Dynamic properties of antennal responses to pheromone in two moth species

Using Head-Mounted Ethanol Sensors to Monitor Olfactory Information and Determine Behavioral Changes Associated with Ethanol-Plume Contact during Mouse Odor-Guided Navigation

Olfaction guides navigation and decision-making in organisms from multiple animal phyla. Understanding how animals use olfactory cues to guide navigation is a complicated problem for two main reasons. First, the sensory cues used to guide animals to the source of an odor consist of volatile molecules, which form plumes. These plumes are governed by turbulent air currents, resulting in an intermittent and spatiotemporally varying olfactory signal. A second problem is that the technologies for chemical quantification are cumbersome and cannot be used to detect what the freely moving animal senses in real time. Understanding how the olfactory system guides this behavior requires knowing the sensory cues and the accompanying brain signals during navigation. Here, we present a method for real-time monitoring of olfactory information using low-cost, lightweight sensors that robustly detect common solvent molecules, like alcohols, and can be easily mounted on the heads of freely behaving mice engaged in odor-guided navigation. To establish the accuracy and temporal response properties of these sensors we compared their responses with those of a photoionization detector (PID) to precisely controlled ethanol stimuli. Ethanol-sensor recordings, deconvolved using a difference-of-exponentials kernel, showed robust correlations with the PID signal at behaviorally relevant time, frequency, and spatial scales. Additionally, calcium imaging of odor responses from the olfactory bulbs (OBs) of awake, head-fixed mice showed strong correlations with ethanol plume contacts detected by these sensors. Finally, freely behaving mice engaged in odor-guided navigation showed robust behavioral changes such as speed reduction that corresponded to ethanol plume contacts.

Access to the odor world: olfactory receptors and their role for signal transduction in insects

The sense of smell enables insects to recognize and discriminate a broad range of volatile chemicals in their environment originating from prey, host plants and conspecifics. These olfactory cues are received by olfactory sensory neurons (OSNs) that relay information about food sources, oviposition sites and mates to the brain and thus elicit distinct odor-evoked behaviors. Research over the last decades has greatly advanced our knowledge concerning the molecular basis underlying the reception of odorous compounds and the mechanisms of signal transduction in OSNs. The emerging picture clearly indicates that OSNs of insects recognize odorants and pheromones by means of ligand-binding membrane proteins encoded by large and diverse families of receptor genes. In contrast, the mechanisms of the chemo-electrical transduction process are not fully understood; the present status suggests a contribution of ionotropic as well as metabotropic mechanisms. In this review, we will summarize current knowledge on the peripheral mechanisms of odor sensing in insects focusing on olfactory receptors and their specific role in the recognition and transduction of odorant and pheromone signals by OSNs.

Cheetah Reunion - The Challenge of Finding Your Friends Again

Animals navigate their environment using a variety of senses and strategies. This multiplicity enables them to respond to different navigational requirements resulting from habitat, scale and purpose. One of the challenges social animals face is how to reunite after periods of separation. We explore a variety of possible mechanisms used to reunite the members of a cheetah coalition dispersed within a large area after prolonged separation. Using GPS data from three cheetahs reuniting after weeks of separation, we determined that 1) the likelihood of purely coincidental reunion is miniscule 2) the reunion occurred in an area not normally frequented 3) with very little time spent in the region in advance of the reunion. We therefore propose that timely encounter of scent markings where paths cross is the most likely mechanism used to aid the reunion.

Spatial memory-based behaviors for locating sources of odor plumes

BACKGROUND

Many animals must locate odorant point sources during key behaviors such as reproduction, foraging and habitat selection. Cues from such sources are typically distributed as air- or water-borne chemical plumes, characterized by high intermittency due to environmental turbulence and episodically rapid changes in position and orientation during wind or current shifts. Well-known examples of such behaviors include male moths, which have physiological and behavioral specializations for locating the sources of pheromone plumes emitted by females. Male moths and many other plume-following organisms exhibit "counter-turning" behavior, in which they execute a pre-planned sequence of cross-stream movements spanning all or part of an odorant plume, combined with upstream movements towards the source. Despite its ubiquity and ecological importance, theoretical investigation of counter-turning has so far been limited to a small subset of plausible behavioral algorithms based largely on classical biased random walk gradient-climbing or oscillator models.

