1
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Meschi E, Duquenoy L, Otto N, Dempsey G, Waddell S. Compensatory enhancement of input maintains aversive dopaminergic reinforcement in hungry Drosophila. Neuron 2024; 112:2315-2332.e8. [PMID: 38795709 DOI: 10.1016/j.neuron.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/12/2024] [Accepted: 04/30/2024] [Indexed: 05/28/2024]
Abstract
Hungry animals need compensatory mechanisms to maintain flexible brain function, while modulation reconfigures circuits to prioritize resource seeking. In Drosophila, hunger inhibits aversively reinforcing dopaminergic neurons (DANs) to permit the expression of food-seeking memories. Multitasking the reinforcement system for motivation potentially undermines aversive learning. We find that chronic hunger mildly enhances aversive learning and that satiated-baseline and hunger-enhanced learning require endocrine adipokinetic hormone (AKH) signaling. Circulating AKH influences aversive learning via its receptor in four neurons in the ventral brain, two of which are octopaminergic. Connectomics revealed AKH receptor-expressing neurons to be upstream of several classes of ascending neurons, many of which are presynaptic to aversively reinforcing DANs. Octopaminergic modulation of and output from at least one of these ascending pathways is required for shock- and bitter-taste-reinforced aversive learning. We propose that coordinated enhancement of input compensates for hunger-directed inhibition of aversive DANs to preserve reinforcement when required.
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Affiliation(s)
- Eleonora Meschi
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Lucille Duquenoy
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Nils Otto
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Georgia Dempsey
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Scott Waddell
- University of Oxford, Centre for Neural Circuits and Behaviour, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK.
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2
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Fulton KA, Zimmerman D, Samuel A, Vogt K, Datta SR. Common principles for odour coding across vertebrates and invertebrates. Nat Rev Neurosci 2024; 25:453-472. [PMID: 38806946 DOI: 10.1038/s41583-024-00822-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.
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Affiliation(s)
- Kara A Fulton
- Department of Neuroscience, Harvard Medical School, Boston, MA, USA
| | - David Zimmerman
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Aravi Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Katrin Vogt
- Department of Physics, Harvard University, Cambridge, MA, USA.
- Department of Biology, University of Konstanz, Konstanz, Germany.
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.
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3
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Chakroborty NK, Leboulle, Einspanier R, Menzel R. Behavioral and genetic correlates of heterogeneity in learning performance in individual honeybees, Apis mellifera. PLoS One 2024; 19:e0304563. [PMID: 38865313 PMCID: PMC11168654 DOI: 10.1371/journal.pone.0304563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Learning an olfactory discrimination task leads to heterogeneous results in honeybees with some bees performing very well and others at low rates. Here we investigated this behavioral heterogeneity and asked whether it was associated with particular gene expression patterns in the bee's brain. Bees were individually conditioned using a sequential conditioning protocol involving several phases of olfactory learning and retention tests. A cumulative score was used to differentiate the tested bees into high and low performers. The rate of CS+ odor learning was found to correlate most strongly with a cumulative performance score extracted from all learning and retention tests. Microarray analysis of gene expression in the mushroom body area of the brains of these bees identified a number of differentially expressed genes between high and low performers. These genes are associated with diverse biological functions, such as neurotransmission, memory formation, cargo trafficking and development.
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Affiliation(s)
- Neloy Kumar Chakroborty
- Institute Biology, Neurobiology, Freie Universität Berlin, Königin Luisestr, Berlin, Germany
| | - Leboulle
- Institute Biology, Neurobiology, Freie Universität Berlin, Königin Luisestr, Berlin, Germany
| | - Ralf Einspanier
- Department of Veterinary Medicine, Institute of Veterinary Biochemistry, Freie Universität Berlin, Oertzenweg, Berlin, Germany
| | - Randolf Menzel
- Institute Biology, Neurobiology, Freie Universität Berlin, Königin Luisestr, Berlin, Germany
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4
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Boto T, Tomchik SM. Functional Imaging of Learning-Induced Plasticity in the Central Nervous System with Genetically Encoded Reporters in Drosophila. Cold Spring Harb Protoc 2024; 2024:pdb.top107799. [PMID: 37197830 DOI: 10.1101/pdb.top107799] [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] [Indexed: 05/19/2023]
Abstract
Learning and memory allow animals to adjust their behavior based on the predictive value of their past experiences. Memories often exist in complex representations, spread across numerous cells and synapses in the brain. Studying relatively simple forms of memory provides insights into the fundamental processes that underlie multiple forms of memory. Associative learning occurs when an animal learns the relationship between two previously unrelated sensory stimuli, such as when a hungry animal learns that a particular odor is followed by a tasty reward. Drosophila is a particularly powerful model to study how this type of memory works. The fundamental principles are widely shared among animals, and there is a wide range of genetic tools available to study circuit function in flies. In addition, the olfactory structures that mediate associative learning in flies, such as the mushroom body and its associated neurons, are anatomically organized, relatively well-characterized, and readily accessible to imaging. Here, we review the olfactory anatomy and physiology of the olfactory system, describe how plasticity in the olfactory pathway mediates learning and memory, and explain the general principles underlying calcium imaging approaches.
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Affiliation(s)
- Tamara Boto
- Department of Physiology, Trinity College Dublin, Dublin 2, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Seth M Tomchik
- Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
- Stead Family Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
- Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, Iowa 52242, USA
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5
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Liu Y, Ye S, Li XN, Li WG. Memory Trace for Fear Extinction: Fragile yet Reinforceable. Neurosci Bull 2024; 40:777-794. [PMID: 37812300 PMCID: PMC11178705 DOI: 10.1007/s12264-023-01129-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/08/2023] [Indexed: 10/10/2023] Open
Abstract
Fear extinction is a biological process in which learned fear behavior diminishes without anticipated reinforcement, allowing the organism to re-adapt to ever-changing situations. Based on the behavioral hypothesis that extinction is new learning and forms an extinction memory, this new memory is more readily forgettable than the original fear memory. The brain's cellular and synaptic traces underpinning this inherently fragile yet reinforceable extinction memory remain unclear. Intriguing questions are about the whereabouts of the engram neurons that emerged during extinction learning and how they constitute a dynamically evolving functional construct that works in concert to store and express the extinction memory. In this review, we discuss recent advances in the engram circuits and their neural connectivity plasticity for fear extinction, aiming to establish a conceptual framework for understanding the dynamic competition between fear and extinction memories in adaptive control of conditioned fear responses.
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Affiliation(s)
- Ying Liu
- Department of Rehabilitation Medicine, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Huashan Hospital, Institute for Translational Brain Research, Fudan University, Shanghai, 200032, China
| | - Shuai Ye
- Department of Rehabilitation Medicine, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Huashan Hospital, Institute for Translational Brain Research, Fudan University, Shanghai, 200032, China
| | - Xin-Ni Li
- Department of Rehabilitation Medicine, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Huashan Hospital, Institute for Translational Brain Research, Fudan University, Shanghai, 200032, China
| | - Wei-Guang Li
- Department of Rehabilitation Medicine, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Huashan Hospital, Institute for Translational Brain Research, Fudan University, Shanghai, 200032, China.
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6
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Eckstein N, Bates AS, Champion A, Du M, Yin Y, Schlegel P, Lu AKY, Rymer T, Finley-May S, Paterson T, Parekh R, Dorkenwald S, Matsliah A, Yu SC, McKellar C, Sterling A, Eichler K, Costa M, Seung S, Murthy M, Hartenstein V, Jefferis GSXE, Funke J. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 2024; 187:2574-2594.e23. [PMID: 38729112 PMCID: PMC11106717 DOI: 10.1016/j.cell.2024.03.016] [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: 04/02/2023] [Revised: 10/04/2023] [Accepted: 03/13/2024] [Indexed: 05/12/2024]
Abstract
High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.
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Affiliation(s)
- Nils Eckstein
- HHMI Janelia Research Campus, Ashburn, VA, USA; Institute of Neuroinformatics UZH/ETHZ, Zurich, Switzerland
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Centre for Neural Circuits and Behaviour, The University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK; Department of Neurobiology and Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Andrew Champion
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Michelle Du
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Yijie Yin
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | | | | | | | | | | | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Volker Hartenstein
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK.
| | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, VA, USA.
