Dimethyl sulphide (DMS) serves as a key olfactory cue for seabird navigation, yet existing DMS products operate at coarse spatiotemporal resolutions (≥ 25km, monthly) mismatched to the scales of individual movement decisions. We ask (I) whether machine learning can produce biologically relevant, high-resolution DMS estimates across the North Atlantic, and (ii) whether such estimates can be readily integrated with animal tracking data to support ecological interpretation of animal trajectories. Using North Atlantic in-situ DMS observations (2002-2024) and five satellite-data-based environmental predictors (chlorophyll, mixed layer depth, nitrate, sea-surface temperature, and photosynthetically available radiation), we developed a machine-learning-based ensemble model for DMS prediction that achieved strong accuracy (test R2 = 0.88; RMSE = 0.859 µmol m-3), exceeding previously reported performance for basin-scale DMS mapping. To identify the key drivers of the model predictions, we conducted SHAP (SHapley Additive exPlanations) analysis, which revealed that mixed layer depth, nitrate concentration, and chlorophyll were the dominant controlling factors, aligning with established understanding of DMS biogeochemistry. We then produced a spatially continuous, daily 4km DMS dataset for the North Atlantic domain (0-60° N, 80° W-15° E), revealing seasonal cycles, persistent hotspots, and fine-scale gradients not captured by coarse climatologies. Finally, we develop AniDMS, an open-source Python package that automates trajectory annotation with gridded DMS and associated covariates, demonstrated with a Manx shearwater case study. Together, the dataset and the tool enable scalable, hypothesis-driven tests of olfactory navigation of seabirds and provide a transferable framework for integrating high-resolution environmental context into movement ecology.

Complex behaviors, such as navigation, rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select appropriate motor sequences remains poorly understood. Here we characterize the architecture of neural circuits that control Caenorhabditis elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to the spatial distribution of attractive and aversive olfactory cues, anticipate upcoming turn directions and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to link sensory cues to motor actions.

Olfactory search in turbulent environments is a sensorimotor challenge solved with remarkable efficiency by many animals, yet replicating this ability in artificial systems remains difficult because detections are intermittent and wind direction fluctuates strongly, rendering standard search strategies unreliable. We introduce a wind-relative reinforcement-learning framework in which an agent navigates a turbulent plume with a single internal variable — the elapsed time since the last odor detection — and selects actions relative to a locally estimated wind direction filtered through an exponential memory kernel. Policies are trained and evaluated in direct numerical simulations of turbulence, capturing the multi-scale characteristics of velocity and odor fields in natural environments, both in the presence and absence of a mean wind. In a mild mean wind, the learned policy outperforms cast-and-surge regardless of the wind memory time, yet adapts its movement pattern to wind-estimation quality. In isotropic turbulence, performance peaks at an intermediate wind memory time, identifying temporal wind integration as a regime-dependent resource. Our results highlight the importance of developing and validating olfactory-navigation strategies under realistic turbulent conditions, and offer a compact design principle for minimal robotic olfactory navigation and testable predictions for biological search behavior.

Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, and strict payload and computation constraints. While prior unmanned aerial vehicle (UAV)-based olfaction systems have demonstrated gas distribution mapping or reactive plume tracing, they rely on predefined coverage patterns, external infrastructure, or extensive sensing and coordination. In this work, we present a complete, open-source UAV system for online odor source localization using a minimal sensor suite. The system integrates custom olfaction hardware, onboard sensing, and a learning-based navigation policy that we train in simulation and deploy on a real quadrotor. Through our minimal framework, the UAV is able to navigate directly toward an odor source without constructing an explicit gas distribution map or relying on external positioning systems. We incorporate vision as an optional complementary modality to accelerate navigation under certain conditions. We validate the proposed system through real-world flight experiments in a large indoor environment using an ethanol source, demonstrating consistent source-finding behavior under realistic airflow conditions. The primary contribution of this work is a reproducible system and methodological framework for UAV-based olfactory navigation and source finding under minimal sensing assumptions. We elaborate on our hardware design and open-source our UAV firmware, simulation code, olfaction–vision dataset, and circuit board to the community.

The ability of flying insects to locate distant food and mates by tracking odor plumes through turbulent and unsteady flow represents a remarkable feat of sensorimotor integration. Successful navigation requires not only extracting a reliable directional estimate from an intermittent olfactory signal, but also contending with the challenging dynamics of variable winds. While prior work has established that insects integrate the history of odor encounters to shape search decisions, whether they also retain a working memory of recently experienced wind conditions has remained unknown. Here, we use optogenetics combined with controlled wind perturbations in a free-flight wind tunnel to investigate how wind history modulates the olfactory search behavior of Drosophila melanogaster . By introducing lateral "gust" flow via auxiliary fans and independently delivering olfactory stimuli, we show that the wind experienced during an olfactory stimulus shapes both the immediate surge response and the subsequent spatial search. Flies that received an olfactory stimulus while being displaced by a crosswind gust were significantly more likely to return to the gust zone during the post-stimulus search phase compared to flies that received the same odor cue in steady laminar flow. Meanwhile, surge responses and course directions exhibited during search indicate that moment-to-moment flight kinematics may be driven more by instantaneous flow. These results reveal that wind experience is tracked in addition to olfactory experience, and provide evidence that Drosophila maintain a short-term working memory of ambient wind conditions to guide olfactory navigation.