RESULTS

We derive a model of plume-tracking behavior that assumes a simple spatially-explicit memory of previous encounters with odorant, an explicit statistical model of uncertainty about the plume's position and extent, and the ability to improve estimates of plume characteristics over sequential encounters using Bayesian updating. The model implements spatial memory and effective cognitive strategies with minimal neural processing. We show that laboratory flight tracks of Manduca sexta moths are consistent with predictions of our spatial memory-based model. We assess plume-following performance of the spatial memory-based algorithm in terms of success and efficiency metrics, and in the context of "contests" in which the winner is the first among multiple simulated moths to locate the source.

CONCLUSIONS

Even rudimentary spatial memory can greatly enhance plume-following. In particular, spatial memory can maintain source-seeking success even when plumes are so intermittent that no pheromone is detected in most cross-wind transits. Performance metrics reflect trade-offs between "risk-averse" strategies (wide cross-wind movements, slow upwind advances) that reliably but slowly locate odor sources, and "risk-tolerant" strategies (narrow cross-wind movements, fast upwind advances) that often fail to locate a source but are fast when successful. Success in contests of risk-averse vs. risk-tolerant behaviors varies strongly with the number of competitors, suggesting empirically testable predictions for diverse plume-following taxa. More generally, spatial memory-based models provide tractable, explicit theoretical linkages between sensory biomechanics, neurophysiology and behavior, and ecological and evolutionary dynamics operating at much larger spatio-temporal scales.

High-speed odor transduction and pulse tracking by insect olfactory receptor neurons

Sensory systems encode both the static quality of a stimulus (e.g., color or shape) and its kinetics (e.g., speed and direction). The limits with which stimulus kinetics can be resolved are well understood in vision, audition, and somatosensation. However, the maximum temporal resolution of olfactory systems has not been accurately determined. Here, we probe the limits of temporal resolution in insect olfaction by delivering high frequency odor pulses and measuring sensory responses in the antennae. We show that transduction times and pulse tracking capabilities of olfactory receptor neurons are faster than previously reported. Once an odorant arrives at the boundary layer of the antenna, odor transduction can occur within less than 2 ms and fluctuating odor stimuli can be resolved at frequencies more than 100 Hz. Thus, insect olfactory receptor neurons can track stimuli of very short duration, as occur when their antennae encounter narrow filaments in an odor plume. These results provide a new upper bound to the kinetics of odor tracking in insect olfactory receptor neurons and to the latency of initial transduction events in olfaction.

Using insect electroantennogram sensors on autonomous robots for olfactory searches

Robots designed to track chemical leaks in hazardous industrial facilities or explosive traces in landmine fields face the same problem as insects foraging for food or searching for mates: the olfactory search is constrained by the physics of turbulent transport. The concentration landscape of wind borne odors is discontinuous and consists of sporadically located patches. A pre-requisite to olfactory search is that intermittent odor patches are detected. Because of its high speed and sensitivity, the olfactory organ of insects provides a unique opportunity for detection. Insect antennae have been used in the past to detect not only sex pheromones but also chemicals that are relevant to humans, e.g., volatile compounds emanating from cancer cells or toxic and illicit substances. We describe here a protocol for using insect antennae on autonomous robots and present a proof of concept for tracking odor plumes to their source. The global response of olfactory neurons is recorded in situ in the form of electroantennograms (EAGs). Our experimental design, based on a whole insect preparation, allows stable recordings within a working day. In comparison, EAGs on excised antennae have a lifetime of 2 hr. A custom hardware/software interface was developed between the EAG electrodes and a robot. The measurement system resolves individual odor patches up to 10 Hz, which exceeds the time scale of artificial chemical sensors. The efficiency of EAG sensors for olfactory searches is further demonstrated in driving the robot toward a source of pheromone. By using identical olfactory stimuli and sensors as in real animals, our robotic platform provides a direct means for testing biological hypotheses about olfactory coding and search strategies. It may also prove beneficial for detecting other odorants of interests by combining EAGs from different insect species in a bioelectronic nose configuration or using nanostructured gas sensors that mimic insect antennae.

