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Cowley BR, Calhoun AJ, Rangarajan N, Ireland E, Turner MH, Pillow JW, Murthy M. Mapping model units to visual neurons reveals population code for social behaviour. Nature 2024; 629:1100-1108. [PMID: 38778103 PMCID: PMC11136655 DOI: 10.1038/s41586-024-07451-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 04/19/2024] [Indexed: 05/25/2024]
Abstract
The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input1-5 but also how each neuron causally contributes to behaviour6,7. Here we demonstrate a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that we introduce is 'knockout training', which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. We apply this approach to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour8-11. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels12, and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour13,14. Our model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, our framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain15 into the model.
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Affiliation(s)
- Benjamin R Cowley
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Adam J Calhoun
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Elise Ireland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Maxwell H Turner
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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2
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Moreno-Sanchez A, Vasserman AN, Jang H, Hina BW, von Reyn CR, Ausborn J. Morphology and synapse topography optimize linear encoding of synapse numbers in Drosophila looming responsive descending neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.591016. [PMID: 38712267 PMCID: PMC11071487 DOI: 10.1101/2024.04.24.591016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Synapses are often precisely organized on dendritic arbors, yet the role of synaptic topography in dendritic integration remains poorly understood. Utilizing electron microscopy (EM) connectomics we investigate synaptic topography in Drosophila melanogaster looming circuits, focusing on retinotopically tuned visual projection neurons (VPNs) that synapse onto descending neurons (DNs). Synapses of a given VPN type project to non-overlapping regions on DN dendrites. Within these spatially constrained clusters, synapses are not retinotopically organized, but instead adopt near random distributions. To investigate how this organization strategy impacts DN integration, we developed multicompartment models of DNs fitted to experimental data and using precise EM morphologies and synapse locations. We find that DN dendrite morphologies normalize EPSP amplitudes of individual synaptic inputs and that near random distributions of synapses ensure linear encoding of synapse numbers from individual VPNs. These findings illuminate how synaptic topography influences dendritic integration and suggest that linear encoding of synapse numbers may be a default strategy established through connectivity and passive neuron properties, upon which active properties and plasticity can then tune as needed.
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Affiliation(s)
- Anthony Moreno-Sanchez
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - Alexander N. Vasserman
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
| | - HyoJong Jang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Bryce W. Hina
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Catherine R. von Reyn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Jessica Ausborn
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, United States
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3
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Keleş MF, Hardcastle BJ, Städele C, Xiao Q, Frye MA. Inhibitory Interactions and Columnar Inputs to an Object Motion Detector in Drosophila. Cell Rep 2021; 30:2115-2124.e5. [PMID: 32075756 DOI: 10.1016/j.celrep.2020.01.061] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 12/06/2019] [Accepted: 01/16/2020] [Indexed: 02/06/2023] Open
Abstract
The direction-selective T4/T5 cells innervate optic-flow processing projection neurons in the lobula plate of the fly that mediate the visual control of locomotion. In the lobula, visual projection neurons coordinate complex behavioral responses to visual features, however, the input circuitry and computations that bestow their feature-detecting properties are less clear. Here, we study a highly specialized small object motion detector, LC11, and demonstrate that its responses are suppressed by local background motion. We show that LC11 expresses GABA-A receptors that serve to sculpt responses to small objects but are not responsible for the rejection of background motion. Instead, LC11 is innervated by columnar T2 and T3 neurons that are themselves highly sensitive to small static or moving objects, insensitive to wide-field motion and, unlike T4/T5, respond to both ON and OFF luminance steps.
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Affiliation(s)
- Mehmet F Keleş
- University of California, Los Angeles, Department of Integrative Biology and Physiology, 610 Charles Young Drive East, Los Angeles, CA 90095-7239, USA
| | - Ben J Hardcastle
- University of California, Los Angeles, Department of Integrative Biology and Physiology, 610 Charles Young Drive East, Los Angeles, CA 90095-7239, USA
| | - Carola Städele
- University of California, Los Angeles, Department of Integrative Biology and Physiology, 610 Charles Young Drive East, Los Angeles, CA 90095-7239, USA
| | - Qi Xiao
- University of California, Los Angeles, Department of Integrative Biology and Physiology, 610 Charles Young Drive East, Los Angeles, CA 90095-7239, USA; University of California, Los Angeles, Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Mark A Frye
- University of California, Los Angeles, Department of Integrative Biology and Physiology, 610 Charles Young Drive East, Los Angeles, CA 90095-7239, USA.
