Sniff-synchronized, gradient-guided olfactory search by freely moving mice

Simple olfactory navigation in air and water

Two simple algorithms based on combining odor concentration differences across time and space along with information on the flow direction are tested for their ability to locate an odor source in four different odor landscapes. Image data taken from air plumes in three different regimes and a water plume are used as test environments for a bilateral ("stereo sampling") algorithm using concentration differences across two sensors and a "casting" algorithm that uses successive samples to decide orientation. Agents are started at random locations and orientations in the landscape and allowed to move until they reach the source of the odor (success) or leave the imaged area (failure). Parameters for the algorithm are chosen to optimize success and to minimize path length to the source. Success rates over 90% are consistently obtained with path lengths that can be as low as twice the starting distance from the source in air and four times the distance in the highly turbulent water plumes. We find that parameters that optimize success often lead to more exploratory pathways to the source. Information about the direction from which the odor is coming is necessary for successful navigation in the water plume and reduces the path length in the three tested air plumes.

Do rodents smell with sound?

Chemosensation via olfaction is a critical process underlying social interactions in many different species. Past studies of olfaction in mammals often have focused on its mechanisms in isolation from other systems, limiting the generalizability of findings from olfactory research to perceptual processes in other modalities. Studies of chemical communication, in particular, have progressed independently of research on vocal behavior and acoustic communication. Those bioacousticians who have considered how sound production and reception might interact with olfaction often portray odors as cues to the kinds of vocalizations that might be functionally useful. In the olfaction literature, vocalizations are rarely mentioned. Here, we propose that ultrasonic vocalizations may affect what rodents smell by altering the deposition of inhaled particles and that rodents coordinate active sniffing with sound production specifically to enhance reception of pheromones. In this scenario, rodent vocalizations may contribute to a unique mode of active olfactory sensing, in addition to whatever roles they serve as social signals. Consideration of this hypothesis highlights the perceptual advantages that parallel coordination of multiple sensorimotor processes may provide to individuals exploring novel situations and environments, especially those involving dynamic social interactions.

How do mammals convert dynamic odor information into neural maps for landscape navigation?

Odors are transported by seemingly chaotic plumes, whose spatiotemporal structure contains rich information about space, with olfaction serving as a gateway for obtaining and processing this spatial information. Beyond tracking odors, olfaction provides localization and chemical communication cues for detecting conspecifics and predators, and linking external environments to internal cognitive maps. In this Essay, we discuss recent physiological, behavioral, and methodological advancements in mammalian olfactory research to present our current understanding of how olfaction can be used to navigate the environment. We also examine potential neural mechanisms that might convert dynamic olfactory inputs into environmental maps along this axis. Finally, we consider technological applications of odor dynamics for developing bio-inspired sensor technologies, robotics, and computational models. By shedding light on the principles underlying the processing of odor dynamics, olfactory research will pave the way for innovative solutions that bridge the gap between biology and technology, enriching our understanding of the natural world.

Wind gates olfaction-driven search states in free flight

For organisms tracking a chemical cue to its source, the motion of their surrounding fluid provides crucial information for success. Swimming and flying animals engaged in olfaction-driven search often start by turning into the direction of an oncoming wind or water current. However, it is unclear how organisms adjust their strategies when directional cues are absent or unreliable, as is often the case in nature. Here, we use the genetic toolkit of Drosophila melanogaster to develop an optogenetic paradigm to deliver temporally precise "virtual" olfactory experiences for free-flying animals in either laminar wind or still air. We first confirm that in laminar wind flies turn upwind. Furthermore, we show that they achieve this using a rapid (∼100 ms) turn, implying that flies estimate the ambient wind direction prior to "surging" upwind. In still air, flies adopt a remarkably stereotyped "sink and circle" search state characterized by ∼60° turns at 3-4 Hz, biased in a consistent direction. Together, our results show that Drosophila melanogaster assesses the presence and direction of ambient wind prior to deploying a distinct search strategy. In both laminar wind and still air, immediately after odor onset, flies decelerate and often perform a rapid turn. Both maneuvers are consistent with predictions from recent control theoretic analyses for how insects may estimate properties of wind while in flight. We suggest that flies may use their deceleration and "anemometric" turn as active sensing maneuvers to rapidly gauge properties of their wind environment before initiating a proximal or upwind search routine.

