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Zhu J, Boivin JC, Garner A, Ning J, Zhao YQ, Ohyama T. Feedback inhibition by a descending GABAergic neuron regulates timing of escape behavior in Drosophila larvae. eLife 2024; 13:RP93978. [PMID: 39196635 DOI: 10.7554/elife.93978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024] Open
Abstract
Escape behaviors help animals avoid harm from predators and other threats in the environment. Successful escape relies on integrating information from multiple stimulus modalities (of external or internal origin) to compute trajectories toward safe locations, choose between actions that satisfy competing motivations, and execute other strategies that ensure survival. To this end, escape behaviors must be adaptive. When a Drosophila melanogaster larva encounters a noxious stimulus, such as the focal pressure a parasitic wasp applies to the larval cuticle via its ovipositor, it initiates a characteristic escape response. The escape sequence consists of an initial abrupt bending, lateral rolling, and finally rapid crawling. Previous work has shown that the detection of noxious stimuli primarily relies on class IV multi-dendritic arborization neurons (Class IV neurons) located beneath the body wall, and more recent studies have identified several important components in the nociceptive neural circuitry involved in rolling. However, the neural mechanisms that underlie the rolling-escape sequence remain unclear. Here, we present both functional and anatomical evidence suggesting that bilateral descending neurons within the subesophageal zone of D. melanogaster larva play a crucial role in regulating the termination of rolling and subsequent transition to escape crawling. We demonstrate that these descending neurons (designated SeIN128) are inhibitory and receive inputs from a second-order interneuron upstream (Basin-2) and an ascending neuron downstream of Basin-2 (A00c). Together with optogenetic experiments showing that co-activation of SeIN128 neurons and Basin-2 influence the temporal dynamics of rolling, our findings collectively suggest that the ensemble of SeIN128, Basin-2, and A00c neurons forms a GABAergic feedback loop onto Basin-2, which inhibits rolling and thereby facilitates the shift to escape crawling.
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Affiliation(s)
- Jiayi Zhu
- Department of Biology, McGill University, Montreal, Canada
- Integrated Program of Neuroscience, McGill University, Montreal, Canada
| | - Jean-Christophe Boivin
- Department of Biology, McGill University, Montreal, Canada
- Integrated Program of Neuroscience, McGill University, Montreal, Canada
| | - Alastair Garner
- Department of Biology, McGill University, Montreal, Canada
- Integrated Program of Neuroscience, McGill University, Montreal, Canada
| | - Jing Ning
- Department of Biology, McGill University, Montreal, Canada
| | - Yi Q Zhao
- Department of Biology, McGill University, Montreal, Canada
| | - Tomoko Ohyama
- Department of Biology, McGill University, Montreal, Canada
- Alan Edwards Center for Research on Pain, McGill University, Montreal, Canada
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He Y, Ding Y, Gong C, Zhou J, Gong Z. The tail segments are required by the performance but not the accomplishment of various modes of Drosophila larval locomotion. Behav Brain Res 2024; 471:115074. [PMID: 38825023 DOI: 10.1016/j.bbr.2024.115074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/20/2024] [Accepted: 05/27/2024] [Indexed: 06/04/2024]
Abstract
The tail plays important roles in locomotion control in many animals. But in animals with multiple body segments, the roles of the hind body segments and corresponding innervating neurons in locomotion control are not clear. Here, using the Drosophila larva as the model animal, we investigated the roles of the posterior terminal segments in various modes of locomotion and found that they participate in all of them. In forward crawling, paralysis of the larval tail by blocking the Abdb-Gal4 labeled neurons in the posterior segments of VNC led to a slower locomotion speed but did not prevent the initiation of forward peristalsis. In backward crawling, larvae with the Abdb-Gal4 neurons inhibited were unable to generate effective displacement although waves of backward peristalsis could be initiated and persist. In head swing where the movement of the tail is not obvious, disabling the larval tail by blocking Abdb-Gal4 neurons led to increased bending amplitude upon touching the head. In the case of larval lateral rolling, larval tail paralysis by inhibition of Abdb-Gal4 neurons did not prevent the accomplishment of rolling, but resulted in slower rolling speed. Our work reveals that the contribution of Drosophila larval posterior VNC segments and corresponding body segments in the tail to locomotion is comprehensive but could be compensated at least partially by other body segments. We suggest that the decentralization in locomotion control with respect to animal body parts helps to maintain the robustness of locomotion in multi-segment animals.
