1
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Westeinde EA, Kellogg E, Dawson PM, Lu J, Hamburg L, Midler B, Druckmann S, Wilson RI. Transforming a head direction signal into a goal-oriented steering command. Nature 2024; 626:819-826. [PMID: 38326621 PMCID: PMC10881397 DOI: 10.1038/s41586-024-07039-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.
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Affiliation(s)
| | - Emily Kellogg
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Paul M Dawson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jenny Lu
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Lydia Hamburg
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Benjamin Midler
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Shaul Druckmann
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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2
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Barth-Maron A, D'Alessandro I, Wilson RI. Interactions between specialized gain control mechanisms in olfactory processing. Curr Biol 2023; 33:5109-5120.e7. [PMID: 37967554 DOI: 10.1016/j.cub.2023.10.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/16/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023]
Abstract
Gain control is a process that adjusts a system's sensitivity when input levels change. Neural systems contain multiple mechanisms of gain control, but we do not understand why so many mechanisms are needed or how they interact. Here, we investigate these questions in the Drosophila antennal lobe, where we identify several types of inhibitory interneurons with specialized gain control functions. We find that some interneurons are nonspiking, with compartmentalized calcium signals, and they specialize in intra-glomerular gain control. Conversely, we find that other interneurons are recruited by strong and widespread network input; they specialize in global presynaptic gain control. Using computational modeling and optogenetic perturbations, we show how these mechanisms can work together to improve stimulus discrimination while also minimizing temporal distortions in network activity. Our results demonstrate how the robustness of neural network function can be increased by interactions among diverse and specialized mechanisms of gain control.
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Affiliation(s)
- Asa Barth-Maron
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Isabel D'Alessandro
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
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3
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Yang HH, Brezovec LE, Capdevila LS, Vanderbeck QX, Adachi A, Mann RS, Wilson RI. Fine-grained descending control of steering in walking Drosophila. bioRxiv 2023:2023.10.15.562426. [PMID: 37904997 PMCID: PMC10614758 DOI: 10.1101/2023.10.15.562426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream from distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Notably, a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from brain cells that drive specific, coordinated modulations of low-level patterns.
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Affiliation(s)
- Helen H. Yang
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
| | - Luke E. Brezovec
- Department of Neurobiology, Stanford University, Stanford, CA 94305 USA
| | | | | | - Atsuko Adachi
- Department of Biochemistry and Molecular Biophysics, Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027 USA
| | - Richard S. Mann
- Department of Biochemistry and Molecular Biophysics, Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027 USA
| | - Rachel I. Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115 USA
- Lead contact
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4
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Wilson RI. Neural Networks for Navigation: From Connections to Computations. Annu Rev Neurosci 2023; 46:403-423. [PMID: 37428603 DOI: 10.1146/annurev-neuro-110920-032645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and reciprocally connected to motor control. This review summarizes recent progress in understanding these networks, focusing on studies in arthropods. One factor driving recent progress is the availability of the Drosophila connectome; however, it is increasingly clear that navigation depends on ongoing synaptic plasticity in these networks. Functional synapses appear to be continually reselected from the set of anatomical potential synapses based on the interaction of Hebbian learning rules, sensory feedback, attractor dynamics, and neuromodulation. This can explain how the brain's maps of space are rapidly updated; it may also explain how the brain can initialize goals as stable fixed points for navigation.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Cambridge, Massachusetts, USA;
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5
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Fisher YE, Marquis M, D'Alessandro I, Wilson RI. Author Correction: Dopamine promotes head direction plasticity during orienting movements. Nature 2023:10.1038/s41586-023-06238-7. [PMID: 37226002 DOI: 10.1038/s41586-023-06238-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Yvette E Fisher
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Molecular and Cellular Biology, University of California Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Michael Marquis
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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6
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Kutschireiter A, Basnak MA, Wilson RI, Drugowitsch J. Bayesian inference in ring attractor networks. Proc Natl Acad Sci U S A 2023; 120:e2210622120. [PMID: 36812206 PMCID: PMC9992764 DOI: 10.1073/pnas.2210622120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/12/2023] [Indexed: 02/24/2023] Open
Abstract
Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This "Bayesian ring attractor" performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms.
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Affiliation(s)
| | | | - Rachel I. Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
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7
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Holtz SL, Wilson RI. Functional specializations of Drosophila primary mechanosensory neurons. Biophys J 2023; 122:87a. [PMID: 36785059 DOI: 10.1016/j.bpj.2022.11.673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Affiliation(s)
- Stephen L Holtz
- Department of Neurobiology, Harvard Medical School/Howard Hughes Medical Institute, Boston, MA, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School/Howard Hughes Medical Institute, Boston, MA, USA
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8
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Marquis M, Wilson RI. Locomotor and olfactory responses in dopamine neurons of the Drosophila superior-lateral brain. Curr Biol 2022; 32:5406-5414.e5. [PMID: 36450284 DOI: 10.1016/j.cub.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/17/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022]
Abstract
The Drosophila brain contains about 50 distinct morphological types of dopamine neurons.1,2,3,4 Physiological studies of Drosophila dopamine neurons have been largely limited to one brain region, the mushroom body,5,6,7,8,9,10,11,12,13 where they are implicated in learning.14,15,16,17,18 By comparison, we know little about the physiology of other Drosophila dopamine neurons. Interestingly, a recent whole-brain imaging study found that dopamine neuron activity in several fly brain regions is correlated with locomotion.19 This is notable because many dopamine neurons in the rodent brain are also correlated with locomotion or other movements20,21,22,23,24,25,26,27,28,29,30; however, most rodent studies have focused on learned and rewarded behaviors, and few have investigated dopamine neuron activity during spontaneous (self-timed) movements. In this study, we monitored dopamine neurons in the Drosophila brain during self-timed locomotor movements, focusing on several previously uncharacterized cell types that arborize in the superior-lateral brain, specifically the lateral horn and superior-lateral protocerebrum. We found that activity of all of these dopamine neurons correlated with spontaneous fluctuations in walking speed, with different cell types showing different speed correlations. Some dopamine neurons also responded to odors, but these responses were suppressed by repeated odor encounters. Finally, we found that the same identifiable dopamine neuron can encode different combinations of locomotion and odor in different individuals. If these dopamine neurons promote synaptic plasticity-like the dopamine neurons of the mushroom body-then, their tuning profiles would imply that plasticity depends on a flexible integration of sensory signals, motor signals, and recent experience.
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Affiliation(s)
- Michael Marquis
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
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9
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Li H, Janssens J, De Waegeneer M, Kolluru SS, Davie K, Gardeux V, Saelens W, David F, Brbić M, Spanier K, Leskovec J, McLaughlin CN, Xie Q, Jones RC, Brueckner K, Shim J, Tattikota SG, Schnorrer F, Rust K, Nystul TG, Carvalho-Santos Z, Ribeiro C, Pal S, Mahadevaraju S, Przytycka TM, Allen AM, Goodwin SF, Berry CW, Fuller MT, White-Cooper H, Matunis EL, DiNardo S, Galenza A, O’Brien LE, Dow JAT, Jasper H, Oliver B, Perrimon N, Deplancke B, Quake SR, Luo L, Aerts S, Agarwal D, Ahmed-Braimah Y, Arbeitman M, Ariss MM, Augsburger J, Ayush K, Baker CC, Banisch T, Birker K, Bodmer R, Bolival B, Brantley SE, Brill JA, Brown NC, Buehner NA, Cai XT, Cardoso-Figueiredo R, Casares F, Chang A, Clandinin TR, Crasta S, Desplan C, Detweiler AM, Dhakan DB, Donà E, Engert S, Floc'hlay S, George N, González-Segarra AJ, Groves AK, Gumbin S, Guo Y, Harris DE, Heifetz Y, Holtz SL, Horns F, Hudry B, Hung RJ, Jan YN, Jaszczak JS, Jefferis GSXE, Karkanias J, Karr TL, Katheder NS, Kezos J, Kim AA, Kim SK, Kockel L, Konstantinides N, Kornberg TB, Krause HM, Labott AT, Laturney M, Lehmann R, Leinwand S, Li J, Li JSS, Li K, Li K, Li L, Li T, Litovchenko M, Liu HH, Liu Y, Lu TC, Manning J, Mase A, Matera-Vatnick M, Matias NR, McDonough-Goldstein CE, McGeever A, McLachlan AD, Moreno-Roman P, Neff N, Neville M, Ngo S, Nielsen T, O'Brien CE, Osumi-Sutherland D, Özel MN, Papatheodorou I, Petkovic M, Pilgrim C, Pisco AO, Reisenman C, Sanders EN, Dos Santos G, Scott K, Sherlekar A, Shiu P, Sims D, Sit RV, Slaidina M, Smith HE, Sterne G, Su YH, Sutton D, Tamayo M, Tan M, Tastekin I, Treiber C, Vacek D, Vogler G, Waddell S, Wang W, Wilson RI, Wolfner MF, Wong YCE, Xie A, Xu J, Yamamoto S, Yan J, Yao Z, Yoda K, Zhu R, Zinzen RP. Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly. Science 2022; 375:eabk2432. [PMID: 35239393 PMCID: PMC8944923 DOI: 10.1126/science.abk2432] [Citation(s) in RCA: 202] [Impact Index Per Article: 101.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.
