1
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Belonio KC, Haile ES, Fyke Z, Vivona L, Konanur VR, Tulabandhula T, Zak JD. Amplification of olfactory transduction currents implements sparse stimulus encoding. J Neurosci 2025; 45:e2008242025. [PMID: 40097179 PMCID: PMC12044040 DOI: 10.1523/jneurosci.2008-24.2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Revised: 03/08/2025] [Accepted: 03/11/2025] [Indexed: 03/19/2025] Open
Abstract
Sensory systems must perform the dual and opposing tasks of being sensitive to weak stimuli while also maintaining information content in dense and variable sensory landscapes. This occurs in the olfactory system, where OSNs are highly sensitive to low concentrations of odors and maintain discriminability in complex odor environments. How olfactory sensory neurons (OSNs) maintain both sensitivity and sparsity is poorly understood. Here, we investigated whether the calcium-activated chloride channel, TMEM16B, may support these dual roles in OSNs in both male and female mice. We used multiphoton microscopy to image the stimulus-response density of OSNs in the olfactory epithelium. In TMEM16B knockout mice, we found that sensory representations were denser, and the magnitude of OSN responses was increased. Behaviorally, these changes in sensory representations were associated with an increased aversion to the odorant trimethylamine, which switches perceptual valence as its concentration increases, and a decreased efficiency of olfactory-guided navigation. Our results indicate that the calcium-activated chloride channel TMEM16B sparsens sensory representations in the peripheral olfactory system and contributes to efficient integrative olfactory-guided behaviors.Significance Statement Sensory systems must build internal neural representations of stimuli found in the external environment. In the olfactory system, molecules that give rise to the perception of odors are detected by olfactory sensory neurons within the nose. Upon odorant binding to sensory neurons, a biochemical signaling cascade transduces neural signals that other areas of the brain can then read out. A key component of this cascade is the calcium-activated chloride channel TMEM16B. We found that despite its role in amplifying transduction currents in olfactory sensory neurons, TMEM16B paradoxically constrains their output, thereby limiting information transfer to the brain. Our findings also indicate that TMEM16B plays an important role in how animals detect and perceive odors.
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Affiliation(s)
- Kai Clane Belonio
- Department of Biological Sciences, University of Illinois Chicago, Chicago, Illinois 60607
| | - Eyerusalem S. Haile
- Graduate Program in Biological Sciences, University of Illinois Chicago, Chicago, Illinois 60607
| | - Zach Fyke
- Graduate Program in Neuroscience, University of Illinois Chicago, Chicago, Illinois 60607
| | - Lindsay Vivona
- Graduate Program in Biological Sciences, University of Illinois Chicago, Chicago, Illinois 60607
| | - Vaibhav R. Konanur
- Department of Biological Sciences, University of Illinois Chicago, Chicago, Illinois 60607
| | - Theja Tulabandhula
- Departments of Information and Decision Sciences, University of Illinois Chicago, Chicago, Illinois 60607
| | - Joseph D. Zak
- Department of Biological Sciences, University of Illinois Chicago, Chicago, Illinois 60607
- Psychology, University of Illinois Chicago, Chicago, Illinois 60607
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2
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Wachowiak M, Dewan A, Bozza T, O'Connell TF, Hong EJ. Recalibrating Olfactory Neuroscience to the Range of Naturally Occurring Odor Concentrations. J Neurosci 2025; 45:e1872242024. [PMID: 40044450 PMCID: PMC11884396 DOI: 10.1523/jneurosci.1872-24.2024] [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: 07/25/2024] [Revised: 11/15/2024] [Accepted: 11/25/2024] [Indexed: 03/09/2025] Open
Abstract
Sensory systems enable organisms to detect and respond to environmental signals relevant for their survival and reproduction. A crucial aspect of any sensory signal is its intensity; understanding how sensory signals guide behavior requires probing sensory system function across the range of stimulus intensities naturally experienced by an organism. In olfaction, defining the range of natural odorant concentrations is difficult. Odors are complex mixtures of airborne chemicals emitting from a source in an irregular pattern that varies across time and space, necessitating specialized methods to obtain an accurate measurement of concentration. Perhaps as a result, experimentalists often choose stimulus concentrations based on empirical considerations rather than with respect to ecological or behavioral context. Here, we attempt to determine naturally relevant concentration ranges for olfactory stimuli by reviewing and integrating data from diverse disciplines. We compare odorant concentrations used in experimental studies in rodents and insects with those reported in different settings including ambient natural environments, the headspace of natural sources, and within the sources themselves. We also compare these values to psychophysical measurements of odorant detection threshold in rodents, where thresholds have been extensively measured. Odorant concentrations in natural regimes rarely exceed a few parts per billion, while most experimental studies investigating olfactory coding and behavior exceed these concentrations by several orders of magnitude. We discuss the implications of this mismatch and the importance of testing odorants in their natural concentration range for understanding neural mechanisms underlying olfactory sensation and odor-guided behaviors.
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Affiliation(s)
- Matt Wachowiak
- Department of Neurobiology, University of Utah School of Medicine, Salt Lake City, Utah 84112
| | - Adam Dewan
- Department of Psychology and Program in Neuroscience, Florida State University, Tallahassee, Florida 32306
| | - Thomas Bozza
- Department of Neurobiology, Northwestern University, Evanston, Illinois 60208
| | - Tom F O'Connell
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, California 91125
| | - Elizabeth J Hong
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, California 91125
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3
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Patel H, Garrido Portilla V, Shneidman AV, Movilli J, Alvarenga J, Dupré C, Aizenberg M, Murthy VN, Tropsha A, Aizenberg J. Design Principles From Natural Olfaction for Electronic Noses. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2412669. [PMID: 39835449 PMCID: PMC11948017 DOI: 10.1002/advs.202412669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2024] [Revised: 11/29/2024] [Indexed: 01/22/2025]
Abstract
Natural olfactory systems possess remarkable sensitivity and precision beyond what is currently achievable by engineered gas sensors. Unlike their artificial counterparts, noses are capable of distinguishing scents associated with mixtures of volatile molecules in complex, typically fluctuating environments and can adapt to changes. This perspective examines the multifaceted biological principles that provide olfactory systems their discriminatory prowess, and how these ideas can be ported to the design of electronic noses for substantial improvements in performance across metrics such as sensitivity and ability to speciate chemical mixtures. The topics examined herein include the fluid dynamics of odorants in natural channels; specificity and kinetics of odorant interactions with olfactory receptors and mucus linings; complex signal processing that spatiotemporally encodes physicochemical properties of odorants; active sampling techniques, like biological sniffing and nose repositioning; biological priming; and molecular chaperoning. Each of these components of natural olfactory systems are systmatically investigated, as to how they have been or can be applied to electronic noses. While not all artificial sensors can employ these strategies simultaneously, integrating a subset of bioinspired principles can address issues like sensitivity, drift, and poor selectivity, offering advancements in many sectors such as environmental monitoring, industrial safety, and disease diagnostics.
