Fast odour dynamics are encoded in the olfactory system and guide behaviour

Perceptual constancy for an odor is acquired through changes in primary sensory neurons

The ability to consistently recognize an object despite variable sensory input is termed perceptual constancy. This ability is not innate; rather, it develops with experience early in life. We show that, when mice are naïve to an odor object, perceptual constancy is absent across increasing concentrations. The perceptual change coincides with a rapid reduction in activity from a single olfactory receptor channel that is most sensitive to the odor. This drop in activity is not a property of circuit interactions within the olfactory bulb; instead, it is due to a sensitivity mismatch of olfactory receptor neurons within the nose. We show that, after forming an association of this odor with food, the sensitivity of the receptor channel is matched to the odor object, preventing transmission failure and promoting perceptual stability. These data show that plasticity of the primary sensory organ enables learning of perceptual constancy.

Interpretable deep learning for deconvolutional analysis of neural signals

The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and network parameters. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the heterogeneity of neural responses in the piriform cortex and in the striatum during unstructured, naturalistic experiments. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural activity.

Neural Network Excitation/Inhibition: A Key to Empathy and Empathy Impairment

Empathy is an ability to fully understand and feel the mental states of others. We emphasize that empathy is elicited by the transmission of pain, fear, and sensory information. In clinical studies, impaired empathy has been observed in most psychiatric conditions. However, the precise impairment mechanism of the network systems on the pathogenesis of empathy impairment in psychiatric disorders is still unclear. Multiple lines of evidence suggest that disturbances in the excitatory/inhibitory balance in neurologic disorders are key to empathetic impairment in psychiatric disorders. Therefore, we here describe the roles played by the anterior cingulate cortex- and medial prefrontal cortex-dependent neural circuits and their impairments in psychiatric disorders, including anxiety, depression, and autism. In addition, we review recent studies on the role of microglia in neural network excitation/inhibition imbalance, which contributes to a better understanding of the neural network excitation/inhibition imbalance and may open up innovative psychiatric therapies.

High-speed odor sensing using miniaturized electronic nose

Animals have evolved to rapidly detect and recognize brief and intermittent encounters with odor packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results-existing solutions are either slow; or bulky, expensive, and power-intensive-limiting applicability in real-world scenarios for mobile robotics. Here, we introduce a miniaturized high-speed electronic nose, characterized by high-bandwidth sensor readouts, tightly controlled sensing parameters, and powerful algorithms. The system is evaluated on a high-fidelity odor delivery benchmark. We showcase successful classification of tens-of-millisecond odor pulses and demonstrate temporal pattern encoding of stimuli switching with up to 60 hertz. Those timescales are unprecedented in miniaturized low-power settings and demonstrably exceed the performance observed in mice. It is now possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.

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.

Human olfactory perception embeds fine temporal resolution within a single sniff

A sniff in humans typically lasts one to three seconds and is commonly considered to produce a long-exposure shot of the chemical environment that sets the temporal limit of olfactory perception. To break this limit, we devised a sniff-triggered apparatus that controls odorant deliveries within a sniff with a precision of 18 milliseconds. Using this apparatus, we show through rigorous psychophysical testing of 229 participants (649 sessions) that two odorants presented in one order and its reverse become perceptually discriminable when the stimulus onset asynchrony is merely 60 milliseconds (Cohen's d = 0.48; 95% confidence interval, (55, 59); 120-millisecond difference). Discrimination performance improves with the length of stimulus onset asynchrony and is independent of explicit knowledge of the temporal order of odorants or the relative amount of odorant molecules accumulated in a sniff. Our findings demonstrate that human olfactory perception is sensitive to chemical dynamics within a single sniff and provide behavioural evidence for a temporal code of odour identity.

Odor representation and coding by the mitral/tufted cells in the olfactory bulb

The olfactory bulb (OB) is the first relay station in the olfactory system and functions as a crucial hub. It can represent odor information precisely and accurately in an ever-changing environment. As the only output neurons in the OB, mitral/tufted cells encode information such as odor identity and concentration. Recently, the neural strategies and mechanisms underlying odor representation and encoding in the OB have been investigated extensively. Here we review the main progress on this topic. We first review the neurons and circuits involved in odor representation, including the different cell types in the OB and the neural circuits within and beyond the OB. We will then discuss how two different coding strategies-spatial coding and temporal coding-work in the rodent OB. Finally, we discuss potential future directions for this research topic. Overall, this review provides a comprehensive description of our current understanding of how odor information is represented and encoded by mitral/tufted cells in the OB.

