1
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Hirota J. Molecular mechanisms of differentiation and class choice of olfactory sensory neurons. Genesis 2024; 62:e23587. [PMID: 38454646 DOI: 10.1002/dvg.23587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/10/2024] [Accepted: 01/29/2024] [Indexed: 03/09/2024]
Abstract
The sense of smell is intricately linked to essential animal behaviors necessary for individual survival and species preservation. During vertebrate evolution, odorant receptors (ORs), responsible for detecting odor molecules, have evolved to adapt to changing environments, transitioning from aquatic to terrestrial habitats and accommodating increasing complex chemical environments. These evolutionary pressures have given rise to the largest gene family in vertebrate genomes. Vertebrate ORs are phylogenetically divided into two major classes; class I and class II. Class I OR genes, initially identified in fish and frog, have persisted across vertebrate species. On the other hand, class II OR genes are unique to terrestrial animals, accounting for ~90% of mammalian OR genes. In mice, each olfactory sensory neuron (OSN) expresses a single functional allele of a single OR gene from either the class I or class II OR repertoire. This one neuron-one receptor rule is established through two sequential steps: specification of OR class and subsequent exclusive OR expression from the corresponding OR class. Consequently, OSNs acquire diverse neuronal identities during the process of OSN differentiation, enabling animals to detect a wide array of odor molecules. This review provides an overview of the OSN differentiation process through which OSN diversity is achieved, primarily using the mouse as a model animal.
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Affiliation(s)
- Junji Hirota
- Department of Life Science and Technology, Graduate School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
- Center for Integrative Biosciences, Tokyo Institute of Technology, Yokohama, Japan
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2
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Schreurs M, Piampongsant S, Roncoroni M, Cool L, Herrera-Malaver B, Vanderaa C, Theßeling FA, Kreft Ł, Botzki A, Malcorps P, Daenen L, Wenseleers T, Verstrepen KJ. Predicting and improving complex beer flavor through machine learning. Nat Commun 2024; 15:2368. [PMID: 38531860 DOI: 10.1038/s41467-024-46346-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/21/2024] [Indexed: 03/28/2024] Open
Abstract
The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.
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Affiliation(s)
- Michiel Schreurs
- VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium
| | - Supinya Piampongsant
- VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium
| | - Miguel Roncoroni
- VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium
| | - Lloyd Cool
- VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium
- Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium
| | - Beatriz Herrera-Malaver
- VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium
| | - Christophe Vanderaa
- Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium
| | - Florian A Theßeling
- VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium
- Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium
| | - Łukasz Kreft
- VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium
| | - Alexander Botzki
- VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium
| | | | - Luk Daenen
- AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium
| | - Tom Wenseleers
- Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium
| | - Kevin J Verstrepen
- VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium.
- CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium.
- Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium.
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3
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Zavatone-Veth JA, Masset P, Tong WL, Zak JD, Murthy VN, Pehlevan C. Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.21.545947. [PMID: 37961548 PMCID: PMC10634677 DOI: 10.1101/2023.06.21.545947] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
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.
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Affiliation(s)
- Jacob A Zavatone-Veth
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Physics, Harvard University Cambridge, MA 02138
| | - Paul Masset
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - William L Tong
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
| | - Joseph D Zak
- Department of Biological Sciences, University of Illinois at Chicago Chicago, IL 60607
| | - Venkatesh N Murthy
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - Cengiz Pehlevan
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
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4
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Lee BK, Mayhew EJ, Sanchez-Lengeling B, Wei JN, Qian WW, Little KA, Andres M, Nguyen BB, Moloy T, Yasonik J, Parker JK, Gerkin RC, Mainland JD, Wiltschko AB. A principal odor map unifies diverse tasks in olfactory perception. Science 2023; 381:999-1006. [PMID: 37651511 DOI: 10.1126/science.ade4401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 08/03/2023] [Indexed: 09/02/2023]
Abstract
Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.