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7
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Dwijesha AS, Eswaran A, Berry JA, Phan A. Diverse memory paradigms in Drosophila reveal diverse neural mechanisms. Learn Mem 2024; 31:a053810. [PMID: 38862165 PMCID: PMC11199951 DOI: 10.1101/lm.053810.123] [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: 10/14/2023] [Accepted: 01/12/2024] [Indexed: 06/13/2024]
Abstract
In this review, we aggregated the different types of learning and memory paradigms developed in adult Drosophila and attempted to assess the similarities and differences in the neural mechanisms supporting diverse types of memory. The simplest association memory assays are conditioning paradigms (olfactory, visual, and gustatory). A great deal of work has been done on these memories, revealing hundreds of genes and neural circuits supporting this memory. Variations of conditioning assays (reversal learning, trace conditioning, latent inhibition, and extinction) also reveal interesting memory mechanisms, whereas mechanisms supporting spatial memory (thermal maze, orientation memory, and heat box) and the conditioned suppression of innate behaviors (phototaxis, negative geotaxis, anemotaxis, and locomotion) remain largely unexplored. In recent years, there has been an increased interest in multisensory and multicomponent memories (context-dependent and cross-modal memory) and higher-order memory (sensory preconditioning and second-order conditioning). Some of this work has revealed how the intricate mushroom body (MB) neural circuitry can support more complex memories. Finally, the most complex memories are arguably those involving social memory: courtship conditioning and social learning (mate-copying and egg-laying behaviors). Currently, very little is known about the mechanisms supporting social memories. Overall, the MBs are important for association memories of multiple sensory modalities and multisensory integration, whereas the central complex is important for place, orientation, and navigation memories. Interestingly, several different types of memory appear to use similar or variants of the olfactory conditioning neural circuitry, which are repurposed in different ways.
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Affiliation(s)
- Amoolya Sai Dwijesha
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Akhila Eswaran
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Jacob A Berry
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Anna Phan
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
- Women and Children's Health Research Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
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8
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Pribbenow C, Owald D. Skewing information flow through pre- and postsynaptic plasticity in the mushroom bodies of Drosophila. Learn Mem 2024; 31:a053919. [PMID: 38876487 PMCID: PMC11199954 DOI: 10.1101/lm.053919.124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/26/2024] [Indexed: 06/16/2024]
Abstract
Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.
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Affiliation(s)
- Carlotta Pribbenow
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - David Owald
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany
- NeuroCure, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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9
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Lv M, Cai R, Zhang R, Xia X, Li X, Wang Y, Wang H, Zeng J, Xue Y, Mao L, Li Y. An octopamine-specific GRAB sensor reveals a monoamine relay circuitry that boosts aversive learning. Natl Sci Rev 2024; 11:nwae112. [PMID: 38798960 PMCID: PMC11126161 DOI: 10.1093/nsr/nwae112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 05/29/2024] Open
Abstract
Octopamine (OA), analogous to norepinephrine in vertebrates, is an essential monoamine neurotransmitter in invertebrates that plays a significant role in various biological functions, including olfactory associative learning. However, the spatial and temporal dynamics of OA in vivo remain poorly understood due to limitations associated with the currently available methods used to detect it. To overcome these limitations, we developed a genetically encoded GPCR activation-based (GRAB) OA sensor called GRABOA1.0. This sensor is highly selective for OA and exhibits a robust and rapid increase in fluorescence in response to extracellular OA. Using GRABOA1.0, we monitored OA release in the Drosophila mushroom body (MB), the fly's learning center, and found that OA is released in response to both odor and shock stimuli in an aversive learning model. This OA release requires acetylcholine (ACh) released from Kenyon cells, signaling via nicotinic ACh receptors. Finally, we discovered that OA amplifies aversive learning behavior by augmenting dopamine-mediated punishment signals via Octβ1R in dopaminergic neurons, leading to alterations in synaptic plasticity within the MB. Thus, our new GRABOA1.0 sensor can be used to monitor OA release in real time under physiological conditions, providing valuable insights into the cellular and circuit mechanisms that underlie OA signaling.
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Affiliation(s)
- Mingyue Lv
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Ruyi Cai
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Renzimo Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Yuanpei College, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Huan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Jianzhi Zeng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Yifei Xue
- College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Lanqun Mao
- College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Yuanpei College, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518107, China
- Chinese Institute for Brain Research, Beijing 102206, China
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10
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Suárez-Grimalt R, Grunwald Kadow IC, Scheunemann L. An integrative sensor of body states: how the mushroom body modulates behavior depending on physiological context. Learn Mem 2024; 31:a053918. [PMID: 38876486 PMCID: PMC11199956 DOI: 10.1101/lm.053918.124] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/08/2024] [Indexed: 06/16/2024]
Abstract
The brain constantly compares past and present experiences to predict the future, thereby enabling instantaneous and future behavioral adjustments. Integration of external information with the animal's current internal needs and behavioral state represents a key challenge of the nervous system. Recent advancements in dissecting the function of the Drosophila mushroom body (MB) at the single-cell level have uncovered its three-layered logic and parallel systems conveying positive and negative values during associative learning. This review explores a lesser-known role of the MB in detecting and integrating body states such as hunger, thirst, and sleep, ultimately modulating motivation and sensory-driven decisions based on the physiological state of the fly. State-dependent signals predominantly affect the activity of modulatory MB input neurons (dopaminergic, serotoninergic, and octopaminergic), but also induce plastic changes directly at the level of the MB intrinsic and output neurons. Thus, the MB emerges as a tightly regulated relay station in the insect brain, orchestrating neuroadaptations due to current internal and behavioral states leading to short- but also long-lasting changes in behavior. While these adaptations are crucial to ensure fitness and survival, recent findings also underscore how circuit motifs in the MB may reflect fundamental design principles that contribute to maladaptive behaviors such as addiction or depression-like symptoms.
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Affiliation(s)
- Raquel Suárez-Grimalt
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany
- Institut für Neurophysiologie and NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | | | - Lisa Scheunemann
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany
- Institut für Neurophysiologie and NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
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11
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Webb B. Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body. Learn Mem 2024; 31:a053824. [PMID: 38862164 PMCID: PMC11199945 DOI: 10.1101/lm.053824.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/01/2024] [Indexed: 06/13/2024]
Abstract
The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.
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Affiliation(s)
- Barbara Webb
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
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12
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Parnas M, Manoim JE, Lin AC. Sensory encoding and memory in the mushroom body: signals, noise, and variability. Learn Mem 2024; 31:a053825. [PMID: 38862174 PMCID: PMC11199953 DOI: 10.1101/lm.053825.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/21/2023] [Indexed: 06/13/2024]
Abstract
To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.
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Affiliation(s)
- Moshe Parnas
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Julia E Manoim
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Andrew C Lin
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, United Kingdom
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13
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Yamada D, Davidson AM, Hige T. Cyclic nucleotide-induced bidirectional long-term synaptic plasticity in Drosophila mushroom body. J Physiol 2024; 602:2019-2045. [PMID: 38488688 PMCID: PMC11068490 DOI: 10.1113/jp285745] [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: 09/30/2023] [Accepted: 02/21/2024] [Indexed: 03/26/2024] Open
Abstract
Activation of the cAMP pathway is one of the common mechanisms underlying long-term potentiation (LTP). In the Drosophila mushroom body, simultaneous activation of odour-coding Kenyon cells (KCs) and reinforcement-coding dopaminergic neurons activates adenylyl cyclase in KC presynaptic terminals, which is believed to trigger synaptic plasticity underlying olfactory associative learning. However, learning induces long-term depression (LTD) at these synapses, contradicting the universal role of cAMP as a facilitator of transmission. Here, we developed a system to electrophysiologically monitor both short-term and long-term synaptic plasticity at KC output synapses and demonstrated that they are indeed an exception in which activation of the cAMP-protein kinase A pathway induces LTD. Contrary to the prevailing model, our cAMP imaging found no evidence for synergistic action of dopamine and KC activity on cAMP synthesis. Furthermore, we found that forskolin-induced cAMP increase alone was insufficient for plasticity induction; it additionally required simultaneous KC activation to replicate the presynaptic LTD induced by pairing with dopamine. On the other hand, activation of the cGMP pathway paired with KC activation induced slowly developing LTP, proving antagonistic actions of the two second-messenger pathways predicted by behavioural study. Finally, KC subtype-specific interrogation of synapses revealed that different KC subtypes exhibit distinct plasticity duration even among synapses on the same postsynaptic neuron. Thus, our work not only revises the role of cAMP in synaptic plasticity by uncovering the unexpected convergence point of the cAMP pathway and neuronal activity, but also establishes the methods to address physiological mechanisms of synaptic plasticity in this important model. KEY POINTS: Although presynaptic cAMP increase generally facilitates synapses, olfactory associative learning in Drosophila, which depends on dopamine and cAMP signalling genes, induces long-term depression (LTD) at the mushroom body output synapses. By combining electrophysiology, pharmacology and optogenetics, we directly demonstrate that these synapses are an exception where activation of the cAMP-protein kinase A pathway leads to presynaptic LTD. Dopamine- or forskolin-induced cAMP increase alone is not sufficient for LTD induction; neuronal activity, which has been believed to trigger cAMP synthesis in synergy with dopamine input, is required in the downstream pathway of cAMP. In contrast to cAMP, activation of the cGMP pathway paired with neuronal activity induces presynaptic long-term potentiation, which explains behaviourally observed opposing actions of transmitters co-released by dopaminergic neurons. Our work not only revises the role of cAMP in synaptic plasticity, but also provides essential methods to address physiological mechanisms of synaptic plasticity in this important model system.