Object-oriented olfaction: challenges for chemosensation and for chemosensory research

Many animal species use olfaction to extract information about objects in their environment. Yet, the specific molecular signature that any given object emits varies due to various factors. Here, we detail why such variability makes chemosensory-mediated object recognition such a hard problem, and we propose that a major function of the elaborate chemosensory network is to overcome it. We describe previous work addressing different elements of the problem and outline future research directions that we consider essential for a full understanding of object-oriented olfaction. In particular, we call for extensive representation of olfactory object variability in chemical, behavioral, and electrophysiological analyses. While written with an emphasis on macrosmatic mammalian species, our arguments apply to all organisms that employ chemosensation to navigate complex environments.

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.

Electrical Signaling Beyond Neurons

Neural action potentials (APs) are difficult to interpret as signal encoders and/or computational primitives. Their relationships with stimuli and behaviors are obscured by the staggering complexity of nervous systems themselves. We can reduce this complexity by observing that "simpler" neuron-less organisms also transduce stimuli into transient electrical pulses that affect their behaviors. Without a complicated nervous system, APs are often easier to understand as signal/response mechanisms. We review examples of nonneural stimulus transductions in domains of life largely neglected by theoretical neuroscience: bacteria, protozoans, plants, fungi, and neuron-less animals. We report properties of those electrical signals-for example, amplitudes, durations, ionic bases, refractory periods, and particularly their ecological purposes. We compare those properties with those of neurons to infer the tasks and selection pressures that neurons satisfy. Throughout the tree of life, nonneural stimulus transductions time behavioral responses to environmental changes. Nonneural organisms represent the presence or absence of a stimulus with the presence or absence of an electrical signal. Their transductions usually exhibit high sensitivity and specificity to a stimulus, but are often slow compared to neurons. Neurons appear to be sacrificing the specificity of their stimulus transductions for sensitivity and speed. We interpret cellular stimulus transductions as a cell's assertion that it detected something important at that moment in time. In particular, we consider neural APs as fast but noisy detection assertions. We infer that a principal goal of nervous systems is to detect extremely weak signals from noisy sensory spikes under enormous time pressure. We discuss neural computation proposals that address this goal by casting neurons as devices that implement online, analog, probabilistic computations with their membrane potentials. Those proposals imply a measurable relationship between afferent neural spiking statistics and efferent neural membrane electrophysiology.

Learning to integrate parts for whole through correlated neural variability

Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

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.

Wireless monitoring of respiration with EEG reveals relationships between respiration, behavior, and brain activity in freely moving mice

Active sampling in the olfactory domain is a fundamental aspect of mouse behavior, and there is increasing evidence that respiration-entrained neural activity outside of the olfactory system sets an important global brain rhythm. It is therefore crucial to accurately measure breathing during natural behaviors. We develop a new approach to do this in freely moving animals, by implanting a telemetry-based pressure sensor into the right jugular vein, which allows for wireless monitoring of thoracic pressure. After verifying this technique against standard head-fixed respiration measurements, we combined it with EEG and EMG recording and used evolving partial coherence analysis to investigate the relationship between respiration and brain activity across a range of experiments in which the mice could move freely. During voluntary exploration of odors and objects, we found that the association between respiration and cortical activity in the delta and theta frequency range decreased, whereas the association between respiration and cortical activity in the alpha range increased. During sleep, however, the presentation of an odor was able to cause a transient increase in sniffing without changing dominant sleep rhythms (delta and theta) in the cortex. Our data align with the emerging idea that the respiration rhythm could act as a synchronizing scaffold for specific brain rhythms during wakefulness and exploration, but suggest that respiratory changes are less able to impact brain activity during sleep. Combining wireless respiration monitoring with different types of brain recording across a variety of behaviors will further increase our understanding of the important links between active sampling, passive respiration, and neural activity.NEW & NOTEWORTHY Animals can alter their respiration rate to actively sample their environment, and increasing evidence suggests that neurons across the brain align their firing to this changing rhythm. We developed a new approach to measure sniffing in freely moving mice while simultaneously recording brain activity, and uncovered how specific cortical rhythms changed their coherence with respiration rhythm during natural behaviors and across arousal states.

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.

Q-Learning to navigate turbulence without a map

We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior information about the odor location. We ask whether navigation strategies to a target can be learned robustly within a sequential decision making framework. We develop a reinforcement learning algorithm using a small set of interpretable olfactory states and train it with realistic turbulent odor cues. By introducing a temporal memory, we demonstrate that two salient features of odor traces, discretized in few olfactory states, are sufficient to learn navigation in a realistic odor plume. Performance is dictated by the sparse nature of turbulent plumes. An optimal memory exists which ignores blanks within the plume and activates a recovery strategy outside the plume. We obtain the best performance by letting agents learn their recovery strategy and show that it is mostly casting cross wind, similar to behavior observed in flying insects. The optimal strategy is robust to substantial changes in the odor plumes, suggesting minor parameter tuning may be sufficient to adapt to different environments.