Carbon dioxide and fruit odor transduction in Drosophila olfactory neurons. What controls their dynamic properties?

We measured frequency response functions between odorants and action potentials in two types of neurons in Drosophila antennal basiconic sensilla. CO₂ was used to stimulate ab1C neurons, and the fruit odor ethyl butyrate was used to stimulate ab3A neurons. We also measured frequency response functions for light-induced action potential responses from transgenic flies expressing H134R-channelrhodopsin-2 (ChR2) in the ab1C and ab3A neurons. Frequency response functions for all stimulation methods were well-fitted by a band-pass filter function with two time constants that determined the lower and upper frequency limits of the response. Low frequency time constants were the same in each type of neuron, independent of stimulus method, but varied between neuron types. High frequency time constants were significantly slower with ethyl butyrate stimulation than light or CO₂ stimulation. In spite of these quantitative differences, there were strong similarities in the form and frequency ranges of all responses. Since light-activated ChR2 depolarizes neurons directly, rather than through a chemoreceptor mechanism, these data suggest that low frequency dynamic properties of Drosophila olfactory sensilla are dominated by neuron-specific ionic processes during action potential production. In contrast, high frequency dynamics are limited by processes associated with earlier steps in odor transduction, and CO₂ is detected more rapidly than fruit odor.

The role of the coreceptor Orco in insect olfactory transduction

Insects sense odorants with specialized odorant receptors (ORs). Each antennal olfactory receptor neuron expresses one OR with an odorant binding site together with a conserved coreceptor called Orco which does not bind odorants. Orco is necessary for localization of ORs to dendritic membranes and, thus, is essential for odorant detection. It forms a spontaneously opening cation channel, activated via phosphorylation by protein kinase C. Thereafter, Orco is also activated via cyclic adenosine monophosphate (cAMP). Orco forms homo-as well as heteromers with ORs with unknown stoichiometry. Contradictory publications suggest different mechanisms of olfactory transduction. On the one hand, evidence accumulates for the employment of more than one G protein-coupled olfactory transduction cascade in different insects. On the other hand, results from other studies suggest that the OR-Orco complex functions as an odorant-gated cation channel mediating ionotropic signal transduction. This review analyzes conflicting hypotheses concerning the role of Orco in insect olfactory transduction. In conclusion, in situ studies in hawkmoths falsify the hypothesis that Orco underlies odorant-induced ionotropic signal transduction in all insect species. Instead, Orco forms a metabotropically gated, slow cation channel which controls odorant response threshold and kinetics of the sensory neuron.

The speed of smell: odor-object segregation within milliseconds

Segregating objects from background, and determining which of many concurrent stimuli belong to the same object, remains one of the most challenging unsolved problems both in neuroscience and in technical applications. While this phenomenon has been investigated in depth in vision and audition it has hardly been investigated in olfaction. We found that for honeybees a 6-ms temporal difference in stimulus coherence is sufficient for odor-object segregation, showing that the temporal resolution of the olfactory system is much faster than previously thought.

What reaches the antenna? How to calibrate odor flux and ligand-receptor affinities

Physiological studies on olfaction frequently ignore the airborne quantities of stimuli reaching the sensory organ. We used a gas chromatography-calibrated photoionization detector to estimate quantities released from standard Pasteur pipette stimulus cartridges during repeated puffing of 27 compounds and verified how lack of quantification could obscure olfactory sensory neuron (OSN) affinities. Chemical structure of the stimulus, solvent, dose, storage condition, puff interval, and puff number all influenced airborne quantities. A model including boiling point and lipophilicity, but excluding vapor pressure, predicted airborne quantities from stimuli in paraffin oil on filter paper. We recorded OSN responses of Drosophila melanogaster, Ips typographus, and Culex quinquefasciatus, to known quantities of airborne stimuli. These demonstrate that inferred OSN tuning width, ligand affinity, and classification can be confounded and require stimulus quantification. Additionally, proper dose-response analysis shows that Drosophila AB3A OSNs are not promiscuous, but highly specific for ethyl hexanoate, with other earlier proposed ligands 10- to 10 000-fold less potent. Finally, we reanalyzed published Drosophila OSN data (DoOR) and demonstrate substantial shifts in affinities after compensation for quantity and puff number. We conclude that consistent experimental protocols are necessary for correct OSN classification and present some simple rules that make calibration, even retroactively, readily possible.

Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go?

Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully “translated” into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms.

The spatial and temporal patterns of odors sampled by lobsters and crabs in a turbulent plume

Odors are dispersed across aquatic habitats by turbulent water flow as filamentous, intermittent plumes. Many crustaceans sniff (take discrete samples of ambient water and the odors it carries) by flicking their olfactory antennules. We used planar laser-induced fluorescence to investigate how flicking antennules of different morphologies (long antennules of spiny lobsters, Panulirus argus; short antennules of blue crabs, Callinectes sapidus) sample fluctuating odor signals at different positions in a turbulent odor plume in a flume to determine whether the patterns of concentrations captured can provide information about an animal's position relative to the odor source. Lobster antennules intercept odors during a greater percentage of flicks and encounter higher peak concentrations than do crab antennules, but because crabs flick at higher frequency, the duration of odor-free gaps between encountered odor pulses is similar. For flicking antennules there were longer time gaps between odor encounters as the downstream distance to the odor source decreases, but shorter gaps along the plume centerline than near the edge. In contrast to the case for antennule flicking, almost all odor-free gaps were <500 ms at all positions in the plume if concentration was measured continuously at the same height as the antennules. Variance in concentration is lower and mean concentration is greater near the substratum, where leg chemosensors continuously sample the plume, than in the water where antennules sniff. Concentrations sampled by legs increase as an animal nears an odor source, but decrease for antennules. Both legs and antennules encounter higher concentrations near the centerline than at the edge of the plume.

A general odorant background affects the coding of pheromone stimulus intermittency in specialist olfactory receptor neurones

In nature the aerial trace of pheromone used by male moths to find a female appears as a train of discontinuous pulses separated by gaps among a complex odorant background constituted of plant volatiles. We investigated the effect of such background odor on behavior and coding of temporal parameters of pheromone pulse trains in the pheromone olfactory receptor neurons of Spodoptera littoralis. Effects of linalool background were tested by measuring walking behavior towards a source of pheromone. While velocity and orientation index did drop when linalool was turned on, both parameters recovered back to pre-background values after 40 s with linalool still present. Photo-ionization detector was used to characterize pulse delivery by our stimulator. The photo-ionization detector signal reached 71% of maximum amplitude at 50 ms pulses and followed the stimulus period at repetition rates up to 10 pulses/s. However, at high pulse rates the concentration of the odorant did not return to base level during inter-pulse intervals. Linalool decreased the intensity and shortened the response of receptor neurons to pulses. High contrast (>10 dB) in firing rate between pulses and inter-pulse intervals was observed for 1 and 4 pulses/s, both with and without background. Significantly more neurons followed the 4 pulses/s pattern when delivered over linalool; at the same time the information content was preserved almost to the control values. Rapid recovery of behavior shows that change of perceived intensity is more important than absolute stimulus intensity. While decreasing the response intensity, background odor preserved the temporal parameters of the specific signal.

Locating a compact odor source using a four-channel insect electroantennogram sensor

Here we demonstrate the feasibility of using an array of live insects to detect concentrated packets of odor and infer the location of an odor source (∼15 m away) using a backward Lagrangian dispersion model based on the Langevin equation. Bayesian inference allows uncertainty to be quantified, which is useful for robotic planning. The electroantennogram (EAG) is the biopotential developed between the tissue at the tip of an insect antenna and its base, which is due to the massed response of the olfactory receptor neurons to an odor stimulus. The EAG signal can carry tens of bits per second of information with a rise time as short as 12 ms (K A Justice 2005 J. Neurophiol. 93 2233-9). Here, instrumentation including a GPS with a digital compass and an ultrasonic 2D anemometer has been integrated with an EAG odor detection scheme, allowing the location of an odor source to be estimated by collecting data at several downwind locations. Bayesian inference in conjunction with a Lagrangian dispersion model, taking into account detection errors, has been implemented resulting in an estimate of the odor source location within 0.2 m of the actual location.