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Affiliation(s)
- Hongjie Li
- Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA,Huffington Center on Aging and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jasper Janssens
- VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium,Laboratory of Computational Biology, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Maxime De Waegeneer
- VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium,Laboratory of Computational Biology, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Sai Saroja Kolluru
- Departments of Bioengineering and Applied Physics, Stanford University, Stanford CA USA, and Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Kristofer Davie
- VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Wouter Saelens
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Fabrice David
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Maria Brbić
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA, and Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Katina Spanier
- VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium,Laboratory of Computational Biology, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA, and Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Colleen N. McLaughlin
- Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Qijing Xie
- Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Robert C. Jones
- Departments of Bioengineering and Applied Physics, Stanford University, Stanford CA USA, and Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Katja Brueckner
- Department of Cell and Tissue Biology, University of California, San Francisco, CA 94143, USA
| | - Jiwon Shim
- Department of Life Science, College of Natural Science, Hanyang University, Seoul, Republic of Korea 04763
| | - Sudhir Gopal Tattikota
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115; Howard Hughes Medical Institute, Boston, MA, USA
| | - Frank Schnorrer
- Aix-Marseille University, CNRS, IBDM (UMR 7288), Turing Centre for Living systems, 13009 Marseille, France
| | - Katja Rust
- Institute of Physiology and Pathophysiology, Department of Molecular Cell Physiology, Philipps-University, Marburg, Germany,Department of Anatomy, University of California, San Francisco, CA 94143, USA
| | - Todd G. Nystul
- Department of Anatomy, University of California, San Francisco, CA 94143, USA
| | - Zita Carvalho-Santos
- Behavior and Metabolism Laboratory, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Carlos Ribeiro
- Behavior and Metabolism Laboratory, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Soumitra Pal
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
| | - Sharvani Mahadevaraju
- Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Kidney and Digestive Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Teresa M. Przytycka
- National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA
| | - Aaron M. Allen
- Centre for Neural Circuits & Behaviour, University of Oxford, Tinsley Building, Mansfield road, Oxford, OX1 3SR, UK
| | - Stephen F. Goodwin
- Centre for Neural Circuits & Behaviour, University of Oxford, Tinsley Building, Mansfield road, Oxford, OX1 3SR, UK
| | - Cameron W. Berry
- Department of Developmental Biology and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Margaret T. Fuller
- Department of Developmental Biology and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Helen White-Cooper
- Molecular Biosciences Division, Cardiff University, Cardiff, CF10 3AX UK
| | - Erika L. Matunis
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Stephen DiNardo
- Perelman School of Medicine, The University of Pennsylvania, and The Penn Institute for Regenerative Medicine Philadelphia, PA 19104, USA
| | - Anthony Galenza
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford CA 94305, USA
| | - Lucy Erin O’Brien
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford CA 94305, USA
| | - Julian A. T. Dow
- Institute of Molecular, Cell & Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - FCA Consortium
- FCA Consortium: All authors listed before Acknowledgements, and all contributions and affiliations listed in the Supplementary Materials
| | - Heinrich Jasper
- Immunology Discovery, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Brian Oliver
- Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Kidney and Digestive Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115; Howard Hughes Medical Institute, Boston, MA, USA,corresponding authors: (N.P.), (B.D.), (S.R.Q.), (L.L.), (S.A.)
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland,corresponding authors: (N.P.), (B.D.), (S.R.Q.), (L.L.), (S.A.)
| | - Stephen R. Quake
- Departments of Bioengineering and Applied Physics, Stanford University, Stanford CA USA, and Chan Zuckerberg Biohub, San Francisco CA, USA,corresponding authors: (N.P.), (B.D.), (S.R.Q.), (L.L.), (S.A.)
| | - Liqun Luo
- Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA,corresponding authors: (N.P.), (B.D.), (S.R.Q.), (L.L.), (S.A.)
| | - Stein Aerts
- VIB-KU Leuven Center for Brain & Disease Research, KU Leuven, Leuven 3000, Belgium,Laboratory of Computational Biology, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium,corresponding authors: (N.P.), (B.D.), (S.R.Q.), (L.L.), (S.A.)
| | - Devika Agarwal
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK
| | | | - Michelle Arbeitman
- Biomedical Sciences Department, Florida State University, Tallahassee, FL, USA
| | - Majd M Ariss
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jordan Augsburger
- Department of Cell and Tissue Biology, University of California, San Francisco, CA 94143, USA
| | - Kumar Ayush
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Catherine C Baker
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Torsten Banisch
- Skirball Institute and HHMI, New York University Langone Medical Center, New York City, NY 10016, USA
| | - Katja Birker
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Rolf Bodmer
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Benjamin Bolival
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Susanna E Brantley
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Julie A Brill
- Cell Biology Program, The Hospital for Sick Children (SickKids), Toronto, ON M5G 0A4, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Nora C Brown
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Norene A Buehner
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Xiaoyu Tracy Cai
- Immunology Discovery, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Rita Cardoso-Figueiredo
- Behavior and Metabolism Laboratory, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Fernando Casares
- CABD (Andalusian Centre for Developmental Biology), CSIC-UPO-JA, Seville 41013, Spain
| | - Amy Chang
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Sheela Crasta
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Applied Physics, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Claude Desplan
- Department of Biology, New York University, New York, New York 10003, USA
| | | | - Darshan B Dhakan
- Behavior and Metabolism Laboratory, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Erika Donà
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Stefanie Engert
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Swann Floc'hlay
- VIB-KU Leuven Center for Brain and Disease Research, KU Leuven, Leuven 3000, Belgium.,Laboratory of Computational Biology, Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Nancy George
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Amanda J González-Segarra
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Andrew K Groves
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.,Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Samantha Gumbin
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yanmeng Guo
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Devon E Harris
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yael Heifetz
- The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Stephen L Holtz
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Felix Horns
- Department of Bioengineering and Biophysics Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Bruno Hudry
- Université Côte d'Azur, CNRS, INSERM, iBV, France
| | - Ruei-Jiun Hung
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Yuh Nung Jan
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Jacob S Jaszczak
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | | | | | - Timothy L Karr
- Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | | | - James Kezos
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Anna A Kim
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA.,University of California, Santa Barbara, CA 93106, USA.,Uppsala University, Sweden
| | - Seung K Kim
- Department of Developmental Biology and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lutz Kockel
- Department of Developmental Biology and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nikolaos Konstantinides
- Institut Jacques Monod, Centre National de la Recherche Scientifique-UMR 7592, Université Paris Diderot, Paris, France
| | - Thomas B Kornberg
- Cardiovascular Research Institute, University of California, San Francisco, CA 94143, USA
| | - Henry M Krause
- Donnelly Centre for Cellular and Biomolecular Research, Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Andrew Thomas Labott
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Meghan Laturney
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Ruth Lehmann
- Skirball Institute, Department of Cell Biology and HHMI, New York University Langone Medical Center, New York City, NY 10016
| | - Sarah Leinwand
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jiefu Li
- Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Joshua Shing Shun Li
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Kai Li
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Ke Li
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Liying Li
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Tun Li
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Maria Litovchenko
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.,Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Han-Hsuan Liu
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Yifang Liu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Tzu-Chiao Lu
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jonathan Manning
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Anjeli Mase
- Department of Cell and Tissue Biology, University of California, San Francisco, CA 94143, USA
| | | | - Neuza Reis Matias
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Caitlin E McDonough-Goldstein
- Department of Biology, Syracuse University, Syracuse, NY, USA.,Department of Evolutionary Biology, University of Vienna, Vienna, Austria
| | | | - Alex D McLachlan
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
| | - Paola Moreno-Roman
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Norma Neff
- Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Megan Neville
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford OX1 3SR, UK