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.

IntelliCage: the development and perspectives of a mouse- and user-friendly automated behavioral test system

IntelliCage for mice is a rodent home-cage equipped with four corner structures harboring symmetrical double panels for operant conditioning at each of the two sides, either by reward (access to water) or by aversion (non-painful stimuli: air-puffs, LED lights). Corner visits, nose-pokes and actual licks at bottle-nipples are recorded individually using subcutaneously implanted transponders for RFID identification of up to 16 adult mice housed in the same home-cage. This allows for recording individual in-cage activity of mice and applying reward/punishment operant conditioning schemes in corners using workflows designed on a versatile graphic user interface. IntelliCage development had four roots: (i) dissatisfaction with standard approaches for analyzing mouse behavior, including standardization and reproducibility issues, (ii) response to handling and housing animal welfare issues, (iii) the increasing number of mouse models had produced a high work burden on classic manual behavioral phenotyping of single mice. and (iv), studies of transponder-chipped mice in outdoor settings revealed clear genetic behavioral differences in mouse models corresponding to those observed by classic testing in the laboratory. The latter observations were important for the development of home-cage testing in social groups, because they contradicted the traditional belief that animals must be tested under social isolation to prevent disturbance by other group members. The use of IntelliCages reduced indeed the amount of classic testing remarkably, while its flexibility was proved in a wide range of applications worldwide including transcontinental parallel testing. Essentially, two lines of testing emerged: sophisticated analysis of spontaneous behavior in the IntelliCage for screening of new genetic models, and hypothesis testing in many fields of behavioral neuroscience. Upcoming developments of the IntelliCage aim at improved stimulus presentation in the learning corners and videotracking of social interactions within the IntelliCage. Its main advantages are (i) that mice live in social context and are not stressfully handled for experiments, (ii) that studies are not restricted in time and can run in absence of humans, (iii) that it increases reproducibility of behavioral phenotyping worldwide, and (iv) that the industrial standardization of the cage permits retrospective data analysis with new statistical tools even after many years.

Olfactory receptor neurons are sensitive to stimulus onset asynchrony: implications for odor source discrimination

In insects, olfactory receptor neurons (ORNs) are localized in sensilla. Within a sensillum, different ORN types are typically co-localized and exhibit nonsynaptic reciprocal inhibition through ephaptic coupling. This inhibition is hypothesized to aid odor source discrimination in environments where odor molecules (odorants) are dispersed by wind, resulting in turbulent plumes. Under these conditions, odorants from a single source arrive at the ORNs synchronously, while those from separate sources arrive asynchronously. Ephaptic inhibition is expected to be weaker for asynchronous arriving odorants from separate sources, thereby enhancing their discrimination. Previous studies have focused on ephaptic inhibition of sustained ORN responses to constant odor stimuli. This begs the question of whether ephaptic inhibition also affects transient ORN responses and if this inhibition is modulated by the temporal arrival patterns of different odorants. To address this, we recorded co-localized ORNs in the fruit fly Drosophila melanogaster and exposed them to dynamic odorant mixtures. We found reciprocal inhibition, strongly suggesting the presence of ephaptic coupling. This reciprocal inhibition does indeed modulate transient ORN responses and is sensitive to the relative timing of odor stimuli. Notably, the strength of inhibition decreases as the synchrony and correlation between arriving odorants decrease. These results support the hypothesis that ephaptic inhibition aids odor source discrimination.

Phospholipid Molecular Layer that Enhances Distinction of Odors Based on Artificial Sniffing

We propose a novel odor-sensing system based on the dynamic response of phospholipid molecular layers for artificial olfaction. Organisms obtain information about their surroundings based on multidimensional information obtained from sniffing, i.e., periodic perturbations. Semiconductor- and receptor-based odor sensors have been developed previously. However, these sensors predominantly identify odors based on one-dimensional information, which limits the type of odor molecule they can identify. Therefore, the development of odor sensors that mimic the olfactory systems of living organisms is useful to overcome this limitation. In this study, we developed a novel odor-sensing system based on the dynamics of phospholipids that responds delicately to chemical substances at room temperature using multidimensional information obtained from periodic perturbations. Odor molecules are periodically supplied to the phospholipid molecular layer as an input sample. The waveform of the surface tension of the phospholipid molecular layer changes depending on the odor molecules and serves as an output. Such characteristic responses originating from the dynamics of odor molecules on the phospholipid molecular layer can be reproduced numerically. The phospholipid molecular layer amplified the information originating from the odor molecule, and the mechanism was evaluated by using surface pressure-area isotherms. This paper offers a platform for an interface-chemistry-based artificial sniffing system as an active sensor and a novel olfactory mechanism via physicochemical responses of the receptor-independent membranes of the organism.

