biological olfaction. This paper tries to cover this gap by applying a biologically derived processing algorithm to a highly redundant array. A webcam recorded the RGB channel color intensities of 15 optical indicators due to exposure to different chemical vapors in real time. Each pixel was assumed as a distinct sensor obtaining about 28,000 copies representative of 45 cross-selective virtual receptors. This outstanding redundancy enables deeply studying a model of the olfactory bulb configured as a network of inhibitory and excitatory elements. Subsequently, a Self-Organizing Map (SOM) is used to create an over-segmented reference space from the network outputs, where the exposure to different stimuli produces adsorption-desorption paths of activated neurons in real-time. Remarkably, even when a large part of the sensors, 65%, provided anomalous responses, the network offers robust odor identification. Using such a large and easy-to-make platform opens the way to a better understanding and facile implementation of algorithms that trace the architecture of the biological olfaction pathway.