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Affiliation(s)
- Haritosh Patel
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityBostonMA02134USA
| | - Vicente Garrido Portilla
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityBostonMA02134USA
| | - Anna V. Shneidman
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityBostonMA02134USA
| | - Jacopo Movilli
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityBostonMA02134USA
- Department of Chemical SciencesUniversity of PadovaPadova35131Italy
| | - Jack Alvarenga
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityBostonMA02134USA
| | - Christophe Dupré
- Department of Molecular & Cellular BiologyHarvard UniversityCambridgeMA02138USA
| | - Michael Aizenberg
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityBostonMA02134USA
| | - Venkatesh N. Murthy
- Department of Molecular & Cellular BiologyHarvard UniversityCambridgeMA02138USA
- Center for Brain ScienceHarvard UniversityCambridgeMA02138USA
- Kempner InstituteHarvard UniversityBostonMA02134USA
| | - Alexander Tropsha
- Department of ChemistryThe University of North Carolina at Chapel HillChapel HillNC27516USA
| | - Joanna Aizenberg
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityBostonMA02134USA
- Department of Chemistry and Chemical BiologyHarvard UniversityCambridgeMA02138USA
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4
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Drnovsek E, Weitkamp K, Murthy VN, Gurbuz E, Haehner A, Hummel T. Detection of odorants in odour mixtures among healthy people and patients with olfactory dysfunction. Eur J Neurosci 2025; 61:e16633. [PMID: 39803925 PMCID: PMC11727005 DOI: 10.1111/ejn.16633] [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: 01/18/2024] [Accepted: 11/18/2024] [Indexed: 01/16/2025]
Abstract
Target odorant detection in mixtures has been shown to become more difficult as the number of background odorants increases and falls below chance level in mixtures with 16 components. Our aim was to investigate target odorant detection in mixtures among healthy people and compare it between dysosmic patients and age- and gender-matched controls. Participants underwent extensive olfactory testing and performed two target odorant detection tasks. Eugenol ('clove') and phenylethanol (PEA, 'rose') were target odorants for all participants, whereas a third target was randomised. For each target odorant in task one (task two), there were four steps. Mixtures contained two (three) odorants in the first step and up to seven (eight) odorants in the fourth step. In each step, participants were asked to choose the sample with the target odorant from the three (two) jars presented. The study included 90 healthy people and 40 patients. As expected, probability of successful target odorant detection decreased as the number of odorants in the mixture increased. However, even when there were seven (eight) odorants in the mixture, around 50% (50%) of healthy people detected Eugenol and around 30% (40%) detected PEA. Furthermore, both distributions of successful target odorant detection differed from the expected binominal distribution of chance (p < 0.001). Patients performed worse at detecting Eugenol or PEA at each step than controls. Furthermore, there were significant positive correlations between task scores and olfactory function. In conclusion, target odorant detection is influenced by the target odorant, number of background odorants, and individual olfactory function.
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Affiliation(s)
- Eva Drnovsek
- Smell and Taste Clinic, Department of OtorhinolaryngologyTechnische Universität DresdenDresdenGermany
| | - Kristina Weitkamp
- Smell and Taste Clinic, Department of OtorhinolaryngologyTechnische Universität DresdenDresdenGermany
| | - Venkatesh N. Murthy
- Center for Brain ScienceHarvard UniversityCambridgeMAUSA
- Department of Molecular & Cellular BiologyHarvard UniversityCambridgeMassachusettsUSA
| | - Edanur Gurbuz
- Smell and Taste Clinic, Department of OtorhinolaryngologyTechnische Universität DresdenDresdenGermany
- Faculty of MedicineMugla Sitki Kocman UniversityMuglaTurkey
| | - Antje Haehner
- Smell and Taste Clinic, Department of OtorhinolaryngologyTechnische Universität DresdenDresdenGermany
| | - Thomas Hummel
- Smell and Taste Clinic, Department of OtorhinolaryngologyTechnische Universität DresdenDresdenGermany
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5
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Belonio KC, Haile ES, Fyke Z, Vivona L, Konanur V, Zak JD. Amplification of olfactory transduction currents implements sparse stimulus encoding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.11.617893. [PMID: 39416025 PMCID: PMC11482904 DOI: 10.1101/2024.10.11.617893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Sensory systems must perform the dual and opposing tasks of being sensitive to weak stimuli while also maintaining information content in dense and variable sensory landscapes. This occurs in the olfactory system, where OSNs are highly sensitive to low concentrations of odors and maintain discriminability in complex odor environments. How olfactory sensory neurons (OSNs) maintain both sensitivity and sparsity is not well understood. Here, we investigated whether the calcium-activated chloride channel, TMEM16B, may support these dual roles in OSNs. We used multiphoton microscopy to image the stimulus-response density of OSNs in the olfactory epithelium. In TMEM16B knockout mice, we found that sensory representations were denser, and the magnitude of OSN responses was increased. Behaviorally, these changes in sensory representations were associated with an increased aversion to the odorant trimethylamine, which switches perceptual valence as its concentration increases, and a decreased efficiency of olfactory-guided navigation. Together, our results indicate that the calcium-activated chloride channel TMEM16B sparsens sensory representations in the peripheral olfactory system and contributes to efficient integrative olfactory-guided behaviors.
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Affiliation(s)
- Kai Clane Belonio
- Department of Biological Sciences, University of Illinois Chicago, 60607
| | - Eyerusalem S. Haile
- Graduate Program in Biological Sciences, University of Illinois Chicago, 60607
| | - Zach Fyke
- Graduate Program in Neuroscience, University of Illinois Chicago, 60607
| | - Lindsay Vivona
- Graduate Program in Biological Sciences, University of Illinois Chicago, 60607
| | - Vaibhav Konanur
- Department of Biological Sciences, University of Illinois Chicago, 60607
| | - Joseph D. Zak
- Department of Biological Sciences, University of Illinois Chicago, 60607
- Department of Psychology, University of Illinois Chicago, 60607
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6
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Rokni D, Ben-Shaul Y. Object-oriented olfaction: challenges for chemosensation and for chemosensory research. Trends Neurosci 2024; 47:834-848. [PMID: 39245626 DOI: 10.1016/j.tins.2024.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 08/02/2024] [Accepted: 08/16/2024] [Indexed: 09/10/2024]
Abstract
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.
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Affiliation(s)
- Dan Rokni
- Department of Medical Neurobiology, The Hebrew University Faculty of Medicine, Institute for Medical Research, Israel-Canada (IMRIC), Jerusalem, Israel.
| | - Yoram Ben-Shaul
- Department of Medical Neurobiology, The Hebrew University Faculty of Medicine, Institute for Medical Research, Israel-Canada (IMRIC), Jerusalem, Israel.
