151
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Valdes-Aleman J, Fetter RD, Sales EC, Heckman EL, Venkatasubramanian L, Doe CQ, Landgraf M, Cardona A, Zlatic M. Comparative Connectomics Reveals How Partner Identity, Location, and Activity Specify Synaptic Connectivity in Drosophila. Neuron 2020; 109:105-122.e7. [PMID: 33120017 PMCID: PMC7837116 DOI: 10.1016/j.neuron.2020.10.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 08/12/2020] [Accepted: 10/05/2020] [Indexed: 01/30/2023]
Abstract
The mechanisms by which synaptic partners recognize each other and establish appropriate numbers of connections during embryonic development to form functional neural circuits are poorly understood. We combined electron microscopy reconstruction, functional imaging of neural activity, and behavioral experiments to elucidate the roles of (1) partner identity, (2) location, and (3) activity in circuit assembly in the embryonic nerve cord of Drosophila. We found that postsynaptic partners are able to find and connect to their presynaptic partners even when these have been shifted to ectopic locations or silenced. However, orderly positioning of axon terminals by positional cues and synaptic activity is required for appropriate numbers of connections between specific partners, for appropriate balance between excitatory and inhibitory connections, and for appropriate functional connectivity and behavior. Our study reveals with unprecedented resolution the fine connectivity effects of multiple factors that work together to control the assembly of neural circuits.
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Affiliation(s)
- Javier Valdes-Aleman
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA; Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Richard D Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA
| | - Emily C Sales
- Institute of Neuroscience, Howard Hughes Medical Institute, University of Oregon, Eugene, OR 97403, USA
| | - Emily L Heckman
- Institute of Neuroscience, Howard Hughes Medical Institute, University of Oregon, Eugene, OR 97403, USA
| | | | - Chris Q Doe
- Institute of Neuroscience, Howard Hughes Medical Institute, University of Oregon, Eugene, OR 97403, USA
| | - Matthias Landgraf
- Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Albert Cardona
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA; Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK; MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Marta Zlatic
- Janelia Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA; Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK; MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK.
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152
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Insulin signaling represents a gating mechanism between different memory phases in Drosophila larvae. PLoS Genet 2020; 16:e1009064. [PMID: 33104728 PMCID: PMC7644093 DOI: 10.1371/journal.pgen.1009064] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 11/05/2020] [Accepted: 08/19/2020] [Indexed: 12/18/2022] Open
Abstract
The ability to learn new skills and to store them as memory entities is one of the most impressive features of higher evolved organisms. However, not all memories are created equal; some are short-lived forms, and some are longer lasting. Formation of the latter is energetically costly and by the reason of restricted availability of food or fluctuations in energy expanses, efficient metabolic homeostasis modulating different needs like survival, growth, reproduction, or investment in longer lasting memories is crucial. Whilst equipped with cellular and molecular pre-requisites for formation of a protein synthesis dependent long-term memory (LTM), its existence in the larval stage of Drosophila remains elusive. Considering it from the viewpoint that larval brain structures are completely rebuilt during metamorphosis, and that this process depends completely on accumulated energy stores formed during the larval stage, investing in LTM represents an unnecessary expenditure. However, as an alternative, Drosophila larvae are equipped with the capacity to form a protein synthesis independent so-called larval anaesthesia resistant memory (lARM), which is consolidated in terms of being insensitive to cold-shock treatments. Motivated by the fact that LTM formation causes an increase in energy uptake in Drosophila adults, we tested the idea of whether an energy surplus can induce the formation of LTM in the larval stage. Suprisingly, increasing the metabolic state by feeding Drosophila larvae the disaccharide sucrose directly before aversive olfactory conditioning led to the formation of a protein synthesis dependent longer lasting memory. Moreover, formation of this memory component is accompanied by the suppression of lARM. We ascertained that insulin receptors (InRs) expressed in the mushroom body Kenyon cells suppresses the formation of lARM and induces the formation of a protein synthesis dependent longer lasting memory in Drosophila larvae. Given the numerical simplicity of the larval nervous system this work offers a unique prospect to study the impact of insulin signaling on the formation of protein synthesis dependent memories on a molecular level.
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153
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Abstract
Scientists have created the most detailed map of the fruit fly brain to date, identifying over 25,000 neurons and 20 million synapses.