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.

Extracting spatial information from temporal odor patterns: insights from insects

Extracting spatial information from temporal stimulus patterns is essential for sensory perception (e.g. visual motion direction detection or concurrent sound segregation), but this process remains understudied in olfaction. Animals rely on olfaction to locate resources and dangers. In open environments, where odors are dispersed by turbulent wind, detection of wind direction seems crucial for odor source localization. However, recent studies showed that insects can extract spatial information from the odor stimulus itself, independently from sensing wind direction. This remarkable ability is achieved by detecting the fine-scale temporal pattern of odor encounters, which contains information about the location and size of an odor source, and the distance between different odor sources.

Olfactory navigation in arthropods

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

Robust odor identification in novel olfactory environments in mice

Relevant odors signaling food, mates, or predators can be masked by unpredictable mixtures of less relevant background odors. Here, we developed a mouse behavioral paradigm to test the role played by the novelty of the background odors. During the task, mice identified target odors in previously learned background odors and were challenged by catch trials with novel background odors, a task similar to visual CAPTCHA. Female wild-type (WT) mice could accurately identify known targets in novel background odors. WT mice performance was higher than linear classifiers and the nearest neighbor classifier trained using olfactory bulb glomerular activation patterns. Performance was more consistent with an odor deconvolution method. We also used our task to investigate the performance of female Cntnap2^-/- mice, which show some autism-like behaviors. Cntnap2^-/- mice had glomerular activation patterns similar to WT mice and matched WT mice target detection for known background odors. However, Cntnap2^-/- mice performance fell almost to chance levels in the presence of novel backgrounds. Our findings suggest that mice use a robust algorithm for detecting odors in novel environments and this computation is impaired in Cntnap2^-/- mice.

Odor encoding by signals in the olfactory bulb

To understand the operation of the olfactory system, it is essential to know how information is encoded in the olfactory bulb. We applied Shannon information theoretic methods to address this, with signals from up to 57 glomeruli simultaneously optically imaged from presynaptic inputs in glomeruli in the mouse dorsal (dOB) and lateral (lOB) olfactory bulb, in response to six exemplar pure chemical odors. We discovered that, first, the tuning of these signals from glomeruli to a set of odors is remarkably broad, with a mean sparseness of 0.83 and a mean signal correlation of 0.64. Second, both of these factors contribute to the low information that is available from the responses of even populations of many tens of glomeruli, which was only 1.35 bits across 33 glomeruli on average, compared with the 2.58 bits required to perfectly encode these six odors. Third, although there is considerable interest in the possibility of temporal encoding of stimulus including odor identity, the amount of information in the temporal aspects of the presynaptic glomerular responses was low (mean 0.11 bits) and, importantly, was redundant with respect to the information available from the rates. Fourth, the information from simultaneously recorded glomeruli asymptotes very gradually and nonlinearly, showing that glomeruli do not have independent responses. Fifth, the information from a population became available quite rapidly, within 100 ms of sniff onset, and the peak of the glomerular response was at 200 ms. Sixth, the information from the lOB was not additive with that of the dOB.NEW & NOTEWORTHY We report broad tuning and low odor information available across the lateral and dorsal bulb populations of glomeruli. Even though response latencies can be significantly predictive of stimulus identity, such contained very little information and none that was not redundant with information based on rate coding alone. Last, in line with the emerging notion of the important role of earliest stages of responses ("primacy"), we report a very rapid rise in information after each inhalation.

Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality

Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed that odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plume's composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated that these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.