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7
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Miyamoto K, Stark J, Kathrotia M, Luu A, Victoriano J, Chan CL, Lee D, Root CM. The Orbitofrontal Cortex Is Required for Learned Modulation of Innate Olfactory Behavior. eNeuro 2024; 11:ENEURO.0343-24.2024. [PMID: 39406479 PMCID: PMC11493560 DOI: 10.1523/eneuro.0343-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 09/17/2024] [Accepted: 10/03/2024] [Indexed: 10/23/2024] Open
Abstract
Animals have evolved innate responses to cues including social, food, and predator odors. In the natural environment, animals are faced with choices that involve balancing risk and reward where innate significance may be at odds with internal need. The ability to update the value of a cue through learning is essential for navigating changing and uncertain environments. However, the mechanisms involved in this modulation are not well defined in mammals. We have established a new olfactory assay that challenges a thirsty mouse to choose an aversive odor over an attractive odor in foraging for water, thus overriding their innate behavioral response to odor. Innately, mice prefer the attractive odor port over the aversive odor port. However, decreasing the probability of water at the attractive port leads mice to prefer the aversive port, reflecting a learned override of the innate response to the odors. The orbitofrontal cortex (OFC) is a fourth-order olfactory brain area, involved in flexible value association, with behaviorally relevant outputs throughout the limbic system. We performed optogenetic and chemogenetic silencing experiments that demonstrate the OFC is necessary for this learned modulation of innate aversion to odor. Further, we characterized odor evoked c-fos expression in learned and control mice and found significant suppression of activity in the bed nucleus of the stria terminalis, lateral septum, and central and medial amygdala. These findings reveal that the OFC is necessary for the learned override of innate behavior and may signal to limbic structures to modulate innate response to odor.
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Affiliation(s)
- Kiana Miyamoto
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
| | - Jeremy Stark
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
| | - Mayuri Kathrotia
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
| | - Amanda Luu
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
| | - Joelle Victoriano
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
| | - Chung Lung Chan
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
| | - Donghyung Lee
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
| | - Cory M Root
- Department of Neurobiology, School of Biological Sciences, University of California San Diego, San Diego, California 92093-0357
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8
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Giaffar H, Shuvaev S, Rinberg D, Koulakov AA. The primacy model and the structure of olfactory space. PLoS Comput Biol 2024; 20:e1012379. [PMID: 39255274 PMCID: PMC11423968 DOI: 10.1371/journal.pcbi.1012379] [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: 12/05/2023] [Revised: 09/25/2024] [Accepted: 07/30/2024] [Indexed: 09/12/2024] Open
Abstract
Understanding sensory processing involves relating the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.
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Affiliation(s)
- Hamza Giaffar
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Sergey Shuvaev
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Dmitry Rinberg
- Neuroscience Institute, New York University Langone Health, New York, New York, United States of America
- Center for Neural Science, New York University, New York, New York, United States of America
| | - Alexei A. Koulakov
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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9
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Zak JD, Reddy G, Konanur V, Murthy VN. Distinct information conveyed to the olfactory bulb by feedforward input from the nose and feedback from the cortex. Nat Commun 2024; 15:3268. [PMID: 38627390 PMCID: PMC11021479 DOI: 10.1038/s41467-024-47366-6] [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: 10/19/2023] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
Sensory systems are organized hierarchically, but feedback projections frequently disrupt this order. In the olfactory bulb (OB), cortical feedback projections numerically match sensory inputs. To unravel information carried by these two streams, we imaged the activity of olfactory sensory neurons (OSNs) and cortical axons in the mouse OB using calcium indicators, multiphoton microscopy, and diverse olfactory stimuli. Here, we show that odorant mixtures of increasing complexity evoke progressively denser OSN activity, yet cortical feedback activity is of similar sparsity for all stimuli. Also, representations of complex mixtures are similar in OSNs but are decorrelated in cortical axons. While OSN responses to increasing odorant concentrations exhibit a sigmoidal relationship, cortical axonal responses are complex and nonmonotonic, which can be explained by a model with activity-dependent feedback inhibition in the cortex. Our study indicates that early-stage olfactory circuits have access to local feedforward signals and global, efficiently formatted information about odor scenes through cortical feedback.
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Affiliation(s)
- Joseph D Zak
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, 60607, USA.
- Department of Psychology, University of Illinois Chicago, Chicago, IL, 60607, USA.
| | - Gautam Reddy
- Physics & Informatics Laboratories, NTT Research, Inc., Sunnyvale, CA, 94085, USA
- Department of Physics, Princeton University, Princeton, NJ, 08540, USA
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
| | - Vaibhav Konanur
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, 60607, USA
| | - Venkatesh N Murthy
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, 02134, USA
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10
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Ryndych D, Sebold A, Strassburg A, Li Y, Ramos RL, Otazu GH. Haploinsufficiency of Shank3 in Mice Selectively Impairs Target Odor Recognition in Novel Background Odors. J Neurosci 2023; 43:7799-7811. [PMID: 37739796 PMCID: PMC10648539 DOI: 10.1523/jneurosci.0255-23.2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 07/30/2023] [Accepted: 09/13/2023] [Indexed: 09/24/2023] Open
Abstract
Individuals with mutations in a single copy of the SHANK3 gene present with social interaction deficits. Although social behavior in mice depends on olfaction, mice with mutations in a single copy of the Shank3 gene do not have olfactory deficits in simple odor identification tasks (Drapeau et al., 2018). Here, we tested olfaction in mice with mutations in a single copy of the Shank3 gene (Peça et al., 2011) using a complex odor task and imaging in awake mice. Average glomerular responses in the olfactory bulb of Shank3B +/- were correlated with WT mice. However, there was increased trial-to-trial variability in the odor responses for Shank3B +/- mice. Simulations demonstrated that this increased variability could affect odor detection in novel environments. To test whether performance was affected by the increased variability, we tested target odor recognition in the presence of novel background odors using a recently developed task (Li et al., 2023). Head-fixed mice were trained to detect target odors in the presence of known background odors. Performance was tested using catch trials where the known background odors were replaced by novel background odors. We compared the performance of eight Shank3B +/- mice (five males, three females) on this task with six WT mice (three males, three females). Performance for known background odors and learning rates were similar between Shank3B +/- and WT mice. However, when tested with novel background odors, the performance of Shank3B +/- mice dropped to almost chance levels. Thus, haploinsufficiency of the Shank3 gene causes a specific deficit in odor detection in novel environments. Our results are discussed in the context of other Shank3 mouse models and have implications for understanding olfactory function in neurodevelopmental disorders.SIGNIFICANCE STATEMENT People and mice with mutations in a single copy in the synaptic gene Shank3 show features seen in autism spectrum disorders, including social interaction deficits. Although mice social behavior uses olfaction, mice with mutations in a single copy of Shank3 have so far not shown olfactory deficits when tested using simple tasks. Here, we used a recently developed task to show that these mice could identify odors in the presence of known background odors as well as wild-type mice. However, their performance fell below that of wild-type mice when challenged with novel background odors. This deficit was also previously reported in the Cntnap2 mouse model of autism, suggesting that odor detection in novel backgrounds is a general deficit across mouse models of autism.