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Affiliation(s)
- Jason Pipkin
- Department of Biology, Brandeis University, Waltham, United States
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154
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Louis M. Mini-brain computations converting dynamic olfactory inputs into orientation behavior. Curr Opin Neurobiol 2020; 64:1-9. [PMID: 31837503 PMCID: PMC7286801 DOI: 10.1016/j.conb.2019.11.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/18/2019] [Accepted: 11/20/2019] [Indexed: 01/15/2023]
Abstract
The neural logic underlying the conversion of non-stationary (dynamic) olfactory inputs into odor-search behaviors has been difficult to crack due to the distributed nature of the olfactory code - food odors typically co-activate multiple olfactory sensory neurons. In the Drosophila larva, the activity of a single olfactory sensory neuron is sufficient to direct accurate reorientation maneuvers in odor gradients (chemotaxis). In this reduced sensory system, a descending pathway essential for larval chemotaxis has been delineated from the peripheral olfactory system down to the premotor system. Here, I review how anatomical and functional inspections of this pathway have advanced our understanding of the neural mechanisms that convert behaviorally relevant sensory signals into orientation responses.
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Affiliation(s)
- Matthieu Louis
- Neuroscience Research Institute & Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Department of Physics, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
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155
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Amin H, Apostolopoulou AA, Suárez-Grimalt R, Vrontou E, Lin AC. Localized inhibition in the Drosophila mushroom body. eLife 2020; 9:56954. [PMID: 32955437 PMCID: PMC7541083 DOI: 10.7554/elife.56954] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/02/2020] [Indexed: 12/12/2022] Open
Abstract
Many neurons show compartmentalized activity, in which activity does not spread readily across the cell, allowing input and output to occur locally. However, the functional implications of compartmentalized activity for the wider neural circuit are often unclear. We addressed this problem in the Drosophila mushroom body, whose principal neurons, Kenyon cells, receive feedback inhibition from a non-spiking interneuron called the anterior paired lateral (APL) neuron. We used local stimulation and volumetric calcium imaging to show that APL inhibits Kenyon cells’ dendrites and axons, and that both activity in APL and APL’s inhibitory effect on Kenyon cells are spatially localized (the latter somewhat less so), allowing APL to differentially inhibit different mushroom body compartments. Applying these results to the Drosophila hemibrain connectome predicts that individual Kenyon cells inhibit themselves via APL more strongly than they inhibit other individual Kenyon cells. These findings reveal how cellular physiology and detailed network anatomy can combine to influence circuit function.
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Affiliation(s)
- Hoger Amin
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom.,Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Anthi A Apostolopoulou
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom.,Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
| | - Raquel Suárez-Grimalt
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - Eleftheria Vrontou
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
| | - Andrew C Lin
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom.,Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom
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156
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Alicea B. Raising the Connectome: The Emergence of Neuronal Activity and Behavior in Caenorhabditis elegans. Front Cell Neurosci 2020; 14:524791. [PMID: 33100971 PMCID: PMC7522492 DOI: 10.3389/fncel.2020.524791] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 08/24/2020] [Indexed: 11/15/2022] Open
Abstract
The differentiation of neurons and formation of connections between cells is the basis of both the adult phenotype and behaviors tied to cognition, perception, reproduction, and survival. Such behaviors are associated with local (circuits) and global (connectome) brain networks. A solid understanding of how these networks emerge is critical. This opinion piece features a guided tour of early developmental events in the emerging connectome, which is crucial to a new view on the connectogenetic process. Connectogenesis includes associating cell identities with broader functional and developmental relationships. During this process, the transition from developmental cells to terminally differentiated cells is defined by an accumulation of traits that ultimately results in neuronal-driven behavior. The well-characterized developmental and cell biology of Caenorhabditis elegans will be used to build a synthesis of developmental events that result in a functioning connectome. Specifically, our view of connectogenesis enables a first-mover model of synaptic connectivity to be demonstrated using data representing larval synaptogenesis. In a first-mover model of Stackelberg competition, potential pre- and postsynaptic relationships are shown to yield various strategies for establishing various types of synaptic connections. By comparing these results to what is known regarding principles for establishing complex network connectivity, these strategies are generalizable to other species and developmental systems. In conclusion, we will discuss the broader implications of this approach, as what is presented here informs an understanding of behavioral emergence and the ability to simulate related biological phenomena.