| | - Sang Ngo
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tanja Nielsen
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Caitlin E O'Brien
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - David Osumi-Sutherland
- European Bioinformatics Institute (EMBL/EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | | | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Maja Petkovic
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Clare Pilgrim
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3DY, UK
| | | | - Carolina Reisenman
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Erin Nicole Sanders
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gilberto Dos Santos
- The Biological Laboratories, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
| | - Kristin Scott
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Aparna Sherlekar
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Philip Shiu
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - David Sims
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK
| | - Rene V Sit
- Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Maija Slaidina
- Skirball Institute, Faculty of Medicine, New York University, New York, NY 10016
| | - Harold E Smith
- Genomics Core, National Institute of Diabetes and Digestive and Kidney Diseases, US National Institutes of Health, Bethesda, MD, USA
| | - Gabriella Sterne
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Yu-Han Su
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daniel Sutton
- Graduate Program in Genetics and Genomics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Marco Tamayo
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | | | - Ibrahim Tastekin
- Behavior and Metabolism Laboratory, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Christoph Treiber
- Centre for Neural Circuits and Behaviour, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3TA, UK
| | - David Vacek
- Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Georg Vogler
- Development, Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Scott Waddell
- Centre for Neural Circuits and Behaviour, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3TA, UK
| | - Wanpeng Wang
- Cardiovascular Research Institute, University of California, San Francisco, CA 94143, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Mariana F Wolfner
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Yiu-Cheung E Wong
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Anthony Xie
- Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Jun Xu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Shinya Yamamoto
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | - Jia Yan
- Chan Zuckerberg Biohub, San Francisco CA, USA
| | - Zepeng Yao
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Kazuki Yoda
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruijun Zhu
- Department of Physiology, Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, San Francisco, CA, USA
| | - Robert P Zinzen
- Laboratory for Systems Biology of Neural Tissue Differentiation, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrueck Centre for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Roessle-Strasse 12, 13125 Berlin, Germany
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10
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Fisher YE, Marquis M, D’Alessandro I, Wilson RI. Dopamine promotes head direction plasticity during orienting movements. Nature 2022; 612:316-322. [PMID: 36450986 PMCID: PMC9729112 DOI: 10.1038/s41586-022-05485-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 10/25/2022] [Indexed: 12/05/2022]
Abstract
In neural networks that store information in their connection weights, there is a tradeoff between sensitivity and stability1,2. Connections must be plastic to incorporate new information, but if they are too plastic, stored information can be corrupted. A potential solution is to allow plasticity only during epochs when task-specific information is rich, on the basis of a 'when-to-learn' signal3. We reasoned that dopamine provides a when-to-learn signal that allows the brain's spatial maps to update when new spatial information is available-that is, when an animal is moving. Here we show that the dopamine neurons innervating the Drosophila head direction network are specifically active when the fly turns to change its head direction. Moreover, their activity scales with moment-to-moment fluctuations in rotational speed. Pairing dopamine release with a visual cue persistently strengthens the cue's influence on head direction cells. Conversely, inhibiting these dopamine neurons decreases the influence of the cue. This mechanism should accelerate learning during moments when orienting movements are providing a rich stream of head direction information, allowing learning rates to be low at other times to protect stored information. Our results show how spatial learning in the brain can be compressed into discrete epochs in which high learning rates are matched to high rates of information intake.
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Affiliation(s)
- Yvette E. Fisher
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA ,grid.47840.3f0000 0001 2181 7878Present Address: Department of Molecular and Cellular Biology, University of California Berkeley, Berkeley, CA USA ,grid.499295.a0000 0004 9234 0175Present Address: Chan Zuckerberg Biohub, San Francisco, CA USA
| | - Michael Marquis
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | - Isabel D’Alessandro
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | - Rachel I. Wilson
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
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11
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Lu J, Behbahani AH, Hamburg L, Westeinde EA, Dawson PM, Lyu C, Maimon G, Dickinson MH, Druckmann S, Wilson RI. Transforming representations of movement from body- to world-centric space. Nature 2022; 601:98-104. [PMID: 34912123 PMCID: PMC10759448 DOI: 10.1038/s41586-021-04191-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 10/28/2021] [Indexed: 12/21/2022]
Abstract
When an animal moves through the world, its brain receives a stream of information about the body's translational velocity from motor commands and sensory feedback signals. These incoming signals are referenced to the body, but ultimately, they must be transformed into world-centric coordinates for navigation1,2. Here we show that this computation occurs in the fan-shaped body in the brain of Drosophila melanogaster. We identify two cell types, PFNd and PFNv3-5, that conjunctively encode translational velocity and heading as a fly walks. In these cells, velocity signals are acquired from locomotor brain regions6 and are multiplied with heading signals from the compass system. PFNd neurons prefer forward-ipsilateral movement, whereas PFNv neurons prefer backward-contralateral movement, and perturbing PFNd neurons disrupts idiothetic path integration in walking flies7. Downstream, PFNd and PFNv neurons converge onto hΔB neurons, with a connectivity pattern that pools together heading and translation direction combinations corresponding to the same movement in world-centric space. This network motif effectively performs a rotation of the brain's representation of body-centric translational velocity according to the current heading direction. Consistent with our predictions, we observe that hΔB neurons form a representation of translational velocity in world-centric coordinates. By integrating this representation over time, it should be possible for the brain to form a working memory of the path travelled through the environment8-10.
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Affiliation(s)
- Jenny Lu
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Amir H Behbahani
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Lydia Hamburg
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Elena A Westeinde
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Paul M Dawson
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Cheng Lyu
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Gaby Maimon
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Michael H Dickinson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Shaul Druckmann
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Rachel I Wilson
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA.
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12
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Okubo TS, Patella P, D'Alessandro I, Wilson RI. A Neural Network for Wind-Guided Compass Navigation. Neuron 2020; 107:924-940.e18. [PMID: 32681825 PMCID: PMC7507644 DOI: 10.1016/j.neuron.2020.06.022] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 05/13/2020] [Accepted: 06/22/2020] [Indexed: 11/27/2022]
Abstract
Spatial maps in the brain are most accurate when they are linked to external sensory cues. Here, we show that the compass in the Drosophila brain is linked to the direction of the wind. Shifting the wind rightward rotates the compass as if the fly were turning leftward, and vice versa. We describe the mechanisms of several computations that integrate wind information into the compass. First, an intensity-invariant representation of wind direction is computed by comparing left-right mechanosensory signals. Then, signals are reformatted to reduce the coding biases inherent in peripheral mechanics, and wind cues are brought into the same circular coordinate system that represents visual cues and self-motion signals. Because the compass incorporates both mechanosensory and visual cues, it should enable navigation under conditions where no single cue is consistently reliable. These results show how local sensory signals can be transformed into a global, multimodal, abstract representation of space.
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Affiliation(s)
- Tatsuo S Okubo
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Paola Patella
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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13
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Isaacman-Beck J, Paik KC, Wienecke CFR, Yang HH, Fisher YE, Wang IE, Ishida IG, Maimon G, Wilson RI, Clandinin TR. SPARC enables genetic manipulation of precise proportions of cells. Nat Neurosci 2020; 23:1168-1175. [PMID: 32690967 PMCID: PMC7939234 DOI: 10.1038/s41593-020-0668-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 06/12/2020] [Indexed: 11/17/2022]
Abstract
Many experimental approaches rely on controlling gene expression in select subsets of cells within an individual animal. However, reproducibly targeting transgene expression to specific fractions of a genetically defined cell type is challenging. We developed Sparse Predictive Activity through Recombinase Competition (SPARC), a generalizable toolkit that can express any effector in precise proportions of post-mitotic cells in Drosophila. Using this approach, we demonstrate targeted expression of many effectors in several cell types and apply these tools to calcium imaging of individual neurons and optogenetic manipulation of sparse cell populations in vivo.
