Explore the vast range of titles available.

The cortex organizes sensory information to enable discrimination and generalization 1 – 4 . As systematic representations of chemical odour space have not yet been described in the olfactory cortex, it remains unclear how odour relationships are encoded to place chemically distinct but similar odours, such as lemon and orange, into perceptual categories, such as citrus 5 – 7 . Here, by combining chemoinformatics and multiphoton imaging in the mouse, we show that both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odour relationships through correlated patterns of activity. However, cortical odour codes differ from those in the bulb: cortex more strongly clusters together representations for related odours, selectively rewrites pairwise odour relationships, and better matches odour perception. The bulb-to-cortex transformation depends on the associative network originating within the piriform cortex, and can be reshaped by passive odour experience. Thus, cortex actively builds a structured representation of chemical odour space that highlights odour relationships; this representation is similar across individuals but remains plastic, suggesting a means through which the olfactory system can assign related odour cues to common and yet personalized percepts. Both piriform cortex and its sensory inputs from the olfactory bulb represent chemical odour relationships, but cortex reshapes relational information inherited from the sensory periphery to enhance odour generalization and to reflect experience.

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.The detection, discrimination and categorization of odours are essential for survival across the animal kingdom. In this Review, Datta and co-workers describe and compare the neural circuits that mediate the processing of olfactory information and the key principles of olfactory coding in insects and mammals.

With less than a million neurons, the western honeybee Apis mellifera is capable of complex olfactory behaviors and provides an ideal model for investigating the neurophysiology of the olfactory circuit and the basis of olfactory perception and learning. Here, we review the most fundamental aspects of honeybee’s olfaction: first, we discuss which odorants dominate its environment, and how bees use them to communicate and regulate colony homeostasis; then, we describe the neuroanatomy and the neurophysiology of the olfactory circuit; finally, we explore the cellular and molecular mechanisms leading to olfactory memory formation. The vastity of histological, neurophysiological, and behavioral data collected during the last century, together with new technological advancements, including genetic tools, confirm the honeybee as an attractive research model for understanding olfactory coding and learning.

Object recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and computational capabilities. Here, we report a tactile-olfactory sensing array, which was inspired by the natural sense-fusion system of star-nose mole, and can permit real-time acquisition of the local topography, stiffness, and odor of a variety of objects without visual input. The tactile-olfactory information is processed by a bioinspired olfactory-tactile associated machine-learning algorithm, essentially mimicking the biological fusion procedures in the neural system of the star-nose mole. Aiming to achieve human identification during rescue missions in challenging environments such as dark or buried scenarios, our tactile-olfactory intelligent sensing system could classify 11 typical objects with an accuracy of 96.9% in a simulated rescue scenario at a fire department test site. The tactile-olfactory bionic sensing system required no visual input and showed superior tolerance to environmental interference, highlighting its great potential for robust object recognition in difficult environments where other methods fall short. For the object recognition in lightless environments, the authors propose the olfactory-tactile machine learning approach, inspired by the star-nose mole’s neural system. They show how bionic flexible sensor arrays allow for real-time acquisition of object’s form and odor when touching it.

We identified the neurons comprising the Drosophila mushroom body (MB), an associative center in invertebrate brains, and provide a comprehensive map describing their potential connections. Each of the 21 MB output neuron (MBON) types elaborates segregated dendritic arbors along the parallel axons of ∼2000 Kenyon cells, forming 15 compartments that collectively tile the MB lobes. MBON axons project to five discrete neuropils outside of the MB and three MBON types form a feedforward network in the lobes. Each of the 20 dopaminergic neuron (DAN) types projects axons to one, or at most two, of the MBON compartments. Convergence of DAN axons on compartmentalized Kenyon cell–MBON synapses creates a highly ordered unit that can support learning to impose valence on sensory representations. The elucidation of the complement of neurons of the MB provides a comprehensive anatomical substrate from which one can infer a functional logic of associative olfactory learning and memory. One of the key goals of neuroscience is to understand how specific circuits of brain cells enable animals to respond optimally to the constantly changing world around them. Such processes are more easily studied in simpler brains, and the fruit fly—with its small size, short life cycle, and well-developed genetic toolkit—is widely used to study the genes and circuits that underlie learning and behavior. Fruit flies can learn to approach odors that have previously been paired with food, and also to avoid any odors that have been paired with an electric shock, and a part of the brain called the mushroom body has a central role in this process. When odorant molecules bind to receptors on the fly's antennae, they activate neurons in the antennal lobe of the brain, which in turn activate cells called Kenyon cells within the mushroom body. The Kenyon cells then activate output neurons that convey signals to other parts of the brain. It is known that relatively few Kenyon cells are activated by any given odor. Moreover, it seems that a given odor activates different sets of Kenyon cells in different flies. Because the association between an odor and the Kenyon cells it activates is unique to each fly, each fly needs to learn through its own experiences what a particular pattern of Kenyon cell activation means. Aso et al. have now applied sophisticated molecular genetic and anatomical techniques to thousands of different transgenic flies to identify the neurons of the mushroom body. The resulting map reveals that the mushroom body contains roughly 2200 neurons, including seven types of Kenyon cells and 21 types of output cells, as well as 20 types of neurons that use the neurotransmitter dopamine. Moreover, this map provides insights into the circuits that support odor-based learning. It reveals, for example, that the mushroom body can be divided into 15 anatomical compartments that are each defined by the presence of a specific set of output and dopaminergic neuron cell types. Since the dopaminergic neurons help to shape a fly's response to odors on the basis of previous experience, this organization suggests that these compartments may be semi-autonomous information processing units. In contrast to the rest of the insect brain, the mushroom body has a flexible organization that is similar to that of the mammalian brain. Elucidating the circuits that support associative learning in fruit flies should therefore make it easier to identify the equivalent mechanisms in vertebrate animals.