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Affiliation(s)
- Brian K Lee
- Google Research, Brain Team, Cambridge, MA, USA
| | - Emily J Mayhew
- Monell Chemical Senses Center, Philadelphia, PA, USA
- Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA
| | | | | | - Wesley W Qian
- Google Research, Brain Team, Cambridge, MA, USA
- Osmo Labs, PBC, Cambridge, MA, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | | | | | | | - Theresa Moloy
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jacob Yasonik
- Google Research, Brain Team, Cambridge, MA, USA
- Osmo Labs, PBC, Cambridge, MA, USA
| | - Jane K Parker
- Department of Food and Nutritional Sciences, University of Reading, Reading, Berkshire, UK
| | - Richard C Gerkin
- Google Research, Brain Team, Cambridge, MA, USA
- Osmo Labs, PBC, Cambridge, MA, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Joel D Mainland
- Monell Chemical Senses Center, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
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5
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Margulis E, Lang T, Tromelin A, Ziaikin E, Behrens M, Niv MY. Bitter Odorants and Odorous Bitters: Toxicity and Human TAS2R Targets. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37263600 DOI: 10.1021/acs.jafc.3c00592] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Flavor is perceived through the olfactory, taste, and trigeminal systems, mediated by designated GPCRs and channels. Signal integration occurs mainly in the brain, but some cross-reactivities occur at the receptor level. Here, we predict potential bitterness and taste receptors targets for thousands of odorants. BitterPredict and BitterIntense classifiers suggest that 3-9% of flavor and food odorants have bitter taste, but almost none are intensely bitter. About 14% of bitter molecules are expected to have an odor. Bitterness is more common for unpleasant smells such as fishy, amine, and ammoniacal, while non-bitter odorants often have pleasant smells. Experimental toxicity values suggest that fishy ammoniac smells are more toxic than pleasant smells, regardless of bitterness. TAS2R14 is predicted as the main bitter receptor for odorants, confirmed by in vitro profiling of 10 odorants. The activity of bitter odorants may have implications for physiology due to ectopic expression of taste and smell receptors.
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Affiliation(s)
- Eitan Margulis
- Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| | - Tatjana Lang
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Anne Tromelin
- Centre des Sciences du Goût et de l'Alimentation, CNRS, INRAE, Institut Agro, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Evgenii Ziaikin
- Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
| | - Maik Behrens
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Masha Y Niv
- Food Science and Nutrition, The Robert H Smith Faculty of Agriculture, Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel
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6
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Schicker D, Singh S, Freiherr J, Grasskamp AT. OWSum: algorithmic odor prediction and insight into structure-odor relationships. J Cheminform 2023; 15:51. [PMID: 37150811 PMCID: PMC10164323 DOI: 10.1186/s13321-023-00722-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
We derived and implemented a linear classification algorithm for the prediction of a molecule's odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.
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Affiliation(s)
- Doris Schicker
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany.
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany.
| | - Satnam Singh
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Jessica Freiherr
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Andreas T Grasskamp
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany.
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7
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Qian WW, Wei JN, Sanchez-Lengeling B, Lee BK, Luo Y, Vlot M, Dechering K, Peng J, Gerkin RC, Wiltschko AB. Metabolic activity organizes olfactory representations. eLife 2023; 12:e82502. [PMID: 37129358 PMCID: PMC10154027 DOI: 10.7554/elife.82502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 04/11/2023] [Indexed: 05/03/2023] Open
Abstract
Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model (Lee et al., 2022) for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience. Using this olfactory representation (principal odor map [POM]), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences (Caspi et al., 2014) also follow smooth paths in POM despite large jumps in molecular structure. Just as the brain's visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brain's representation of the olfactory world.
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Affiliation(s)
- Wesley W Qian
- OsmoCambridgeUnited States
- Google Research, Brain TeamCambridgeUnited States
| | | | | | - Brian K Lee
- Google Research, Brain TeamCambridgeUnited States
| | - Yunan Luo
- Department of Computer Science, University of IllinoisUrbanaUnited States
| | | | | | - Jian Peng
- Department of Computer Science, University of IllinoisUrbanaUnited States
| | - Richard C Gerkin
- OsmoCambridgeUnited States
- Google Research, Brain TeamCambridgeUnited States
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8
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Billesbølle CB, de March CA, van der Velden WJC, Ma N, Tewari J, Del Torrent CL, Li L, Faust B, Vaidehi N, Matsunami H, Manglik A. Structural basis of odorant recognition by a human odorant receptor. Nature 2023; 615:742-749. [PMID: 36922591 PMCID: PMC10580732 DOI: 10.1038/s41586-023-05798-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 02/06/2023] [Indexed: 03/17/2023]
Abstract
Our sense of smell enables us to navigate a vast space of chemically diverse odour molecules. This task is accomplished by the combinatorial activation of approximately 400 odorant G protein-coupled receptors encoded in the human genome1-3. How odorants are recognized by odorant receptors remains unclear. Here we provide mechanistic insight into how an odorant binds to a human odorant receptor. Using cryo-electron microscopy, we determined the structure of the active human odorant receptor OR51E2 bound to the fatty acid propionate. Propionate is bound within an occluded pocket in OR51E2 and makes specific contacts critical to receptor activation. Mutation of the odorant-binding pocket in OR51E2 alters the recognition spectrum for fatty acids of varying chain length, suggesting that odorant selectivity is controlled by tight packing interactions between an odorant and an odorant receptor. Molecular dynamics simulations demonstrate that propionate-induced conformational changes in extracellular loop 3 activate OR51E2. Together, our studies provide a high-resolution view of chemical recognition of an odorant by a vertebrate odorant receptor, providing insight into how this large family of G protein-coupled receptors enables our olfactory sense.