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Affiliation(s)
- Yinhui He
- Department of neurology of the fourth Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China; Zhejiang Lab, Hangzhou 311121, China
| | - Yimiao Ding
- Department of neurology of the fourth Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Caixia Gong
- Department of Geriatrics, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang province 310003, China; Zhejiang Provincial Key Laboratory for Diagnosis and Treatment of Aging and Physic-chemical Injury Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang province 310003, China
| | - Jinrun Zhou
- Department of neurology of the fourth Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China; Zhejiang Lab, Hangzhou 311121, China
| | - Zhefeng Gong
- Department of neurology of the fourth Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China; Zhejiang Lab, Hangzhou 311121, China.
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3
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Laurent F, Blanc A, May L, Gándara L, Cocanougher BT, Jones BMW, Hague P, Barré C, Vestergaard CL, Crocker J, Zlatic M, Jovanic T, Masson JB. LarvaTagger: manual and automatic tagging of Drosophila larval behaviour. Bioinformatics 2024; 40:btae441. [PMID: 38970365 PMCID: PMC11262801 DOI: 10.1093/bioinformatics/btae441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/03/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024] Open
Abstract
MOTIVATION As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. RESULTS We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. AVAILABILITY AND IMPLEMENTATION All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.
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Affiliation(s)
- François Laurent
- Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, 75015 Paris, France
- Épiméthée, INRIA, 75015 Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France
| | - Alexandre Blanc
- Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, 75015 Paris, France
- Épiméthée, INRIA, 75015 Paris, France
| | - Lilly May
- Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, 75015 Paris, France
- TUM School of Computation, Information and Technology, 80333 Munich, Germany
| | - Lautaro Gándara
- European Molecular Biology Laboratory, Developmental Biology, 69117 Heidelberg, Germany
| | - Benjamin T Cocanougher
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, United States
- MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom
| | - Benjamin M W Jones
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, United States
- MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom
| | - Peter Hague
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, United States
- MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom
| | - Chloé Barré
- Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, 75015 Paris, France
- Épiméthée, INRIA, 75015 Paris, France
| | - Christian L Vestergaard
- Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, 75015 Paris, France
- Épiméthée, INRIA, 75015 Paris, France
| | - Justin Crocker
- European Molecular Biology Laboratory, Developmental Biology, 69117 Heidelberg, Germany
| | - Marta Zlatic
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, United States
- MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom
| | - Tihana Jovanic
- Institut des Neurosciences Paris-Saclay, Université Paris-Saclay, Centre National de la Recherche Scientifique, UMR 9197, 91400 Saclay, France
| | - Jean-Baptiste Masson
- Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, 75015 Paris, France
- Épiméthée, INRIA, 75015 Paris, France
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4
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Kohsaka H. Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion. Front Neural Circuits 2023; 17:1175899. [PMID: 37711343 PMCID: PMC10499525 DOI: 10.3389/fncir.2023.1175899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/13/2023] [Indexed: 09/16/2023] Open
Abstract
The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.
