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Abstract Neuromorphic computing, exemplified by breakthroughs in machine vision through concepts like address-event representation and send-on-delta sampling, has revolutionised sensor technology, enabling low-latency and high dynamic range perception with minimal bandwidth. While these advancements are prominent in vision and auditory perception, their potential in machine olfaction remains under-explored, particularly in the context of fast sensing. Here, we outline the perspectives for neuromorphic principles in machine olfaction. Considering the physical characteristics of turbulent odour environments, we argue that event-driven signal processing is optimally suited to the inherent properties of olfactory signals. We highlight the lack of bandwidth limitation due to turbulent dispersal processes, the characteristic temporal and chemical sparsity, as well as the high information density of the odour landscape. Further, we critically review and discuss the literature on neuromorphic olfaction; particularly focusing on neuromorphic principles such as event generation algorithms, information encoding mechanisms, event processing schemes (spiking neural networks), and learning. We discuss that the application of neuromorphic principles may significantly enhance response time and task performance in robotic olfaction, enabling autonomous systems to perform complex tasks in turbulent environments—such as environmental monitoring, odour guided search and rescue operations, and hazard detection. 

Abstract Daily experience suggests that we perceive distances near us linearly. However, the actual geometry of spatial representation in the brain is unknown. Here we report that neurons in the CA1 region of rat hippocampus that mediate spatial perception represent space according to a non-linear hyperbolic geometry. This geometry uses an exponential scale and yields greater positional information than a linear scale. We found that the size of the representation matches the optimal predictions for the number of CA1 neurons. The representations also dynamically expanded proportional to the logarithm of time that the animal spent exploring the environment, in correspondence with the maximal mutual information that can be received. The dynamic changes tracked even small variations due to changes in the running speed of the animal. These results demonstrate how neural circuits achieve efficient representations using dynamic hyperbolic geometry. 

Abstract Using odors to find food and mates is one of the most ancient and highly conserved behaviors. Arthropods from flies to moths to crabs use broadly similar strategies to navigate toward odor sources—such as integrating flow information with odor information, comparing odor concentration across sensors, and integrating odor information over time. Because arthropods share many homologous brain structures—antennal lobes for processing olfactory information, mechanosensors for processing flow, mushroom bodies (or hemi-ellipsoid bodies) for associative learning, and central complexes for navigation, it is likely that these closely related behaviors are mediated by conserved neural circuits. However, differences in the types of odors they seek, the physics of odor dispersal, and the physics of locomotion in water, air, and on substrates mean that these circuits must have adapted to generate a wide diversity of odor-seeking behaviors. In this review, we discuss common strategies and specializations observed in olfactory navigation behavior across arthropods, and review our current knowledge about the neural circuits subserving this behavior. We propose that a comparative study of arthropod nervous systems may provide insight into how a set of basic circuit structures has diversified to generate behavior adapted to different environments. 

Abstract Sensory processing in olfactory systems is organized across olfactory bulb glomeruli, wherein axons of peripheral sensory neurons expressing the same olfactory receptor co-terminate to transmit receptor-specific activity to central neurons. Understanding how receptors map to glomeruli is therefore critical to understanding olfaction. High-throughput spatial transcriptomics is a rapidly advancing field, but low-abundance olfactory receptor expression within glomeruli has previously precluded high-throughput mapping of receptors to glomeruli in the mouse. Here we combined sequential sectioning along the anteroposterior, dorsoventral, and mediolateral axes with target capture enrichment sequencing to overcome low-abundance target expression. This strategy allowed us to spatially map 86% of olfactory receptors across the olfactory bulb and uncover a relationship between OR sequence and glomerular position. 

Abstract To navigate towards a food source, animals frequently combine odor cues about source identity with wind direction cues about source location. Where and how these two cues are integrated to support navigation is unclear. Here we describe a pathway to theDrosophilafan-shaped body that encodes attractive odor and promotes upwind navigation. We show that neurons throughout this pathway encode odor, but not wind direction. Using connectomics, we identify fan-shaped body local neurons called h∆C that receive input from this odor pathway and a previously described wind pathway. We show that h∆C neurons exhibit odor-gated, wind direction-tuned activity, that sparse activation of h∆C neurons promotes navigation in a reproducible direction, and that h∆C activity is required for persistent upwind orientation during odor. Based on connectome data, we develop a computational model showing how h∆C activity can promote navigation towards a goal such as an upwind odor source. Our results suggest that odor and wind cues are processed by separate pathways and integrated within the fan-shaped body to support goal-directed navigation. 

Abstract We consider a common measurement paradigm, where an unknown subset of an affine space is measured by unknown continuous quasi-convex functions. Given the measurement data, can one determine the dimension of this space? In this paper, we develop a method for inferring the intrinsic dimension of the data from measurements by quasi-convex functions, under natural assumptions. The dimension inference problem depends only on discrete data of the ordering of the measured points of space, induced by the sensor functions. We construct a filtration of Dowker complexes, associated to measurements by quasi-convex functions. Topological features of these complexes are then used to infer the intrinsic dimension. We prove convergence theorems that guarantee obtaining the correct intrinsic dimension in the limit of large data, under natural assumptions. We also illustrate the usability of this method in simulations. 

In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior, switching between long-range dispersal and local search depending on resource availability. How premotor circuits regulate locomotor statistics is not clear. Here, we analyze and model locomotor statistics and their modulation by attractive food odor in walkingDrosophila. Food odor evokes three motor regimes in flies: baseline walking, upwind running during odor, and search behavior following odor loss. During search, we find that flies adopt higher angular velocities and slower ground speeds and turn for longer periods in the same direction. We further find that flies adopt periods of different mean ground speed and that these state changes influence the length of odor-evoked runs. We next developed a simple model of neural locomotor control that suggests that contralateral inhibition plays a key role in regulating the statistical features of locomotion. As the fly connectome predicts decussating inhibitory neurons in the premotor lateral accessory lobe (LAL), we gained genetic access to a subset of these neurons and tested their effects on behavior. We identified one population whose activation induces all three signature of local search and that regulates angular velocity at odor offset. We identified a second population, including a single LAL neuron pair, that bidirectionally regulates ground speed. Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies neural substrates of these computations. 

Sensory systems enable organisms to detect and respond to environmental signals relevant for their survival and reproduction. A crucial aspect of any sensory signal is its intensity; understanding how sensory signals guide behavior requires probing sensory system function across the range of stimulus intensities naturally experienced by an organism. In olfaction, defining the range of natural odorant concentrations is difficult. Odors are complex mixtures of airborne chemicals emitting from a source in an irregular pattern that varies across time and space, necessitating specialized methods to obtain an accurate measurement of concentration. Perhaps as a result, experimentalists often choose stimulus concentrations based on empirical considerations rather than with respect to ecological or behavioral context. Here, we attempt to determine naturally relevant concentration ranges for olfactory stimuli by reviewing and integrating data from diverse disciplines. We compare odorant concentrations used in experimental studies in rodents and insects with those reported in different settings including ambient natural environments, the headspace of natural sources, and within the sources themselves. We also compare these values to psychophysical measurements of odorant detection threshold in rodents, where thresholds have been extensively measured. Odorant concentrations in natural regimes rarely exceed a few parts per billion, while most experimental studies investigating olfactory coding and behavior exceed these concentrations by several orders of magnitude. We discuss the implications of this mismatch and the importance of testing odorants in their natural concentration range for understanding neural mechanisms underlying olfactory sensation and odor-guided behaviors.