System identification of Drosophila olfactory sensory neurons

The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in the spike domain by Drosophila OSNs. In order to apply system identification techniques, we built a novel low-turbulence odor delivery system that allowed us to deliver airborne stimuli in a precise and reproducible fashion. The system provides a 1% tolerance in stimulus reproducibility and an exact control of odor concentration and concentration gradient on a millisecond time scale. Using this novel setup, we recorded and analyzed the in-vivo response of OSNs to a wide range of time-varying odor waveforms. We report for the first time that across trials the response of OR59b OSNs is very precise and reproducible. Further, we empirically show that the response of an OSN depends not only on the concentration, but also on the rate of change of the odor concentration. Moreover, we demonstrate that a two-dimensional (2D) Encoding Manifold in a concentration-concentration gradient space provides a quantitative description of the neuron's response. We then use the white noise system identification methodology to construct one-dimensional (1D) and two-dimensional (2D) Linear-Nonlinear-Poisson (LNP) cascade models of the sensory neuron for a fixed mean odor concentration and fixed contrast. We show that in terms of predicting the intensity rate of the spike train, the 2D LNP model performs on par with the 1D LNP model, with a root mean-square error (RMSE) increase of about 5 to 10%. Surprisingly, we find that for a fixed contrast of the white noise odor waveforms, the nonlinear block of each of the two models changes with the mean input concentration. The shape of the nonlinearities of both the 1D and the 2D LNP model appears to be, for a fixed mean of the odor waveform, independent of the stimulus contrast. This suggests that white noise system identification of Or59b OSNs only depends on the first moment of the odor concentration. Finally, by comparing the 2D Encoding Manifold and the 2D LNP model, we demonstrate that the OSN identification results depend on the particular type of the employed test odor waveforms. This suggests an adaptive neural encoding model for Or59b OSNs that changes its nonlinearity in response to the odor concentration waveforms.

Pheromone transduction in moths

Calling female moths attract their mates late at night with intermittent release of a species-specific sex-pheromone blend. Mean frequency of pheromone filaments encodes distance to the calling female. In their zig-zagging upwind search male moths encounter turbulent pheromone blend filaments at highly variable concentrations and frequencies. The male moth antennae are delicately designed to detect and distinguish even traces of these sex pheromones amongst the abundance of other odors. Its olfactory receptor neurons sense even single pheromone molecules and track intermittent pheromone filaments of highly variable frequencies up to about 30 Hz over a wide concentration range. In the hawkmoth Manduca sexta brief, weak pheromone stimuli as encountered during flight are detected via a metabotropic PLCβ-dependent signal transduction cascade which leads to transient changes in intracellular Ca²⁺ concentrations. Strong or long pheromone stimuli, which are possibly perceived in direct contact with the female, activate receptor-guanylyl cyclases causing long-term adaptation. In addition, depending on endogenous rhythms of the moth's physiological state, hormones such as the stress hormone octopamine modulate second messenger levels in sensory neurons. High octopamine levels during the activity phase maximize temporal resolution cAMP-dependently as a prerequisite to mate location. Thus, I suggest that sliding adjustment of odor response threshold and kinetics is based upon relative concentration ratios of intracellular Ca²⁺ and cyclic nucleotide levels which gate different ion channels synergistically. In addition, I propose a new hypothesis for the cyclic nucleotide-dependent ion channel formed by insect olfactory receptor/coreceptor complexes. Instead of being employed for an ionotropic mechanism of odor detection it is proposed to control subthreshold membrane potential oscillation of sensory neurons, as a basis for temporal encoding of odors.