Dynamics of odor-source localization: Insights from real-time odor plume recordings and head-motion tracking in freely moving mice

Animals navigating turbulent odor plumes exhibit a rich variety of behaviors, and employ efficient strategies to locate odor sources. A growing body of literature has started to probe this complex task of localizing airborne odor sources in walking mammals to further our understanding of neural encoding and decoding of naturalistic sensory stimuli. However, correlating the intermittent olfactory information with behavior has remained a long-standing challenge due to the stochastic nature of the odor stimulus. We recently reported a method to record real-time olfactory information available to freely moving mice during odor-guided navigation, hence overcoming that challenge. Here we combine our odor-recording method with head-motion tracking to establish correlations between plume encounters and head movements. We show that mice exhibit robust head-pitch motions in the 5-14Hz range during an odor-guided navigation task, and that these head motions are modulated by plume encounters. Furthermore, mice reduce their angles with respect to the source upon plume contact. Head motions may thus be an important part of the sensorimotor behavioral repertoire during naturalistic odor-source localization.

Olfactory investigation in the home cage

We have developed a behavioral paradigm to study volitional olfactory investigation in mice over several months. We placed odor ports in the wall of a standard cage that administer a neutral odorant stimulus when a mouse pokes its nose inside. Even though animals were fed and watered ad libitum, and sampling from the port elicited no outcome other than the delivery of an odor, mice readily sampled these stimuli hundreds of times per day. This self-paced olfactory investigation persisted for weeks with only modest habituation following the first day of exposure to a given set of odorants. If an unexpected odorant stimulus was administered at the port, the sampling rate increased transiently (in the first 20 min) by an order of magnitude and remained higher than baseline throughout the subsequent day, indicating learned implicit knowledge. Thus, this system may be used to study naturalistic olfactory learning over extended time scales outside of conventional task structures.

Sound-seeking before and after hearing loss in mice

How we move our bodies affects how we perceive sound. For instance, head movements help us to better localize the source of a sound and to compensate for asymmetric hearing loss. However, many auditory experiments are designed to restrict head and body movements. To study the role of movement in hearing, we developed a behavioral task called sound-seeking that rewarded freely moving mice for tracking down an ongoing sound source. Over the course of learning, mice more efficiently navigated to the sound. Next, we asked how sound-seeking was affected by hearing loss induced by surgical removal of the malleus from the middle ear. After bilateral hearing loss sound-seeking performance drastically declined and did not recover. In striking contrast, after unilateral hearing loss mice were only transiently impaired and then recovered their sound-seek ability over about a week. Throughout recovery, unilateral mice increasingly relied on a movement strategy of sequentially checking potential locations for the sound source. In contrast, the startle reflex (an innate auditory behavior) was preserved after unilateral hearing loss and abolished by bilateral hearing loss without recovery over time. In sum, mice compensate with body movement for permanent unilateral damage to the peripheral auditory system. Looking forward, this paradigm provides an opportunity to examine how movement enhances perception and enables resilient adaptation to sensory disorders.

Miniscope Recording Calcium Signals at Hippocampus of Mice Navigating an Odor Plume

Mice navigate an odor plume with a complex spatiotemporal structure in the dark to find the source of odorants. This article describes a protocol to monitor behavior and record Ca 2+ transients in dorsal CA1 stratum pyramidale neurons in hippocampus (dCA1) in mice navigating an odor plume in a 50 cm x 50 cm x 25 cm odor arena. An epifluorescence miniscope focused through a GRIN lens imaged Ca 2+ transients in dCA1 neurons expressing the calcium sensor GCaMP6f in Thy1-GCaMP6f mice. The paper describes the behavioral protocol to train the mice to perform this odor plume navigation task in an automated odor arena. The methods include a step-by-step procedure for the surgery for GRIN lens implantation and baseplate placement for imaging GCaMP6f in CA1. The article provides information on real-time tracking of the mouse position to automate the start of the trials and delivery of a sugar water reward. In addition, the protocol includes information on using of an interface board to synchronize metadata describing the automation of the odor navigation task and frame times for the miniscope and a digital camera tracking mouse position. Moreover, the methods delineate the pipeline used to process GCaMP6f fluorescence movies by motion correction using NorMCorre followed by identification of regions of interest with EXTRACT. Finally, the paper describes an artificial neural network approach to decode spatial paths from CA1 neural ensemble activity to predict mouse navigation of the odor plume.