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Affiliation(s)
- Daichi Yamada
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Andrew M. Davidson
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, United States
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14
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Davidson AM, Hige T. Roles of feedback and feed-forward networks of dopamine subsystems: insights from Drosophila studies. Learn Mem 2024; 31:a053807. [PMID: 38862171 PMCID: PMC11199952 DOI: 10.1101/lm.053807.123] [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: 08/30/2023] [Accepted: 11/10/2023] [Indexed: 06/13/2024]
Abstract
Across animal species, dopamine-operated memory systems comprise anatomically segregated, functionally diverse subsystems. Although individual subsystems could operate independently to support distinct types of memory, the logical interplay between subsystems is expected to enable more complex memory processing by allowing existing memory to influence future learning. Recent comprehensive ultrastructural analysis of the Drosophila mushroom body revealed intricate networks interconnecting the dopamine subsystems-the mushroom body compartments. Here, we review the functions of some of these connections that are beginning to be understood. Memory consolidation is mediated by two different forms of network: A recurrent feedback loop within a compartment maintains sustained dopamine activity required for consolidation, whereas feed-forward connections across compartments allow short-term memory formation in one compartment to open the gate for long-term memory formation in another compartment. Extinction and reversal of aversive memory rely on a similar feed-forward circuit motif that signals omission of punishment as a reward, which triggers plasticity that counteracts the original aversive memory trace. Finally, indirect feed-forward connections from a long-term memory compartment to short-term memory compartments mediate higher-order conditioning. Collectively, these emerging studies indicate that feedback control and hierarchical connectivity allow the dopamine subsystems to work cooperatively to support diverse and complex forms of learning.
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Affiliation(s)
- Andrew M Davidson
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
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15
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Zárate-Rochín AM. Contemporary neurocognitive models of memory: A descriptive comparative analysis. Neuropsychologia 2024; 196:108846. [PMID: 38430963 DOI: 10.1016/j.neuropsychologia.2024.108846] [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: 11/03/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.
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Affiliation(s)
- Alba Marcela Zárate-Rochín
- Instituto de Investigaciones Cerebrales, Universidad Veracruzana, Dr. Castelazo Ayala s/n, Industrial Animas, 91190, Xalapa-Enríquez, Veracruz, Mexico.
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16
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Lv M, Cai R, Zhang R, Xia X, Li X, Wang Y, Wang H, Zeng J, Xue Y, Mao L, Li Y. An octopamine-specific GRAB sensor reveals a monoamine relay circuitry that boosts aversive learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.09.584200. [PMID: 38559104 PMCID: PMC10979849 DOI: 10.1101/2024.03.09.584200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Octopamine (OA), analogous to norepinephrine in vertebrates, is an essential monoamine neurotransmitter in invertebrates that plays a significant role in various biological functions, including olfactory associative learning. However, the spatial and temporal dynamics of OA in vivo remain poorly understood due to limitations associated with the currently available methods used to detect it. To overcome these limitations, we developed a genetically encoded GPCR activation-based (GRAB) OA sensor called GRABOA1.0. This sensor is highly selective for OA and exhibits a robust and rapid increase in fluorescence in response to extracellular OA. Using GRABOA1.0, we monitored OA release in the Drosophila mushroom body (MB), the fly's learning center, and found that OA is released in response to both odor and shock stimuli in an aversive learning model. This OA release requires acetylcholine (ACh) released from Kenyon cells, signaling via nicotinic ACh receptors. Finally, we discovered that OA amplifies aversive learning behavior by augmenting dopamine-mediated punishment signals via Octβ1R in dopaminergic neurons, leading to alterations in synaptic plasticity within the MB. Thus, our new GRABOA1.0 sensor can be used to monitor OA release in real-time under physiological conditions, providing valuable insights into the cellular and circuit mechanisms that underlie OA signaling.
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Affiliation(s)
- Mingyue Lv
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Ruyi Cai
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Renzimo Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Yuanpei College, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Huan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Jianzhi Zeng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Yifei Xue
- College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Lanqun Mao
- College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Yuanpei College, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518107, China
- Chinese Institute for Brain Research, Beijing 102206, China
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17
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Cornean J, Molina-Obando S, Gür B, Bast A, Ramos-Traslosheros G, Chojetzki J, Lörsch L, Ioannidou M, Taneja R, Schnaitmann C, Silies M. Heterogeneity of synaptic connectivity in the fly visual system. Nat Commun 2024; 15:1570. [PMID: 38383614 PMCID: PMC10882054 DOI: 10.1038/s41467-024-45971-z] [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: 12/15/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Visual systems are homogeneous structures, where repeating columnar units retinotopically cover the visual field. Each of these columns contain many of the same neuron types that are distinguished by anatomic, genetic and - generally - by functional properties. However, there are exceptions to this rule. In the 800 columns of the Drosophila eye, there is an anatomically and genetically identifiable cell type with variable functional properties, Tm9. Since anatomical connectivity shapes functional neuronal properties, we identified the presynaptic inputs of several hundred Tm9s across both optic lobes using the full adult female fly brain (FAFB) electron microscopic dataset and FlyWire connectome. Our work shows that Tm9 has three major and many sparsely distributed inputs. This differs from the presynaptic connectivity of other Tm neurons, which have only one major, and more stereotypic inputs than Tm9. Genetic synapse labeling showed that the heterogeneous wiring exists across individuals. Together, our data argue that the visual system uses heterogeneous, distributed circuit properties to achieve robust visual processing.
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Affiliation(s)
- Jacqueline Cornean
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Sebastian Molina-Obando
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Burak Gür
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Annika Bast
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Giordano Ramos-Traslosheros
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Jonas Chojetzki
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Lena Lörsch
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Maria Ioannidou
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Rachita Taneja
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Christopher Schnaitmann
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany
| | - Marion Silies
- Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University, 55128, Mainz, Germany.
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18
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Toshima N, Schleyer M. IR76b-expressing neurons in Drosophila melanogaster are necessary for associative reward learning of an amino acid mixture. Biol Lett 2024; 20:20230519. [PMID: 38351746 PMCID: PMC10865000 DOI: 10.1098/rsbl.2023.0519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/18/2024] [Indexed: 02/16/2024] Open
Abstract
Learning where to find nutrients while at the same time avoiding toxic food is essential for survival of any animal. Using Drosophila melanogaster larvae as a study case, we investigate the role of gustatory sensory neurons expressing IR76b for associative learning of amino acids, the building blocks of proteins. We found surprising complexity in the neuronal underpinnings of sensing amino acids, and a functional division of sensory neurons. We found that the IR76b receptor is dispensable for amino acid learning, whereas the neurons expressing IR76b are specifically required for the rewarding but not the punishing effect of amino acids. This unexpected dissociation in neuronal processing of amino acids for different behavioural functions provides a study case for functional divisions of labour in gustatory systems.
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Affiliation(s)
- Naoko Toshima
- Department Genetics of Learning and Memory, Leibniz-Institute for Neurobiology, Magdeburg 39118, Germany
- Institute for the Advancement of Higher Education, Hokkaido University, Sapporo 060-0810, Japan
| | - Michael Schleyer
- Department Genetics of Learning and Memory, Leibniz-Institute for Neurobiology, Magdeburg 39118, Germany
- Institute for the Advancement of Higher Education, Hokkaido University, Sapporo 060-0810, Japan
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19
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Jürgensen AM, Sakagiannis P, Schleyer M, Gerber B, Nawrot MP. Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva. iScience 2024; 27:108640. [PMID: 38292165 PMCID: PMC10824792 DOI: 10.1016/j.isci.2023.108640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 11/10/2023] [Accepted: 12/01/2023] [Indexed: 02/01/2024] Open
Abstract
Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.
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Affiliation(s)
- Anna-Maria Jürgensen
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Panagiotis Sakagiannis
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for the Advancement of Higher Education, Faculty of Science, Hokkaido University, Sapporo 060-08080, Japan
| | - Bertram Gerber
- Leibniz Institute for Neurobiology (LIN), Department of Genetics, 39118 Magdeburg, Germany
- Institute for Biology, Otto-von-Guericke University, 39120 Magdeburg, Germany
- Center for Brain and Behavioral Sciences (CBBS), Otto-von-Guericke University, 39118 Magdeburg, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, 50674 Cologne, Germany
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20
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Jürgensen AM, Schmitt FJ, Nawrot MP. Minimal circuit motifs for second-order conditioning in the insect mushroom body. Front Physiol 2024; 14:1326307. [PMID: 38269060 PMCID: PMC10806035 DOI: 10.3389/fphys.2023.1326307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024] Open
Abstract
In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. In the insect mushroom body, a brain region central to learning and memory, such associations are encoded in the synapses between its intrinsic and output neurons. This process is mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explored minimal circuit motifs in the mushroom body for their ability to support second-order conditioning using mechanistic models. We found that dopaminergic neurons can either be activated directly by the mushroom body's intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrated that the circuit motifs differ in their computational efficiency and robustness. Beyond previous research, we suggest an additional motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.