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Affiliation(s)
| | - Kristine C Paik
- Department of Neurobiology, Stanford University, Stanford, CA, USA.,Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | | | - Helen H Yang
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Yvette E Fisher
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Irving E Wang
- Department of Neurobiology, Stanford University, Stanford, CA, USA.,Freenome, South San Francisco, CA, USA
| | - Itzel G Ishida
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Gaby Maimon
- Laboratory of Integrative Brain Function and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
| | - Rachel I Wilson
- Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
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14
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Fisher YE, Lu J, D'Alessandro I, Wilson RI. Sensorimotor experience remaps visual input to a heading-direction network. Nature 2019; 576:121-125. [PMID: 31748749 PMCID: PMC7753972 DOI: 10.1038/s41586-019-1772-4] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 10/24/2019] [Indexed: 11/09/2022]
Abstract
In the Drosophila brain, 'compass' neurons track the orientation of the body and head (the fly's heading) during navigation 1,2. In the absence of visual cues, the compass neuron network estimates heading by integrating self-movement signals over time3,4. When a visual cue is present, the estimate of the network is more accurate1,3. Visual inputs to compass neurons are thought to originate from inhibitory neurons called R neurons (also known as ring neurons); the receptive fields of R neurons tile visual space5. The axon of each R neuron overlaps with the dendrites of every compass neuron6, raising the question of how visual cues are integrated into the compass. Here, using in vivo whole-cell recordings, we show that a visual cue can evoke synaptic inhibition in compass neurons and that R neurons mediate this inhibition. Each compass neuron is inhibited only by specific visual cue positions, indicating that many potential connections from R neurons onto compass neurons are actually weak or silent. We also show that the pattern of visually evoked inhibition can reorganize over minutes as the fly explores an altered virtual-reality environment. Using ensemble calcium imaging, we demonstrate that this reorganization causes persistent changes in the compass coordinate frame. Taken together, our data suggest a model in which correlated pre- and postsynaptic activity triggers associative long-term synaptic depression of visually evoked inhibition in compass neurons. Our findings provide evidence for the theoretical proposal that associative plasticity of sensory inputs, when combined with attractor dynamics, can reconcile self-movement information with changing external cues to generate a coherent sense of direction7-12.
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Affiliation(s)
- Yvette E Fisher
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jenny Lu
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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15
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Batchelor AV, Wilson RI. Sound localization behavior in Drosophila melanogaster depends on inter-antenna vibration amplitude comparisons. ACTA ACUST UNITED AC 2019; 222:222/3/jeb191213. [PMID: 30733260 DOI: 10.1242/jeb.191213] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 11/20/2018] [Indexed: 12/25/2022]
Abstract
Drosophila melanogaster hear with their antennae: sound evokes vibration of the distal antennal segment, and this vibration is transduced by specialized mechanoreceptor cells. The left and right antennae vibrate preferentially in response to sounds arising from different azimuthal angles. Therefore, by comparing signals from the two antennae, it should be possible to obtain information about the azimuthal angle of a sound source. However, behavioral evidence of sound localization has not been reported in Drosophila Here, we show that walking D. melanogaster do indeed turn in response to lateralized sounds. We confirm that this behavior is evoked by vibrations of the distal antennal segment. The rule for turning is different for sounds arriving from different locations: flies turn toward sounds in their front hemifield, but they turn away from sounds in their rear hemifield, and they do not turn at all in response to sounds from 90 or -90 deg. All of these findings can be explained by a simple rule: the fly steers away from the antenna with the larger vibration amplitude. Finally, we show that these behaviors generalize to sound stimuli with diverse spectro-temporal features, and that these behaviors are found in both sexes. Our findings demonstrate the behavioral relevance of the antenna's directional tuning properties. They also pave the way for investigating the neural implementation of sound localization, as well as the potential roles of sound-guided steering in courtship and exploration.
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Affiliation(s)
- Alexandra V Batchelor
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave., Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave., Boston, MA 02115, USA
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16
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Jeanne JM, Fişek M, Wilson RI. The Organization of Projections from Olfactory Glomeruli onto Higher-Order Neurons. Neuron 2018; 98:1198-1213.e6. [PMID: 29909998 PMCID: PMC6051339 DOI: 10.1016/j.neuron.2018.05.011] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 04/19/2018] [Accepted: 05/04/2018] [Indexed: 11/27/2022]
Abstract
Each odorant receptor corresponds to a unique glomerulus in the brain. Projections from different glomeruli then converge in higher brain regions, but we do not understand the logic governing which glomeruli converge and which do not. Here, we use two-photon optogenetics to map glomerular connections onto neurons in the lateral horn, the region of the Drosophila brain that receives the majority of olfactory projections. We identify 39 morphological types of lateral horn neurons (LHNs) and show that different types receive input from different combinations of glomeruli. We find that different LHN types do not have independent inputs; rather, certain combinations of glomeruli converge onto many of the same LHNs and so are over-represented. Notably, many over-represented combinations are composed of glomeruli that prefer chemically dissimilar ligands whose co-occurrence indicates a behaviorally relevant "odor scene." The pattern of glomerulus-LHN connections thus represents a prediction of what ligand combinations will be most salient.
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Affiliation(s)
- James M Jeanne
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Mehmet Fişek
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
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17
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Patella P, Wilson RI. Functional Maps of Mechanosensory Features in the Drosophila Brain. Curr Biol 2018; 28:1189-1203.e5. [PMID: 29657118 PMCID: PMC5952606 DOI: 10.1016/j.cub.2018.02.074] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 02/19/2018] [Accepted: 02/27/2018] [Indexed: 01/04/2023]
Abstract
Johnston's organ is the largest mechanosensory organ in Drosophila. It contributes to hearing, touch, vestibular sensing, proprioception, and wind sensing. In this study, we used in vivo 2-photon calcium imaging and unsupervised image segmentation to map the tuning properties of Johnston's organ neurons (JONs) at the site where their axons enter the brain. We then applied the same methodology to study two key brain regions that process signals from JONs: the antennal mechanosensory and motor center (AMMC) and the wedge, which is downstream of the AMMC. First, we identified a diversity of JON response types that tile frequency space and form a rough tonotopic map. Some JON response types are direction selective; others are specialized to encode amplitude modulations over a specific range (dynamic range fractionation). Next, we discovered that both the AMMC and the wedge contain a tonotopic map, with a significant increase in tonotopy-and a narrowing of frequency tuning-at the level of the wedge. Whereas the AMMC tonotopic map is unilateral, the wedge tonotopic map is bilateral. Finally, we identified a subregion of the AMMC/wedge that responds preferentially to the coherent rotation of the two mechanical organs in the same angular direction, indicative of oriented steady air flow (directional wind). Together, these maps reveal the broad organization of the primary and secondary mechanosensory regions of the brain. They provide a framework for future efforts to identify the specific cell types and mechanisms that underlie the hierarchical re-mapping of mechanosensory information in this system.
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Affiliation(s)
- Paola Patella
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave., Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave., Boston, MA 02115, USA.