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Affiliation(s)
- Hiroshi Kohsaka
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan
- Department of Complexity Science and Engineering, Graduate School of Frontier Science, The University of Tokyo, Chiba, Japan
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5
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Patel AA, Cardona A, Cox DN. Neural substrates of cold nociception in Drosophila larva. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.31.551339. [PMID: 37577520 PMCID: PMC10418107 DOI: 10.1101/2023.07.31.551339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Metazoans detect and differentiate between innocuous (non-painful) and/or noxious (harmful) environmental cues using primary sensory neurons, which serve as the first node in a neural network that computes stimulus specific behaviors to either navigate away from injury-causing conditions or to perform protective behaviors that mitigate extensive injury. The ability of an animal to detect and respond to various sensory stimuli depends upon molecular diversity in the primary sensors and the underlying neural circuitry responsible for the relevant behavioral action selection. Recent studies in Drosophila larvae have revealed that somatosensory class III multidendritic (CIII md) neurons function as multimodal sensors regulating distinct behavioral responses to innocuous mechanical and nociceptive thermal stimuli. Recent advances in circuit bases of behavior have identified and functionally validated Drosophila larval somatosensory circuitry involved in innocuous (mechanical) and noxious (heat and mechanical) cues. However, central processing of cold nociceptive cues remained unexplored. We implicate multisensory integrators (Basins), premotor (Down-and-Back) and projection (A09e and TePns) neurons as neural substrates required for cold-evoked behavioral and calcium responses. Neural silencing of cell types downstream of CIII md neurons led to significant reductions in cold-evoked behaviors and neural co-activation of CIII md neurons plus additional cell types facilitated larval contraction (CT) responses. We further demonstrate that optogenetic activation of CIII md neurons evokes calcium increases in these neurons. Collectively, we demonstrate how Drosophila larvae process cold stimuli through functionally diverse somatosensory circuitry responsible for generating stimulus specific behaviors.
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Affiliation(s)
- Atit A. Patel
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Albert Cardona
- HHMI Janelia Research Campus, Ashburn, VA, USA
- MRC Laboratory of Molecular Biology, Cambridge, UK
- Department of Physiology, Development, and Neuroscience, University of Cambridge, UK
| | - Daniel N. Cox
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
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6
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Zhu J, Boivin JC, Pang S, Xu CS, Lu Z, Saalfeld S, Hess HF, Ohyama T. Comparative connectomics and escape behavior in larvae of closely related Drosophila species. Curr Biol 2023:S0960-9822(23)00675-9. [PMID: 37285846 DOI: 10.1016/j.cub.2023.05.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 05/02/2023] [Accepted: 05/17/2023] [Indexed: 06/09/2023]
Abstract
Evolution has generated an enormous variety of morphological, physiological, and behavioral traits in animals. How do behaviors evolve in different directions in species equipped with similar neurons and molecular components? Here we adopted a comparative approach to investigate the similarities and differences of escape behaviors in response to noxious stimuli and their underlying neural circuits between closely related drosophilid species. Drosophilids show a wide range of escape behaviors in response to noxious cues, including escape crawling, stopping, head casting, and rolling. Here we find that D. santomea, compared with its close relative D. melanogaster, shows a higher probability of rolling in response to noxious stimulation. To assess whether this behavioral difference could be attributed to differences in neural circuitry, we generated focused ion beam-scanning electron microscope volumes of the ventral nerve cord of D. santomea to reconstruct the downstream partners of mdIV, a nociceptive sensory neuron in D. melanogaster. Along with partner interneurons of mdVI (including Basin-2, a multisensory integration neuron necessary for rolling) previously identified in D. melanogaster, we identified two additional partners of mdVI in D. santomea. Finally, we showed that joint activation of one of the partners (Basin-1) and a common partner (Basin-2) in D. melanogaster increased rolling probability, suggesting that the high rolling probability in D. santomea is mediated by the additional activation of Basin-1 by mdIV. These results provide a plausible mechanistic explanation for how closely related species exhibit quantitative differences in the likelihood of expressing the same behavior.
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Affiliation(s)
- Jiayi Zhu
- Department of Biology, McGill University, Docteur Penfield, Montreal, QC H3A 1B1, Canada; Integrated Program of Neuroscience, McGill University, Pine Avenue W., Montreal, QC H3A 1A1, Canada
| | - Jean-Christophe Boivin
- Department of Biology, McGill University, Docteur Penfield, Montreal, QC H3A 1B1, Canada; Integrated Program of Neuroscience, McGill University, Pine Avenue W., Montreal, QC H3A 1A1, Canada
| | - Song Pang
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Zhiyuan Lu
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Harald F Hess
- Janelia Research Campus, Howard Hughes Medical Institute, Helix Drive, Ashburn, VA 20147, USA
| | - Tomoko Ohyama
- Department of Biology, McGill University, Docteur Penfield, Montreal, QC H3A 1B1, Canada; Alan Edwards Center for Research on Pain, McGill University, University Street, Montreal, QC H3A 2B4, Canada.