SUMMARY

This protocol describes how to investigate the brain-behavior relationship in hippocampal CA1 in mice navigating an odor plume. This article provides a step-by-step protocol, including the surgery to access imaging of the hippocampus, behavioral training, miniscope GCaMP6f recording and processing of the brain and behavioral data to decode the mouse position from ROI neural activity.

Dynamics of odor-source localization: Insights from real-time odor plume recordings and head-motion tracking in freely moving mice

Animals navigating turbulent odor plumes exhibit a rich variety of behaviors, and employ efficient strategies to locate odor sources. A growing body of literature has started to probe this complex task of localizing airborne odor sources in walking mammals to further our understanding of neural encoding and decoding of naturalistic sensory stimuli. However, correlating the intermittent olfactory information with behavior has remained a long-standing challenge due to the stochastic nature of the odor stimulus. We recently reported a method to record real-time olfactory information available to freely moving mice during odor-guided navigation, hence overcoming that challenge. Here we combine our odor-recording method with head-motion tracking to establish correlations between plume encounters and head movements. We show that mice exhibit robust head-pitch motions in the 5-14Hz range during an odor-guided navigation task, and that these head motions are modulated by plume encounters. Furthermore, mice reduce their angles with respect to the source upon plume contact. Head motions may thus be an important part of the sensorimotor behavioral repertoire during naturalistic odor-source localization.

Common principles for odour coding across vertebrates and invertebrates

The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ('syllables') from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings.

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.

The spiking output of the mouse olfactory bulb encodes large-scale temporal features of natural odor environments

In natural odor environments, odor travels in plumes. Odor concentration dynamics change in characteristic ways across the width and length of a plume. Thus, spatiotemporal dynamics of plumes have informative features for animals navigating to an odor source. Population activity in the olfactory bulb (OB) has been shown to follow odor concentration across plumes to a moderate degree (Lewis et al., 2021). However, it is unknown whether the ability to follow plume dynamics is driven by individual cells or whether it emerges at the population level. Previous research has explored the responses of individual OB cells to isolated features of plumes, but it is difficult to adequately sample the full feature space of plumes as it is still undetermined which features navigating mice employ during olfactory guided search. Here we released odor from an upwind odor source and simultaneously recorded both odor concentration dynamics and cellular response dynamics in awake, head-fixed mice. We found that longer timescale features of odor concentration dynamics were encoded at both the cellular and population level. At the cellular level, responses were elicited at the beginning of the plume for each trial, signaling plume onset. Plumes with high odor concentration elicited responses at the end of the plume, signaling plume offset. Although cellular level tracking of plume dynamics was observed to be weak, we found that at the population level, OB activity distinguished whiffs and blanks (accurately detected odor presence versus absence) throughout the duration of a plume. Even ~20 OB cells were enough to accurately discern odor presence throughout a plume. Our findings indicate that the full range of odor concentration dynamics and high frequency fluctuations are not encoded by OB spiking activity. Instead, relatively lower-frequency temporal features of plumes, such as plume onset, plume offset, whiffs, and blanks, are represented in the OB.

Odors in space

As an evolutionarily ancient sense, olfaction is key to learning where to find food, shelter, mates, and important landmarks in an animal's environment. Brain circuitry linking odor and navigation appears to be a well conserved multi-region system among mammals; the anterior olfactory nucleus, piriform cortex, entorhinal cortex, and hippocampus each represent different aspects of olfactory and spatial information. We review recent advances in our understanding of the neural circuits underlying odor-place associations, highlighting key choices of behavioral task design and neural circuit manipulations for investigating learning and memory.