An optofluidic platform for interrogating chemosensory behavior and brainwide neural representation in larval zebrafish

Studying chemosensory processing desires precise chemical cue presentation, behavioral response monitoring, and large-scale neuronal activity recording. Here we present Fish-on-Chips, a set of optofluidic tools for highly-controlled chemical delivery while simultaneously imaging behavioral outputs and whole-brain neuronal activities at cellular resolution in larval zebrafish. These include a fluidics-based swimming arena and an integrated microfluidics-light sheet fluorescence microscopy (µfluidics-LSFM) system, both of which utilize laminar fluid flows to achieve spatiotemporally precise chemical cue presentation. To demonstrate the strengths of the platform, we used the navigation arena to reveal binasal input-dependent behavioral strategies that larval zebrafish adopt to evade cadaverine, a death-associated odor. The µfluidics-LSFM system enables sequential presentation of odor stimuli to individual or both nasal cavities separated by only ~100 µm. This allowed us to uncover brainwide neural representations of cadaverine sensing and binasal input summation in the vertebrate model. Fish-on-Chips is readily generalizable and will empower the investigation of neural coding in the chemical senses.

Odour motion sensing enhances navigation of complex plumes

Odour plumes in the wild are spatially complex and rapidly fluctuating structures carried by turbulent airflows^1-4. To successfully navigate plumes in search of food and mates, insects must extract and integrate multiple features of the odour signal, including odour identity⁵, intensity⁶ and timing^6-12. Effective navigation requires balancing these multiple streams of olfactory information and integrating them with other sensory inputs, including mechanosensory and visual cues^9,12,13. Studies dating back a century have indicated that, of these many sensory inputs, the wind provides the main directional cue in turbulent plumes, leading to the longstanding model of insect odour navigation as odour-elicited upwind motion^6,8-12,14,15. Here we show that Drosophila melanogaster shape their navigational decisions using an additional directional cue-the direction of motion of odours-which they detect using temporal correlations in the odour signal between their two antennae. Using a high-resolution virtual-reality paradigm to deliver spatiotemporally complex fictive odours to freely walking flies, we demonstrate that such odour-direction sensing involves algorithms analogous to those in visual-direction sensing¹⁶. Combining simulations, theory and experiments, we show that odour motion contains valuable directional information that is absent from the airflow alone, and that both Drosophila and virtual agents are aided by that information in navigating naturalistic plumes. The generality of our findings suggests that odour-direction sensing may exist throughout the animal kingdom and could improve olfactory robot navigation in uncertain environments.

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.

The structures and functions of correlations in neural population codes

The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.

Getting out the caliper: Behavioral quantification of perceptual odor similarity

Rigorously quantifying perceptual similarity is essential to link sensory stimuli to neural activity and to define the dimensionality of perceptual space, which is challenging for the chemical senses in particular. Nakayama, Gerkin, and Rinberg present an efficient delayed match-to-sample behavioral paradigm that promises to provide a metric for odor similarity.

Sample Preparation and Warping Accuracy for Correlative Multimodal Imaging in the Mouse Olfactory Bulb Using 2-Photon, Synchrotron X-Ray and Volume Electron Microscopy

Integrating physiology with structural insights of the same neuronal circuit provides a unique approach to understanding how the mammalian brain computes information. However, combining the techniques that provide both streams of data represents an experimental challenge. When studying glomerular column circuits in the mouse olfactory bulb, this approach involves e.g., recording the neuronal activity with in vivo 2-photon (2P) calcium imaging, retrieving the circuit structure with synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT) and/or serial block-face scanning electron microscopy (SBEM) and correlating these datasets. Sample preparation and dataset correlation are two key bottlenecks in this correlative workflow. Here, we first quantify the occurrence of different artefacts when staining tissue slices with heavy metals to generate X-ray or electron contrast. We report improvements in the staining procedure, ultimately achieving perfect staining in ∼67% of the 0.6 mm thick olfactory bulb slices that were previously imaged in vivo with 2P. Secondly, we characterise the accuracy of the spatial correlation between functional and structural datasets. We demonstrate that direct, single-cell precise correlation between in vivo 2P and SXRT tissue volumes is possible and as reliable as correlating between 2P and SBEM. Altogether, these results pave the way for experiments that require retrieving physiology, circuit structure and synaptic signatures in targeted regions. These correlative function-structure studies will bring a more complete understanding of mammalian olfactory processing across spatial scales and time.

Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy

Understanding the function of biological tissues requires a coordinated study of physiology and structure, exploring volumes that contain complete functional units at a detail that resolves the relevant features. Here, we introduce an approach to address this challenge: Mouse brain tissue sections containing a region where function was recorded using in vivo 2-photon calcium imaging were stained, dehydrated, resin-embedded and imaged with synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT). SXRT provided context at subcellular detail, and could be followed by targeted acquisition of multiple volumes using serial block-face electron microscopy (SBEM). In the olfactory bulb, combining SXRT and SBEM enabled disambiguation of in vivo-assigned regions of interest. In the hippocampus, we found that superficial pyramidal neurons in CA1a displayed a larger density of spine apparati than deeper ones. Altogether, this approach can enable a functional and structural investigation of subcellular features in the context of cells and tissues.