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Affiliation(s)
- Anna-Maria Jürgensen
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany
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21
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Yamada D, Davidson AM, Hige T. Cyclic nucleotide-induced bidirectional long-term synaptic plasticity in Drosophila mushroom body. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.28.560058. [PMID: 37808762 PMCID: PMC10557778 DOI: 10.1101/2023.09.28.560058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Activation of the cAMP pathway is one of the common mechanisms underlying long-term potentiation (LTP). In the Drosophila mushroom body, simultaneous activation of odor-coding Kenyon cells (KCs) and reinforcement-coding dopaminergic neurons activates adenylyl cyclase in KC presynaptic terminals, which is believed to trigger synaptic plasticity underlying olfactory associative learning. However, learning induces long-term depression (LTD) at these synapses, contradicting the universal role of cAMP as a facilitator of transmission. Here, we develop a system to electrophysiologically monitor both short-term and long-term synaptic plasticity at KC output synapses and demonstrate that they are indeed an exception where activation of the cAMP/protein kinase A pathway induces LTD. Contrary to the prevailing model, our cAMP imaging finds no evidence for synergistic action of dopamine and KC activity on cAMP synthesis. Furthermore, we find that forskolin-induced cAMP increase alone is insufficient for plasticity induction; it additionally requires simultaneous KC activation to replicate the presynaptic LTD induced by pairing with dopamine. On the other hand, activation of the cGMP pathway paired with KC activation induces slowly developing LTP, proving antagonistic actions of the two second-messenger pathways predicted by behavioral study. Finally, KC subtype-specific interrogation of synapses reveals that different KC subtypes exhibit distinct plasticity duration even among synapses on the same postsynaptic neuron. Thus, our work not only revises the role of cAMP in synaptic plasticity by uncovering the unexpected convergence point of the cAMP pathway and neuronal activity, but also establishes the methods to address physiological mechanisms of synaptic plasticity in this important model.
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Affiliation(s)
- Daichi Yamada
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Andrew M. Davidson
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, United States
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22
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Jovanoski KD, Duquenoy L, Mitchell J, Kapoor I, Treiber CD, Croset V, Dempsey G, Parepalli S, Cognigni P, Otto N, Felsenberg J, Waddell S. Dopaminergic systems create reward seeking despite adverse consequences. Nature 2023; 623:356-365. [PMID: 37880370 PMCID: PMC10632144 DOI: 10.1038/s41586-023-06671-8] [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: 07/04/2022] [Accepted: 09/22/2023] [Indexed: 10/27/2023]
Abstract
Resource-seeking behaviours are ordinarily constrained by physiological needs and threats of danger, and the loss of these controls is associated with pathological reward seeking1. Although dysfunction of the dopaminergic valuation system of the brain is known to contribute towards unconstrained reward seeking2,3, the underlying reasons for this behaviour are unclear. Here we describe dopaminergic neural mechanisms that produce reward seeking despite adverse consequences in Drosophila melanogaster. Odours paired with optogenetic activation of a defined subset of reward-encoding dopaminergic neurons become cues that starved flies seek while neglecting food and enduring electric shock punishment. Unconstrained seeking of reward is not observed after learning with sugar or synthetic engagement of other dopaminergic neuron populations. Antagonism between reward-encoding and punishment-encoding dopaminergic neurons accounts for the perseverance of reward seeking despite punishment, whereas synthetic engagement of the reward-encoding dopaminergic neurons also impairs the ordinary need-dependent dopaminergic valuation of available food. Connectome analyses reveal that the population of reward-encoding dopaminergic neurons receives highly heterogeneous input, consistent with parallel representation of diverse rewards, and recordings demonstrate state-specific gating and satiety-related signals. We propose that a similar dopaminergic valuation system dysfunction is likely to contribute to maladaptive seeking of rewards by mammals.
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Affiliation(s)
| | - Lucille Duquenoy
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Jessica Mitchell
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Ishaan Kapoor
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | | | - Vincent Croset
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Department of Biosciences, Durham University, Durham, UK
| | - Georgia Dempsey
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Sai Parepalli
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Paola Cognigni
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Northern Medical Physics and Clinical Engineering, Newcastle upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK
| | - Nils Otto
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Institute of Anatomy and Molecular Neurobiology, Westfälische Wilhelms-University, Münster, Germany
| | - Johannes Felsenberg
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Scott Waddell
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK.
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23
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Kato A, Ohta K, Okanoya K, Kazama H. Dopaminergic neurons dynamically update sensory values during olfactory maneuver. Cell Rep 2023; 42:113122. [PMID: 37757823 DOI: 10.1016/j.celrep.2023.113122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 07/29/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
Dopaminergic neurons (DANs) drive associative learning to update the value of sensory cues, but their contribution to the assessment of sensory values outside the context of association remains largely unexplored. Here, we show in Drosophila that DANs in the mushroom body encode the innate value of odors and constantly update the current value by inducing plasticity during olfactory maneuver. Our connectome-based network model linking all the way from the olfactory neurons to DANs reproduces the characteristics of DAN responses, proposing a concrete circuit mechanism for computation. Downstream of DANs, odors alone induce value- and dopamine-dependent changes in the activity of mushroom body output neurons, which store the current value of odors. Consistent with this neural plasticity, specific sets of DANs bidirectionally modulate flies' steering in a virtual olfactory environment. Thus, the DAN circuit known for discrete, associative learning also continuously updates odor values in a nonassociative manner.
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Affiliation(s)
- Ayaka Kato
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kazumi Ohta
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; RIKEN CBS-KAO Collaboration Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kazuo Okanoya
- Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Hokto Kazama
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; RIKEN CBS-KAO Collaboration Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
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24
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Shen Y, Dasgupta S, Navlakha S. Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies. Neural Comput 2023; 35:1797-1819. [PMID: 37725710 DOI: 10.1162/neco_a_01615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 07/14/2023] [Indexed: 09/21/2023]
Abstract
Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learning (retaining associations between inputs and outputs, even after good representations are learned) plays an important function in the brain; however, its role in continual learning has not been carefully studied. Here, we identified a two-layer neural circuit in the fruit fly olfactory system that performs continual associative learning between odors and their associated valences. In the first layer, inputs (odors) are encoded using sparse, high-dimensional representations, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer, only the synapses between odor-activated neurons and the odor's associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We prove theoretically that these two perceptron-like layers help reduce catastrophic forgetting compared to the original perceptron algorithm, under continual learning. We then show empirically on benchmark data sets that this simple and lightweight architecture outperforms other popular neural-inspired algorithms when also using a two-layer feedforward architecture. Overall, fruit flies evolved an efficient continual associative learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation.
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Affiliation(s)
- Yang Shen
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY 11724, U.S.A.
| | - Sanjoy Dasgupta
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, U.S.A.
| | - Saket Navlakha
- Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, Cold Spring Harbor, NY 11724, U.S.A.
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Arican C, Schmitt FJ, Rössler W, Strube-Bloss MF, Nawrot MP. The mushroom body output encodes behavioral decision during sensory-motor transformation. Curr Biol 2023; 33:4217-4224.e4. [PMID: 37657449 DOI: 10.1016/j.cub.2023.08.016] [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: 03/21/2023] [Revised: 06/30/2023] [Accepted: 08/03/2023] [Indexed: 09/03/2023]
Abstract
Animals form a behavioral decision by evaluating sensory evidence on the background of past experiences and the momentary motivational state. In insects, we still lack understanding of how and at which stage of the recurrent sensory-motor pathway behavioral decisions are formed. The mushroom body (MB), a central brain structure in insects1 and crustaceans,2,3 integrates sensory input of different modalities4,5,6 with the internal state, the behavioral state, and external sensory context7,8,9,10 through a large number of recurrent, mostly neuromodulatory inputs,11,12 implicating a functional role for MBs in state-dependent sensory-motor transformation.13,14 A number of classical conditioning studies in honeybees15,16 and fruit flies17,18,19 have provided accumulated evidence that at its output, the MB encodes the valence of a sensory stimulus with respect to its behavioral relevance. Recent work has extended this notion of valence encoding to the context of innate behaviors.8,20,21,22 Here, we co-analyzed a defined feeding behavior and simultaneous extracellular single-unit recordings from MB output neurons (MBONs) in the cockroach in response to timed sensory stimulation with odors. We show that clear neuronal responses occurred almost exclusively during behaviorally responded trials. Early MBON responses to the sensory stimulus preceded the feeding behavior and predicted its occurrence or non-occurrence from the single-trial population activity. Our results therefore suggest that at its output, the MB does not merely encode sensory stimulus valence. We hypothesize instead that the MB output represents an integrated signal of internal state, momentary environmental conditions, and experience-dependent memory to encode a behavioral decision.
