Parallel encoding of CO2 in attractive and aversive glomeruli by selective lateral signaling between olfactory afferents

Nonlinear high-activity neuronal excitation enhances odor discrimination

Discrimination between different signals is crucial for animals' survival. Inhibition that suppresses weak neural activity is crucial for pattern decorrelation. Our understanding of alternative mechanics that allow efficient signal classification remains incomplete. We show that Drosophila olfactory receptor neurons (ORNs) have numerous intraglomerular axo-axonal connections mediated by the G protein-coupled receptor (GPCR), muscarinic type B receptor (mAChR-B). Contrary to its usual inhibitory role, mAChR-B participates in ORN excitation. The excitatory effect of mAChR-B only occurs at high ORN firing rates. A computational model demonstrates that nonlinear intraglomerular or global excitation decorrelates the activity patterns of ORNs of different types and improves odor classification and discrimination, while acting in concert with the previously known inhibition. Indeed, knocking down mAChR-B led to increased correlation in odor-induced ORN activity, which was associated with impaired odor discrimination, as shown in behavioral experiments. Furthermore, knockdown (KD) of mAChR-B and the GABAergic GPCR, GABAB-R, has an additive behavioral effect, causing reduced odor discrimination relative to single-KD flies. Together, this study unravels a novel mechanism for neuronal pattern decorrelation, which is based on nonlinear intraglomerular excitation.

Olfactory coding in the mosquito antennal lobe: labeled lines or combinatorial code?

Odors serve as important cues for many behaviors in mosquitoes, including host-seeking, foraging, and oviposition. They are detected by olfactory receptor neurons present in the sensory organs, whose axons take this signal to the antennal lobe, the first olfactory processing center in the insect brain. We review the organization and the functioning of the antennal lobe in mosquitoes, focusing on two populations of interneurons present there: the local neurons (LNs) and the projection neurons (PNs). LNs enable information processing in the antennal lobe by providing lateral inhibition and excitation. PNs carry the processed output to downstream neurons in the lateral horn and the mushroom body. We compare the ideas of labeled lines and combinatorial codes, and argue that the PN population encodes odors combinatorially. Throughout this review, we discuss the observations from Aedes, Anopheles, and Culex mosquitoes in the context of previous findings from Drosophila and other insects.

Olfactory combinatorial coding supports risk-reward decision making in C. elegans

Olfactory-driven behaviors are essential for animal survival, but mechanisms for decoding olfactory inputs remain poorly understood. We have used whole-network Ca ++ imaging to study olfactory coding in Caenorhabditis elegans. We show that the odorant 1-octanol is encoded combinatorially in the periphery as both an attractant and a repellant. These inputs are integrated centrally, and their relative strengths determine the sensitivity and valence of the behavioral response through modulation of locomotory reversals and speed. The balance of these pathways also dictates the activity of the locomotory command interneurons, which control locomotory reversals. This balance serves as a regulatory node for response modulation, allowing C. elegans to weigh opportunities and hazards in its environment when formulating behavioral responses. Thus, an odorant can be encoded simultaneously as inputs of opposite valence, focusing attention on the integration of these inputs in determining perception, response, and plasticity.

Odour source distance is predictable from a time history of odour statistics for large scale outdoor plumes

Odour plumes in turbulent environments are intermittent and sparse. Laboratory-scaled experiments suggest that information about the source distance may be encoded in odour signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here, we analyse odour signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odour signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5 to 10 s, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source distance estimates in a 60 × 60 m2 search area with median errors of 3-8 m, a distance at which point odour sources are often within visual range for animals such as mosquitoes.

Sensorimotor transformation underlying odor-modulated locomotion in walking Drosophila

Most real-world behaviors - such as odor-guided locomotion - are performed with incomplete information. Activity in olfactory receptor neuron (ORN) classes provides information about odor identity but not the location of its source. In this study, we investigate the sensorimotor transformation that relates ORN activation to locomotion changes in Drosophila by optogenetically activating different combinations of ORN classes and measuring the resulting changes in locomotion. Three features describe this sensorimotor transformation: First, locomotion depends on both the instantaneous firing frequency (f) and its change (df); the two together serve as a short-term memory that allows the fly to adapt its motor program to sensory context automatically. Second, the mapping between (f, df) and locomotor parameters such as speed or curvature is distinct for each pattern of activated ORNs. Finally, the sensorimotor mapping changes with time after odor exposure, allowing information integration over a longer timescale.

Olfactory search with finite-state controllers

Long-range olfactory search is an extremely difficult task in view of the sparsity of odor signals that are available to the searcher and the complex encoding of the information about the source location. Current algorithmic approaches typically require a continuous memory space, sometimes of large dimensionality, which may hamper their optimization and often obscure their interpretation. Here, we show how finite-state controllers with a small set of discrete memory states are expressive enough to display rich, time-extended behavioral modules that resemble the ones observed in living organisms. Finite-state controllers optimized for olfactory search have an immediate interpretation in terms of approximate clocks and coarse-grained spatial maps, suggesting connections with neural models of search behavior.

Olfactory navigation in arthropods

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.

Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

To survive, insects must effectively navigate odor plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency-the fraction of time signal can be detected-but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

Restructuring of olfactory representations in the fly brain around odor relationships in natural sources

A core challenge of olfactory neuroscience is to understand how neural representations of odor are generated and progressively transformed across different layers of the olfactory circuit into formats that support perception and behavior. The encoding of odor by odorant receptors in the input layer of the olfactory system reflects, at least in part, the chemical relationships between odor compounds. Neural representations of odor in higher order associative olfactory areas, generated by random feedforward networks, are expected to largely preserve these input odor relationships1-3. We evaluated these ideas by examining how odors are represented at different stages of processing in the olfactory circuit of the vinegar fly D. melanogaster. We found that representations of odor in the mushroom body (MB), a third-order associative olfactory area in the fly brain, are indeed structured and invariant across flies. However, the structure of MB representational space diverged significantly from what is expected in a randomly connected network. In addition, odor relationships encoded in the MB were better correlated with a metric of the similarity of their distribution across natural sources compared to their similarity with respect to chemical features, and the converse was true for odor relationships encoded in primary olfactory receptor neurons (ORNs). Comparison of odor coding at primary, secondary, and tertiary layers of the circuit revealed that odors were significantly regrouped with respect to their representational similarity across successive stages of olfactory processing, with the largest changes occurring in the MB. The non-linear reorganization of odor relationships in the MB indicates that unappreciated structure exists in the fly olfactory circuit, and this structure may facilitate the generalization of odors with respect to their co-occurence in natural sources.

Sensory biology: Olfactory crosstalk reshapes odor coding

New research uncovers a novel form of crosstalk between olfactory pathways in the antennal lobe, the first olfactory center of the fly brain. This crosstalk reshapes odor coding and may explain how carbon dioxide can elicit either attraction or aversion.