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Current Opinion in Neurobiology Oct 2022Volatile chemicals in the environment provide ethologically important information to many animals. However, how animals learn to use this information is only beginning... (Review)
Review
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.
Topics: Animals; Brain; Conditioning, Classical; Learning; Smell
PubMed: 35998474
DOI: 10.1016/j.conb.2022.102623 -
Journal of Comparative Physiology. A,... Jul 2023Using 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... (Review)
Review
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.
Topics: Animals; Arthropods; Olfactory Pathways; Smell; Odorants; Brain
PubMed: 36658447
DOI: 10.1007/s00359-022-01611-9 -
Neuron Apr 2023The coincidence between conditioned stimulus (CS) and unconditioned stimulus (US) is essential for associative learning; however, the mechanism regulating the duration...
The coincidence between conditioned stimulus (CS) and unconditioned stimulus (US) is essential for associative learning; however, the mechanism regulating the duration of this temporal window remains unclear. Here, we found that serotonin (5-HT) bi-directionally regulates the coincidence time window of olfactory learning in Drosophila and affects synaptic plasticity of Kenyon cells (KCs) in the mushroom body (MB). Utilizing GPCR-activation-based (GRAB) neurotransmitter sensors, we found that KC-released acetylcholine (ACh) activates a serotonergic dorsal paired medial (DPM) neuron, which in turn provides inhibitory feedback to KCs. Physiological stimuli induce spatially heterogeneous 5-HT signals, which proportionally gate the intrinsic coincidence time windows of different MB compartments. Artificially reducing or increasing the DPM neuron-released 5-HT shortens or prolongs the coincidence window, respectively. In a sequential trace conditioning paradigm, this serotonergic neuromodulation helps to bridge the CS-US temporal gap. Altogether, we report a model circuitry for perceiving the temporal coincidence and determining the causal relationship between environmental events.
Topics: Animals; Smell; Serotonin; Drosophila; Conditioning, Classical; Neurons; Mushroom Bodies
PubMed: 36706757
DOI: 10.1016/j.neuron.2022.12.034 -
The Journal of Neuroscience : the... Oct 2018Localizing the sources of stimuli is essential. Most organisms cannot eat, mate, or escape without knowing where the relevant stimuli originate. For many, if not most,... (Review)
Review
Localizing the sources of stimuli is essential. Most organisms cannot eat, mate, or escape without knowing where the relevant stimuli originate. For many, if not most, animals, olfaction plays an essential role in search. While microorganismal chemotaxis is relatively well understood, in larger animals the algorithms and mechanisms of olfactory search remain mysterious. In this symposium, we will present recent advances in our understanding of olfactory search in flies and rodents. Despite their different sizes and behaviors, both species must solve similar problems, including meeting the challenges of turbulent airflow, sampling the environment to optimize olfactory information, and incorporating odor information into broader navigational systems.
Topics: Algorithms; Animals; Environment; Humans; Memory; Odorants; Smell; Species Specificity
PubMed: 30381430
DOI: 10.1523/JNEUROSCI.1668-18.2018 -
Frontiers in Neural Circuits 2022In 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... (Review)
Review
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.
Topics: Amygdala; Animals; Mice; Odorants; Olfactory Bulb; Olfactory Pathways; Respiration; Smell
PubMed: 35431818
DOI: 10.3389/fncir.2022.861800 -
Chemical Senses Jan 2019The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that... (Review)
Review
The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and data science, with its currently most popular implementation as machine learning, facilitate complex data-driven research approaches. The use of machine learning in human olfactory research included major approaches comprising 1) the study of the physiology of pattern-based odor detection and recognition processes, 2) pattern recognition in olfactory phenotypes, 3) the development of complex disease biomarkers including olfactory features, 4) odor prediction from physico-chemical properties of volatile molecules, and 5) knowledge discovery in publicly available big databases. A limited set of unsupervised and supervised machine-learned methods has been used in these projects, however, the increasing use of contemporary methods of computational science is reflected in a growing number of reports employing machine learning for human olfactory research. This review provides key concepts of machine learning and summarizes current applications on human olfactory data.
