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Neuron Dec 2021The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in...
The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in artificial neural networks trained to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity onto a larger expansion layer of Kenyon cells. When trained to both classify odor identity and to impart innate valence onto odors, the network develops independent pathways for identity and valence classification. Thus, the defining features of fly and mouse olfactory systems also evolved in artificial neural networks trained to perform olfactory tasks. This implies that convergent evolution reflects an underlying logic rather than shared developmental principles.
Topics: Animals; Machine Learning; Mice; Odorants; Olfactory Bulb; Olfactory Pathways; Olfactory Perception; Olfactory Receptor Neurons; Smell
PubMed: 34619093
DOI: 10.1016/j.neuron.2021.09.010 -
Brain Structure & Function Mar 2022Sensory information, sampled by sensory organs positioned on each side of the body may play a crucial role in organizing brain lateralization. This question is of... (Review)
Review
Sensory information, sampled by sensory organs positioned on each side of the body may play a crucial role in organizing brain lateralization. This question is of particular interest with regard to the growing evidence of alteration in lateralization in several psychiatric conditions. In this context, the olfactory system, an ancient, mostly ipsilateral and well-conserved system across phylogeny may prove an interesting model system to understand the behavioral significance of brain lateralization. Here, we focused on behavioral data in vertebrates and non-vertebrates, suggesting that the two hemispheres of the brain differentially processed olfactory cues to achieve diverse sensory operations, such as detection, discrimination, identification of behavioral valuable cues or learning. These include reports across different species on best performances with one nostril or the other or odorant active sampling by one nostril or the other, depending on odorants or contexts. In some species, hints from peripheral anatomical or functional asymmetry were proposed to explain these asymmetries in behavior. Instigations of brain activation or more rarely of brain connectivity evoked by odorants revealed a complex picture with regards to asymmetric patterns which is discussed with respect to behavioral data. Along the steps of the discussed literature, we propose avenues for future research.
Topics: Animals; Behavior, Animal; Brain; Learning; Odorants; Smell
PubMed: 34596756
DOI: 10.1007/s00429-021-02390-w -
Journal of Insect Physiology 2020Pheromones are chemical communication signals known to elicit stereotyped behaviours and/or physiological processes in individuals of the same species, generally in...
Pheromones are chemical communication signals known to elicit stereotyped behaviours and/or physiological processes in individuals of the same species, generally in relation to a specific function (e.g. mate finding in moths). However, recent research suggests that pheromones can modulate behaviours, which are not directly related to their usual function and thus potentially affect behavioural plasticity. To test this hypothesis, we studied the possible modulatory effects of pheromones on olfactory learning and memory in Agrotis ipsilon moths, which are well-established models to study sex-pheromones. To achieve this, sexually mature male moths were trained to associate an odour with either a reward (appetitive learning) or punishment (aversive learning) and olfactory memory was tested at medium- and long-term (1 h or 1.5 h, and 24 h). Our results show that male moths can learn to associate an odour with a sucrose reward, as well as a mild electric shock, and that olfactory memory persists over medium- and long-term range. Pheromones facilitated both appetitive and aversive olfactory learning: exposure to the conspecific sex-pheromone before conditioning enhanced appetitive but not aversive learning, while exposure to a sex-pheromone component of a heterospecific species (repellent) facilitated aversive but not appetitive learning. However, this effect was short-term, as medium- and long-term memory were not improved. Thus, in moths, pheromones can modulate olfactory learning and memory, indicating that they contribute to behavioural plasticity allowing optimization of the animal's behaviour under natural conditions. This might occur through an alteration of sensitization.
Topics: Animals; Appetitive Behavior; Learning; Male; Memory; Moths; Punishment; Sex Attractants; Smell
PubMed: 33127358
DOI: 10.1016/j.jinsphys.2020.104159 -
Trends in Parasitology Mar 2022Female mosquitoes use chemical and physical cues, including vision, smell, heat, and humidity, to orient toward hosts. Body odors are produced by skin resident bacteria... (Review)
Review
Female mosquitoes use chemical and physical cues, including vision, smell, heat, and humidity, to orient toward hosts. Body odors are produced by skin resident bacteria that convert metabolites secreted in sweat into odorants that confer the characteristic body scent. Mosquitoes detect these compounds using olfactory receptors in their antennal olfactory receptor neurons. Such information is further integrated with the senses of temperature and humidity, as well as vision, processed in the brain into a behavioral output, leading to host finding. Knowledge of human scent components unveils a variety of odorants that are attractive to mosquitoes, but also odor-triggering repellency. Finding ways to divert human-seeking behavior by female mosquitoes using odorants can possibly mitigate mosquito-borne pathogen transmission.
Topics: Animals; Cues; Culicidae; Female; Host-Seeking Behavior; Humans; Odorants; Smell
PubMed: 34674963
DOI: 10.1016/j.pt.2021.09.012 -
The Journal of Neuroscience : the... May 2020Aminergic signaling modulates associative learning and memory. Substantial advance has been made in on the dopamine receptors and circuits mediating olfactory learning;...