One nose but two nostrils: Learn to align with sparse connections between two olfactory cortices

The integration of neural representations in the two hemispheres is an important problem in neuroscience. Recent experiments revealed that odor responses in cortical neurons driven by separate stimulation of the two nostrils are highly correlated. This bilateral alignment points to structured inter-hemispheric connections, but detailed mechanism remains unclear. Here, we hypothesized that continuous exposure to environmental odors shapes these projections and modeled it as online learning with local Hebbian rule. We found that Hebbian learning with sparse connections achieves bilateral alignment, exhibiting a linear trade-off between speed and accuracy. We identified an inverse scaling relationship between the number of cortical neurons and the inter-hemispheric projection density required for desired alignment accuracy, i.e., more cortical neurons allow sparser inter-hemispheric projections. We next compared the alignment performance of local Hebbian rule and the global stochastic-gradient-descent (SGD) learning for artificial neural networks. We found that although SGD leads to the same alignment accuracy with modestly sparser connectivity, the same inverse scaling relation holds. We showed that their similar performance originates from the fact that the update vectors of the two learning rules align significantly throughout the learning process. This insight may inspire efficient sparse local learning algorithms for more complex problems.

Behavioral discrimination and olfactory bulb encoding of odor plume intermittency

In order to survive, animals often need to navigate a complex odor landscape where odors can exist in airborne plumes. Several odor plume properties change with distance from the odor source, providing potential navigational cues to searching animals. Here, we focus on odor intermittency, a temporal odor plume property that measures the fraction of time odor is above a threshold at a given point within the plume and decreases with increasing distance from the odor source. We sought to determine if mice can use changes in intermittency to locate an odor source. To do so, we trained mice on an intermittency discrimination task. We establish that mice can discriminate odor plume samples of low and high intermittency and that the neural responses in the olfactory bulb can account for task performance and support intermittency encoding. Modulation of sniffing, a behavioral parameter that is highly dynamic during odor-guided navigation, affects both behavioral outcome on the intermittency discrimination task and neural representation of intermittency. Together, this work demonstrates that intermittency is an odor plume property that can inform olfactory search and more broadly supports the notion that mammalian odor-based navigation can be guided by temporal odor plume properties.

Sound-seeking before and after hearing loss in mice

How we move our bodies affects how we perceive sound. For instance, we can explore an environment to seek out the source of a sound and we can use head movements to compensate for hearing loss. How we do this is not well understood because many auditory experiments are designed to limit head and body movements. To study the role of movement in hearing, we developed a behavioral task called sound-seeking that rewarded mice for tracking down an ongoing sound source. Over the course of learning, mice more efficiently navigated to the sound. We then asked how auditory behavior was affected by hearing loss induced by surgical removal of the malleus from the middle ear. An innate behavior, the auditory startle response, was abolished by bilateral hearing loss and unaffected by unilateral hearing loss. Similarly, performance on the sound-seeking task drastically declined after bilateral hearing loss and did not recover. In striking contrast, mice with unilateral hearing loss were only transiently impaired on sound-seeking; over a recovery period of about a week, they regained high levels of performance, increasingly reliant on a different spatial sampling strategy. Thus, even in the face of permanent unilateral damage to the peripheral auditory system, mice recover their ability to perform a naturalistic sound-seeking task. This paradigm provides an opportunity to examine how body movement enables better hearing and resilient adaptation to sensory deprivation.

Olfactory cortex: Temporal segregation of inputs from the two nostrils

Piriform cortex processes odor information coming from two nostrils to give rise to unified perception of odorant identity and intensity. A new study reveals that human piriform cortex harbours distinct representations of odor input from ipsilateral and contralateral nostrils through temporal segregation.