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Affiliation(s)
- Cansu Arican
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany.
| | - Felix Johannes Schmitt
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany
| | - Wolfgang Rössler
- Behavioral Physiology and Sociobiology (Zoology II), Biozentrum, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
| | - Martin Fritz Strube-Bloss
- Department of Biological Cybernetics and Theoretical Biology, University of Bielefeld, Universitätsstr. 25, 33615 Bielefeld, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, 50674 Cologne, Germany.
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Davidson AM, Kaushik S, Hige T. Dopamine-Dependent Plasticity Is Heterogeneously Expressed by Presynaptic Calcium Activity across Individual Boutons of the Drosophila Mushroom Body. eNeuro 2023; 10:ENEURO.0275-23.2023. [PMID: 37848287 PMCID: PMC10616905 DOI: 10.1523/eneuro.0275-23.2023] [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: 07/31/2023] [Revised: 10/01/2023] [Accepted: 10/08/2023] [Indexed: 10/19/2023] Open
Abstract
The Drosophila mushroom body (MB) is an important model system for studying the synaptic mechanisms of associative learning. In this system, coincidence of odor-evoked calcium influx and dopaminergic input in the presynaptic terminals of Kenyon cells (KCs), the principal neurons of the MB, triggers long-term depression (LTD), which plays a critical role in olfactory learning. However, it is controversial whether such synaptic plasticity is accompanied by a corresponding decrease in odor-evoked calcium activity in the KC presynaptic terminals. Here, we address this question by inducing LTD by pairing odor presentation with optogenetic activation of dopaminergic neurons (DANs). This allows us to rigorously compare the changes at the presynaptic and postsynaptic sites in the same conditions. By imaging presynaptic acetylcholine release in the condition where LTD is reliably observed in the postsynaptic calcium signals, we show that neurotransmitter release from KCs is depressed selectively in the MB compartments innervated by activated DANs, demonstrating the presynaptic nature of LTD. However, total odor-evoked calcium activity of the KC axon bundles does not show concurrent depression. We further conduct calcium imaging in individual presynaptic boutons and uncover the highly heterogeneous nature of calcium plasticity. Namely, only a subset of boutons, which are strongly activated by associated odors, undergo calcium activity depression, while weakly responding boutons show potentiation. Thus, our results suggest an unexpected nonlinear relationship between presynaptic calcium influx and the results of plasticity, challenging the simple view of cooperative actions of presynaptic calcium and dopaminergic input.
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Affiliation(s)
- Andrew M Davidson
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Shivam Kaushik
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
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27
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Rajagopalan AE, Darshan R, Hibbard KL, Fitzgerald JE, Turner GC. Reward expectations direct learning and drive operant matching in Drosophila. Proc Natl Acad Sci U S A 2023; 120:e2221415120. [PMID: 37733736 PMCID: PMC10523640 DOI: 10.1073/pnas.2221415120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 08/11/2023] [Indexed: 09/23/2023] Open
Abstract
Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnstein's operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here, we discovered operant matching in Drosophila and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a dynamic foraging paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each fly's sequential choice behavior using a family of biologically realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synapse-level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.
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Affiliation(s)
- Adithya E. Rajagopalan
- Janelia Research Campus, HHMI, Ashburn, VA20147
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD21205
| | - Ran Darshan
- Janelia Research Campus, HHMI, Ashburn, VA20147
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Sagol School of Neuroscience, The School of Physics and Astronomy, Tel Aviv University, Tel Aviv6997801, Israel
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28
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Aso Y, Yamada D, Bushey D, Hibbard KL, Sammons M, Otsuna H, Shuai Y, Hige T. Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement. eLife 2023; 12:e85756. [PMID: 37721371 PMCID: PMC10588983 DOI: 10.7554/elife.85756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 09/07/2023] [Indexed: 09/19/2023] Open
Abstract
How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign a valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After formation of appetitive memory, UpWiNs acquire enhanced response to reward-predicting odors as the response of the inhibitory presynaptic MBON undergoes depression. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was terminated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.
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Affiliation(s)
- Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Daichi Yamada
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Megan Sammons
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Yichun Shuai
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel HillChapel HillUnited States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel HillChapel HillUnited States
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29
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Nguyen R, Koukoutselos K, Forro T, Ciocchi S. Fear extinction relies on ventral hippocampal safety codes shaped by the amygdala. SCIENCE ADVANCES 2023; 9:eadg4881. [PMID: 37256958 PMCID: PMC10413664 DOI: 10.1126/sciadv.adg4881] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/25/2023] [Indexed: 06/02/2023]
Abstract
Extinction memory retrieval is influenced by spatial contextual information that determines responding to conditioned stimuli (CS). However, it is poorly understood whether contextual representations are imbued with emotional values to support memory selection. Here, we performed activity-dependent engram tagging and in vivo single-unit electrophysiological recordings from the ventral hippocampus (vH) while optogenetically manipulating basolateral amygdala (BLA) inputs during the formation of cued fear extinction memory. During fear extinction when CS acquire safety properties, we found that CS-related activity in the vH reactivated during sleep consolidation and was strengthened upon memory retrieval. Moreover, fear extinction memory was facilitated when the extinction context exhibited precise coding of its affective zones. Last, these activity patterns along with the retrieval of the fear extinction memory were dependent on glutamatergic transmission from the BLA during extinction learning. Thus, fear extinction memory relies on the formation of contextual and stimulus safety representations in the vH instructed by the BLA.
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Affiliation(s)
| | | | - Thomas Forro
- Laboratory of Systems Neuroscience, Department of Physiology, University of Bern, Bern, Switzerland
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30
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Shen P, Wan X, Wu F, Shi K, Li J, Gao H, Zhao L, Zhou C. Neural circuit mechanisms linking courtship and reward in Drosophila males. Curr Biol 2023; 33:2034-2050.e8. [PMID: 37160122 DOI: 10.1016/j.cub.2023.04.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 03/15/2023] [Accepted: 04/17/2023] [Indexed: 05/11/2023]
Abstract
Courtship has evolved to achieve reproductive success in animal species. However, whether courtship itself has a positive value remains unclear. In the present work, we report that courtship is innately rewarding and can induce the expression of appetitive short-term memory (STM) and long-term memory (LTM) in Drosophila melanogaster males. Activation of male-specific P1 neurons is sufficient to mimic courtship-induced preference and memory performance. Surprisingly, P1 neurons functionally connect to a large proportion of dopaminergic neurons (DANs) in the protocerebral anterior medial (PAM) cluster. The acquisition of STM and LTM depends on two distinct subsets of PAM DANs that convey the courtship-reward signal to the restricted regions of the mushroom body (MB) γ and α/β lobes through two dopamine receptors, D1-like Dop1R1 and D2-like Dop2R. Furthermore, the retrieval of STM stored in the MB α'/β' lobes and LTM stored in the MB α/β lobe relies on two distinct MB output neurons. Finally, LTM consolidation requires two subsets of PAM DANs projecting to the MB α/β lobe and corresponding MB output neurons. Taken together, our findings demonstrate that courtship is a potent rewarding stimulus and reveal the underlying neural circuit mechanisms linking courtship and reward in Drosophila males.
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Affiliation(s)
- Peng Shen
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xiaolu Wan
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fengming Wu
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Kai Shi
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Li
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Hongjiang Gao
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Lilin Zhao
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Chuan Zhou
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China
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31
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Okray Z, Jacob PF, Stern C, Desmond K, Otto N, Talbot CB, Vargas-Gutierrez P, Waddell S. Multisensory learning binds neurons into a cross-modal memory engram. Nature 2023; 617:777-784. [PMID: 37100911 PMCID: PMC10208976 DOI: 10.1038/s41586-023-06013-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/24/2023] [Indexed: 04/28/2023]
Abstract
Associating multiple sensory cues with objects and experience is a fundamental brain process that improves object recognition and memory performance. However, neural mechanisms that bind sensory features during learning and augment memory expression are unknown. Here we demonstrate multisensory appetitive and aversive memory in Drosophila. Combining colours and odours improved memory performance, even when each sensory modality was tested alone. Temporal control of neuronal function revealed visually selective mushroom body Kenyon cells (KCs) to be required for enhancement of both visual and olfactory memory after multisensory training. Voltage imaging in head-fixed flies showed that multisensory learning binds activity between streams of modality-specific KCs so that unimodal sensory input generates a multimodal neuronal response. Binding occurs between regions of the olfactory and visual KC axons, which receive valence-relevant dopaminergic reinforcement, and is propagated downstream. Dopamine locally releases GABAergic inhibition to permit specific microcircuits within KC-spanning serotonergic neurons to function as an excitatory bridge between the previously 'modality-selective' KC streams. Cross-modal binding thereby expands the KCs representing the memory engram for each modality into those representing the other. This broadening of the engram improves memory performance after multisensory learning and permits a single sensory feature to retrieve the memory of the multimodal experience.