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Affiliation(s)
- Darya Ryndych
- Department of Biomedical Sciences, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, New York 11568
| | - Alison Sebold
- Department of Biomedical Sciences, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, New York 11568
| | - Alyssa Strassburg
- Department of Biomedical Sciences, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, New York 11568
| | - Yan Li
- Department of Biomedical Sciences, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, New York 11568
| | - Raddy L Ramos
- Department of Biomedical Sciences, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, New York 11568
| | - Gonzalo H Otazu
- Department of Biomedical Sciences, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, New York 11568
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11
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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: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [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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12
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Robust odor identification in novel olfactory environments in mice. Nat Commun 2023; 14:673. [PMID: 36781878 PMCID: PMC9925783 DOI: 10.1038/s41467-023-36346-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/26/2023] [Indexed: 02/15/2023] Open
Abstract
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.
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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. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2023; 36:64793-64828. [PMID: 40376274 PMCID: PMC12079577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/18/2025]
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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14
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Chae H, Banerjee A, Dussauze M, Albeanu DF. Long-range functional loops in the mouse olfactory system and their roles in computing odor identity. Neuron 2022; 110:3970-3985.e7. [PMID: 36174573 PMCID: PMC9742324 DOI: 10.1016/j.neuron.2022.09.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 07/12/2022] [Accepted: 09/02/2022] [Indexed: 12/15/2022]
Abstract
Elucidating the neural circuits supporting odor identification remains an open challenge. Here, we analyze the contribution of the two output cell types of the mouse olfactory bulb (mitral and tufted cells) to decode odor identity and concentration and its dependence on top-down feedback from their respective major cortical targets: piriform cortex versus anterior olfactory nucleus. We find that tufted cells substantially outperform mitral cells in decoding both odor identity and intensity. Cortical feedback selectively regulates the activity of its dominant bulb projection cell type and implements different computations. Piriform feedback specifically restructures mitral responses, whereas feedback from the anterior olfactory nucleus preferentially controls the gain of tufted representations without altering their odor tuning. Our results identify distinct functional loops involving the mitral and tufted cells and their cortical targets. We suggest that in addition to the canonical mitral-to-piriform pathway, tufted cells and their target regions are ideally positioned to compute odor identity.
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Affiliation(s)
- Honggoo Chae
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Arkarup Banerjee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Cold Spring Harbor Laboratory School for Biological Sciences, Cold Spring Harbor, NY, USA
| | - Marie Dussauze
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Cold Spring Harbor Laboratory School for Biological Sciences, Cold Spring Harbor, NY, USA
| | - Dinu F Albeanu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Cold Spring Harbor Laboratory School for Biological Sciences, Cold Spring Harbor, NY, USA.
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15
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Adefuin AM, Lindeman S, Reinert JK, Fukunaga I. State-dependent representations of mixtures by the olfactory bulb. eLife 2022; 11:76882. [PMID: 35254262 PMCID: PMC8937304 DOI: 10.7554/elife.76882] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/05/2022] [Indexed: 12/02/2022] Open
Abstract
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.
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Affiliation(s)
- Aliya Mari Adefuin
- Sensory and Behavioural Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Sander Lindeman
- Sensory and Behavioural Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Janine K Reinert
- Sensory and Behavioural Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Izumi Fukunaga
- Sensory and Behavioural Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
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16
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Derby CD, McClintock TS, Caprio J. Understanding responses to chemical mixtures: looking forward from the past. Chem Senses 2022; 47:bjac002. [PMID: 35226060 PMCID: PMC8883806 DOI: 10.1093/chemse/bjac002] [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] [Indexed: 11/12/2022] Open
Abstract
Our goal in this article is to provide a perspective on how to understand the nature of responses to chemical mixtures. In studying responses to mixtures, researchers often identify "mixture interactions"-responses to mixtures that are not accurately predicted from the responses to the mixture's individual components. Critical in these studies is how to predict responses to mixtures and thus to identify a mixture interaction. We explore this issue with a focus on olfaction and on the first level of neural processing-olfactory sensory neurons-although we use examples from taste systems as well and we consider responses beyond sensory neurons, including behavior and psychophysics. We provide a broadly comparative perspective that includes examples from vertebrates and invertebrates, from genetic and nongenetic animal models, and from literature old and new. In the end, we attempt to recommend how to approach these problems, including possible future research directions.
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Affiliation(s)
- Charles D Derby
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | | | - John Caprio
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
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17
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Lebovich L, Yunerman M, Scaiewicz V, Loewenstein Y, Rokni D. Paradoxical relationship between speed and accuracy in olfactory figure-background segregation. PLoS Comput Biol 2021; 17:e1009674. [PMID: 34871306 PMCID: PMC8675919 DOI: 10.1371/journal.pcbi.1009674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 12/16/2021] [Accepted: 11/20/2021] [Indexed: 11/19/2022] Open
Abstract
In natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We first considered pure effects of background odors on either signal or noise in the decision-making dynamics and showed that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. In this task, the inter-trial interval was independent of behavioral reaction times to avoid motivating rapid responses. We found that increased backgrounds reduce mouse performance but paradoxically also decrease reaction times, suggesting that noise in the decision making process is increased by backgrounds. We further assessed the contributions of background effects on both noise and signal by fitting the DDM to the behavioral data. The models showed that background odors affect both the signal and the noise, but that the paradoxical relationship between trial difficulty and reaction time is caused by the added noise. Sensory systems are constantly stimulated by signals from many objects in the environment. Segmentation of important signals from the cluttered background is therefore a task that is faced by all sensory systems. For many mammalians, the sense of smell is the primary sense that guides many daily behaviors. As such, the olfactory system must be able to detect and identify odors of interest against varying and dynamic backgrounds. Here we studied how background odors interfere with the detection of target odors. We trained mice on a task in which they are presented with odor mixtures and are required to report whether they include either of two target odors. We analyze the behavioral data using a common model of sensory-guided decision-making—the drift-diffusion-model. In this model, decisions are influenced by two elements: a drift which is the signal produced by the stimulus, and noise. We show that the addition of background odors has a dual effect—a reduction in the drift, as well as an increase in the noise. The increased noise also causes more rapid decisions, thereby producing a paradoxical relationship between trial difficulty and decision speed; mice make faster decisions on more difficult trials.