A model of visual-olfactory integration for odour localisation in free-flying fruit flies

Flying fruit flies (Drosophila melanogaster) locate a concealed appetitive odour source most accurately in environments containing vertical visual contrasts. To investigate how visuomotor and olfactory responses may be integrated, we examine the free-flight behaviour of flies in three visual conditions, with and without food odour present. While odour localisation is facilitated by uniformly distributed vertical contrast as compared with purely horizontal contrast, localised vertical contrast also facilitates odour localisation, but only if the odour source is situated close to it. We implement a model of visuomotor control consisting of three parallel subsystems: an optomotor response stabilising the model fly's yaw orientation; a collision avoidance system to saccade away from looming obstacles; and a speed regulation system. This model reproduces many of the behaviours we observe in flies, including visually mediated 'rebound' turns following saccades. Using recordings of real odour plumes, we simulate the presence of an odorant in the arena, and investigate ways in which the olfactory input could modulate visuomotor control. We reproduce the experimental results by using the change in odour intensity to regulate the sensitivity of collision avoidance, resulting in visually mediated chemokinesis. Additionally, it is necessary to amplify the optomotor response whenever odour is present, increasing the model fly's tendency to steer towards features of the visual environment. We conclude that visual and olfactory responses of Drosophila are not independent, but that relatively simple interaction between these modalities can account for the observed visual dependence of odour source localisation.

Two interacting olfactory transduction mechanisms have linked polarities and dynamics in Drosophila melanogaster antennal basiconic sensilla neurons

We measured frequency response functions between concentrations of fruit odorants and individual action potentials in large basiconic sensilla of the Drosophila melanogaster antenna. A new method of randomly varying odorant concentration was combined with rapid, continuous measurement of concentration at the antenna by a miniature photoionization detector. All frequency responses decreased progressively at frequencies approaching 100 Hz, providing an upper limit for the dynamics of Drosophila olfaction. We found two distinct response patterns: excitatory band-pass frequency responses were seen with ethyl acetate, ethyl butyrate, and hexanol, whereas inhibitory low-pass responses were seen with methyl salicylate and phenylethyl acetate. Band-pass responses peaked at 1-10 Hz. Frequency responses could be well fitted by simple linear filter equations, and the fitted parameters were consistent within each of the two types of responses. Experiments with equal mixtures of excitatory and inhibitory odorants gave responses that were characteristic of the inhibitory components, indicating that interaction during transduction causes inhibitory odorants to suppress the responses to excitatory odorants. Plots of response amplitude versus odorant concentration indicated that the odorant concentrations used were within approximately linear regions of the dose response relationships. We also estimated linear information capacity from the coherence function of each recording. Although coherence was relatively high, indicating a large signal-to-noise ratio, information capacity for olfaction was much lower than comparable estimates for mechanotransduction or visual transduction because of the limited bandwidth of olfaction. These data offer new insights into transduction by primary chemoreceptors and place temporal constraints on Drosophila olfactory behavior.

Real-time odor discrimination using a bioelectronic sensor array based on the insect electroantennogram

Current trends in artificial nose research are strongly influenced by knowledge of biological olfactory systems. Insects have evolved over millions of years to detect and maneuver toward a food source or mate, or away from predators. The insect olfactory system is able to identify volatiles on a time scale that matches their ability to maneuver. Here, biological olfactory sense organs, insect antennae, have been exploited in a hybrid-device biosensor, demonstrating the ability to identify individual strands of odor in a plume passing over the sensor on a sub-second time scale. A portable system was designed to utilize the electrophysiological responses recorded from a sensor array composed of male or female antennae from four or eight different species of insects (a multi-channel electroantennogram, EAG). A computational analysis strategy that allows discrimination between odors in real time is described in detail. Following a training period, both semi-parametric and k-nearest neighbor (k-NN) classifiers with the ability to discard ambiguous responses are applied toward the classification of up to eight odors. EAG responses to individual strands in an odor plume are classified or discarded as ambiguous with a delay (sensor response to classification report) on the order of 1 s. The dependence of classification error rate on several parameters is described. Finally, the performance of the approach is compared to that of a minimal conditional risk classifier.