Coupling of Mouse Olfactory Bulb Projection Neurons to Fluctuating Odor Pulses

Odors are transported by turbulent air currents, creating complex temporal fluctuations in odor concentration that provide a potentially informative stimulus dimension. We have shown that mice are able to discriminate odor stimuli based on their temporal structure, indicating that information contained in the temporal structure of odor plumes can be extracted by the mouse olfactory system. Here, using in vivo extracellular and intracellular electrophysiological recordings, we show that mitral cells (MCs) and tufted cells (TCs) of the male C57BL/6 mouse olfactory bulb can encode the dominant temporal frequencies present in odor stimuli up to at least 20 Hz. A substantial population of cell-odor pairs showed significant coupling of their subthreshold membrane potential with the odor stimulus at both 2 Hz (29/70) and the suprasniff frequency 20 Hz (24/70). Furthermore, mitral/tufted cells (M/TCs) show differential coupling of their membrane potential to odor concentration fluctuations with tufted cells coupling more strongly for the 20 Hz stimulation. Frequency coupling was always observed to be invariant to odor identity, and M/TCs that coupled well to a mixture also coupled to at least one of the components of the mixture. Interestingly, pharmacological blocking of the inhibitory circuitry strongly modulated frequency coupling of cell-odor pairs at both 2 Hz (10/15) and 20 Hz (9/15). These results provide insight into how both cellular and circuit properties contribute to the encoding of temporal odor features in the mouse olfactory bulb.SIGNIFICANCE STATEMENT Odors in the natural environment have a strong temporal structure that can be extracted and used by mice in their behavior. Here, using in vivo extracellular and intracellular electrophysiological techniques, we show that the projection neurons in the olfactory bulb can encode and couple to the dominant frequency present in an odor stimulus. Furthermore, frequency coupling was observed to be differential between mitral and tufted cells and was odor invariant but strongly modulated by local inhibitory circuits. In summary, this study provides insight into how both cellular and circuit properties modulate encoding of odor temporal features in the mouse olfactory bulb.

Transgenic mice encoding modern imaging probes: Properties and applications

Modern biology is increasingly reliant on optical technologies, including visualization and longitudinal monitoring of cellular processes. The major limitation here is the availability of animal models to track the molecules and cells in their natural environment in vivo. Owing to the integrity of the studied tissue and the high stability of transgene expression throughout life, transgenic mice encoding fluorescent proteins and biosensors represent unique tools for in vivo studies in norm and pathology. We review the strategies for targeting probe expression in specific tissues, cell subtypes, or cellular compartments. We describe the application of transgenic mice expressing fluorescent proteins for tracking protein expression patterns, apoptotic events, tissue differentiation and regeneration, neurogenesis, tumorigenesis, and cell fate mapping. We overview the possibilities of functional imaging of secondary messengers, neurotransmitters, and ion fluxes. Finally, we provide the rationale and perspectives for the use of transgenic imaging probes in translational research and drug discovery.

Processing of Odor Information During the Respiratory Cycle in Mice

In the mouse olfactory system, odor signals detected in the olfactory epithelium are converted to a topographic map of activated glomeruli in the olfactory bulb. The map information is then conveyed by projection neurons, mitral cells and tufted cells, to various areas in the olfactory cortex. An odor map is transmitted to the anterior olfactory nucleus by tufted cells for odor identification and recollection of associated memory for learned decisions. For instinct decisions, odor information is directly transmitted to the valence regions in the amygdala by specific subsets of mitral cells. Transmission of orthonasal odor signals through these two distinct pathways, innate and learned, are closely related with exhalation and inhalation, respectively. Furthermore, the retronasal/interoceptive and orthonasal/exteroceptive signals are differentially processed during the respiratory cycle, suggesting that these signals are processed in separate areas of the olfactory bulb and olfactory cortex. In this review article, the recent progress is summarized for our understanding of the olfactory circuitry and processing of odor signals during respiration.

Top-down feedback enables flexible coding strategies in the olfactory cortex

In chemical sensation, multiple models have been proposed to explain how odors are represented in the olfactory cortex. One hypothesis is that the combinatorial identity of active neurons within sniff-related time windows is critical, whereas another model proposes that it is the temporal structure of neural activity that is essential for encoding odor information. We find that top-down feedback to the main olfactory bulb dictates the information transmitted to the piriform cortex and switches between these coding strategies. Using a detailed network model, we demonstrate that feedback control of inhibition influences the excitation-inhibition balance in mitral cells, restructuring the dynamics of piriform cortical cells. This results in performance improvement in odor discrimination tasks. These findings present a framework for early olfactory computation, where top-down feedback to the bulb flexibly shapes the temporal structure of neural activity in the piriform cortex, allowing the early olfactory system to dynamically switch between two distinct coding models.