Recruitment of grid-like responses in human entorhinal and piriform cortices by odor landmark-based navigation

Olfactory navigation is universal across the animal kingdom. Humans, however, have rarely been considered in this context. Here, we combined olfactometry techniques, virtual reality (VR) software, and neuroimaging methods to investigate whether humans can navigate an olfactory landscape by learning the spatial relationships among discrete odor cues and integrating this knowledge into a spatial map. Our data show that over time, participants improved their performance on the odor navigation task by taking more direct paths toward targets and completing more trials within a given time period. This suggests that humans can successfully navigate a complex odorous environment, reinforcing the notion of human olfactory navigation. fMRI data collected during the olfactory navigation task revealed the emergence of grid-like responses in entorhinal and piriform cortices that were attuned to the same grid orientation. This result implies the existence of a specialized olfactory grid network tasked with guiding spatial navigation based on odor landmarks.

A map of spatial navigation for neuroscience

Spatial navigation has received much attention from neuroscientists, leading to the identification of key brain areas and the discovery of numerous spatially selective cells. Despite this progress, our understanding of how the pieces fit together to drive behavior is generally lacking. We argue that this is partly caused by insufficient communication between behavioral and neuroscientific researchers. This has led the latter to under-appreciate the relevance and complexity of spatial behavior, and to focus too narrowly on characterizing neural representations of space-disconnected from the computations these representations are meant to enable. We therefore propose a taxonomy of navigation processes in mammals that can serve as a common framework for structuring and facilitating interdisciplinary research in the field. Using the taxonomy as a guide, we review behavioral and neural studies of spatial navigation. In doing so, we validate the taxonomy and showcase its usefulness in identifying potential issues with common experimental approaches, designing experiments that adequately target particular behaviors, correctly interpreting neural activity, and pointing to new avenues of research.

A change in behavioral state switches the pattern of motor output that underlies rhythmic head and orofacial movements

The breathing rhythm serves as a reference that paces orofacial motor actions and orchestrates active sensing. Past work has reported that pacing occurs solely at a fixed phase relative to sniffing. We re-evaluated this constraint as a function of exploratory behavior. Allocentric and egocentric rotations of the head and the electromyogenic activity of the motoneurons for head and orofacial movements were recorded in free-ranging rats as they searched for food. We found that a change in state from foraging to rearing is accompanied by a large phase shift in muscular activation relative to sniffing, and a concurrent change in the frequency of sniffing, so that pacing now occurs at one of the two phases. Further, head turning is biased such that an animal gathers a novel sample of its environment upon inhalation. In total, the coordination of active sensing has a previously unrealized computational complexity. This can emerge from hindbrain circuits with fixed architecture and credible synaptic time delays.

A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior

Accurate and efficient quantification of animal behavior facilitates the understanding of the brain. An emerging approach within machine learning (ML) field is to combine multiple ML-based algorithms to quantify animal behavior. These so-called hybrid models have emerged because of limitations associated with supervised [e.g., random forest (RF)] and unsupervised [e.g., hidden Markov model (HMM)] ML models. For example, RF models lack temporal information across video frames, and HMM latent states are often difficult to interpret. We sought to develop a hybrid model, and did so in the context of a study of mouse risk assessment behavior. We used DeepLabCut to estimate the positions of mouse body parts. Positional features were calculated using DeepLabCut outputs and were used to train RF and HMM models with equal number of states, separately. The per-frame predictions from RF and HMM models were then passed to a second HMM model layer ("reHMM"). The outputs of the reHMM layer showed improved interpretability over the initial HMM output. Finally, we combined predictions from RF and HMM models with selected positional features to train a third HMM model ("reHMM+"). This reHMM+ layered hybrid model unveiled distinctive temporal and human-interpretable behavioral patterns. We applied this workflow to investigate risk assessment to trimethylthiazoline and snake feces odor, finding unique behavioral patterns to each that were separable from attractive and neutral stimuli. We conclude that this layered, hybrid ML workflow represents a balanced approach for improving the depth and reliability of ML classifiers in chemosensory and other behavioral contexts.