Flying Drosophila critically depend on high-contrast visual surroundings to localize odor sources in still air, yet the neural mechanisms of visual integration for active odor tracking are unknown. We demonstrate that E-PG neurons—head direction cells in the central complex—work in concert with self-generated visual motion signals to maintain a stable heading metric during olfactory navigation in flight. Using a magnetic tether system and a digital “visual clamp”, we show that removing the visual feedback generated by a fly’s own turns (reafference) causes the animal to lose its heading within an odor plume. Thus, olfactory and mechanosensory signals alone are insufficient for plume stabilization. E-PG neurons have been shown to store visual changes in heading during flight. Genetically hyperpolarizing E-PG neurons significantly compromised the flies’ ability to both acquire and maintain heading toward a food odor. Notably, silencing these neurons did not disrupt basic visual reflexes, such as optomotor gaze stabilization or object tracking, indicating a specific role in odor-directed visual navigation rather than basic visual flight control. While odor was found to modulate the frequency and amplitude of turns independently, E-PG neurons are essential for directing the orientation of corrective saccades toward the plume center. These results establish that visual reafference engages the internal visual compass to sustain a spatial working memory of heading changes between saccades, allowing flies to maintain a straight course and navigate effectively toward an invisible odor source in flight.

Working memory and evidence integration are fundamental components of cognition thought to arise from distributed circuits throughout the brain1–2. Theoretical3,4 and behavioral studies5,6 argue that both processes are required for plume navigation, an innate task in which animals use stochastic sensory cues to navigate towards the unknown location of an odor source7–10. Here we identify a small population of local neurons in the navigation center of Drosophila11–13 that exhibits both evidence integration and working memory dynamics during goal-directed olfactory navigation. Developing a closed-loop virtual plume navigation paradigm, we show that a bump of activity in this population ramps up with successive odor encounters, and can persist for variable intervals after odor loss. While bump activity persists, the fly maintains the goal heading it adopted during odor. Silencing these neurons impairs the persistence of upwind heading after odor loss. Simulations show that the time constant of persistence observed in these neurons optimizes navigation in a turbulent boundary layer plume. Our work localizes working memory and evidence integration to a specific group of genetically-identified neurons, which will facilitate the mechanistic dissection of these building blocks of cognition.

Individually foraging ants use egocentric views as a dominant navigation strategy for learning and retracing routes. Evidence suggests that route retracing can be achieved by algorithms which use views as 'visual compasses', where individuals choose the heading that leads to the most familiar visual scene when compared to route memories. However, such a mechanism does not naturally lead to route approach, and alternative strategies are required to enable convergence when off-route and for correcting on-route divergence. In this work we investigate how behaviour incorporated into visual compass like route learning and recapitulation strategies might enable convergence to a learned route and its destination. Without alterations to the basic form of the initial learning route, the most successful recapitulation method comes from a 'cast and surge' approach, a mechanism seen across arthropods for olfactory navigation. In this strategy casts form a 'zig-zagged' or oscillatory search in space for familiar views, and surges exploit visual familiarity gradients. We also find that performance improves if the learned route consists of an oscillatory motor mechanism with learning gated to occur when the agent approaches the central axis of the oscillation. Furthermore, such oscillations combined with the cast and surge method additively enhance performance, showing that it benefits to incorporate oscillatory behaviour in both learning and recapitulation. As destination reaching is the primary goal of navigation, we show that a suitably sized goal-orientated learning walk might suffice, but that the scale of this is dependent on the degree of divergence, and thus depends on route length and the route learning and recapitulation strategies used. Finally we show that view familiarity can modulate on-the-spot scans performed by an agent, providing a better reflection of ant behaviour. Overall, our results show that the visual compass can provide a basis for robust visual navigation, so long as it is considered holistically with the details of basic motor and sensory-motor patterns of ants undertaking route learning and recapitulation.

Individually foraging ants use egocentric views as a dominant navigation strategy for learning and retracing routes. Evidence suggests that route retracing can be achieved by algorithms which use views as ‘visual compasses’, where individuals choose the heading that leads to the most familiar visual scene when compared to route memories. However, such a mechanism does not naturally lead to route approach, and alternative strategies are required to enable convergence when off-route and for correcting on-route divergence. In this work we investigate how behaviour incorporated into visual compass like route learning and recapitulation strategies might enable convergence to a learned route and its destination. Without alterations to the basic form of the initial learning route, the most successful recapitulation method comes from a ‘cast and surge’ approach, a mechanism seen across arthropods for olfactory navigation. In this strategy casts form a ‘zig-zagged’ or oscillatory search in space for familiar views, and surges exploit visual familiarity gradients. We also find that performance improves if the learned route consists of an oscillatory motor mechanism with learning gated to occur when the agent approaches the central axis of the oscillation. Furthermore, such oscillations combined with the cast and surge method additively enhance performance, showing that it benefits to incorporate oscillatory behaviour in both learning and recapitulation. As destination reaching is the primary goal of navigation, we show that a suitably sized goal-orientated learning walk might suffice, but that the scale of this is dependent on the degree of divergence, and thus depends on route length and the route learning and recapitulation strategies used. Finally we show that view familiarity can modulate on-the-spot scans performed by an agent, providing a better reflection of ant behaviour. Overall, our results show that the visual compass can provide a basis for robust visual navigation, so long as it is considered holistically with the details of basic motor and sensory-motor patterns of ants undertaking route learning and recapitulation.