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18
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Azevedo AW, Wilson RI. Active Mechanisms of Vibration Encoding and Frequency Filtering in Central Mechanosensory Neurons. Neuron 2017; 96:446-460.e9. [PMID: 28943231 DOI: 10.1016/j.neuron.2017.09.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 07/26/2017] [Accepted: 09/05/2017] [Indexed: 12/11/2022]
Abstract
To better understand biophysical mechanisms of mechanosensory processing, we investigated two cell types in the Drosophila brain (A2 and B1 cells) that are postsynaptic to antennal vibration receptors. A2 cells receive excitatory synaptic currents in response to both directions of movement: thus, twice per vibration cycle. The membrane acts as a low-pass filter, so that voltage and spiking mainly track the vibration envelope rather than individual cycles. By contrast, B1 cells are excited by only forward or backward movement, meaning they are sensitive to vibration phase. They receive oscillatory synaptic currents at the stimulus frequency, and they bandpass filter these inputs to favor specific frequencies. Different cells prefer different frequencies, due to differences in their voltage-gated conductances. Both Na+ and K+ conductances suppress low-frequency synaptic inputs, so cells with larger voltage-gated conductances prefer higher frequencies. These results illustrate how membrane properties and voltage-gated conductances can extract distinct stimulus features into parallel channels.
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Affiliation(s)
- Anthony W Azevedo
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
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Tobin WF, Wilson RI, Lee WCA. Wiring variations that enable and constrain neural computation in a sensory microcircuit. eLife 2017; 6. [PMID: 28530904 PMCID: PMC5440167 DOI: 10.7554/elife.24838] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Accepted: 04/23/2017] [Indexed: 11/13/2022] Open
Abstract
Neural network function can be shaped by varying the strength of synaptic connections. One way to achieve this is to vary connection structure. To investigate how structural variation among synaptic connections might affect neural computation, we examined primary afferent connections in the Drosophila olfactory system. We used large-scale serial section electron microscopy to reconstruct all the olfactory receptor neuron (ORN) axons that target a left-right pair of glomeruli, as well as all the projection neurons (PNs) postsynaptic to these ORNs. We found three variations in ORN→PN connectivity. First, we found a systematic co-variation in synapse number and PN dendrite size, suggesting total synaptic conductance is tuned to postsynaptic excitability. Second, we discovered that PNs receive more synapses from ipsilateral than contralateral ORNs, providing a structural basis for odor lateralization behavior. Finally, we found evidence of imprecision in ORN→PN connections that can diminish network performance. DOI:http://dx.doi.org/10.7554/eLife.24838.001
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Affiliation(s)
- William F Tobin
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, United States
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Abstract
The ability of animals to flexibly navigate through complex environments depends on the integration of sensory information with motor commands. The sensory modality most tightly linked to motor control is mechanosensation. Adaptive motor control depends critically on an animal's ability to respond to mechanical forces generated both within and outside the body. The compact neural circuits of insects provide appealing systems to investigate how mechanical cues guide locomotion in rugged environments. Here, we review our current understanding of mechanosensation in insects and its role in adaptive motor control. We first examine the detection and encoding of mechanical forces by primary mechanoreceptor neurons. We then discuss how central circuits integrate and transform mechanosensory information to guide locomotion. Because most studies in this field have been performed in locusts, cockroaches, crickets, and stick insects, the examples we cite here are drawn mainly from these 'big insects'. However, we also pay particular attention to the tiny fruit fly, Drosophila, where new tools are creating new opportunities, particularly for understanding central circuits. Our aim is to show how studies of big insects have yielded fundamental insights relevant to mechanosensation in all animals, and also to point out how the Drosophila toolkit can contribute to future progress in understanding mechanosensory processing.
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Affiliation(s)
- John C Tuthill
- Department of Physiology and Biophysics, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA.
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
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Tuthill JC, Wilson RI. Parallel Transformation of Tactile Signals in Central Circuits of Drosophila. Cell 2016; 164:1046-59. [PMID: 26919434 DOI: 10.1016/j.cell.2016.01.014] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Revised: 11/23/2015] [Accepted: 01/04/2016] [Indexed: 01/30/2023]
Abstract
To distinguish between complex somatosensory stimuli, central circuits must combine signals from multiple peripheral mechanoreceptor types, as well as mechanoreceptors at different sites in the body. Here, we investigate the first stages of somatosensory integration in Drosophila using in vivo recordings from genetically labeled central neurons in combination with mechanical and optogenetic stimulation of specific mechanoreceptor types. We identify three classes of central neurons that process touch: one compares touch signals on different parts of the same limb, one compares touch signals on right and left limbs, and the third compares touch and proprioceptive signals. Each class encodes distinct features of somatosensory stimuli. The axon of an individual touch receptor neuron can diverge to synapse onto all three classes, meaning that these computations occur in parallel, not hierarchically. Representing a stimulus as a set of parallel comparisons is a fast and efficient way to deliver somatosensory signals to motor circuits.
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Affiliation(s)
- John C Tuthill
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
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Bell JS, Wilson RI. Behavior Reveals Selective Summation and Max Pooling among Olfactory Processing Channels. Neuron 2016; 91:425-38. [PMID: 27373835 DOI: 10.1016/j.neuron.2016.06.011] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Revised: 03/28/2016] [Accepted: 06/02/2016] [Indexed: 11/30/2022]
Abstract
The olfactory system is divided into processing channels (glomeruli), each receiving input from a different type of olfactory receptor neuron (ORN). Here we investigated how glomeruli combine to control behavior in freely walking Drosophila. We found that optogenetically activating single ORN types typically produced attraction, although some ORN types produced repulsion. Attraction consisted largely of a behavioral program with the following rules: at fictive odor onset, flies walked upwind, and at fictive odor offset, they reversed. When certain pairs of attractive ORN types were co-activated, the level of the behavioral response resembled the sum of the component responses. However, other pairs of attractive ORN types produced a response resembling the larger component (max pooling). Although activation of different ORN combinations produced different levels of behavior, the rules of the behavioral program were consistent. Our results illustrate a general method for inferring how groups of neurons work together to modulate behavioral programs.
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Affiliation(s)
- Joseph S Bell
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
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Adamantidis A, Arber S, Bains JS, Bamberg E, Bonci A, Buzsáki G, Cardin JA, Costa RM, Dan Y, Goda Y, Graybiel AM, Häusser M, Hegemann P, Huguenard JR, Insel TR, Janak PH, Johnston D, Josselyn SA, Koch C, Kreitzer AC, Lüscher C, Malenka RC, Miesenböck G, Nagel G, Roska B, Schnitzer MJ, Shenoy KV, Soltesz I, Sternson SM, Tsien RW, Tsien RY, Turrigiano GG, Tye KM, Wilson RI. Optogenetics: 10 years after ChR2 in neurons--views from the community. Nat Neurosci 2015; 18:1202-12. [PMID: 26308981 DOI: 10.1038/nn.4106] [Citation(s) in RCA: 88] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Antoine Adamantidis
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montréal, Canada, and the Department of Neurology, Inselspital University Hospital, University of Bern, Bern, Switzerland
| | - Silvia Arber
- Biozentrum, Department of Cell Biology, University of Basel, Basel, Switzerland, and the Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Jaideep S Bains
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Ernst Bamberg
- Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Antonello Bonci
- Intramural Research Program, Synaptic Plasticity Section, National Institute on Drug Abuse, Baltimore, Maryland, USA, the Solomon H. Snyder Neuroscience Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA, and in the Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - György Buzsáki
- The Neuroscience Institute, School of Medicine and Center for Neural Science, New York University, New York, New York, USA
| | - Jessica A Cardin
- Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, USA, and at the Kavli Institute of Neuroscience, Yale University, New Haven, Connecticut, USA
| | - Rui M Costa
- Champalimaud Neuroscience Programme, Champalimaud Center for the Unknown, Lisbon, Portugal
| | - Yang Dan
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, California, USA
| | - Yukiko Goda
- RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
| | - Ann M Graybiel
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Michael Häusser
- Wolfson Institute for Biomedical Research and Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Peter Hegemann
- Institut für Biologie/Experimentelle Biophysik, Humboldt Universität zu Berlin, Berlin, Germany
| | - John R Huguenard
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Thomas R Insel