Physical processes and real-time chemical measurement of the insect olfactory environment

Odor-mediated insect navigation in airborne chemical plumes is vital to many ecological interactions, including mate finding, flower nectaring, and host locating (where disease transmission or herbivory may begin). After emission, volatile chemicals become rapidly mixed and diluted through physical processes that create a dynamic olfactory environment. This review examines those physical processes and some of the analytical technologies available to characterize those behavior-inducing chemical signals at temporal scales equivalent to the olfactory processing in insects. In particular, we focus on two areas of research that together may further our understanding of olfactory signal dynamics and its processing and perception by insects. First, measurement of physical atmospheric processes in the field can provide insight into the spatiotemporal dynamics of the odor signal available to insects. Field measurements in turn permit aspects of the physical environment to be simulated in the laboratory, thereby allowing careful investigation into the links between odor signal dynamics and insect behavior. Second, emerging analytical technologies with high recording frequencies and field-friendly inlet systems may offer new opportunities to characterize natural odors at spatiotemporal scales relevant to insect perception and behavior. Characterization of the chemical signal environment allows the determination of when and where olfactory-mediated behaviors may control ecological interactions. Finally, we argue that coupling of these two research areas will foster increased understanding of the physicochemical environment and enable researchers to determine how olfactory environments shape insect behaviors and sensory systems.

Dynamic properties of Drosophila olfactory electroantennograms

Time-dependent properties of chemical signals are probably crucially important to many animals, but little is known about the dynamics of chemoreceptors. Behavioral evidence of dynamic sensitivity includes the control of moth flight by pheromone plume structure, and the ability of some blood-sucking insects to detect varying concentrations of carbon dioxide, possibly matched to host breathing rates. Measurement of chemoreceptor dynamics has been limited by the technical challenge of producing controlled, accurate modulation of olfactory and gustatory chemical concentrations over suitably wide ranges of amplitude and frequency. We used a new servo-controlled laminar flow system, combined with photoionization detection of surrogate tracer gas, to characterize electroantennograms (EAG) of Drosophila antennae during stimulation with fruit odorants or aggregation pheromone in air. Frequency response functions and coherence functions measured over a bandwidth of 0-100 Hz were well characterized by first-order low-pass linear filter functions. Filter time constant varied over almost a tenfold range, and was characteristic for each odorant, indicating that several dynamically different chemotransduction mechanisms are present. Pheromone response was delayed relative to fruit odors. Amplitude of response, and consequently signal-to-noise ratio, also varied consistently with different compounds. Accurate dynamic characterization promises to provide important new information about chemotransduction and odorant-stimulated behavior.

Simulation of the neural response to the odour stimulus

The paper is aimed at the description of the newly introduced model used to simulate the neural response to the chemical stimulus. A potential difference across the living cell or tissue depends usually on the chemical excitation of this living entity. The peculiar case of chemical excitation is the odour recognition across animal kingdom, in particular by various insects. A potential difference is characterised by relatively rapid variation with time elapsed since the application of the stimulus. A complete mathematical model giving results similar to the real ones is outlined and discussed in light of the potential application to the various experimental patterns recognition. The saturation effects due to overlapping spikes are discussed in some detail. It is proposed to use semi-invariants as the semi-quantitative method to compare various data sets obtained either in response to various stimuli or to the same stimulus applied to various species.

Winging it: moth flight behavior and responses of olfactory neurons are shaped by pheromone plume dynamics

Terrestrial odor plumes have a physical structure that results from turbulence in the fluid environment. The rapidity of insect flight maneuvers within a plume indicates that their responses are dictated by fleeting (<1 s) rather than longer (>1 s) exposures to odor imposed by physical variables that distribute odor molecules in time and space. Even though encounters with pheromone filaments are brief, male moths responding to female-produced pheromones are remarkably able to extract information relating to the biological properties of these olfactory signals. These properties include the types of molecule present and their relative abundances. Thus, peripheral and central olfactory neurons are capable of representing these biological properties of a pheromone plume within the context of a temporally irregular and unpredictable signal. The mechanisms underlying olfactory processing of these signals with respect to their biological and physical properties are discussed in the context of a behavioral framework.