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Affiliation(s)
- Lior Lebovich
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
| | - Michael Yunerman
- Department of Medical Neurobiology, School of Medicine and IMRIC, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Viviana Scaiewicz
- Department of Medical Neurobiology, School of Medicine and IMRIC, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yonatan Loewenstein
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
- The Alexander Silberman Institute of Life Sciences, The Hebrew University, Jerusalem, Israel
- Department of Cognitive Sciences and The Federmann Center for the Study of Rationality, The Hebrew University, Jerusalem, Israel
| | - Dan Rokni
- Department of Medical Neurobiology, School of Medicine and IMRIC, The Hebrew University of Jerusalem, Jerusalem, Israel
- * E-mail:
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18
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Downer JD, Verhein JR, Rapone BC, O'Connor KN, Sutter ML. An Emergent Population Code in Primary Auditory Cortex Supports Selective Attention to Spectral and Temporal Sound Features. J Neurosci 2021; 41:7561-7577. [PMID: 34210783 PMCID: PMC8425978 DOI: 10.1523/jneurosci.0693-20.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/19/2021] [Accepted: 05/28/2021] [Indexed: 11/21/2022] Open
Abstract
Textbook descriptions of primary sensory cortex (PSC) revolve around single neurons' representation of low-dimensional sensory features, such as visual object orientation in primary visual cortex (V1), location of somatic touch in primary somatosensory cortex (S1), and sound frequency in primary auditory cortex (A1). Typically, studies of PSC measure neurons' responses along few (one or two) stimulus and/or behavioral dimensions. However, real-world stimuli usually vary along many feature dimensions and behavioral demands change constantly. In order to illuminate how A1 supports flexible perception in rich acoustic environments, we recorded from A1 neurons while rhesus macaques (one male, one female) performed a feature-selective attention task. We presented sounds that varied along spectral and temporal feature dimensions (carrier bandwidth and temporal envelope, respectively). Within a block, subjects attended to one feature of the sound in a selective change detection task. We found that single neurons tend to be high-dimensional, in that they exhibit substantial mixed selectivity for both sound features, as well as task context. We found no overall enhancement of single-neuron coding of the attended feature, as attention could either diminish or enhance this coding. However, a population-level analysis reveals that ensembles of neurons exhibit enhanced encoding of attended sound features, and this population code tracks subjects' performance. Importantly, surrogate neural populations with intact single-neuron tuning but shuffled higher-order correlations among neurons fail to yield attention- related effects observed in the intact data. These results suggest that an emergent population code not measurable at the single-neuron level might constitute the functional unit of sensory representation in PSC.SIGNIFICANCE STATEMENT The ability to adapt to a dynamic sensory environment promotes a range of important natural behaviors. We recorded from single neurons in monkey primary auditory cortex (A1), while subjects attended to either the spectral or temporal features of complex sounds. Surprisingly, we found no average increase in responsiveness to, or encoding of, the attended feature across single neurons. However, when we pooled the activity of the sampled neurons via targeted dimensionality reduction (TDR), we found enhanced population-level representation of the attended feature and suppression of the distractor feature. This dissociation of the effects of attention at the level of single neurons versus the population highlights the synergistic nature of cortical sound encoding and enriches our understanding of sensory cortical function.
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Affiliation(s)
- Joshua D Downer
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, California 94143
| | - Jessica R Verhein
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- School of Medicine, Stanford University, Stanford, California 94305
| | - Brittany C Rapone
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- School of Social Sciences, Oxford Brookes University, Oxford, OX4 0BP, United Kingdom
| | - Kevin N O'Connor
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, California 95618
| | - Mitchell L Sutter
- Center for Neuroscience, University of California, Davis, Davis, California 95618
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, California 95618
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19
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Abstract
Olfaction is fundamentally distinct from other sensory modalities. Natural odor stimuli are complex mixtures of volatile chemicals that interact in the nose with a receptor array that, in rodents, is built from more than 1,000 unique receptors. These interactions dictate a peripheral olfactory code, which in the brain is transformed and reformatted as it is broadcast across a set of highly interconnected olfactory regions. Here we discuss the problems of characterizing peripheral population codes for olfactory stimuli, of inferring the specific functions of different higher olfactory areas given their extensive recurrence, and of ultimately understanding how odor representations are linked to perception and action. We argue that, despite the differences between olfaction and other sensory modalities, addressing these specific questions will reveal general principles underlying brain function.
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Affiliation(s)
- David H Brann
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA;
| | - Sandeep Robert Datta
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA;
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20
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Penker S, Licht T, Hofer KT, Rokni D. Mixture Coding and Segmentation in the Anterior Piriform Cortex. Front Syst Neurosci 2020; 14:604718. [PMID: 33328914 PMCID: PMC7710992 DOI: 10.3389/fnsys.2020.604718] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/02/2020] [Indexed: 12/14/2022] Open
Abstract
Coding of odorous stimuli has been mostly studied using single isolated stimuli. However, a single sniff of air in a natural environment is likely to introduce airborne chemicals emitted by multiple objects into the nose. The olfactory system is therefore faced with the task of segmenting odor mixtures to identify objects in the presence of rich and often unpredictable backgrounds. The piriform cortex is thought to be the site of object recognition and scene segmentation, yet the nature of its responses to odorant mixtures is largely unknown. In this study, we asked two related questions. (1) How are mixtures represented in the piriform cortex? And (2) Can the identity of individual mixture components be read out from mixture representations in the piriform cortex? To answer these questions, we recorded single unit activity in the piriform cortex of naïve mice while sequentially presenting single odorants and their mixtures. We find that a normalization model explains mixture responses well, both at the single neuron, and at the population level. Additionally, we show that mixture components can be identified from piriform cortical activity by pooling responses of a small population of neurons-in many cases a single neuron is sufficient. These results indicate that piriform cortical representations are well suited to perform figure-background segmentation without the need for learning.
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Affiliation(s)
| | | | | | - Dan Rokni
- Department of Medical Neurobiology, School of Medicine and IMRIC, The Hebrew University of Jerusalem, Jerusalem, Israel
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21
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Hiratani N, Latham PE. Rapid Bayesian learning in the mammalian olfactory system. Nat Commun 2020; 11:3845. [PMID: 32737295 PMCID: PMC7395793 DOI: 10.1038/s41467-020-17490-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 07/02/2020] [Indexed: 01/17/2023] Open
Abstract
Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular, it is not clear how olfactory circuits achieve rapid, data efficient learning with local synaptic plasticity. Here, we formulate olfactory learning as a Bayesian optimization process, then map the learning rules into a computational model of the mammalian olfactory circuit. The model is capable of odor identification from a small number of observations, while reproducing cellular plasticity commonly observed during development. We extend the framework to reward-based learning, and show that the circuit is able to rapidly learn odor-reward association with a plausible neural architecture. These results deepen our theoretical understanding of unsupervised learning in the mammalian brain. How can rodents make sense of the olfactory environment without supervision? Here, the authors formulate olfactory learning as an integrated Bayesian inference problem, then derive a set of synaptic plasticity rules and neural dynamics that enables near-optimal learning of odor identification.