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4
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Ji X, Yuan D, Wei H, Cheng Y, Wang X, Yang J, Hu P, Gestrich JY, Liu L, Zhu Y. Differentiation of Theta Visual Motion from Fourier Motion Requires LC16 and R18C12 Neurons in Drosophila. iScience 2020; 23:101041. [PMID: 32325414 PMCID: PMC7176990 DOI: 10.1016/j.isci.2020.101041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/09/2020] [Accepted: 04/01/2020] [Indexed: 11/19/2022] Open
Abstract
Many animals perceive features of higher-order visual motion that are beyond the spatiotemporal correlations of luminance defined in first-order motion. Although the neural mechanisms of first-order motion detection have become understood in recent years, those underlying higher-order motion perception remain unclear. Here, we established a paradigm to assess the detection of theta motion—a type of higher-order motion—in freely walking Drosophila. Behavioral screening using this paradigm identified two clusters of neurons in the central brain, designated as R18C12, which were required for perception of theta motion but not for first-order motion. Furthermore, theta motion-activated R18C12 neurons were structurally and functionally located downstream of visual projection neurons in lobula, lobula columnar cells LC16, which activated R18C12 neurons via interactions of acetylcholine (ACh) and muscarinic acetylcholine receptors (mAChRs). The current study provides new insights into LC neurons and the neuronal mechanisms underlying visual information processing in complex natural scenes. Perception of theta motion requires LC16 and R18C12 neurons R18C12 neurons are activated by theta motion R18C12 neurons form synaptic connections with LC16 neurons LC16 neurons activate R18C12 neurons through ACh acting on mAChR
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Affiliation(s)
- Xiaoxiao Ji
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Deliang Yuan
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Hongying Wei
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yaxin Cheng
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Xinwei Wang
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Jihua Yang
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Pengbo Hu
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Julia Yvonne Gestrich
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Li Liu
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China; CAS Key Laboratory of Mental Health, Beijing 100101, P. R. China.
| | - Yan Zhu
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Chaoyang District, Beijing 100101, P. R. China; College of Life Sciences, University of the Chinese Academy of Sciences, Beijing 100049, P. R. China.
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5
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Städele C, Rimniceanu M, Frye MA. Drosophila Neuroscience: Should I Land or Should I Jump? Curr Biol 2019; 29:R1089-R1091. [PMID: 31639356 DOI: 10.1016/j.cub.2019.08.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Information about behavioral states can be integrated in decision-making circuits. In Drosophila, the behavioral state - flying versus not flying - determines whether flies land or jump by dynamically coupling visual information to pre-motor descending neurons.
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Affiliation(s)
- Carola Städele
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90095, USA.
| | - Martha Rimniceanu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90095, USA
| | - Mark A Frye
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90095, USA
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6
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Liang L, Fratzl A, Goldey G, Ramesh RN, Sugden AU, Morgan JL, Chen C, Andermann ML. A Fine-Scale Functional Logic to Convergence from Retina to Thalamus. Cell 2018; 173:1343-1355.e24. [PMID: 29856953 DOI: 10.1016/j.cell.2018.04.041] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 01/31/2018] [Accepted: 04/27/2018] [Indexed: 11/26/2022]
Abstract
Numerous well-defined classes of retinal ganglion cells innervate the thalamus to guide image-forming vision, yet the rules governing their convergence and divergence remain unknown. Using two-photon calcium imaging in awake mouse thalamus, we observed a functional arrangement of retinal ganglion cell axonal boutons in which coarse-scale retinotopic ordering gives way to fine-scale organization based on shared preferences for other visual features. Specifically, at the ∼6 μm scale, clusters of boutons from different axons often showed similar preferences for either one or multiple features, including axis and direction of motion, spatial frequency, and changes in luminance. Conversely, individual axons could "de-multiplex" information channels by participating in multiple, functionally distinct bouton clusters. Finally, ultrastructural analyses demonstrated that retinal axonal boutons in a local cluster often target the same dendritic domain. These data suggest that functionally specific convergence and divergence of retinal axons may impart diverse, robust, and often novel feature selectivity to visual thalamus.
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Affiliation(s)
- Liang Liang
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Alex Fratzl
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Laboratory of Synaptic Mechanisms, Brain Mind Institute, School of Life Science, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Glenn Goldey
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Rohan N Ramesh
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA
| | - Arthur U Sugden
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Josh L Morgan
- Department of Ophthalmology and Visual Sciences, Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Chinfei Chen
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA.
| | - Mark L Andermann
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA.
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