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Affiliation(s)
| | - Claire A de March
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA
- Institut de Chimie des Substances Naturelles, UPR2301 CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Wijnand J C van der Velden
- Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Ning Ma
- Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Jeevan Tewari
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA
| | - Claudia Llinas Del Torrent
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
- Laboratory of Computational Medicine, Biostatistics Unit, Faculty of Medicine, Universitat Autònoma Barcelona, Bellaterra, Barcelona, Spain
| | - Linus Li
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | - Bryan Faust
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | - Nagarajan Vaidehi
- Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, Duarte, CA, USA.
| | - Hiroaki Matsunami
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA.
- Department of Neurobiology, Duke Institute for Brain Sciences, Duke University, Durham, NC, USA.
| | - Aashish Manglik
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA.
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, USA.
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9
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Tesileanu T, Piasini E, Balasubramanian V. Efficient processing of natural scenes in visual cortex. Front Cell Neurosci 2022; 16:1006703. [PMID: 36545653 PMCID: PMC9760692 DOI: 10.3389/fncel.2022.1006703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.
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Affiliation(s)
- Tiberiu Tesileanu
- Center for Computational Neuroscience, Flatiron Institute, New York, NY, United States,*Correspondence: Tiberiu Tesileanu
| | - Eugenio Piasini
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy,Eugenio Piasini
| | - Vijay Balasubramanian
- Department of Physics and Astronomy, David Rittenhouse Laboratory, University of Pennsylvania, Philadelphia, PA, United States,Santa Fe Institute, Santa Fe, NM, United States
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10
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Klyuchnikova MA, Kvasha IG, Laktionova TK, Voznessenskaya VV. Olfactory perception of 5α-androst-16-en-3-one: Data obtained in the residents of central Russia. Data Brief 2022; 45:108704. [DOI: 10.1016/j.dib.2022.108704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/14/2022] [Accepted: 10/21/2022] [Indexed: 11/05/2022] Open
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11
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Barwich AS, Lloyd EA. More than meets the AI: The possibilities and limits of machine learning in olfaction. Front Neurosci 2022; 16:981294. [PMID: 36117640 PMCID: PMC9475214 DOI: 10.3389/fnins.2022.981294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system's biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd's "Logic of Research Questions," a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question.
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Affiliation(s)
- Ann-Sophie Barwich
- Department of History and Philosophy of Science and Medicine, College of Arts and Sciences, Indiana University Bloomington, Bloomington, IN, United States
- Cognitive Science Program, College of Arts and Sciences, Indiana University, Bloomington, IN, United States
| | - Elisabeth A. Lloyd
- Department of History and Philosophy of Science and Medicine, College of Arts and Sciences, Indiana University Bloomington, Bloomington, IN, United States
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12
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Burton SD, Brown A, Eiting TP, Youngstrom IA, Rust TC, Schmuker M, Wachowiak M. Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb. eLife 2022; 11:80470. [PMID: 35861321 PMCID: PMC9352350 DOI: 10.7554/elife.80470] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022] Open
Abstract
In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better characterize this relationship in the mouse olfactory system by defining glomeruli in terms of the odorants to which they are most sensitive. Using high-throughput odorant delivery and ultrasensitive imaging of sensory inputs, we imaged responses to 185 odorants presented at concentrations determined to activate only one or a few glomeruli across the dorsal olfactory bulb. The resulting datasets defined the tuning properties of glomeruli - and, by inference, their cognate odorant receptors - in a low-concentration regime, and yielded consensus maps of glomerular sensitivity across a wide range of chemical space. Glomeruli were extremely narrowly tuned, with ~25% responding to only one odorant, and extremely sensitive, responding to their effective odorants at sub-picomolar to nanomolar concentrations. Such narrow tuning in this concentration regime allowed for reliable functional identification of many glomeruli based on a single diagnostic odorant. At the same time, the response spectra of glomeruli responding to multiple odorants was best predicted by straightforward odorant structural features, and glomeruli sensitive to distinct odorants with common structural features were spatially clustered. These results define an underlying structure to the primary representation of sensory space by the mouse olfactory system.
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Affiliation(s)
- Shawn D Burton
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Audrey Brown
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Thomas P Eiting
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Isaac A Youngstrom
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Thomas C Rust
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Michael Schmuker
- Biocomputation Group, Centre of Data Innovation Research, Department of Computer Science, University of HertfordshireHertfordshireUnited Kingdom
| | - Matt Wachowiak
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
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