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Affiliation(s)
- Zeynep Okray
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK.
| | - Pedro F Jacob
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | - Ciara Stern
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | - Kieran Desmond
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | - Nils Otto
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
- Institute of Anatomy and Molecular Neurobiology, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Clifford B Talbot
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK
| | | | - Scott Waddell
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford, UK.
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32
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How flies remember a rich experience from its individual components. Nature 2023:10.1038/d41586-023-01333-1. [PMID: 37101066 DOI: 10.1038/d41586-023-01333-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
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33
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Zeng J, Li X, Zhang R, Lv M, Wang Y, Tan K, Xia X, Wan J, Jing M, Zhang X, Li Y, Yang Y, Wang L, Chu J, Li Y, Li Y. Local 5-HT signaling bi-directionally regulates the coincidence time window for associative learning. Neuron 2023; 111:1118-1135.e5. [PMID: 36706757 PMCID: PMC11152601 DOI: 10.1016/j.neuron.2022.12.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/03/2022] [Accepted: 12/30/2022] [Indexed: 01/27/2023]
Abstract
The coincidence between conditioned stimulus (CS) and unconditioned stimulus (US) is essential for associative learning; however, the mechanism regulating the duration of this temporal window remains unclear. Here, we found that serotonin (5-HT) bi-directionally regulates the coincidence time window of olfactory learning in Drosophila and affects synaptic plasticity of Kenyon cells (KCs) in the mushroom body (MB). Utilizing GPCR-activation-based (GRAB) neurotransmitter sensors, we found that KC-released acetylcholine (ACh) activates a serotonergic dorsal paired medial (DPM) neuron, which in turn provides inhibitory feedback to KCs. Physiological stimuli induce spatially heterogeneous 5-HT signals, which proportionally gate the intrinsic coincidence time windows of different MB compartments. Artificially reducing or increasing the DPM neuron-released 5-HT shortens or prolongs the coincidence window, respectively. In a sequential trace conditioning paradigm, this serotonergic neuromodulation helps to bridge the CS-US temporal gap. Altogether, we report a model circuitry for perceiving the temporal coincidence and determining the causal relationship between environmental events.
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Affiliation(s)
- Jianzhi Zeng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Hefei National Laboratory for Physical Sciences at the Microscale, CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, Anhui, China.
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Renzimo Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China
| | - Mingyue Lv
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Ke Tan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China
| | - Jinxia Wan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Miao Jing
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xiuning Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yu Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yang Yang
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Wang
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jun Chu
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yan Li
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China; Chinese Institute for Brain Research, Beijing 102206, China.
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34
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Crossley M, Benjamin PR, Kemenes G, Staras K, Kemenes I. A circuit mechanism linking past and future learning through shifts in perception. SCIENCE ADVANCES 2023; 9:eadd3403. [PMID: 36961898 PMCID: PMC10038338 DOI: 10.1126/sciadv.add3403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Long-term memory formation is energetically costly. Neural mechanisms that guide an animal to identify fruitful associations therefore have important survival benefits. Here, we elucidate a circuit mechanism in Lymnaea, which enables past memory to shape new memory formation through changes in perception. Specifically, strong classical conditioning drives a positive shift in perception that facilitates the robust learning of a subsequent and otherwise ineffective weak association. Circuit dissection approaches reveal the neural control network responsible, characterized by a mutual inhibition motif. This both sets perceptual state and acts as the master controller for gating new learning. Pharmacological circuit manipulation in vivo fully substitutes for strong paradigm learning, shifting the network into a more receptive state to enable subsequent weak paradigm learning. Thus, perceptual change provides a conduit to link past and future memory storage. We propose that this mechanism alerts animals to learning-rich periods, lowering the threshold for new memory acquisition.
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35
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Hafez OA, Escribano B, Ziegler RL, Hirtz JJ, Niebur E, Pielage J. The cellular architecture of memory modules in Drosophila supports stochastic input integration. eLife 2023; 12:e77578. [PMID: 36916672 PMCID: PMC10069864 DOI: 10.7554/elife.77578] [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: 02/03/2022] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroom body (MB), the fly's center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-α3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.
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Affiliation(s)
- Omar A Hafez
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
| | - Benjamin Escribano
- Division of Neurobiology and Zoology, Department of Biology, University of KaiserslauternKaiserslauternGermany
| | - Rouven L Ziegler
- Division of Neurobiology and Zoology, Department of Biology, University of KaiserslauternKaiserslauternGermany
| | - Jan J Hirtz
- Physiology of Neuronal Networks Group, Department of Biology, University of KaiserslauternKaiserslauternGermany
| | - Ernst Niebur
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreUnited States
- Solomon Snyder Department of Neuroscience, Johns Hopkins UniversityBaltimoreUnited States
| | - Jan Pielage
- Division of Neurobiology and Zoology, Department of Biology, University of KaiserslauternKaiserslauternGermany
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36
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Yang Q, Zhou J, Wang L, Hu W, Zhong Y, Li Q. Spontaneous recovery of reward memory through active forgetting of extinction memory. Curr Biol 2023; 33:838-848.e3. [PMID: 36731465 DOI: 10.1016/j.cub.2023.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 12/16/2022] [Accepted: 01/11/2023] [Indexed: 02/04/2023]
Abstract
Learned behavior can be suppressed by the extinction procedure. Such extinguished memory often returns spontaneously over time, making it difficult to treat diseases such as addiction. However, the biological mechanisms underlying such spontaneous recovery remain unclear. Here, we report that the extinguished reward memory in Drosophila recovers spontaneously because extinction training forms an aversive memory that can be actively forgotten via the Rac1/Dia pathway. Manipulating Rac1 activity does not affect sugar-reward memory and its immediate extinction effect but bidirectionally regulates spontaneous recovery-the decay process of extinction. Experiments using thermogenetic inhibition and functional imaging support that such extinction appears to be coded as an aversive experience. Genetic and pharmacological inhibition of formin Dia, a downstream effector of Rac1, specifically prevents spontaneous recovery after extinction in both behavioral performance and corresponding physiological traces. Together, our data suggest that spontaneous recovery is caused by active forgetting of the opposing extinction memory.
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Affiliation(s)
- Qi Yang
- School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Jun Zhou
- School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Lingling Wang
- School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Wantong Hu
- School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Yi Zhong
- School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
| | - Qian Li
- School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
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37
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Yamada D, Bushey D, Li F, Hibbard KL, Sammons M, Funke J, Litwin-Kumar A, Hige T, Aso Y. Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila. eLife 2023; 12:79042. [PMID: 36692262 PMCID: PMC9937650 DOI: 10.7554/elife.79042] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective 'teacher' by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the 'student' compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.
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Affiliation(s)
- Daichi Yamada
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Karen L Hibbard
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Megan Sammons
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Ashok Litwin-Kumar
- Department of Neuroscience, Columbia University, New York, United States
| | - Toshihide Hige
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, United States
- Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
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Pardo-Garcia TR, Gu K, Woerner RKR, Dus M. Food memory circuits regulate eating and energy balance. Curr Biol 2023; 33:215-227.e3. [PMID: 36528025 PMCID: PMC9877168 DOI: 10.1016/j.cub.2022.11.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 09/16/2022] [Accepted: 11/17/2022] [Indexed: 12/23/2022]
Abstract
In mammals, learning circuits play an essential role in energy balance by creating associations between sensory cues and the rewarding qualities of food. This process is altered by diet-induced obesity, but the causes and mechanisms are poorly understood. Here, we exploited the relative simplicity and wealth of knowledge about the D. melanogaster reinforcement learning network, the mushroom body, in order to study the relationship between the dietary environment, dopamine-induced plasticity, and food associations. We show flies that are fed a high-sugar diet cannot make associations between sensory cues and the rewarding properties of sugar. This deficit was caused by diet exposure, not fat accumulation, and specifically by lower dopamine-induced plasticity onto mushroom body output neurons (MBONs) during learning. Importantly, food memories dynamically tune the output of MBONs during eating, which instead remains fixed in sugar-diet animals. Interestingly, manipulating the activity of MBONs influenced eating and fat mass, depending on the diet. Altogether, this work advances our fundamental understanding of the mechanisms, causes, and consequences of the dietary environment on reinforcement learning and ingestive behavior.
