Vibration-based biomimetic odor classification

Olfaction is not as well-understood as vision or audition, nor technologically addressed. Here, Chemical Graph Theory is shown to connect the vibrational spectrum of an odorant molecule, invoked in the Vibration Theory of Olfaction, to its structure, which is germane to the orthodox Shape Theory. Atomistic simulations yield the Eigen-VAlue (EVA) vibrational pseudo-spectra for 20 odorant molecules grouped into 6 different ‘perceptual’ classes by odour. The EVA is decomposed into peaks corresponding to different types of vibrational modes. A novel secondary pseudo-spectrum, informed by this physical insight—the Peak-Decomposed EVA (PD-EVA)—has been proposed here. Unsupervised Machine Learning (spectral clustering), applied to the PD-EVA, clusters the odours into different ‘physical’ (vibrational) classes that match the ‘perceptual’, and also reveal inherent perceptual subclasses. This establishes a physical basis for vibration-based odour classification, harmonizes the Shape and Vibration theories, and points to vibration-based sensing as a promising path towards a biomimetic electronic nose.

Probabilistic skeletons endow brain-like neural networks with innate computing capabilities

Genetically encoded structure endows neural networks of the brain with innate computational capabilities that enable odor classification and basic motor control right after birth. It is also conjectured that the stereotypical laminar organization of neocortical microcircuits provides basic computing capabilities on which subsequent learning can build. However, it has remained unknown how nature achieves this. Insight from artificial neural networks does not help to solve this problem, since their computational capabilities result from learning. We show that genetically encoded control over connection probabilities between different types of neurons suffices for programming substantial computing capabilities into neural networks. This insight also provides a method for enhancing computing and learning capabilities of artificial neural networks and neuromorphic hardware through clever initialization.

Evolving the Olfactory System with Machine Learning

The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic in vivo would evolve in artificial neural networks constructed to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity to a larger, expansion layer. When trained to both classify odor and impart innate valence on odors, the network develops independent pathways for innate output and odor classification. Thus, artificial networks evolve even without the biological mechanisms necessary to build these systems in vivo, providing a rationale for the convergent evolution of olfactory circuits.

Front-end Weber-Fechner gain control enhances the fidelity of combinatorial odor coding

We showed previously (Gorur-Shandilya et al., 2017) that Drosophila olfactory receptor neurons (ORNs) expressing the co-receptor Orco scale their gain inversely with mean odor intensity according to Weber-Fechner’s law. Here, we show that this front-end adaptation promotes the reconstruction of odor identity from dynamic odor signals, even in the presence of confounding background odors and rapid intensity fluctuations. These enhancements are further aided by known downstream transformations in the antennal lobe and mushroom body. Our results, which are applicable to various odor classification and reconstruction schemes, stem from the fact that this adaptation mechanism is not intrinsic to the identity of the receptor involved. Instead, a feedback mechanism adjusts receptor sensitivity based on the activity of the receptor-Orco complex, according to Weber-Fechner’s law. Thus, a common scaling of the gain across Orco-expressing ORNs may be a key feature of ORN adaptation that helps preserve combinatorial odor codes in naturalistic landscapes.