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7
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Boivin JC, Zhu J, Ohyama T. Nociception in fruit fly larvae. FRONTIERS IN PAIN RESEARCH 2023; 4:1076017. [PMID: 37006412 PMCID: PMC10063880 DOI: 10.3389/fpain.2023.1076017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
Nociception, the process of encoding and processing noxious or painful stimuli, allows animals to detect and avoid or escape from potentially life-threatening stimuli. Here, we provide a brief overview of recent technical developments and studies that have advanced our understanding of the Drosophila larval nociceptive circuit and demonstrated its potential as a model system to elucidate the mechanistic basis of nociception. The nervous system of a Drosophila larva contains roughly 15,000 neurons, which allows for reconstructing the connectivity among them directly by transmission electron microscopy. In addition, the availability of genetic tools for manipulating the activity of individual neurons and recent advances in computational and high-throughput behavior analysis methods have facilitated the identification of a neural circuit underlying a characteristic nocifensive behavior. We also discuss how neuromodulators may play a key role in modulating the nociceptive circuit and behavioral output. A detailed understanding of the structure and function of Drosophila larval nociceptive neural circuit could provide insights into the organization and operation of pain circuits in mammals and generate new knowledge to advance the development of treatment options for pain in humans.
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Affiliation(s)
- Jean-Christophe Boivin
- Department of Biology, McGill University, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Jiayi Zhu
- Department of Biology, McGill University, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Tomoko Ohyama
- Department of Biology, McGill University, Montreal, QC, Canada
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
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Winding M, Pedigo BD, Barnes CL, Patsolic HG, Park Y, Kazimiers T, Fushiki A, Andrade IV, Khandelwal A, Valdes-Aleman J, Li F, Randel N, Barsotti E, Correia A, Fetter RD, Hartenstein V, Priebe CE, Vogelstein JT, Cardona A, Zlatic M. The connectome of an insect brain. Science 2023; 379:eadd9330. [PMID: 36893230 PMCID: PMC7614541 DOI: 10.1126/science.add9330] [Citation(s) in RCA: 89] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023]
Abstract
Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.
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Affiliation(s)
- Michael Winding
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Benjamin D. Pedigo
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
| | - Christopher L. Barnes
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Heather G. Patsolic
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Accenture, Arlington, VA, USA
| | - Youngser Park
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- kazmos GmbH, Dresden, Germany
| | - Akira Fushiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ingrid V. Andrade
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Avinash Khandelwal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Javier Valdes-Aleman
- University of Cambridge, Department of Zoology, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nadine Randel
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
| | - Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Richard D. Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Stanford University, Stanford, CA, USA
| | - Volker Hartenstein
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Carey E. Priebe
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Joshua T. Vogelstein
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Marta Zlatic
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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9
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Patel AA, Sakurai A, Himmel NJ, Cox DN. Modality specific roles for metabotropic GABAergic signaling and calcium induced calcium release mechanisms in regulating cold nociception. Front Mol Neurosci 2022; 15:942548. [PMID: 36157080 PMCID: PMC9502035 DOI: 10.3389/fnmol.2022.942548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Calcium (Ca2+) plays a pivotal role in modulating neuronal-mediated responses to modality-specific sensory stimuli. Recent studies in Drosophila reveal class III (CIII) multidendritic (md) sensory neurons function as multimodal sensors regulating distinct behavioral responses to innocuous mechanical and nociceptive thermal stimuli. Functional analyses revealed CIII-mediated multimodal behavioral output is dependent upon activation levels with stimulus-evoked Ca2+ displaying relatively low vs. high intracellular