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Affiliation(s)
- Thibaut R Pardo-Garcia
- The Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA; The Department of Molecular, Cellular, and Developmental Biology, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kathleen Gu
- The Undergraduate Program in Neuroscience, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Riley K R Woerner
- The Department of Molecular, Cellular, and Developmental Biology, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Monica Dus
- The Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA; The Department of Molecular, Cellular, and Developmental Biology, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA; The Undergraduate Program in Neuroscience, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA.
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39
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Marquand K, Roselli C, Cervantes-Sandoval I, Boto T. Sleep benefits different stages of memory in Drosophila. Front Physiol 2023; 14:1087025. [PMID: 36744027 PMCID: PMC9892949 DOI: 10.3389/fphys.2023.1087025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/06/2023] [Indexed: 01/20/2023] Open
Abstract
Understanding the physiological mechanisms that modulate memory acquisition and consolidation remains among the most ambitious questions in neuroscience. Massive efforts have been dedicated to deciphering how experience affects behavior, and how different physiological and sensory phenomena modulate memory. Our ability to encode, consolidate and retrieve memories depends on internal drives, and sleep stands out among the physiological processes that affect memory: one of the most relatable benefits of sleep is the aiding of memory that occurs in order to both prepare the brain to learn new information, and after a learning task, to consolidate those new memories. Drosophila lends itself to the study of the interactions between memory and sleep. The fruit fly provides incomparable genetic resources, a mapped connectome, and an existing framework of knowledge on the molecular, cellular, and circuit mechanisms of memory and sleep, making the fruit fly a remarkable model to decipher the sophisticated regulation of learning and memory by the quantity and quality of sleep. Research in Drosophila has stablished not only that sleep facilitates learning in wild-type and memory-impaired animals, but that sleep deprivation interferes with the acquisition of new memories. In addition, it is well-accepted that sleep is paramount in memory consolidation processes. Finally, studies in Drosophila have shown that that learning itself can promote sleep drive. Nevertheless, the molecular and network mechanisms underlying this intertwined relationship are still evasive. Recent remarkable work has shed light on the neural substrates that mediate sleep-dependent memory consolidation. In a similar way, the mechanistic insights of the neural switch control between sleep-dependent and sleep-independent consolidation strategies were recently described. This review will discuss the regulation of memory by sleep in Drosophila, focusing on the most recent advances in the field and pointing out questions awaiting to be investigated.
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Affiliation(s)
- Katie Marquand
- Department of Physiology, School of Medicine, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Camilla Roselli
- Trinity College Institute of Neuroscience, School of Genetics and Microbiology, Smurfit Institute of Genetics and School of Natural Sciences, Trinity College Dublin, Dublin, Ireland
| | - Isaac Cervantes-Sandoval
- Department of Biology, Georgetown University, Washington, DC, United States,Interdisciplinary Program in Neuroscience, Georgetown University, Washington, DC, United States
| | - Tamara Boto
- Department of Physiology, School of Medicine, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland,*Correspondence: Tamara Boto,
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40
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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] [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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41
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Galili DS, Jefferis GS, Costa M. Connectomics and the neural basis of behaviour. CURRENT OPINION IN INSECT SCIENCE 2022; 54:100968. [PMID: 36113710 PMCID: PMC7614087 DOI: 10.1016/j.cois.2022.100968] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/30/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
Methods to acquire and process synaptic-resolution electron-microscopy datasets have progressed very rapidly, allowing production and annotation of larger, more complete connectomes. More accurate neuronal matching techniques are enriching cell type data with gene expression, neuron activity, behaviour and developmental information, providing ways to test hypotheses of circuit function. In a variety of behaviours such as learned and innate olfaction, navigation and sexual behaviour, connectomics has already revealed interconnected modules with a hierarchical structure, recurrence and integration of sensory streams. Comparing individual connectomes to determine which circuit features are robust and which are variable is one key research area; new work in comparative connectomics across development, experience, sex and species will establish strong links between neuronal connectivity and brain function.
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Affiliation(s)
- Dana S Galili
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
| | - Gregory Sxe Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
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42
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Villar ME, Pavão-Delgado M, Amigo M, Jacob PF, Merabet N, Pinot A, Perry SA, Waddell S, Perisse E. Differential coding of absolute and relative aversive value in the Drosophila brain. Curr Biol 2022; 32:4576-4592.e5. [DOI: 10.1016/j.cub.2022.08.058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/24/2022] [Accepted: 08/19/2022] [Indexed: 11/30/2022]
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43
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Zheng Z, Li F, Fisher C, Ali IJ, Sharifi N, Calle-Schuler S, Hsu J, Masoodpanah N, Kmecova L, Kazimiers T, Perlman E, Nichols M, Li PH, Jain V, Bock DD. Structured sampling of olfactory input by the fly mushroom body. Curr Biol 2022; 32:3334-3349.e6. [PMID: 35797998 PMCID: PMC9413950 DOI: 10.1016/j.cub.2022.06.031] [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: 06/05/2020] [Revised: 02/07/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
Associative memory formation and recall in the fruit fly Drosophila melanogaster is subserved by the mushroom body (MB). Upon arrival in the MB, sensory information undergoes a profound transformation from broadly tuned and stereotyped odorant responses in the olfactory projection neuron (PN) layer to narrowly tuned and nonstereotyped responses in the Kenyon cells (KCs). Theory and experiment suggest that this transformation is implemented by random connectivity between KCs and PNs. However, this hypothesis has been challenging to test, given the difficulty of mapping synaptic connections between large numbers of brain-spanning neurons. Here, we used a recent whole-brain electron microscopy volume of the adult fruit fly to map PN-to-KC connectivity at synaptic resolution. The PN-KC connectome revealed unexpected structure, with preponderantly food-responsive PN types converging at above-chance levels on downstream KCs. Axons of the overconvergent PN types tended to arborize near one another in the MB main calyx, making local KC dendrites more likely to receive input from those types. Overconvergent PN types preferentially co-arborize and connect with dendrites of αβ and α'β' KC subtypes. Computational simulation of the observed network showed degraded discrimination performance compared with a random network, except when all signal flowed through the overconvergent, primarily food-responsive PN types. Additional theory and experiment will be needed to fully characterize the impact of the observed non-random network structure on associative memory formation and recall.
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Affiliation(s)
- Zhihao Zheng
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; The Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University, Baltimore, MD 21205, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Corey Fisher
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Iqbal J Ali
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Nadiya Sharifi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Steven Calle-Schuler
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Joseph Hsu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Najla Masoodpanah
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Lucia Kmecova
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Kazmos GmbH, Dresden, Germany
| | - Eric Perlman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Yikes LLC, Baltimore, MD, USA
| | - Matthew Nichols
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | | | | | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Neurological Sciences, University of Vermont, Burlington, VT 05405, USA.
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44
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Hancock CE, Rostami V, Rachad EY, Deimel SH, Nawrot MP, Fiala A. Visualization of learning-induced synaptic plasticity in output neurons of the Drosophila mushroom body γ-lobe. Sci Rep 2022; 12:10421. [PMID: 35729203 PMCID: PMC9213513 DOI: 10.1038/s41598-022-14413-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/07/2022] [Indexed: 12/21/2022] Open
Abstract
By learning, through experience, which stimuli coincide with dangers, it is possible to predict outcomes and act pre-emptively to ensure survival. In insects, this process is localized to the mushroom body (MB), the circuitry of which facilitates the coincident detection of sensory stimuli and punishing or rewarding cues and, downstream, the execution of appropriate learned behaviors. Here, we focused our attention on the mushroom body output neurons (MBONs) of the γ-lobes that act as downstream synaptic partners of the MB γ-Kenyon cells (KCs) to ask how the output of the MB γ-lobe is shaped by olfactory associative conditioning, distinguishing this from non-associative stimulus exposure effects, and without the influence of downstream modulation. This was achieved by employing a subcellularly localized calcium sensor to specifically monitor activity at MBON postsynaptic sites. Therein, we identified a robust associative modulation within only one MBON postsynaptic compartment (MBON-γ1pedc > α/β), which displayed a suppressed postsynaptic response to an aversively paired odor. While this MBON did not undergo non-associative modulation, the reverse was true across the remainder of the γ-lobe, where general odor-evoked adaptation was observed, but no conditioned odor-specific modulation. In conclusion, associative synaptic plasticity underlying aversive olfactory learning is localized to one distinct synaptic γKC-to-γMBON connection.
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Affiliation(s)
- Clare E Hancock
- Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute of Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077, Göttingen, Germany
| | - Vahid Rostami
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicherstraße 47b, 50674, Cologne, Germany
| | - El Yazid Rachad
- Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute of Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077, Göttingen, Germany
| | - Stephan H Deimel
- Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute of Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077, Göttingen, Germany
| | - Martin P Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Zülpicherstraße 47b, 50674, Cologne, Germany
| | - André Fiala
- Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute of Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077, Göttingen, Germany.