- National Institute of Mental Health, Bethesda, Maryland, USA
| | - Patricia H Janak
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland, USA, and the Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland, USA
| | - Daniel Johnston
- Department of Neuroscience and Center for Learning and Memory, University of Texas at Austin, Austin, Texas, USA
| | - Sheena A Josselyn
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, Toronto, Ontario, Canada, and the Departments of Psychology and Physiology and Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Anatol C Kreitzer
- The Gladstone Institutes, San Francisco, California, USA, and the Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, California, USA
| | - Christian Lüscher
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland, and the Service of Neurology, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, Switzerland
| | - Robert C Malenka
- Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, USA
| | - Gero Miesenböck
- Centre for Neural Circuits and Behaviour, University of Oxford, Tinsley Building, Mansfield Road, Oxford, UK
| | - Georg Nagel
- Institute for Molecular Plant Physiology and Biophysics, Biocenter, Julius-Maximilians-University of Würzburg, Würzburg, Germany
| | - Botond Roska
- Neural Circuit Laboratories, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Mark J Schnitzer
- James H. Clark Center for Biomedical Engineering and Sciences, Stanford University, Stanford, California, USA, the Howard Hughes Medical Institute, Stanford University, Stanford, California, USA, and the CNC Program, Stanford University, Stanford, California, USA
| | - Krishna V Shenoy
- Departments of Electrical Engineering, Bioengineering and Neurobiology, the Neurosciences and Bio-X Programs, the Stanford Neurosciences Institute and the Howard Hughes Medical Institute, Stanford University, Stanford, California, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Scott M Sternson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Richard W Tsien
- Department of Neuroscience and Physiology, Neuroscience Institute, New York University Langone Medical Center, New York, New York, USA
| | - Roger Y Tsien
- Department of Pharmacology, Howard Hughes Medical Institute, Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, USA
| | - Gina G Turrigiano
- Department of Biology and Center for Behavioral Genomics, Brandeis University, Waltham, Massachusetts, USA
| | - Kay M Tye
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA
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Jeanne JM, Wilson RI. Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy. Neuron 2015; 88:1014-1026. [PMID: 26586183 DOI: 10.1016/j.neuron.2015.10.018] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Revised: 09/24/2015] [Accepted: 10/08/2015] [Indexed: 11/16/2022]
Abstract
One of the proposed canonical circuit motifs employed by the brain is a feedforward network where parallel signals converge, diverge, and reconverge. Here we investigate a network with this architecture in the Drosophila olfactory system. We focus on a glomerulus whose receptor neurons converge in an all-to-all manner onto six projection neurons that then reconverge onto higher-order neurons. We find that both convergence and reconvergence improve the ability of a decoder to detect a stimulus based on a single neuron's spike train. The first transformation implements averaging, and it improves peak detection accuracy but not speed; the second transformation implements coincidence detection, and it improves speed but not peak accuracy. In each case, the integration time and threshold of the postsynaptic cell are matched to the statistics of convergent spike trains.
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Affiliation(s)
- James M Jeanne
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA
| | - Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA.
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25
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Abstract
In the olfactory system of Drosophila melanogaster, it is relatively straightforward to target in vivo measurements of neural activity to specific processing channels. This, together with the numerical simplicity of the Drosophila olfactory system, has produced rapid gains in our understanding of Drosophila olfaction. This review summarizes the neurophysiology of the first two layers of this system: the peripheral olfactory receptor neurons and their postsynaptic targets in the antennal lobe. We now understand in some detail the cellular and synaptic mechanisms that shape odor representations in these neurons. Together, these mechanisms imply that interesting neural adaptations to environmental statistics have occurred. These mechanisms also place some fundamental constraints on early sensory processing that pose challenges for higher brain regions. These findings suggest some general principles with broad relevance to early sensory processing in other modalities.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
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26
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Liu WW, Wilson RI. Transient and specific inactivation of Drosophila neurons in vivo using a native ligand-gated ion channel. Curr Biol 2013; 23:1202-8. [PMID: 23770187 DOI: 10.1016/j.cub.2013.05.016] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Revised: 04/22/2013] [Accepted: 05/09/2013] [Indexed: 02/04/2023]
Abstract
A key tool in neuroscience is the ability to transiently inactivate specific neurons on timescales of milliseconds to minutes. In Drosophila, there are two available techniques for accomplishing this (shibire(ts) and halorhodopsin [1-3]), but both have shortcomings [4-9]. Here we describe a complementary technique using a native histamine-gated chloride channel (Ort). Ort is the receptor at the first synapse in the visual system. It forms large-conductance homomeric channels that desensitize only modestly in response to ligand [10]. Many regions of the CNS are devoid of histaminergic neurons [11, 12], raising the possibility that Ort could be used to artificially inactivate specific neurons in these regions. To test this idea, we performed in vivo whole-cell recordings from antennal lobe neurons misexpressing Ort. In these neurons, histamine produced a rapid and reversible drop in input resistance, clamping the membrane potential below spike threshold and virtually abolishing spontaneous and odor-evoked activity. Every neuron type in this brain region could be inactivated in this manner. Neurons that did not misexpress Ort showed negligible responses to histamine. Ort also performed favorably in comparison to the available alternative effector transgenes. Thus, Ort misexpression is a useful tool for probing functional connectivity among Drosophila neurons.
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Affiliation(s)
- Wendy W Liu
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
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Lehnert BP, Baker AE, Gaudry Q, Chiang AS, Wilson RI. Distinct roles of TRP channels in auditory transduction and amplification in Drosophila. Neuron 2013; 77:115-28. [PMID: 23312520 PMCID: PMC3811118 DOI: 10.1016/j.neuron.2012.11.030] [Citation(s) in RCA: 101] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2012] [Indexed: 11/26/2022]
Abstract
Auditory receptor cells rely on mechanically gated channels to transform sound stimuli into neural activity. Several TRP channels have been implicated in Drosophila auditory transduction, but mechanistic studies have been hampered by the inability to record subthreshold signals from receptor neurons. Here, we develop a non-invasive method for measuring these signals by recording from a central neuron that is electrically coupled to a genetically defined population of auditory receptor cells. We find that the TRPN family member NompC, which is necessary for the active amplification of sound-evoked motion by the auditory organ, is not required for transduction in auditory receptor cells. Instead, NompC sensitizes the transduction complex to movement and precisely regulates the static forces on the complex. In contrast, the TRPV channels Nanchung and Inactive are required for responses to sound, suggesting they are components of the transduction complex. Thus, transduction and active amplification are genetically separable processes in Drosophila hearing.
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Affiliation(s)
- Brendan P Lehnert
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
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28
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Gaudry Q, Hong EJ, Kain J, de Bivort BL, Wilson RI. Asymmetric neurotransmitter release enables rapid odour lateralization in Drosophila. Nature 2013; 493:424-8. [PMID: 23263180 PMCID: PMC3590906 DOI: 10.1038/nature11747] [Citation(s) in RCA: 108] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2012] [Accepted: 11/07/2012] [Indexed: 01/17/2023]
Abstract
In Drosophila, most individual olfactory receptor neurons (ORNs) project bilaterally to both sides of the brain. Having bilateral rather than unilateral projections may represent a useful redundancy. However, bilateral ORN projections to the brain should also compromise the ability to lateralize odours. Nevertheless, walking or flying Drosophila reportedly turn towards the antenna that is more strongly stimulated by odour. Here we show that each ORN spike releases approximately 40% more neurotransmitter from the axon branch ipsilateral to the soma than from the contralateral branch. As a result, when an odour activates the antennae asymmetrically, ipsilateral central neurons begin to spike a few milliseconds before contralateral neurons, and at a 30 to 50% higher rate than contralateral neurons. We show that a walking fly can detect a 5% asymmetry in total ORN input to its left and right antennal lobes, and can turn towards the odour in less time than it requires the fly to complete a stride. These results demonstrate that neurotransmitter release properties can be tuned independently at output synapses formed by a single axon onto two target cells with identical functions and morphologies. Our data also show that small differences in spike timing and spike rate can produce reliable differences in olfactory behaviour.