levels in response to gentle touch vs. noxious cold, respectively. However, the mechanistic bases underlying modality-specific differential Ca2+ responses in CIII neurons remain incompletely understood. We hypothesized that noxious cold-evoked high intracellular Ca2+ responses in CIII neurons may rely upon Ca2+ induced Ca2+ release (CICR) mechanisms involving transient receptor potential (TRP) channels and/or metabotropic G protein coupled receptor (GPCR) activation to promote cold nociceptive behaviors. Mutant and/or CIII-specific knockdown of GPCR and CICR signaling molecules [GABA B -R2, Gαq, phospholipase C, ryanodine receptor (RyR) and Inositol trisphosphate receptor (IP3R)] led to impaired cold-evoked nociceptive behavior. GPCR mediated signaling, through GABA B -R2 and IP3R, is not required in CIII neurons for innocuous touch evoked behaviors. However, CICR via RyR is required for innocuous touch-evoked behaviors. Disruptions in GABA B -R2, IP3R, and RyR in CIII neurons leads to significantly lower levels of cold-evoked Ca2+ responses indicating GPCR and CICR signaling mechanisms function in regulating Ca2+ release. CIII neurons exhibit bipartite cold-evoked firing patterns, where CIII neurons burst during rapid temperature change and tonically fire during steady state cold temperatures. GABA B -R2 knockdown in CIII neurons resulted in disorganized firing patterns during cold exposure. We further demonstrate that application of GABA or the GABA B specific agonist baclofen potentiates cold-evoked CIII neuron activity. Upon ryanodine application, CIII neurons exhibit increased bursting activity and with CIII-specific RyR knockdown, there is an increase in cold-evoked tonic firing and decrease in bursting. Lastly, our previous studies implicated the TRPP channel Pkd2 in cold nociception, and here, we show that Pkd2 and IP3R genetically interact to specifically regulate cold-evoked behavior, but not innocuous mechanosensation. Collectively, these analyses support novel, modality-specific roles for metabotropic GABAergic signaling and CICR mechanisms in regulating intracellular Ca2+ levels and cold-evoked behavioral output from multimodal CIII neurons.
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Affiliation(s)
| | | | | | - Daniel N. Cox
- Neuroscience Institute, Georgia State University, Atlanta, GA, United States
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10
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Furuya K, Katsumata Y, Ishibashi M, Matsumoto Y, Morimoto T, Aonishi T. Computational model predicts the neural mechanisms of prepulse inhibition in Drosophila larvae. Sci Rep 2022; 12:15211. [PMID: 36075992 PMCID: PMC9458643 DOI: 10.1038/s41598-022-19210-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 08/25/2022] [Indexed: 11/09/2022] Open
Abstract
Prepulse inhibition (PPI) is a behavioural phenomenon in which a preceding weaker stimulus suppresses the startle response to a subsequent stimulus. The effect of PPI has been found to be reduced in psychiatric patients and is a promising neurophysiological indicator of psychiatric disorders. Because the neural circuit of the startle response has been identified at the cellular level, investigating the mechanism underlying PPI in Drosophila melanogaster larvae through experiment-based mathematical modelling can provide valuable insights. We recently identified PPI in Drosophila larvae and found that PPI was reduced in larvae mutated with the Centaurin gamma 1A (CenG1A) gene, which may be associated with autism. In this study, we used numerical simulations to investigate the neural mechanisms underlying PPI in Drosophila larvae. We adjusted the parameters of a previously developed Drosophila larvae computational model and demonstrated that the model could reproduce several behaviours, including PPI. An analysis of the temporal changes in neuronal activity when PPI occurs using our neural circuit model suggested that the activity of specific neurons triggered by prepulses has a considerable effect on PPI. Furthermore, we validated our speculations on PPI reduction in CenG1A mutants with simulations.
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Affiliation(s)
- Kotaro Furuya
- School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama-shi, Kanagawa, 226-8503, Japan.
| | - Yuki Katsumata
- School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama-shi, Kanagawa, 226-8503, Japan
| | - Masayuki Ishibashi
- School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama-shi, Kanagawa, 226-8503, Japan
| | - Yutaro Matsumoto
- School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji-shi, Tokyo, 192-0392, Japan
| | - Takako Morimoto
- School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji-shi, Tokyo, 192-0392, Japan
| | - Toru Aonishi
- School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Yokohama-shi, Kanagawa, 226-8503, Japan.