Aminergic signaling modulates associative learning and memory. Substantial advance has been made in on the dopamine receptors and circuits mediating olfactory learning; however, our knowledge of other aminergic modulation lags behind. To address this knowledge gap, we investigated the role of octopamine in olfactory conditioning. Here, we report that octopamine activity through the β-adrenergic-like receptor Octβ1R drives aversive and appetitive learning: Octβ1R in the mushroom body αβ neurons processes aversive learning, whereas Octβ1R in the projection neurons mediates appetitive learning. Our genetic interaction and imaging studies pinpoint cAMP signaling as a key downstream effector for Octβ1R function. The -adenylyl cyclase synthesizes cAMP in a Ca/calmodulin-dependent manner, serving as a coincidence detector for associative learning and likely representing a downstream target for Octβ1R. Supporting this notion, the double heterozygous /+;β/+ flies perform poorly in both aversive and appetitive conditioning, while individual heterozygous /+ and β/+ flies behave like the wild-type control. Consistently, the mushroom body and projection neurons in the β brain exhibit blunted responses to octopamine when cAMP levels are monitored through the cAMP sensor. We previously demonstrated the pivotal functions of the D receptor dDA1 in aversive and appetitive learning, and the α1 adrenergic-like receptor OAMB in appetitive learning. As expected, β genetically interacts with (dDA1 mutant) in aversive and appetitive learning, but it interacts with only in appetitive learning. This study uncovers the indispensable contributions of dopamine and octopamine signaling to aversive and appetitive learning. All experiments were performed on mixed sex unless otherwise noted. Animals make flexible behavioral choices that are constantly shaped by experience. This plasticity is vital for animals to appropriately respond to the cues predicting benefit or harm. In , dopamine is known to mediate both reward-based and punishment-based learning while octopamine function is important only for reward. Here, we demonstrate that the octopamine-Octβ1R-cAMP pathway processes both aversive and appetitive learning in distinct neural sites of the olfactory circuit. Furthermore, we show that the octopamine-Octβ1R and dopamine-dDA1 signals together drive both aversive and appetitive learning, whereas the octopamine-Octβ1R and octopamine-OAMB pathways jointly facilitate appetitive, but not aversive, learning. This study identifies the cognate actions of octopamine and dopamine signaling as a key neural mechanism for associative learning.
Topics: Animals; Animals, Genetically Modified; Association Learning; Behavior, Animal; Dopamine; Drosophila melanogaster; Mushroom Bodies; Neurons; Octopamine; Receptors, Biogenic Amine; Receptors, Dopamine; Signal Transduction; Smell
PubMed: 32277043
DOI: 10.1523/JNEUROSCI.1756-19.2020 -
ELife Sep 2022Learning and memory storage is a complex process that has proven challenging to tackle. It is likely that, in nature, the instructive value of reinforcing experiences is...
Learning and memory storage is a complex process that has proven challenging to tackle. It is likely that, in nature, the instructive value of reinforcing experiences is acquired rather than innate. The association between seemingly neutral stimuli increases the gamut of possibilities to create meaningful associations and the predictive power of moment-by-moment experiences. Here, we report physiological and behavioral evidence of olfactory unimodal sensory preconditioning in fruit flies. We show that the presentation of a pair of odors (S1 and S2) before one of them (S1) is associated with electric shocks elicits a conditional response not only to the trained odor (S1) but to the odor previously paired with it (S2). This occurs even if the S2 odor was never presented in contiguity with the aversive stimulus. In addition, we show that inhibition of the small G protein , a known forgetting regulator, facilitates the association between S1/S2 odors. These results indicate that flies can infer value to olfactory stimuli based on the previous associative structure between odors, and that inhibition of lengthens the time window of the olfactory 'sensory buffer', allowing the establishment of associations between odors presented in sequence.
Topics: Animals; Conditioning, Classical; Drosophila; Drosophila melanogaster; Monomeric GTP-Binding Proteins; Odorants; Smell
PubMed: 36129180
DOI: 10.7554/eLife.79107 -
Advanced Science (Weinheim,... Jun 2024Portable and personalized artificial intelligence (AI)-driven sensors mimicking human olfactory and gustatory systems have immense potential for the large-scale...
Portable and personalized artificial intelligence (AI)-driven sensors mimicking human olfactory and gustatory systems have immense potential for the large-scale deployment and autonomous monitoring systems of Internet of Things (IoT) devices. In this study, an artificial Q-grader comprising surface-engineered zinc oxide (ZnO) thin films is developed as the artificial nose, tongue, and AI-based statistical data analysis as the artificial brain for identifying both aroma and flavor chemicals in coffee beans. A poly(vinylidene fluoride-co-hexafluoropropylene)/ZnO thin film transistor (TFT)-based liquid sensor is the artificial tongue, and an Au, Ag, or Pd nanoparticles/ZnO nanohybrid gas sensor is the artificial nose. In order to classify the flavor of coffee beans (acetic acid (sourness), ethyl butyrate and 2-furanmethanol (sweetness), caffeine (bitterness)) and the origin of coffee beans (Papua New Guinea, Brazil, Ethiopia, and Colombia-decaffeine), rational combination of TFT transfer and dynamic response curves capture the liquids and gases-dependent electrical transport behavior and principal component analysis (PCA)-assisted machine learning (ML) is implemented. A PCA-assisted ML model distinguished the four target flavors with >92% prediction accuracy. ML-based regression model predicts the flavor chemical concentrations with >99% accuracy. Also, the classification model successfully distinguished four different types of coffee-bean with 100% accuracy.