Honeybees are highly social insects with a rich behavioral repertoire and are a versatile model for neurobiological research. Their gut microbiota comprises a limited number of host-restricted bacterial phylotypes that are important for honeybee health. However, it remains unclear how specific gut members affect honeybee behaviors. Here, we find that antibiotic exposure disturbs the gut community and influences honeybee phenotypes under field conditions. Using laboratory-generated gnotobiotic bees, we show that a normal gut microbiota is required for olfactory learning and memory abilities. Brain transcriptomic profiling reveals distinct brain gene expression patterns between microbiota-free and conventional bees. Subsequent metabolomic analyses of both hemolymph and gut samples show that the microbiota mainly regulates tryptophan metabolism. Our results indicate that host-specific Lactobacillus strains promote memory behavior by transforming tryptophan to indole derivatives that activate the host aryl hydrocarbon receptor. Our findings highlight the contributions of specific gut members to honeybee neurological processes, thus providing a promising model to understand host-microbe interactions. Eusocial honeybee is a versatile model for neurobiological research. Here, the authors established a link between honeybee gut Lactobacillus in bee learning and memory ability via indole derivatives that activate host’s aryl hydrocarbon receptor.

The olfactory system’s ability to detect and discriminate between the vast array of chemicals present in the environment is critical for an animal’s survival. In mammals, the first step of this odor processing is executed by olfactory sensory neurons, which project their axons to a stereotyped location in the olfactory bulb (OB) to form glomeruli. The stereotyped positioning of glomeruli in the OB suggests an importance for this organization in odor perception. However, because the location of only a limited subset of glomeruli has been determined, it has been challenging to determine the relationship between glomerular location and odor discrimination. Using a combination of single-cell RNA sequencing, spatial transcriptomics and machine learning, we have generated a map of most glomerular positions in the mouse OB. These observations significantly extend earlier studies and suggest an overall organizational principle in the OB that may be used by the brain to assist in odor decoding. Using single-cell RNA sequencing and spatial transcriptomics, Wang et al. recreate a map of glomerular locations in the mouse olfactory bulb. This work describes a spatial organization that may be used by the brain to assist in odor decoding.

Discriminative learning enhances contrast between sensory inputs to allow fast and accurate decision-making. However, the neural mechanisms that selectively enhance sensory representations to improve discrimination remain unclear. Here, we show that learning-induced differential gating of olfactory inputs takes place at the first stage of sensory processing in the mouse olfactory bulb and requires dopaminergic short axons cells (SACs). Optical imaging, spatial transcriptomics, and electron microscopy experiments reveal that synaptic and structural plasticity in SACs allows odor valence-based modulation of their interactions with other cell types in the olfactory glomeruli. Importantly, an increase in tyrosine hydroxylase expression by SACs surrounding responding glomeruli, with a bias towards those activated by reward odors, creates a valence-based modulation of sensory input. Further, we identify cholinergic input from the horizontal limb of the diagonal band as the valence-dependent signal that modulates SAC activities and refines sensory representation via disinhibition. Our findings reveal a circuit mechanism where an interneuron population serves as a central hub integrating sensory input and top–down signal to enhance sensory acuity.

Postnatal neurogenesis provides an opportunity to understand how newborn neurons integrate into circuits to restore function. Newborn olfactory sensory neurons (OSNs) wire into highly organized olfactory bulb (OB) circuits throughout life, enabling lifelong plasticity and regeneration. Immature OSNs form functional synapses capable of evoking firing in OB projection neurons but what contribution, if any, they make to odor processing is unknown. Here, we show that immature OSNs provide odor input to the mouse OB, where they form monosynaptic connections with excitatory neurons. Importantly, immature OSNs respond as selectively to odorants as mature OSNs and exhibit graded responses across a wider range of odorant concentrations than mature OSNs, suggesting that immature and mature OSNs provide distinct odor input streams. Furthermore, mice can successfully perform odor detection and discrimination tasks using sensory input from immature OSNs alone. Together, our findings suggest that immature OSNs play a previously unappreciated role in olfactory-guided behavior. New olfactory sensory neurons (OSNs) wire into highly organized olfactory bulb circuits throughout life. Here, the authors show that immature OSNs provide behaviourally relevant sensory input to olfactory bulb neurons that is functionally distinct from that provided by mature OSNs.

Olfactory systems have evolved the extraordinary capability to detect and discriminate volatile odorous molecules (odorants) in the environment. Fundamentally, this process relies on the interaction of odorants and their cognate olfactory receptors (ORs) encoded in the genome. Here, we conducted a cell-based screen using over 800 mouse ORs against seven odorants, resulting in the identification of a set of high-affinity and/or broadly-tuned ORs. We then test whether heterologously expressed ORs respond to odors presented in vapor phase by individually expressing 31 ORs to measure cAMP responses against vapor phase odor stimulation. Comparison of response profiles demonstrates this platform is capable of discriminating between structural analogs. Lastly, co-expression of carboxyl esterase Ces1d expressed in olfactory mucosa resulted in marked changes in activation of specific odorant-OR combinations. Altogether, these results establish a cell-based volatile odor detection and discrimination platform and form the basis for an OR-based volatile odor sensor. Biomimetic “noses” have been proposed to replace trained animals for chemical detection. Here the authors select 31 mouse olfactory receptors (ORs), based on a large cell-based screen of >800 ORs against seven chemicals, to build an OR-based sensor able to discriminate structurally similar compounds.