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11
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Guérinot C, Marcon V, Godard C, Blanc T, Verdier H, Planchon G, Raimondi F, Boddaert N, Alonso M, Sailor K, Lledo PM, Hajj B, El Beheiry M, Masson JB. New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing. FRONTIERS IN BIOINFORMATICS 2022; 1:777101. [PMID: 36303792 PMCID: PMC9580868 DOI: 10.3389/fbinf.2021.777101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/15/2021] [Indexed: 01/02/2023] Open
Abstract
Three-dimensional imaging is at the core of medical imaging and is becoming a standard in biological research. As a result, there is an increasing need to visualize, analyze and interact with data in a natural three-dimensional context. By combining stereoscopy and motion tracking, commercial virtual reality (VR) headsets provide a solution to this critical visualization challenge by allowing users to view volumetric image stacks in a highly intuitive fashion. While optimizing the visualization and interaction process in VR remains an active topic, one of the most pressing issue is how to utilize VR for annotation and analysis of data. Annotating data is often a required step for training machine learning algorithms. For example, enhancing the ability to annotate complex three-dimensional data in biological research as newly acquired data may come in limited quantities. Similarly, medical data annotation is often time-consuming and requires expert knowledge to identify structures of interest correctly. Moreover, simultaneous data analysis and visualization in VR is computationally demanding. Here, we introduce a new procedure to visualize, interact, annotate and analyze data by combining VR with cloud computing. VR is leveraged to provide natural interactions with volumetric representations of experimental imaging data. In parallel, cloud computing performs costly computations to accelerate the data annotation with minimal input required from the user. We demonstrate multiple proof-of-concept applications of our approach on volumetric fluorescent microscopy images of mouse neurons and tumor or organ annotations in medical images.
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Affiliation(s)
- Corentin Guérinot
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
- Sorbonne Université, Collège Doctoral, Paris, France
| | - Valentin Marcon
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
| | - Charlotte Godard
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- École Doctorale Physique en Île-de-France, PSL University, Paris, France
| | - Thomas Blanc
- Sorbonne Université, Collège Doctoral, Paris, France
- Laboratoire Physico-Chimie, Institut Curie, PSL Research University, CNRS UMR168, Paris, France
| | - Hippolyte Verdier
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- Histopathology and Bio-Imaging Group, Sanofi R&D, Vitry-Sur-Seine, France
- Université de Paris, UFR de Physique, Paris, France
| | - Guillaume Planchon
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
| | - Francesca Raimondi
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
- Unité Médicochirurgicale de Cardiologie Congénitale et Pédiatrique, Centre de Référence des Malformations Cardiaques Congénitales Complexes M3C, Hôpital Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
- Pediatric Radiology Unit, Hôpital Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
- UMR-1163 Institut Imagine, Hôpital Universitaire Necker-Enfants Malades, AP-HP, Paris, France
| | - Nathalie Boddaert
- Pediatric Radiology Unit, Hôpital Universitaire Necker-Enfants Malades, Université de Paris, Paris, France
- UMR-1163 Institut Imagine, Hôpital Universitaire Necker-Enfants Malades, AP-HP, Paris, France
| | - Mariana Alonso
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
| | - Kurt Sailor
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
| | - Pierre-Marie Lledo
- Perception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France
| | - Bassam Hajj
- Sorbonne Université, Collège Doctoral, Paris, France
- École Doctorale Physique en Île-de-France, PSL University, Paris, France
| | - Mohamed El Beheiry
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
| | - Jean-Baptiste Masson
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France
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12
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Croteau-Chonka EC, Clayton MS, Venkatasubramanian L, Harris SN, Jones BMW, Narayan L, Winding M, Masson JB, Zlatic M, Klein KT. High-throughput automated methods for classical and operant conditioning of Drosophila larvae. eLife 2022; 11:70015. [PMID: 36305588 PMCID: PMC9678368 DOI: 10.7554/elife.70015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/26/2022] [Indexed: 02/02/2023] Open
Abstract
Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i.e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i.e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.