Neural and behavioral plasticity across the female reproductive cycle

Sex is fundamental for the evolution and survival of most species. However, sex can also pose danger, because it increases the risk of predation and disease transmission, among others. Thus, in many species, cyclic fluctuations in the concentration of sex hormones coordinate sexual receptivity and attractiveness with female reproductive capacity, promoting copulation when fertilization is possible and preventing it otherwise. In recent decades, numerous studies have reported a wide variety of sex hormone-dependent plastic rearrangements across the entire brain, including areas relevant for female sexual behavior. By contrast, how sex hormone-induced plasticity alters the computations performed by such circuits, such that collectively they produce the appropriate periodic switches in female behavior, is mostly unknown. In this review, we highlight the myriad sex hormone-induced neuronal changes known so far, the full repertoire of behavioral changes across the reproductive cycle, and the few examples where the relationship between sex hormone-dependent plasticity, neural activity, and behavior has been established. We also discuss current challenges to causally link the actions of sex hormones to the modification of specific cellular pathways and behavior, focusing on rodents as a model system while drawing a comparison between rodents and humans wherever possible.

Oscillations for active sensing in olfaction: bioinspiration from insect antennal movements

Crustacean and insect antennal scanning movements have been postulated to increase odorant capture but the exact mechanisms as well as measures of efficiency are wanting. The aim of this work is to test the hypothesis that an increase in oscillation frequency of a simplified insect antenna model translates to an increase of odorant capture, and to quantify by how much and through which mechanism. We approximate the antennal movements of bumblebees, quantified in a previous study, by a vertical oscillatory movement of a cylinder in a homogeneous horizontal flow with odorants. We test our multiphysics flow and mass transfer numerical model with dedicated experiments using particle image velocimetry. A new entire translating experimental measurement setup containing an oil tank enables us to work at appropriate Strouhal and Reynolds numbers. Increasing antennal oscillating frequency does increase the odorant capture rate, up to 200%, proving this behavior being active sensing. This result holds however only up to a critical frequency. A decrease of efficiency characterizes higher frequencies, due to molecules depletion within oversampled regions, themselves defined by overlaying boundary layers. Despite decades of work on thermal and mass transfer studies on oscillating cylinders, no analogy with published cases was found. This is due to the unique flow regimes studied here, resulting from the combination of organ small size and low frequencies of oscillations. A theory for such flow regimes is thus to be developed, with applications to fundamental research on animal perception up to bioinspired olfaction.

The facets of olfactory learning

Volatile chemicals in the environment provide ethologically important information to many animals. However, how animals learn to use this information is only beginning to be understood. This review highlights recent experimental advances elucidating olfactory learning in rodents, ranging from adaptations to the environment to task-dependent refinement and multisensory associations. The broad range of phenomena, mechanisms, and brain areas involved demonstrate the complex and multifaceted nature of olfactory learning.

Learning to predict target location with turbulent odor plumes

Animal behavior and neural recordings show that the brain is able to measure both the intensity and the timing of odor encounters. However, whether intensity or timing of odor detections is more informative for olfactory-driven behavior is not understood. To tackle this question, we consider the problem of locating a target using the odor it releases. We ask whether the position of a target is best predicted by measures of timing vs intensity of its odor, sampled for a short period of time. To answer this question, we feed data from accurate numerical simulations of odor transport to machine learning algorithms that learn how to connect odor to target location. We find that both intensity and timing can separately predict target location even from a distance of several meters; however, their efficacy varies with the dilution of the odor in space. Thus, organisms that use olfaction from different ranges may have to switch among different modalities. This has implications on how the brain should represent odors as the target is approached. We demonstrate simple strategies to improve accuracy and robustness of the prediction by modifying odor sampling and appropriately combining distinct measures together. To test the predictions, animal behavior and odor representation should be monitored as the animal moves relative to the target, or in virtual conditions that mimic concentrated vs dilute environments.

Neural mechanisms underlying the temporal organization of naturalistic animal behavior

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.