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45
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Mo H, Wang L, Chen Y, Zhang X, Huang N, Liu T, Hu W, Zhong Y, Li Q. Age-related memory vulnerability to interfering stimuli is caused by gradual loss of MAPK-dependent protection in Drosophila. Aging Cell 2022; 21:e13628. [PMID: 35570367 PMCID: PMC9197400 DOI: 10.1111/acel.13628] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/14/2022] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
Age‐related memory impairment (AMI) is a common phenomenon across species. Vulnerability to interfering stimuli has been proposed to be an important cause of AMI. However, the molecular mechanisms underlying this vulnerability‐related AMI remain unknown. Here we show that learning‐activated MAPK signals are gradually lost with age, leading to vulnerability‐related AMI in Drosophila. Young flies (2‐ or 3‐day‐old) exhibited a significant increase in phosphorylated MAPK levels within 15 min after learning, whereas aged flies (25‐day‐old) did not. Compared to 3‐day‐old flies, significant 1 h memory impairments were observed in 15‐, 20‐, and 30‐day‐old flies, but not in 10‐day‐old flies. However, with post‐learning interfering stimuli such as cooling or electric stimuli, 10‐day‐old flies had worse memory performance at 1 h than 3‐day‐old flies, showing a premature AMI phenomenon. Increasing learning‐activated MAPK signals through acute transgene expression in mushroom body (MB) neurons restored physiological trace of 1 h memory in a pair of MB output neurons in aged flies. Decreasing such signals in young flies mimicked the impairment of 1 h memory trace in aged flies. Restoring learning‐activated MAPK signals in MB neurons in aged flies significantly suppressed AMI even with interfering stimuli. Thus, our data suggest that age‐related loss of learning‐activated neuronal MAPK signals causes memory vulnerability to interfering stimuli, thereby leading to AMI.
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Affiliation(s)
- Han Mo
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Linghan Wang
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Yuting Chen
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Xuchen Zhang
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Ning Huang
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Tingting Liu
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Wantong Hu
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Yi Zhong
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
| | - Qian Li
- School of Life Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China
- Tsinghua‐Peking Center for Life Sciences Beijing China
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46
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Adel M, Chen N, Zhang Y, Reed ML, Quasney C, Griffith LC. Pairing-Dependent Plasticity in a Dissected Fly Brain Is Input-Specific and Requires Synaptic CaMKII Enrichment and Nighttime Sleep. J Neurosci 2022; 42:4297-4310. [PMID: 35474278 PMCID: PMC9145224 DOI: 10.1523/jneurosci.0144-22.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/23/2022] [Accepted: 04/19/2022] [Indexed: 11/21/2022] Open
Abstract
In Drosophila, in vivo functional imaging studies revealed that associative memory formation is coupled to a cascade of neural plasticity events in distinct compartments of the mushroom body (MB). In-depth investigation of the circuit dynamics, however, will require an ex vivo model that faithfully mirrors these events to allow direct manipulations of circuit elements that are inaccessible in the intact fly. The current ex vivo models have been able to reproduce the fundamental plasticity of aversive short-term memory, a potentiation of the MB intrinsic neuron (Kenyon cells [KCs]) responses after artificial learning ex vivo However, this potentiation showed different localization and encoding properties from those reported in vivo and failed to generate the previously reported suppression plasticity in the MB output neurons (MBONs). Here, we develop an ex vivo model using the female Drosophila brain that recapitulates behaviorally evoked plasticity in the KCs and MBONs. We demonstrate that this plasticity accurately localizes to the MB α'3 compartment and is encoded by a coincidence between KC activation and dopaminergic input. The formed plasticity is input-specific, requiring pairing of the conditioned stimulus and unconditioned stimulus pathways; hence, we name it pairing-dependent plasticity. Pairing-dependent plasticity formation requires an intact CaMKII gene and is blocked by previous-night sleep deprivation but is rescued by rebound sleep. In conclusion, we show that our ex vivo preparation recapitulates behavioral and imaging results from intact animals and can provide new insights into mechanisms of memory formation at the level of molecules, circuits, and brain state.SIGNIFICANCE STATEMENT The mammalian ex vivo LTP model enabled in-depth investigation of the hippocampal memory circuit. We develop a parallel model to study the Drosophila mushroom body (MB) memory circuit. Pairing activation of the conditioned stimulus and unconditioned stimulus pathways in dissected brains induces a potentiation pairing-dependent plasticity (PDP) in the axons of α'β' Kenyon cells and a suppression PDP in the dendrites of their postsynaptic MB output neurons, localized in the MB α'3 compartment. This PDP is input-specific and requires the 3' untranslated region of CaMKII Interestingly, ex vivo PDP carries information about the animal's experience before dissection; brains from sleep-deprived animals fail to form PDP, whereas those from animals who recovered 2 h of their lost sleep form PDP.
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Affiliation(s)
- Mohamed Adel
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02454-9110
| | - Nannan Chen
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02454-9110
| | - Yunpeng Zhang
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02454-9110
| | - Martha L Reed
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02454-9110
| | - Christina Quasney
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02454-9110
| | - Leslie C Griffith
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02454-9110
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47
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Interplay between metabolic energy regulation and memory pathways in Drosophila. Trends Neurosci 2022; 45:539-549. [PMID: 35597687 DOI: 10.1016/j.tins.2022.04.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/10/2022] [Accepted: 04/21/2022] [Indexed: 12/17/2022]
Abstract
Regulating energy metabolism is critical to maintain homeostasis of cellular and systemic functions. In the brain, specialised centres for energy storage regulation finely communicate with the periphery and integrate signals about internal states. As a result, the behavioural responses can be directly adjusted accordingly to the energetic demands. In the fruit fly Drosophila melanogaster, one of these regulatory centres is the mushroom bodies (MBs), a brain region involved in olfactory memory. The integration of metabolic cues by the MBs has a crucial impact on learned behaviour. In this review, we explore recent advances supporting the interplay between energy metabolism and memory establishment, as well as the instructive role of energy during the switch between memory phases.
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48
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Klappenbach M, Lara AE, Locatelli FF. Honey bees can store and retrieve independent memory traces after complex experiences that combine appetitive and aversive associations. J Exp Biol 2022; 225:275573. [PMID: 35485192 DOI: 10.1242/jeb.244229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/19/2022] [Indexed: 11/20/2022]
Abstract
Real-world experiences do often mix appetitive and aversive events. Understanding the ability of animals to extract, store and use this information is an important issue in neurobiology. We used honey bees as model organism to study learning and memory after a differential conditioning that combines appetitive and aversive training trials. First of all, we describe an aversive conditioning paradigm that constitutes a clear opposite of the well known appetitive olfactory conditioning of the proboscis extension response. A neutral odour is presented paired with the bitter substance quinine. Aversive memory is evidenced later as an odour-specific impairment in appetitive conditioning. Then we tested the effect of mixing appetitive and aversive conditioning trials distributed along the same training session. Differential conditioning protocols like this were used before to study the ability to discriminate odours, however they were not focused on whether appetitive and aversive memories are formed. We found that after a differential conditioning, honey bees establish independent appetitive and aversive memories that do not interfere with each other during acquisition or storage. Finally, we moved the question forward to retrieval and memory expression to evaluate what happens when appetitive and the aversive learned odours are mixed during test. Interestingly, opposite memories compete in a way that they do not cancel each other out. Honey bees showed the ability to switch from expressing appetitive to aversive memory depending on their satiation level.
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Affiliation(s)
- Martín Klappenbach
- Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Instituto de Fisiología, Biología Molecular y Neurociencias, Universidad de Buenos Aires-CONICET), Ciudad Universitaria, Buenos Aires, Argentina
| | - Agustín E Lara
- Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Instituto de Fisiología, Biología Molecular y Neurociencias, Universidad de Buenos Aires-CONICET), Ciudad Universitaria, Buenos Aires, Argentina
| | - Fernando F Locatelli
- Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Instituto de Fisiología, Biología Molecular y Neurociencias, Universidad de Buenos Aires-CONICET), Ciudad Universitaria, Buenos Aires, Argentina
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49
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Gkanias E, McCurdy LY, Nitabach MN, Webb B. An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster. eLife 2022; 11:75611. [PMID: 35363138 PMCID: PMC8975552 DOI: 10.7554/elife.75611] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/07/2022] [Indexed: 11/30/2022] Open
Abstract
Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting, and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short term to long term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.
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Affiliation(s)
- Evripidis Gkanias
- Institute of Perception Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Li Yan McCurdy
- Department of Cellular and Molecular Physiology, Yale University, New Haven, United States
| | - Michael N Nitabach
- Department of Cellular and Molecular Physiology, Yale University, New Haven, United States.,Department of Genetics, Yale University, New Haven, United States.,Department of Neuroscience, Yale University, New Haven, United States
| | - Barbara Webb
- Institute of Perception Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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Abstract
This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.
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