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Affiliation(s)
- Elise C Croteau-Chonka
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom,Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | | | | | | | - Lakshmi Narayan
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Michael Winding
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom,Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Jean-Baptiste Masson
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States,Decision and Bayesian Computation, Neuroscience Department & Computational Biology Department, Institut PasteurParisFrance
| | - Marta Zlatic
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom,Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States,MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Kristina T Klein
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom,Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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13
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Development of motor circuits: From neuronal stem cells and neuronal diversity to motor circuit assembly. Curr Top Dev Biol 2020; 142:409-442. [PMID: 33706923 DOI: 10.1016/bs.ctdb.2020.11.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
In this review, we discuss motor circuit assembly starting from neuronal stem cells. Until recently, studies of neuronal stem cells focused on how a relatively small pool of stem cells could give rise to a large diversity of different neuronal identities. Historically, neuronal identity has been assayed in embryos by gene expression, gross anatomical features, neurotransmitter expression, and physiological properties. However, these definitions of identity are largely unlinked to mature functional neuronal features relevant to motor circuits. Such mature neuronal features include presynaptic and postsynaptic partnerships, dendrite morphologies, as well as neuronal firing patterns and roles in behavior. This review focuses on recent work that links the specification of neuronal molecular identity in neuronal stem cells to mature, circuit-relevant identity specification. Specifically, these studies begin to address the question: to what extent are the decisions that occur during motor circuit assembly controlled by the same genetic information that generates diverse embryonic neuronal diversity? Much of the research addressing this question has been conducted using the Drosophila larval motor system. Here, we focus largely on Drosophila motor circuits and we point out parallels to other systems. And we highlight outstanding questions in the field. The main concepts addressed in this review are: (1) the description of temporal cohorts-novel units of developmental organization that link neuronal stem cell lineages to motor circuit configuration and (2) the discovery that temporal transcription factors expressed in neuronal stem cells control aspects of circuit assembly by controlling the size of temporal cohorts and influencing synaptic partner choice.
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14
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Eschbach C, Zlatic M. Useful road maps: studying Drosophila larva's central nervous system with the help of connectomics. Curr Opin Neurobiol 2020; 65:129-137. [PMID: 33242722 PMCID: PMC7773133 DOI: 10.1016/j.conb.2020.09.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/21/2020] [Accepted: 09/24/2020] [Indexed: 12/16/2022]
Abstract
The larva of Drosophila melanogaster is emerging as a powerful model system for comprehensive brain-wide understanding of the circuit implementation of neural computations. With an unprecedented amount of tools in hand, including synaptic-resolution connectomics, whole-brain imaging, and genetic tools for selective targeting of single neuron types, it is possible to dissect which circuits and computations are at work behind behaviors that have an interesting level of complexity. Here we present some of the recent advances regarding multisensory integration, learning, and action selection in Drosophila larva.
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Affiliation(s)
- Claire Eschbach
- Department of Zoology, University of Cambridge, United Kingdom.
| | - Marta Zlatic
- Department of Zoology, University of Cambridge, United Kingdom; MRC Laboratory of Molecular Biology, United Kingdom.
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15
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Jovanic T. Studying neural circuits of decision-making in Drosophila larva. J Neurogenet 2020; 34:162-170. [PMID: 32054384 DOI: 10.1080/01677063.2020.1719407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
To study neural circuits underlying decisions, the model organism used for that purpose has to be simple enough to be able to dissect the circuitry neuron by neuron across the nervous system and in the same time complex enough to be able to perform different types of decisions. Here, I lay out the case: (1) that Drosophila larva is an advantageous model system that balances well these two requirements and (2) the insights gained from this model, assuming that circuit principles may be shared across species, can be used to advance our knowledge of neural circuit implementation of decision-making in general, including in more complex brains.
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Affiliation(s)
- Tihana Jovanic
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris Saclay, Gif-sur-Yvette, France.,Decision and Bayesian Computation, UMR 3571 Neuroscience Department & USR 3756 (C3BI/DBC), Institut Pasteur & CNRS, Paris, France
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