Inertial Measurement of Head Tilt in Rodents: Principles and Applications to Vestibular Research

Inertial sensors are increasingly used in rodent research, in particular for estimating head orientation relative to gravity, or head tilt. Despite this growing interest, the accuracy of tilt estimates computed from rodent head inertial data has never been assessed. Using readily available inertial measurement units mounted onto the head of freely moving rats, we benchmarked a set of tilt estimation methods against concurrent 3D optical motion capture. We show that, while low-pass filtered head acceleration signals only provided reliable tilt estimates in static conditions, sensor calibration combined with an appropriate choice of orientation filter and parameters could yield average tilt estimation errors below 1.5∘ during movement. We then illustrate an application of inertial head tilt measurements in a preclinical rat model of unilateral vestibular lesion and propose a set of metrics describing the severity of associated postural and motor symptoms and the time course of recovery. We conclude that headborne inertial sensors are an attractive tool for quantitative rodent behavioral analysis in general and for the study of vestibulo-postural functions in particular.

Using your nose to find your way: Ethological comparisons between human and non-human species

Olfaction is arguably the least valued among our sensory systems, and its significance for human behavior is often neglected. Spatial navigation represents no exception to the rule: humans are often characterized as purely visual navigators, a view that undermines the contribution of olfactory cues. Accordingly, research investigating whether and how humans use olfaction to navigate space is rare. In comparison, research on olfactory navigation in non-human species is abundant, and identifies behavioral strategies along with neural mechanisms characterizing the use of olfactory cues during spatial tasks. Using an ethological approach, our review draws from studies on olfactory navigation across species to describe the adaptation of strategies under the influence of selective pressure. Mammals interact with spatial environments by abstracting multisensory information into cognitive maps. We thus argue that olfactory cues, alongside inputs from other sensory modalities, play a crucial role in spatial navigation for mammalian species, including humans; that is, odors constitute one of the many building blocks in the formation of cognitive maps.

Stimulus Driven Functional Transformations in the Early Olfactory System

Olfactory stimuli are encountered across a wide range of odor concentrations in natural environments. Defining the neural computations that support concentration invariant odor perception, odor discrimination, and odor-background segmentation across a wide range of stimulus intensities remains an open question in the field. In principle, adaptation could allow the olfactory system to adjust sensory representations to the current stimulus conditions, a well-known process in other sensory systems. However, surprisingly little is known about how adaptation changes olfactory representations and affects perception. Here we review the current understanding of how adaptation impacts processing in the first two stages of the vertebrate olfactory system, olfactory receptor neurons (ORNs), and mitral/tufted cells.

Correlated decision making across multiple phases of olfactory guided search in Drosophila improves search efficiency

Nearly all motile organisms must search for food, often requiring multiple phases of exploration across heterogeneous environments. The fruit fly, Drosophila, has emerged as an effective model system for studying this behavior, however, little is known about the extent to which experiences at one point in their search might influence decisions in another. To investigate whether prior experiences impact flies' search behavior after landing, I tracked individually labelled fruit flies as they explored three odor emitting but food-barren objects. I found two features of their behavior that are correlated with the distance they travel on foot. First, flies walked larger distances when they approached the odor source, which they were almost twice as likely to do when landing on the patch farthest downwind. Computational fluid dynamics simulations suggest this patch may have had a stronger baseline odor, but only ∼15% higher than the other two. This small increase, together with flies' high olfactory sensitivity, suggests that perhaps their flight trajectory used to approach the patches plays a role. Second, flies also walked larger distances when the time elapsed since their last visit was longer. However, the correlation is subtle and subject to a large degree of variability. Using agent-based models, I show that this small correlation can increase search efficiency by 25-50% across many scenarios. Furthermore, my models provide mechanistic hypotheses explaining the variability through either a noisy or straightforward decision-making process. Surprisingly, these stochastic decision-making algorithms enhance search efficiency in challenging but realistic search scenarios compared to deterministic strategies.

Sniff-synchronized, gradient-guided olfactory search by freely moving mice

For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in microorganisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To reveal the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. The allocentric structure of investigation and approach indicates an advantage to sampling both sides of the sharpest part of the odor gradient, consistent with a serial-sniff strategy for gradient sensing. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms.

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.