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Affiliation(s)
- Naoki Hiratani
- Gatsby Computational Neuroscience Unit, University College London, 25 Howland Street, London, W1T 4JG, UK.
| | - Peter E Latham
- Gatsby Computational Neuroscience Unit, University College London, 25 Howland Street, London, W1T 4JG, UK
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22
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Chong E, Moroni M, Wilson C, Shoham S, Panzeri S, Rinberg D. Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception. Science 2020; 368:368/6497/eaba2357. [PMID: 32554567 DOI: 10.1126/science.aba2357] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/01/2020] [Indexed: 12/26/2022]
Abstract
How does neural activity generate perception? Finding the combinations of spatial or temporal activity features (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns of optogenetic activation, then measured perceptual changes during extensive and controlled perturbations across spatiotemporal dimensions. We modeled recognition as the matching of patterns to learned templates. The templates that best predicted recognition were sequences of spatially identified units, ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individual units contributed additively, with larger contributions from earlier-activated units. Our synthetic approach reveals the fundamental logic of the olfactory code and provides a general framework for testing links between sensory activity and perception.
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Affiliation(s)
- Edmund Chong
- Neuroscience Institute, NYU Langone Health, New York, NY 10016, USA.
| | - Monica Moroni
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy. .,CIMeC, University of Trento, Rovereto, Italy
| | | | - Shy Shoham
- Neuroscience Institute, NYU Langone Health, New York, NY 10016, USA.,Center for Neural Science, New York University, New York, NY 10003, USA.,Tech4Health Institute, NYU Langone Health, New York, NY 10010, USA.,Department of Ophthalmology, NYU Langone Health, New York, NY 10017, USA
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Dmitry Rinberg
- Neuroscience Institute, NYU Langone Health, New York, NY 10016, USA. .,Center for Neural Science, New York University, New York, NY 10003, USA
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23
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Zak JD, Reddy G, Vergassola M, Murthy VN. Antagonistic odor interactions in olfactory sensory neurons are widespread in freely breathing mice. Nat Commun 2020; 11:3350. [PMID: 32620767 PMCID: PMC7335155 DOI: 10.1038/s41467-020-17124-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 06/10/2020] [Indexed: 12/24/2022] Open
Abstract
Odor landscapes contain complex blends of molecules that each activate unique, overlapping populations of olfactory sensory neurons (OSNs). Despite the presence of hundreds of OSN subtypes in many animals, the overlapping nature of odor inputs may lead to saturation of neural responses at the early stages of stimulus encoding. Information loss due to saturation could be mitigated by normalizing mechanisms such as antagonism at the level of receptor-ligand interactions, whose existence and prevalence remains uncertain. By imaging OSN axon terminals in olfactory bulb glomeruli as well as OSN cell bodies within the olfactory epithelium in freely breathing mice, we find widespread antagonistic interactions in binary odor mixtures. In complex mixtures of up to 12 odorants, antagonistic interactions are stronger and more prevalent with increasing mixture complexity. Therefore, antagonism is a common feature of odor mixture encoding in OSNs and helps in normalizing activity to reduce saturation and increase information transfer.
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Affiliation(s)
- Joseph D Zak
- Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, 02138, USA.
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.
| | - Gautam Reddy
- NSF-Simons Center for Mathematical & Statistical Analysis of Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Massimo Vergassola
- Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, F-75005, France
| | - Venkatesh N Murthy
- Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA, 02138, USA.
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.
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24
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Rapid Learning of Odor-Value Association in the Olfactory Striatum. J Neurosci 2020; 40:4335-4347. [PMID: 32321744 DOI: 10.1523/jneurosci.2604-19.2020] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 03/30/2020] [Accepted: 04/06/2020] [Indexed: 11/21/2022] Open
Abstract
Rodents can successfully learn multiple novel stimulus-response associations after only a few repetitions when the contingencies predict reward. The circuits modified during such reinforcement learning to support decision-making are not known, but the olfactory tubercle (OT) and posterior piriform cortex (pPC) are candidates for decoding reward category from olfactory sensory input and relaying this information to cognitive and motor areas. Through single-cell recordings in behaving male and female C57BL/6 mice, we show here that an explicit representation for reward category emerges in the OT within minutes of learning a novel odor-reward association, whereas the pPC lacks an explicit representation even after weeks of overtraining. The explicit reward category representation in OT is visible in the first sniff (50-100 ms) of an odor on each trial, and precedes the motor action. Together, these results suggest that the coding of stimulus information required for reward prediction does not occur within olfactory cortex, but rather in circuits involving the olfactory striatum.SIGNIFICANCE STATEMENT Rodents are olfactory specialists and can use odors to learn contingencies quickly and well. We have found that mice can readily learn to place multiple odors into rewarded and unrewarded categories. Once they have learned the rule, they can do such categorization in a matter of minutes (<10 trials). We found that neural activity in olfactory cortex largely reflects sensory coding, with very little explicit information about categories. By contrast, neural activity in a brain region in the ventral striatum is rapidly modified in a matter of minutes to reflect reward category. Our experiments set up a paradigm for studying rapid sensorimotor reinforcement in a circuit that is right at the interface of sensory input and reward areas.
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25
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Primacy coding facilitates effective odor discrimination when receptor sensitivities are tuned. PLoS Comput Biol 2019; 15:e1007188. [PMID: 31323033 PMCID: PMC6692051 DOI: 10.1371/journal.pcbi.1007188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 08/13/2019] [Accepted: 06/17/2019] [Indexed: 11/19/2022] Open
Abstract
The olfactory system faces the difficult task of identifying an enormous variety of odors independent of their intensity. Primacy coding, where the odor identity is encoded by the receptor types that respond earliest, might provide a compact and informative representation that can be interpreted efficiently by the brain. In this paper, we analyze the information transmitted by a simple model of primacy coding using numerical simulations and statistical descriptions. We show that the encoded information depends strongly on the number of receptor types included in the primacy representation, but only weakly on the size of the receptor repertoire. The representation is independent of the odor intensity and the transmitted information is useful to perform typical olfactory tasks with close to experimentally measured performance. Interestingly, we find situations in which a smaller receptor repertoire is advantageous for discriminating odors. The model also suggests that overly sensitive receptor types could dominate the entire response and make the whole array useless, which allows us to predict how receptor arrays need to adapt to stay useful during environmental changes. Taken together, we show that primacy coding is more useful than simple binary and normalized coding, essentially because the sparsity of the odor representations is independent of the odor statistics, in contrast to the alternatives. Primacy coding thus provides an efficient odor representation that is independent of the odor intensity and might thus help to identify odors in the olfactory cortex. Humans can identify odors independent of their intensity. Experimental data suggest that this is accomplished by representing the odor identity by the earliest responding receptor types. Using theoretical modeling, we here show that such a primacy code outperforms alternative encodings and allows discriminating odors with close to experimentally measured performance. This performance depends strongly on the number of receptors considered in the primacy code, but the receptor repertoire size is less important. The model also suggests a strong evolutionary pressure on the receptor sensitivities, which could explain observed receptor copy number adaptations. By predicting psycho-physical experiments, the model will thus contribute to our understanding of the olfactory system.