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Affiliation(s)
- Bradly Alicea
- Orthogonal Research and Education Laboratory, Champaign, IL, United States
- OpenWorm Foundation, Boston, MA, United States
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157
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Scheffer LK, Xu CS, Januszewski M, Lu Z, Takemura SY, Hayworth KJ, Huang GB, Shinomiya K, Maitlin-Shepard J, Berg S, Clements J, Hubbard PM, Katz WT, Umayam L, Zhao T, Ackerman D, Blakely T, Bogovic J, Dolafi T, Kainmueller D, Kawase T, Khairy KA, Leavitt L, Li PH, Lindsey L, Neubarth N, Olbris DJ, Otsuna H, Trautman ET, Ito M, Bates AS, Goldammer J, Wolff T, Svirskas R, Schlegel P, Neace E, Knecht CJ, Alvarado CX, Bailey DA, Ballinger S, Borycz JA, Canino BS, Cheatham N, Cook M, Dreher M, Duclos O, Eubanks B, Fairbanks K, Finley S, Forknall N, Francis A, Hopkins GP, Joyce EM, Kim S, Kirk NA, Kovalyak J, Lauchie SA, Lohff A, Maldonado C, Manley EA, McLin S, Mooney C, Ndama M, Ogundeyi O, Okeoma N, Ordish C, Padilla N, Patrick CM, Paterson T, Phillips EE, Phillips EM, Rampally N, Ribeiro C, Robertson MK, Rymer JT, Ryan SM, Sammons M, Scott AK, Scott AL, Shinomiya A, Smith C, Smith K, Smith NL, Sobeski MA, Suleiman A, Swift J, Takemura S, Talebi I, Tarnogorska D, Tenshaw E, Tokhi T, Walsh JJ, Yang T, Horne JA, Li F, Parekh R, Rivlin PK, Jayaraman V, Costa M, Jefferis GSXE, Ito K, Saalfeld S, George R, Meinertzhagen IA, Rubin GM, Hess HF, Jain V, Plaza SM. A connectome and analysis of the adult Drosophila central brain. eLife 2020; 9:e57443. [PMID: 32880371 PMCID: PMC7546738 DOI: 10.7554/elife.57443] [Citation(s) in RCA: 430] [Impact Index Per Article: 107.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 09/01/2020] [Indexed: 12/26/2022] Open
Abstract
The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.
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Affiliation(s)
- Louis K Scheffer
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - C Shan Xu
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Zhiyuan Lu
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Life Sciences Centre, Dalhousie UniversityHalifaxCanada
| | - Shin-ya Takemura
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Kenneth J Hayworth
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gary B Huang
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Kazunori Shinomiya
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Stuart Berg
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Jody Clements
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Philip M Hubbard
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - William T Katz
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Lowell Umayam
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ting Zhao
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - David Ackerman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - John Bogovic
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Tom Dolafi
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Dagmar Kainmueller
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Takashi Kawase
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Khaled A Khairy
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Peter H Li
- Google ResearchMountain ViewUnited States
| | | | - Nicole Neubarth
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Donald J Olbris
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hideo Otsuna
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Eric T Trautman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Masayoshi Ito
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Institute for Quantitative Biosciences, University of TokyoTokyoJapan
| | | | - Jens Goldammer
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Institute of Zoology, Biocenter Cologne, University of CologneCologneGermany
| | - Tanya Wolff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Robert Svirskas
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Erika Neace
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Chelsea X Alvarado
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Dennis A Bailey
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Samantha Ballinger
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Brandon S Canino
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Natasha Cheatham
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Michael Cook
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Octave Duclos
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Bryon Eubanks
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Kelli Fairbanks
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Samantha Finley
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Nora Forknall
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Audrey Francis
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Emily M Joyce
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - SungJin Kim
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Nicole A Kirk
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Julie Kovalyak
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Shirley A Lauchie
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Alanna Lohff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Charli Maldonado
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Emily A Manley
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Sari McLin
- Life Sciences Centre, Dalhousie UniversityHalifaxCanada
| | - Caroline Mooney
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Miatta Ndama
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Omotara Ogundeyi
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Nneoma Okeoma
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Christopher Ordish
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Nicholas Padilla
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Tyler Paterson
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Elliott E Phillips
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Emily M Phillips
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Neha Rampally
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Caitlin Ribeiro
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Jon Thomson Rymer