Insects rely on their olfactory systems to detect odors and locate odor sources through highly efficient flapping-wing mechanisms. While previous studies on bio-inspired unsteady flows have primarily examined the aerodynamic functions of flapping wings, they have largely overlooked the effects of wing-induced unsteady flows on airborne odor stimuli. This study aims to explore how flapping kinematics influence odorant transport. Computational fluid dynamics simulations were employed to investigate unsteady flow fields and odorant transport by solving the Navier–Stokes and odor advection–diffusion equations. Both two-dimensional (2D) and three-dimensional (3D) simulations were conducted to visualize the flow fields and odor concentration distributions generated by pitching–plunging airfoils. Our findings reveal that higher Strouhal numbers, characterized by increased flapping frequency, produce stronger flow jets that enhance odor advection and dissipation downstream, while reducing odor concentration on the airfoil surface. In 2D simulations, symmetry breaking at high Strouhal numbers causes oblique advection of vortices and odor plumes. In contrast, 3D simulations exhibit bifurcated horseshoe-like vortex rings and corresponding odor plume bifurcations. These findings highlight the intricate coupling between unsteady aerodynamics and odor transport, offering valuable insights for bio-inspired designs and advanced olfactory navigation systems.

Odors serve as essential cues for navigation. Although tracking an odor plume has been modeled as a reflexive process, it remains unclear whether animals can use memories of their past odor encounters to infer the spatial structure of their chemical environment or their location within it. Here we developed a virtual-reality olfactory paradigm that allows head-fixed Drosophila to navigate structured chemical landscapes, offering insight into how memory mechanisms shape their navigational strategies. We found that flies track an appetitive odor corridor by following its boundary, alternating between rapid counterturns to exit the plume and directed returns to its edge. Using a combination of behavioral modeling, functional calcium imaging, and neural perturbations, we demonstrate that this ‘edge-tracking’ strategy relies on vector-based computations within the Drosophila central complex in which flies store and dynamically update memories of the direction to return them to the plume’s boundary. Consistent with this, we find that FC2 neurons within the fan-shaped body, which encode a fly’s navigational goal, signal the direction back to the odor boundary when flies are outside the plume. Together, our studies suggest that flies leverage the plume’s boundary as a dynamic landmark to guide their navigation, analogous to the memory-based strategies other insects use for long-distance migration or homing to their nests. Plume tracking thus uses components of a conserved navigational toolkit, enabling flies to use memory mechanisms to navigate through a complex shifting chemical landscape.

Abstract Bio-hybrid drones, which combine biological odor sensors with small drones, introduce an innovative navigation method that compensates for traditional image-based navigation, enhancing the capabilities of aerial robots. Inspired by the odor-source search behavior observed in biological organisms, we identified two key elements for improving odor source direction estimation accuracy for bio-hybrid drones: (1) increasing the anisotropy of the odor sensor using a sensor enclosure, and (2) implementing a stepped rotation algorithm that strategically incorporates pauses during scanning. This integration resulted in a doubling of both search accuracy and range, achieving a search distance of up to 5 m, significantly exceeding the performance of a previous algorithm that sequentially combined rotational and linear motions. Although these elements are commonly observed in various arthropods, they are underapplied in robotics applications. This study provides a novel perspective to robotic olfactory navigation techniques by leveraging these biological behaviors to enhance robotic functionality.

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

Olfactory-based powered descent guidance for Mars methane plume exploration in long-time-average wind environment

Olfactory navigation in fluctuating environments

Physics of bacterial chemotaxis

SummaryOdors are a fundamental part of the sensory environment used by animals to inform behaviors such as foraging and navigation1,2. Primary olfactory (piriform) cortex is thought to be dedicated to encoding odor identity3–8. Here, using neural ensemble recordings in freely moving rats performing a novel odor-cued spatial choice task, we show that posterior piriform cortex neurons also carry a robust spatial map of the environment. Piriform spatial maps were stable across behavioral contexts independent of olfactory drive or reward availability, and the accuracy of spatial information carried by individual neurons depended on the strength of their functional coupling to the hippocampal theta rhythm. Ensembles of piriform neurons concurrently represented odor identity as well as spatial locations of animals, forming an “olfactory-place map”. Our results reveal a previously unknown function for piriform cortex in spatial cognition and suggest that it is well-suited to form odor-place associations and guide olfactory cued spatial navigation.

Rapid distance estimation of odor sources by electronic nose with multi-sensor fusion based on spiking neural network

Simple olfactory navigation in air and water