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Methods in Rodent Chemosensory Cognition. Methods Mol Biol 2019. [PMID: 29884949 DOI: 10.1007/978-1-4939-8609-5_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Olfactory information processing and learning are highly developed computational abilities of rodents. These attributes can be exploited to ask questions at several levels of complexity, from aspects of odorant binding by olfactory receptors to higher order learning about the predictive consequences of odorant stimulus presentation. Quantitative understanding of rodent odorant sampling patterns, both baseline nasal breathing and odorant-stimulated sniffing, is critical to elucidating mechanisms of olfactory information processing, from primary olfactory receptors to cortical centers that synthesize olfactory percepts from preprocessed multimodal inputs. This chapter outlines an innovative new method for measuring breathing and sniffing rates in unrestrained mice while the mice are performing odor-guided tasks in a computer controlled olfactometer.The method described here involves implantation of a wireless pressure sensor in the mouse that reports on thoracic pressure transients caused by breathing and sniffing. Recordings of pressure sensor outputs are made simultaneously with optically-sensed nose pokes by the mouse into an odor delivery port or a water delivery port. Odorant delivery timing and water reward delivery are also recorded simultaneously. This method allows for breathing and sniffing dependent thoracic pressure transients to be recorded with high temporal precision before, during, and after the mouse approaches an odor delivery port, samples the delivered odor, and obtains a water reward contingent on the identity of the odor that was presented and sampled.
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Abstract
Sensory stimuli evoke spiking activities patterned across neurons and time that are hypothesized to encode information about their identity. Since the same stimulus can be encountered in a multitude of ways, how stable or flexible are these stimulus-evoked responses? Here we examine this issue in the locust olfactory system. In the antennal lobe, we find that both spatial and temporal features of odor-evoked responses vary in a stimulus-history dependent manner. The response variations are not random, but allow the antennal lobe circuit to enhance the uniqueness of the current stimulus. Nevertheless, information about the odorant identity is conf ounded due to this contrast enhancement computation. Notably, predictions from a linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of neurons match results from behavioral experiments. In sum, our results suggest that a trade-off between stability and flexibility in sensory coding can be achieved using a simple computational logic. Sensory stimuli are encountered in multiple ways necessitating a flexible and adaptive neural population code for identification. Here, the authors show that the dynamics of odor coding in the locust antennal lobe varies with stimulus context so as to enhance the target stimulus representation.
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Reddy G, Zak JD, Vergassola M, Murthy VN. Antagonism in olfactory receptor neurons and its implications for the perception of odor mixtures. eLife 2018; 7:34958. [PMID: 29687778 PMCID: PMC5915184 DOI: 10.7554/elife.34958] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 03/30/2018] [Indexed: 11/16/2022] Open
Abstract
Natural environments feature mixtures of odorants of diverse quantities, qualities and complexities. Olfactory receptor neurons (ORNs) are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. Yet, the response of ORNs to mixtures is strongly non-additive, and exhibits antagonistic interactions among odorants. Here, we model the processing of mixtures by mammalian ORNs, focusing on the role of inhibitory mechanisms. We show how antagonism leads to an effective ‘normalization’ of the ensemble ORN response, that is, the distribution of responses of the ORN population induced by any mixture is largely independent of the number of components in the mixture. This property arises from a novel mechanism involving the distinct statistical properties of receptor binding and activation, without any recurrent neuronal circuitry. Normalization allows our encoding model to outperform non-interacting models in odor discrimination tasks, leads to experimentally testable predictions and explains several psychophysical experiments in humans. When ordering in a coffee shop, you probably recognize and enjoy the aroma of freshly roasted coffee beans. But as well as coffee, you can also smell the croissants behind the counter and maybe even the perfume or cologne of the person next to you. Each of these scents consists of a collection of chemicals, or odorants. To distinguish between the aroma of coffee and that of croissants, your brain must group the odorants appropriately and then keep the groups separate from each other. This is not a trivial task. Odorants bind to proteins called odorant receptors found on the surface of cells in the nose called olfactory receptor neurons. But each odorant does not have its own dedicated receptor. Instead, a single odorant will bind to multiple types of odorant receptors, and thus, each olfactory receptor neuron may respond to multiple odorants. So how does the brain encode mixtures of odorants in a way that allows us to distinguish one aroma from another? Reddy, Zak et al. have developed a computational model to explain how this process works. The model assumes that an odorant triggers a response in an olfactory receptor neuron via two steps. First, the odorant binds to an odorant receptor. Second, the bound odorant activates the receptor. But the odorant that binds most strongly to a receptor will not necessarily be the odorant that is best at activating that receptor. This allows a phenomenon called competitive antagonism to occur. This is when one odorant in a mixture binds more strongly to a receptor than the other odorants, but only weakly activates that receptor. In so doing, the strongly bound odorant prevents the other odorants from binding to and activating the receptor. This helps tame the dominating influence of background odors, which might otherwise saturate the responses of individual olfactory receptor neurons. Reddy, Zak et al. show that processes such as competitive antagonism enable olfactory receptor neurons to encode all of the odors within a mixture. The model can explain various phenomena observed in experiments and it adds to our understanding of how the brain generates our sense of smell. The model may also be relevant to other biological systems that must filter weak signals from a dominant background. These include the immune system, which must distinguish a small set of foreign proteins from the much larger number of proteins that make up our bodies.
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Affiliation(s)
- Gautam Reddy
- Department of Physics, University of California, San Diego, La Jolla, United States
| | - Joseph D Zak
- Department of Molecular Cellular Biology, Harvard University, Cambridge, United States.,Center for Brain Science, Harvard University, Cambridge, United States
| | - Massimo Vergassola
- Department of Physics, University of California, San Diego, La Jolla, United States
| | - Venkatesh N Murthy
- Department of Molecular Cellular Biology, Harvard University, Cambridge, United States.,Center for Brain Science, Harvard University, Cambridge, United States
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Behavioral readout of spatio-temporal codes in olfaction. Curr Opin Neurobiol 2018; 52:18-24. [PMID: 29694923 DOI: 10.1016/j.conb.2018.04.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 03/10/2018] [Accepted: 04/07/2018] [Indexed: 11/21/2022]
Abstract
Neural recordings performed at an increasing scale and resolution have revealed complex, spatio-temporally precise patterns of activity in the olfactory system. Multiple models may explain the functional consequences of the spatio-temporal olfactory code, but the link to behavior remains unclear. Recent evidence in the field suggests a behavioral sensitivity to both fine spatial and temporal features in the code. How these features and combinations of features give rise to olfactory behavior is the subject of active research in the field. Modern genetic and optogenetic methods show great promise in testing the link between olfactory codes and behavior.