Topics: Biomarkers; Databases, Factual; Electronic Nose; Humans; Machine Learning; Odorants; Smell; Volatile Organic Compounds
PubMed: 30371751
DOI: 10.1093/chemse/bjy067 -
Current Biology : CB Feb 2015Sensory cues that predict reward or punishment are fundamental drivers of animal behavior. For example, attractive odors of palatable food or a potential mate predict... (Review)
Review
Sensory cues that predict reward or punishment are fundamental drivers of animal behavior. For example, attractive odors of palatable food or a potential mate predict reward, while aversive odors of pathogen-laced food or a predator predict punishment. Aversive and attractive odors can be detected by intermingled sensory neurons that express highly related olfactory receptors and display similar central projections. These findings raise basic questions of how innate odor valence is extracted from olfactory circuits, how such circuits are developmentally endowed and modulated by state, and how innate and learned odor responses are related. Here, we review odors, receptors and neural circuits associated with stimulus valence, discussing salient principles derived from studies on nematodes, insects and vertebrates. Understanding the organization of neural circuitry that mediates odor aversion and attraction will provide key insights into how the brain functions.
Topics: Aedes; Animals; Avoidance Learning; Behavior, Animal; Caenorhabditis elegans; Cues; Discrimination Learning; Drosophila; Mice; Models, Neurological; Olfactory Pathways; Olfactory Receptor Neurons; Smell
PubMed: 25649823
DOI: 10.1016/j.cub.2014.11.044 -
Scientific Reports Mar 2021Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an...
Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.
Topics: Animals; Avoidance Learning; Dopaminergic Neurons; Drosophila; Models, Biological; Mushroom Bodies; Neuronal Plasticity; Smell; Stochastic Processes; Synapses
PubMed: 33762640
DOI: 10.1038/s41598-021-85841-y -
Behavioral Neuroscience Oct 2021Learning associations between sensory stimuli and outcomes, and generalizing these associations to novel stimuli, are a fundamental feature of adaptive behavior. Given a...
Learning associations between sensory stimuli and outcomes, and generalizing these associations to novel stimuli, are a fundamental feature of adaptive behavior. Given a noisy olfactory world, stimulus generalization holds unique relevance for the olfactory system. Recent studies suggest that aversive outcomes induce wider generalization curves by modulating discrimination thresholds, but evidence for similar processes in olfaction does not exist. Here, we use a novel olfactory discrimination learning paradigm to address the question of how outcome valence impacts associative learning and generalization in humans. Subjects underwent discrimination learning, where they learned to associate odor mixtures with either aversive (shock) or neutral (air puff) outcomes. We find better olfactory learning for odors associated with aversive compared to neutral outcomes. We further show that generalization gradients are also modulated by outcome valence, with the shock group exhibiting a steeper gradient. Computational modeling revealed that differences in generalization are driven by a narrower excitatory gradient in the shock group, indicating more discriminatory responses. These findings provide novel evidence that olfactory learning and generalization are strongly affected by the valence of outcomes. This adaptive mechanism allows for behavioral flexibility in novel situations with related stimuli and with outcomes of different valences. Because odor stimuli differ considerably from one encounter to the next, adaptive generalization may be especially important in the olfactory system. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Topics: Conditioning, Classical; Discrimination Learning; Generalization, Psychological; Humans; Odorants; Smell
PubMed: 34197137
DOI: 10.1037/bne0000476 -
Current Opinion in Neurobiology Apr 2015Neural oscillations are ubiquitous in olfactory systems of mammals, insects and molluscs. Neurophysiological and computational investigations point to common mechanisms... (Review)
Review
Neural oscillations are ubiquitous in olfactory systems of mammals, insects and molluscs. Neurophysiological and computational investigations point to common mechanisms for gamma or odor associated oscillations across phyla (40-100Hz in mammals, 20-30Hz in insects, 0.5-1.5Hz in molluscs), engaging the reciprocal dendrodendritic synapse between excitatory principle neurons and inhibitory interneurons in the olfactory bulb (OB), antennal lobe (AL), or procerebrum (PrC). Recent studies suggest important mechanisms that may modulate gamma oscillations, including neuromodulators and centrifugal input to the OB and AL. Beta (20Hz) and theta (2-12Hz) oscillations coordinate activity within and across brain regions. Olfactory beta oscillations are associated with odor learning and depend on centrifugal OB input, while theta oscillations are strongly associated with respiration.
Topics: Animals; Biological Clocks; Membrane Potentials; Neurons; Neuropil; Odorants; Olfactory Pathways; Phylogeny; Smell
PubMed: 25460070
DOI: 10.1016/j.conb.2014.10.004