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Affiliation(s)
- Quentin Gaudry
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, Massachusetts 02115, USA
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Abstract
In the vertebrate nose, increasing air speed tends to increase the magnitude of odor-evoked activity in olfactory receptor neurons (ORNs), given constant odor concentration and duration. It is often assumed that the same is true of insect olfactory organs, but this has not been directly tested. In this study, we examined the effect of air speed on ORN responses in Drosophila melanogaster. We constructed an odor delivery device that allowed us to independently vary concentration and air speed, and we used a fast photoionization detector to precisely measure the actual odor concentration at the antenna while simultaneously recording spikes from ORNs in vivo. Our results demonstrate that Drosophila ORN odor responses are invariant to air speed, as long as odor concentration is kept constant. This finding was true across a >100-fold range of air speeds. Because odor hydrophobicity has been proposed to affect the air speed dependence of olfactory transduction, we tested a >1,000-fold range of hydrophobicity values and found that ORN responses are invariant to air speed across this full range. These results have implications for the mechanisms of odor delivery to Drosophila ORNs. Our findings are also significant because flies have a limited ability to control air flow across their antennae, unlike terrestrial vertebrates, which can control air flow within their nasal cavity. Thus, for the fly, invariance to air speed may be adaptive because it confers robustness to changing wind conditions.
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Affiliation(s)
- Yi Zhou
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA
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Bhandawat V, Maimon G, Dickinson MH, Wilson RI. Olfactory modulation of flight in Drosophila is sensitive, selective and rapid. ACTA ACUST UNITED AC 2011; 213:3625-35. [PMID: 20952610 DOI: 10.1242/jeb.040402] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Freely flying Drosophila melanogaster respond to odors by increasing their flight speed and turning upwind. Both these flight behaviors can be recapitulated in a tethered fly, which permits the odor stimulus to be precisely controlled. In this study, we investigated the relationship between these behaviors and odor-evoked activity in primary sensory neurons. First, we verified that these behaviors are abolished by mutations that silence olfactory receptor neurons (ORNs). We also found that antennal mechanosensors in Johnston's organ are required to guide upwind turns. Flight responses to an odor depend on the identity of the ORNs that are active, meaning that these behaviors involve odor discrimination and not just odor detection. Flight modulation can begin rapidly (within about 85 ms) after the onset of olfactory transduction. Moreover, just a handful of spikes in a single ORN type is sufficient to trigger these behaviors. Finally, we found that the upwind turn is triggered independently from the increase in wingbeat frequency, implying that ORN signals diverge to activate two independent and parallel motor commands. Together, our results show that odor-evoked flight modulations are rapid and sensitive responses to specific patterns of sensory neuron activity. This makes these behaviors a useful paradigm for studying the relationship between sensory neuron activity and behavioral decision-making in a simple and genetically tractable organism.
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Affiliation(s)
- Vikas Bhandawat
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
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32
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Bhandawat V, Maimon G, Dickinson MH, Wilson RI. Olfactory modulation of flight in Drosophila is sensitive, selective and rapid. J Exp Biol 2010. [DOI: 10.1242/jeb.053546] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA.
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35
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Abstract
In the Drosophila antennal lobe, excitation can spread between glomerular processing channels. In this study, we investigated the mechanism of lateral excitation. Dual recordings from excitatory local neurons (eLNs) and projection neurons (PNs) showed that eLN-to-PN synapses transmit both hyperpolarization and depolarization, are not diminished by blocking chemical neurotransmission, and are abolished by a gap-junction mutation. This mutation eliminates odor-evoked lateral excitation in PNs and diminishes some PN odor responses. This implies that lateral excitation is mediated by electrical synapses from eLNs onto PNs. In addition, eLNs form synapses onto inhibitory LNs. Eliminating these synapses boosts some PN odor responses and reduces the disinhibitory effect of GABA receptor antagonists on PNs. Thus, eLNs have two opposing effects on PNs, driving both direct excitation and indirect inhibition. We propose that when stimuli are weak, lateral excitation promotes sensitivity, whereas when stimuli are strong, lateral excitation helps recruit inhibitory gain control.
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Affiliation(s)
- Emre Yaksi
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA
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36
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Abstract
The TRPN1 ion channel has a role in both hearing and bristle mechanosensation in fruit flies and in proprioception in nematodes. In this issue of Neuron, two papers present evidence that TRPN1 is also required for proprioception in fruit fly larvae and that it is a bona fide mechanoreceptor channel for nematode feeding behavior.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
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Chou YH, Spletter ML, Yaksi E, Leong JCS, Wilson RI, Luo L. Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe. Nat Neurosci 2010; 13:439-49. [PMID: 20139975 PMCID: PMC2847188 DOI: 10.1038/nn.2489] [Citation(s) in RCA: 267] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2009] [Accepted: 12/22/2009] [Indexed: 12/11/2022]
Abstract
Local interneurons are essential in information processing by neural circuits. Here we present a comprehensive genetic, anatomical and electrophysiological analysis of local interneurons (LNs) in the Drosophila melanogaster antennal lobe, the first olfactory processing center in the brain. We found LNs to be diverse in their neurotransmitter profiles, connectivity and physiological properties. Analysis of >1,500 individual LNs revealed principal morphological classes characterized by coarsely stereotyped glomerular innervation patterns. Some of these morphological classes showed distinct physiological properties. However, the finer-scale connectivity of an individual LN varied considerably across brains, and there was notable physiological variability within each morphological or genetic class. Finally, LN innervation required interaction with olfactory receptor neurons during development, and some individual variability also likely reflected LN-LN interactions. Our results reveal an unexpected degree of complexity and individual variation in an invertebrate neural circuit, a result that creates challenges for solving the Drosophila connectome.
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Affiliation(s)
- Ya-Hui Chou
- Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, California, USA
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38
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Abstract
Multineuronal recordings often reveal synchronized spikes in different neurons. How correlated spike timing affects neural codes depends on the statistics of correlations, which in turn reflects the connectivity that gives rise to correlations. However, determining the connectivity of neurons recorded in vivo can be difficult. Here, we investigate the origins of correlated activity in genetically-labeled neurons of the Drosophila antennal lobe. Dual recordings show synchronized spontaneous spikes in projection neurons (PNs) postsynaptic to the same type of olfactory receptor neuron (ORN). Odors increase these correlations. The primary origin of correlations lies in the divergence of each ORN onto every PN in its glomerulus. Reciprocal PN-PN connections make a smaller contribution to correlations, and PN spike trains in different glomeruli are only weakly correlated. PN axons from the same glomerulus reconverge in the lateral horn, where pooling redundant signals may allow lateral horn neurons to average out noise that arises independently in these PNs.
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Affiliation(s)
- Hokto Kazama
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA
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Wilson RI. Neural and behavioral mechanisms of olfactory perception. Curr Opin Neurobiol 2008; 18:408-12. [PMID: 18809492 DOI: 10.1016/j.conb.2008.08.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2008] [Revised: 08/27/2008] [Accepted: 08/27/2008] [Indexed: 10/21/2022]
Abstract
Recent in vivo and in vitro studies have challenged existing models of olfactory processing in the vertebrate olfactory bulb and insect antennal lobe. Whereas lateral connectivity between olfactory glomeruli was previously thought to form a dense, topographically organized inhibitory surround, new evidence suggests that lateral connections may be sparse, nontopographic, and partly excitatory. Other recent studies highlight the role of active sensing (sniffing) in shaping odor-evoked neural activity and perception.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, United States.
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Olsen SR, Wilson RI. Cracking neural circuits in a tiny brain: new approaches for understanding the neural circuitry of Drosophila. Trends Neurosci 2008; 31:512-20. [PMID: 18775572 DOI: 10.1016/j.tins.2008.07.006] [Citation(s) in RCA: 110] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2008] [Revised: 07/25/2008] [Accepted: 07/30/2008] [Indexed: 11/17/2022]
Abstract
Genetic screens in Drosophila have identified many genes involved in neural development and function. However, until recently, it has been impossible to monitor neural signals in Drosophila central neurons, and it has been difficult to make specific perturbations to central neural circuits. This has changed in the past few years with the development of new tools for measuring and manipulating neural activity in the fly. Here we review how these new tools enable novel conceptual approaches to 'cracking circuits' in this important model organism. We discuss recent studies aimed at defining the cognitive demands on the fly brain, identifying the cellular components of specific neural circuits, mapping functional connectivity in those circuits and defining causal relationships between neural activity and behavior.