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A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system. PLoS Comput Biol 2017; 13:e1005780. [PMID: 28968384 PMCID: PMC5638622 DOI: 10.1371/journal.pcbi.1005780] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 10/12/2017] [Accepted: 09/15/2017] [Indexed: 12/27/2022] Open
Abstract
Determining how synaptic coupling within and between regions is modulated during sensory processing is an important topic in neuroscience. Electrophysiological recordings provide detailed information about neural spiking but have traditionally been confined to a particular region or layer of cortex. Here we develop new theoretical methods to study interactions between and within two brain regions, based on experimental measurements of spiking activity simultaneously recorded from the two regions. By systematically comparing experimentally-obtained spiking statistics to (efficiently computed) model spike rate statistics, we identify regions in model parameter space that are consistent with the experimental data. We apply our new technique to dual micro-electrode array in vivo recordings from two distinct regions: olfactory bulb (OB) and anterior piriform cortex (PC). Our analysis predicts that: i) inhibition within the afferent region (OB) has to be weaker than the inhibition within PC, ii) excitation from PC to OB is generally stronger than excitation from OB to PC, iii) excitation from PC to OB and inhibition within PC have to both be relatively strong compared to presynaptic inputs from OB. These predictions are validated in a spiking neural network model of the OB–PC pathway that satisfies the many constraints from our experimental data. We find when the derived relationships are violated, the spiking statistics no longer satisfy the constraints from the data. In principle this modeling framework can be adapted to other systems and be used to investigate relationships between other neural attributes besides network connection strengths. Thus, this work can serve as a guide to further investigations into the relationships of various neural attributes within and across different regions during sensory processing. Sensory processing is known to span multiple regions of the nervous system. However, electrophysiological recordings during sensory processing have traditionally been limited to a single region or brain layer. With recent advances in experimental techniques, recorded spiking activity from multiple regions simultaneously is feasible. However, other important quantities— such as inter-region connection strengths—cannot yet be measured. Here, we develop new theoretical tools to leverage data obtained by recording from two different brain regions simultaneously. We address the following questions: what are the crucial neural network attributes that enable sensory processing across different regions, and how are these attributes related to one another? With a novel theoretical framework to efficiently calculate spiking statistics, we can characterize a high dimensional parameter space that satisfies data constraints. We apply our results to the olfactory system to make specific predictions about effective network connectivity. Our framework relies on incorporating relatively easy-to-measure quantities to predict hard-to-measure interactions across multiple brain regions. Because this work is adaptable to other systems, we anticipate it will be a valuable tool for analysis of other larger scale brain recordings.
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Herz AV, Mathis A, Stemmler M. Periodic population codes: From a single circular variable to higher dimensions, multiple nested scales, and conceptual spaces. Curr Opin Neurobiol 2017; 46:99-108. [PMID: 28888183 DOI: 10.1016/j.conb.2017.07.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 07/06/2017] [Accepted: 07/19/2017] [Indexed: 12/27/2022]
Abstract
Across the nervous system, neurons often encode circular stimuli using tuning curves that are not sine or cosine functions, but that belong to the richer class of von Mises functions, which are periodic variants of Gaussians. For a population of neurons encoding a single circular variable with such canonical tuning curves, computing a simple population vector is the optimal read-out of the most likely stimulus. We argue that the advantages of population vector read-outs are so compelling that even the neural representation of the outside world's flat Euclidean geometry is curled up into a torus (a circle times a circle), creating the hexagonal activity patterns of mammalian grid cells. Here, the circular scale is not set a priori, so the nervous system can use multiple scales and gain fields to overcome the ambiguity inherent in periodic representations of linear variables. We review the experimental evidence for this framework and discuss its testable predictions and generalizations to more abstract grid-like neural representations.
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Affiliation(s)
- Andreas Vm Herz
- Bernstein Center for Computational Neuroscience Munich and Faculty of Biology, Ludwig-Maximilians-Universität München, Grosshadernerstrasse 2, 82152 Planegg-Martinsried, Germany.
| | - Alexander Mathis
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA; Werner Reichardt Centre for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, 72076 Tübingen, Germany
| | - Martin Stemmler
- Bernstein Center for Computational Neuroscience Munich and Faculty of Biology, Ludwig-Maximilians-Universität München, Grosshadernerstrasse 2, 82152 Planegg-Martinsried, Germany
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A probabilistic approach to demixing odors. Nat Neurosci 2016; 20:98-106. [PMID: 27918530 DOI: 10.1038/nn.4444] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 10/21/2016] [Indexed: 12/15/2022]
Abstract
The olfactory system faces a hard problem: on the basis of noisy information from olfactory receptor neurons (the neurons that transduce chemicals to neural activity), it must figure out which odors are present in the world. Odors almost never occur in isolation, and different odors excite overlapping populations of olfactory receptor neurons, so the central challenge of the olfactory system is to demix its input. Because of noise and the large number of possible odors, demixing is fundamentally a probabilistic inference task. We propose that the early olfactory system uses approximate Bayesian inference to solve it. The computations involve a dynamical loop between the olfactory bulb and the piriform cortex, with cortex explaining incoming activity from the olfactory receptor neurons in terms of a mixture of odors. The model is compatible with known anatomy and physiology, including pattern decorrelation, and it performs better than other models at demixing odors.
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Abstract
The olfactory system removes correlations in natural odors using a network of inhibitory neurons in the olfactory bulb. It has been proposed that this network integrates the response from all olfactory receptors and inhibits them equally. However, how such global inhibition influences the neural representations of odors is unclear. Here, we study a simple statistical model of the processing in the olfactory bulb, which leads to concentration-invariant, sparse representations of the odor composition. We show that the inhibition strength can be tuned to obtain sparse representations that are still useful to discriminate odors that vary in relative concentration, size, and composition. The model reveals two generic consequences of global inhibition: (i) odors with many molecular species are more difficult to discriminate and (ii) receptor arrays with heterogeneous sensitivities perform badly. Comparing these predictions to experiments will help us to understand the role of global inhibition in shaping normalized odor representations in the olfactory bulb.
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Affiliation(s)
- David Zwicker
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, United States of America
- Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, MA 02138, United States of America
- * E-mail:
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