State-dependent representations of mixtures by the olfactory bulb

Sensory systems are often tasked to analyse complex signals from the environment, separating relevant from irrelevant parts. This process of decomposing signals is challenging when a mixture of signals does not equal the sum of its parts, leading to an unpredictable corruption of signal patterns. In olfaction, nonlinear summation is prevalent at various stages of sensory processing. Here, we investigate how the olfactory system deals with binary mixtures of odours under different brain states by two-photon imaging of olfactory bulb (OB) output neurons. Unlike previous studies using anaesthetised animals, we found that mixture summation is more linear in the early phase of evoked responses in awake, head-fixed mice performing an odour detection task, due to dampened responses. Despite smaller and more variable responses, decoding analyses indicated that the data from behaving mice was well discriminable. Curiously, the time course of decoding accuracy did not correlate strictly with the linearity of summation. Further, a comparison with naïve mice indicated that learning to accurately perform the mixture detection task is not accompanied by more linear mixture summation. Finally, using a simulation, we demonstrate that, while saturating sublinearity tends to degrade the discriminability, the extent of the impairment may depend on other factors, including pattern decorrelation. Altogether, our results demonstrate that the mixture representation in the primary olfactory area is state-dependent, but the analytical perception may not strictly correlate with linearity in summation.

jULIEs: nanostructured polytrodes for low traumatic extracellular recordings and stimulation in the mammalian brain

Objective Extracellular microelectrode techniques are the most widely used approach to interrogate neuronal populations. Regardless of the manufacturing method, damage to the vasculature and circuit function during probe insertion remains a concern. Reducing the footprint of the penetrating probes is a potential solution to this issue. However, coupling to the extracellular signals requires careful surface engineering. Approach Here, we show that continuously drawn SiO2-insulated ultra-microelectrode fibres offer an attractive substrate to address these challenges. Individual fibres can be fabricated to >10m continuous stretches and a selection of diameters below 30 µm with a low resistance (<100 Ω/m), continuous metal core of <10 µm and atomically flat smooth shank surfaces. To optimize the properties of the miniaturised electrode-tissue interface, we electrodeposit rough Au structures followed by ~20nm IrOx film by electrodeposition resulting in reduction of the interfacial impedance to <500kΩ at 1 kHz. Main results We demonstrate that these ultra-low impedance electrodes (jULIEs) can record and stimulate single and multi-unit activity with minimal tissue disturbance and exceptional signal-to-noise ratio in both superficial (~40µm) and deep (~6mm) structures of the mouse brain. We further show that sensor modifications are stable and probe manufacturing is reproducible. Significance Minimally perturbing bidirectional neural interfacing can reveal circuit function in the mammalian brain in vivo.

Sensing complementary temporal features of odor signals enhances navigation of diverse turbulent plumes

We and others have shown that during odor plume navigation, walking Drosophila melanogaster bias their motion upwind in response to both the frequency of their encounters with the odor (Demir et al., 2020) and the intermittency of the odor signal, which we define to be the fraction of time the signal is above a detection threshold (Alvarez-Salvado et al., 2018). Here, we combine and simplify previous mathematical models that recapitulated these data to investigate the benefits of sensing both of these temporal features and how these benefits depend on the spatiotemporal statistics of the odor plume. Through agent-based simulations, we find that navigators that only use frequency or intermittency perform well in some environments - achieving maximal performance when gains are near those inferred from experiment - but fail in others. Robust performance across diverse environments requires both temporal modalities. However, we also find a steep trade-off when using both sensors simultaneously, suggesting a strong benefit to modulating how much each sensor is weighted, rather than using both in a fixed combination across plumes. Finally, we show that the circuitry of the Drosophila olfactory periphery naturally enables simultaneous intermittency and frequency sensing, enhancing robust navigation through a diversity of odor environments. Together, our results suggest that the first stage of olfactory processing selects and encodes temporal features of odor signals critical to real-world navigation tasks.

Sparse connectivity for MAP inference in linear models using sister mitral cells

Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly. Although there are typically more variables than neurons, this problem is still solvable because only a small number of variables appear at any one time (sparse prior). However, previous solutions require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so using sparse connectivity. Our algorithm is inspired by the circuit of the mouse olfactory bulb, but our approach is general enough to apply to other modalities. In addition, it should be possible to extend it to nonlinear encoding models.