Topics: Machine Learning; Electronic Nose; Humans; Artificial Intelligence; Taste; Coffee; Odorants; Smell; Tongue; Zinc Oxide; Principal Component Analysis
PubMed: 38582529
DOI: 10.1002/advs.202308976 -
Proceedings of the National Academy of... Aug 2023Long-range olfactory search is an extremely difficult task in view of the sparsity of odor signals that are available to the searcher and the complex encoding of the...
Long-range olfactory search is an extremely difficult task in view of the sparsity of odor signals that are available to the searcher and the complex encoding of the information about the source location. Current algorithmic approaches typically require a continuous memory space, sometimes of large dimensionality, which may hamper their optimization and often obscure their interpretation. Here, we show how finite-state controllers with a small set of discrete memory states are expressive enough to display rich, time-extended behavioral modules that resemble the ones observed in living organisms. Finite-state controllers optimized for olfactory search have an immediate interpretation in terms of approximate clocks and coarse-grained spatial maps, suggesting connections with neural models of search behavior.
Topics: Smell; Odorants
PubMed: 37579168
DOI: 10.1073/pnas.2304230120 -
Journal of Comparative Physiology. A,... Sep 2021This study was designed to test whether Cynopterus sphinx distress calls influence olfactory learning and memory in conspecifics. Bats were exposed to distress...
This study was designed to test whether Cynopterus sphinx distress calls influence olfactory learning and memory in conspecifics. Bats were exposed to distress calls/playbacks (PBs) of distress calls/modified calls and were then trained to novel odors. Bats exposed to distress calls/PBs made significantly fewer feeding attempts and bouts of PBs exposed to modified calls, which significantly induced the expression of c-Fos in the caudomedial neostriatum (NCM) and the amygdala compared to bats exposed to modified calls and trained controls. However, the expression of c-Fos in the hippocampus was not significantly different between the experimental groups. Further, protein phosphatase-1 (PP-1) expression was significantly lower, and the expression levels of E1A homologue of CREB-binding protein (CBP) (P300), brain-derived neurotrophic factor (BDNF) and its tyrosine kinase B1 (TrkB1) receptor were significantly higher in the hippocampus of control/bats exposed to modified calls compared to distress calls/PBs of distress call-exposed bats. Exposure to the call possibly alters the reciprocal interaction between the amygdala and the hippocampus, accordingly regulating the expression levels of PP1, P300 and BDNF and its receptor TrkB1 following training to the novel odor. Thus, the learning and memory consolidation processes were disrupted and showed fewer feeding attempts and bouts. This model may be helpful for understanding the contributions of stressful social communications to human disorders.
Topics: Amygdala; Animal Communication; Animals; Brain-Derived Neurotrophic Factor; CREB-Binding Protein; Chiroptera; Gene Expression; Genes, fos; Hippocampus; Learning; Male; Memory; Neostriatum; Odorants; Protein Phosphatase 1; Receptor, trkB; Smell
PubMed: 34426872
DOI: 10.1007/s00359-021-01505-2 -
Scientific Reports Aug 2023There is growing interest in canine behavioral research specifically for working dogs. Here we take advantage of a dataset of a Transportation Safety Administration...
There is growing interest in canine behavioral research specifically for working dogs. Here we take advantage of a dataset of a Transportation Safety Administration olfactory detection cohort of 628 Labrador Retrievers to perform Machine Learning (ML) prediction and classification studies of behavioral traits and environmental effects. Data were available for four time points over a 12 month foster period after which dogs were accepted into a training program or eliminated. Three supervised ML algorithms had robust performance in correctly predicting which dogs would be accepted into the training program, but poor performance in distinguishing those that were eliminated (~ 25% of the cohort). The 12 month testing time point yielded the best ability to distinguish accepted and eliminated dogs (AUC = 0.68). Classification studies using Principal Components Analysis and Recursive Feature Elimination using Cross-Validation revealed the importance of olfaction and possession-related traits for an airport terminal search and retrieve test, and possession, confidence, and initiative traits for an environmental test. Our findings suggest which tests, environments, behavioral traits, and time course are most important for olfactory detection dog selection. We discuss how this approach can guide further research that encompasses cognitive and emotional, and social and environmental effects.
Topics: Dogs; Animals; Smell; Machine Learning; Supervised Machine Learning; Algorithms; Mental Processes
PubMed: 37528118
DOI: 10.1038/s41598-023-39112-7