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Affiliation(s)
- Shawn R Olsen
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
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41
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Kazama H, Wilson RI. Homeostatic matching and nonlinear amplification at identified central synapses. Neuron 2008; 58:401-13. [PMID: 18466750 DOI: 10.1016/j.neuron.2008.02.030] [Citation(s) in RCA: 137] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2007] [Revised: 01/30/2008] [Accepted: 02/25/2008] [Indexed: 01/27/2023]
Abstract
Here we describe the properties of a synapse in the Drosophila antennal lobe and show how they can explain certain sensory computations in this brain region. The synapse between olfactory receptor neurons (ORNs) and projection neurons (PNs) is very strong, reflecting a large number of release sites and high release probability. This is likely one reason why weak ORN odor responses are amplified in PNs. Furthermore, the amplitude of unitary synaptic currents in a PN is matched to the size of its dendritic arbor. This matching may compensate for a lower input resistance of larger dendrites to produce uniform depolarization across PN types. Consistent with this idea, a genetic manipulation that lowers input resistance increases unitary synaptic currents. Finally, strong stimuli produce short-term depression at this synapse. This helps explain why PN odor responses are transient, and why strong ORN odor responses are not amplified as powerfully as weak responses.
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Affiliation(s)
- Hokto Kazama
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston MA 02115, USA
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA.
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Bhandawat V, Olsen SR, Gouwens NW, Schlief ML, Wilson RI. Sensory processing in the Drosophila antennal lobe increases reliability and separability of ensemble odor representations. Nat Neurosci 2007; 10:1474-82. [PMID: 17922008 DOI: 10.1038/nn1976] [Citation(s) in RCA: 240] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2007] [Accepted: 08/16/2007] [Indexed: 11/09/2022]
Abstract
Here we describe several fundamental principles of olfactory processing in the Drosophila melanogaster antennal lobe (the analog of the vertebrate olfactory bulb), through the systematic analysis of input and output spike trains of seven identified glomeruli. Repeated presentations of the same odor elicit more reproducible responses in second-order projection neurons (PNs) than in their presynaptic olfactory receptor neurons (ORNs). PN responses rise and accommodate rapidly, emphasizing odor onset. Furthermore, weak ORN inputs are amplified in the PN layer but strong inputs are not. This nonlinear transformation broadens PN tuning and produces more uniform distances between odor representations in PN coding space. In addition, portions of the odor response profile of a PN are not systematically related to their direct ORN inputs, which probably indicates the presence of lateral connections between glomeruli. Finally, we show that a linear discriminator classifies odors more accurately using PN spike trains than using an equivalent number of ORN spike trains.
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Affiliation(s)
- Vikas Bhandawat
- Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, Massachusetts 02115, USA
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Olsen SR, Bhandawat V, Wilson RI. Excitatory interactions between olfactory processing channels in the Drosophila antennal lobe. Neuron 2007; 54:89-103. [PMID: 17408580 PMCID: PMC2048819 DOI: 10.1016/j.neuron.2007.03.010] [Citation(s) in RCA: 219] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2007] [Revised: 03/13/2007] [Accepted: 03/15/2007] [Indexed: 11/28/2022]
Abstract
Each odorant receptor gene defines a unique type of olfactory receptor neuron (ORN) and a corresponding type of second-order neuron. Because each odor can activate multiple ORN types, information must ultimately be integrated across these processing channels to form a unified percept. Here, we show that, in Drosophila, integration begins at the level of second-order projection neurons (PNs). We genetically silence all the ORNs that normally express a particular odorant receptor and find that PNs postsynaptic to the silent glomerulus receive substantial lateral excitatory input from other glomeruli. Genetically confining odor-evoked ORN input to just one glomerulus reveals that most PNs postsynaptic to other glomeruli receive indirect excitatory input from the single ORN type that is active. Lateral connections between identified glomeruli vary in strength, and this pattern of connections is stereotyped across flies. Thus, a dense network of lateral connections distributes odor-evoked excitation between channels in the first brain region of the olfactory processing stream.
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Schlief ML, Wilson RI. Olfactory processing and behavior downstream from highly selective receptor neurons. Nat Neurosci 2007; 10:623-30. [PMID: 17417635 PMCID: PMC2838507 DOI: 10.1038/nn1881] [Citation(s) in RCA: 120] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2006] [Accepted: 02/27/2007] [Indexed: 11/09/2022]
Abstract
In both the vertebrate nose and the insect antenna, most olfactory receptor neurons (ORNs) respond to multiple odors. However, some ORNs respond to just a single odor, or at most to a few highly related odors. It has been hypothesized that narrowly tuned ORNs project to narrowly tuned neurons in the brain, and that these dedicated circuits mediate innate behavioral responses to a particular ligand. Here we have investigated neural activity and behavior downstream from two narrowly tuned ORN types in Drosophila melanogaster. We found that genetically ablating either of these ORN types impairs innate behavioral attraction to their cognate ligand. Neurons in the antennal lobe postsynaptic to one of these ORN types are, like their presynaptic ORNs, narrowly tuned to a pheromone. However, neurons postsynaptic to the second ORN type are broadly tuned. These results demonstrate that some narrowly tuned ORNs project to dedicated central circuits, ensuring a tight connection between stimulus and behavior, whereas others project to central neurons that participate in the ensemble representations of many odors.
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Affiliation(s)
- Michelle L Schlief
- Department of Neurobiology, Harvard Medical School, 220 Longwood Ave., Boston, Massachusetts 02115, USA
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Abstract
Olfactory space has a higher dimensionality than does any other class of sensory stimuli, and the olfactory system receives input from an unusually large number of unique information channels. This suggests that aspects of olfactory processing may differ fundamentally from processing in other sensory modalities. This review summarizes current understanding of early events in olfactory processing. We focus on how odors are encoded by the activity of primary olfactory receptor neurons, how odor codes may be transformed in the olfactory bulb, and what relevance these codes may have for downstream neurons in higher brain centers. Recent findings in synaptic physiology, neural coding, and psychophysics are discussed, with reference to both vertebrate and insect model systems.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA.
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Abstract
Drosophila olfactory receptor neurons project to the antennal lobe, the insect analog of the mammalian olfactory bulb. GABAergic synaptic inhibition is thought to play a critical role in olfactory processing in the antennal lobe and olfactory bulb. However, the properties of GABAergic neurons and the cellular effects of GABA have not been described in Drosophila, an important model organism for olfaction research. We have used whole-cell patch-clamp recording, pharmacology, immunohistochemistry, and genetic markers to investigate how GABAergic inhibition affects olfactory processing in the Drosophila antennal lobe. We show that many axonless local neurons (LNs) in the adult antennal lobe are GABAergic. GABA hyperpolarizes antennal lobe projection neurons (PNs) via two distinct conductances, blocked by a GABAA- and GABAB-type antagonist, respectively. Whereas GABAA receptors shape PN odor responses during the early phase of odor responses, GABAB receptors mediate odor-evoked inhibition on longer time scales. The patterns of odor-evoked GABAB-mediated inhibition differ across glomeruli and across odors. Finally, we show that LNs display broad but diverse morphologies and odor preferences, suggesting a cellular basis for odor- and glomerulus-dependent patterns of inhibition. Together, these results are consistent with a model in which odors elicit stimulus-specific spatial patterns of GABA release, and as a result, GABAergic inhibition increases the degree of difference between the neural representations of different odors.
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Affiliation(s)
- Rachel I Wilson
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA.
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Abstract
Molecular genetics has revealed a precise stereotypy in the projection of primary olfactory sensory neurons onto secondary neurons. A major challenge is to understand how this mapping translates into odor responses in these second-order neurons. We investigated this question in Drosophila using whole-cell recordings in vivo. We observe that monomolecular odors generally elicit responses in large ensembles of antennal lobe neurons. Comparison of odor-evoked activity from afferents and postsynaptic neurons in the same glomerulus revealed that second-order neurons display broader tuning and more complex responses than their primary afferents. This indicates a major transformation of odor representations, implicating lateral interactions within the antennal lobe.
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Affiliation(s)
- Rachel I Wilson
- Division of Biology, 139-74, California Institute of Technology, Pasadena, CA 91125, USA
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