A potential biomarker of preclinical Alzheimer's disease: The olfactory dysfunction and its pathogenesis-based neural circuitry impairments

The olfactory dysfunction can signal and act as a potential biomarker of preclinical AD. However, the precise regulatory mechanism of olfactory function on the neural pathogenesis of AD is still unclear. The impairment of neural networks in olfaction system has been shown to be tightly associated with AD. As key brain regions of the olfactory system, the olfactory bulb (OB) and the piriform cortex (PCx) have a profound influence on the olfactory function. Therefore, this review will explore the mechanism of olfactory dysfunction in preclinical AD in the perspective of abnormal neural networks in the OB and PCx and their associated brain regions, especially from two aspects of aberrant oscillations and synaptic plasticity damages, which help better understand the underlying mechanism of olfactory neural network damages related to AD.

Biological Roles of Lipocalins in Chemical Communication, Reproduction, and Regulation of Microbiota

Major evolutionary transitions were always accompanied by genetic remodelling of phenotypic traits. For example, the vertebrate transition from water to land was accompanied by rapid evolution of olfactory receptors and by the expansion of genes encoding lipocalins, which - due to their transporting functions - represent an important interface between the external and internal organic world of an individual and also within an individual. Similarly, some lipocalin genes were lost along other genes when this transition went in the opposite direction leading, for example, to cetaceans. In terrestrial vertebrates, lipocalins are involved in the transport of lipophilic substances, chemical signalling, odour reception, antimicrobial defence and background odour clearance during ventilation. Many ancestral lipocalins have clear physiological functions across the vertebrate taxa while many other have - due to pleiotropic effects of their genes - multiple or complementary functions within the body homeostasis and development. The aim of this review is to deconstruct the physiological functions of lipocalins in light of current OMICs techniques. We concentrated on major findings in the house mouse in comparison to other model taxa (e.g., voles, humans, and birds) in which all or most coding genes within their genomes were repeatedly sequenced and their annotations are sufficiently informative.

The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals

Cognition, defined as the processes concerned with the acquisition, retention and use of information, underlies animals’ abilities to navigate their local surroundings, embark on long-distance seasonal migrations, and socially learn information relevant to movement. Hence, in order to fully understand and predict animal movement, researchers must know the cognitive mechanisms that generate such movement. Work on a few model systems indicates that most animals possess excellent spatial learning and memory abilities, meaning that they can acquire and later recall information about distances and directions among relevant objects. Similarly, field work on several species has revealed some of the mechanisms that enable them to navigate over distances of up to several thousand kilometers. Key behaviors related to movement such as the choice of nest location, home range location and migration route are often affected by parents and other conspecifics. In some species, such social influence leads to the formation of aggregations, which in turn may lead to further social learning about food locations or other resources. Throughout the review, we note a variety of topics at the interface of cognition and movement that invite further investigation. These include the use of social information embedded in trails, the likely important roles of soundscapes and smellscapes, the mechanisms that large mammals rely on for long-distance migration, and the effects of expertise acquired over extended periods.

Olfaction: Source separation in a single sniff

A new study finds that mammalian olfaction may be far faster than previously thought. Mice can discriminate between olfactory stimuli that differ in fine temporal structure, at frequencies of up to 40 Hz. But how might mammals achieve high-bandwidth olfaction, and why?

Principles of odor coding in vertebrates and artificial chemosensory systems

The biological olfactory system is the sensory system responsible for the detection of the chemical composition of the environment. Several attempts to mimic biological olfactory systems have led to various artificial olfactory systems using different technical approaches. Here we provide a parallel description of biological olfactory systems and their technical counterparts. We start with a presentation of the input to the systems, the stimuli, and treat the interface between the external world and the environment where receptor neurons or artificial chemosensors reside. We then delineate the functions of receptor neurons and chemosensors as well as their overall I-O relationships. Up to this point, our account of the systems goes along similar lines. The next processing steps differ considerably: while in biology the processing step following the receptor neurons is the "integration" and "processing" of receptor neuron outputs in the olfactory bulb, this step has various realizations in electronic noses. For a long period of time, the signal processing stages beyond the olfactory bulb, i.e., the higher olfactory centers were little studied. Only recently there has been a marked growth of studies tackling the information processing in these centers. In electronic noses, a third stage of processing has virtually never been considered. In this review, we provide an up-to-date overview of the current knowledge of both fields and, for the first time, attempt to tie them together. We hope it will be a breeding ground for better information, communication, and data exchange between very related but so far little connected fields.