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Sean M Ryan
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Megan Sammons
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Anne K Scott
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ashley L Scott
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Aya Shinomiya
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Claire Smith
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Kelsey Smith
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Natalie L Smith
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Margaret A Sobeski
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Alia Suleiman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Jackie Swift
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Satoko Takemura
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Iris Talebi
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Emily Tenshaw
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Temour Tokhi
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - John J Walsh
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Tansy Yang
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Feng Li
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Patricia K Rivlin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Vivek Jayaraman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marta Costa
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Gregory SXE Jefferis
- MRC Laboratory of Molecular BiologyCambridgeUnited States
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Kei Ito
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Institute for Quantitative Biosciences, University of TokyoTokyoJapan
- Institute of Zoology, Biocenter Cologne, University of CologneCologneGermany
| | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Reed George
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ian A Meinertzhagen
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Life Sciences Centre, Dalhousie UniversityHalifaxCanada
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Harald F Hess
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Viren Jain
- Google Research, Google LLCZurichSwitzerland
| | - Stephen M Plaza
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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158
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Michels B, Franke K, Weiglein A, Sultani H, Gerber B, Wessjohann LA. Rewarding compounds identified from the medicinal plant Rhodiola rosea. ACTA ACUST UNITED AC 2020; 223:223/16/jeb223982. [PMID: 32848044 DOI: 10.1242/jeb.223982] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 06/23/2020] [Indexed: 01/06/2023]
Abstract
Preparations of Rhodiola rosea root are widely used in traditional medicine. They can increase life span in worms and flies, and have various effects related to nervous system function in different animal species and humans. However, which of the compounds in R. rosea is mediating any one of these effects has remained unknown in most cases. Here, an analysis of the volatile and non-volatile low-molecular-weight constituents of R. rosea root samples was accompanied by an investigation of their behavioral impact on Drosophila melanogaster larvae. Rhodiola rosea root samples have an attractive smell and taste to the larvae, and exert a rewarding effect. This rewarding effect was also observed for R. rosea root extracts, and did not require activity of dopamine neurons that mediate known rewards such as sugar. Based on the chemical profiles of R. rosea root extracts and resultant fractions, a bioactivity-correlation analysis (AcorA) was performed to identify candidate rewarding compounds. This suggested positive correlations for - among related compounds - ferulic acid eicosyl ester (FAE-20) and β-sitosterol glucoside. A validation using these as pure compounds confirmed that the correlations were causal. Their rewarding effects can be observed even at low micromolar concentrations and thus at remarkably lower doses than for any known taste reward in the larva. We discuss whether similar rewarding effects, should they be observed in humans, would indicate a habit-forming or addictive potential.
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Affiliation(s)
- Birgit Michels
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Katrin Franke
- Leibniz Institute of Plant Biochemistry (IPB), Department of Bioorganic Chemistry, 06120 Halle (Saale), Germany
| | - Aliće Weiglein
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Haider Sultani
- Leibniz Institute of Plant Biochemistry (IPB), Department of Bioorganic Chemistry, 06120 Halle (Saale), Germany
| | - Bertram Gerber
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, 39118 Magdeburg, Germany .,Otto von Guericke University, Institute of Biology, 39106 Magdeburg, Germany.,Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University, 39106 Magdeburg, Germany
| | - Ludger A Wessjohann
- Leibniz Institute of Plant Biochemistry (IPB), Department of Bioorganic Chemistry, 06120 Halle (Saale), Germany
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159
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Ocker GK, Buice MA. Flexible neural connectivity under constraints on total connection strength. PLoS Comput Biol 2020; 16:e1008080. [PMID: 32745134 PMCID: PMC7425997 DOI: 10.1371/journal.pcbi.1008080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 08/13/2020] [Accepted: 06/19/2020] [Indexed: 12/23/2022] Open
Abstract
Neural computation is determined by neurons’ dynamics and circuit connectivity. Uncertain and dynamic environments may require neural hardware to adapt to different computational tasks, each requiring different connectivity configurations. At the same time, connectivity is subject to a variety of constraints, placing limits on the possible computations a given neural circuit can perform. Here we examine the hypothesis that the organization of neural circuitry favors computational flexibility: that it makes many computational solutions available, given physiological constraints. From this hypothesis, we develop models of connectivity degree distributions based on constraints on a neuron’s total synaptic weight. To test these models, we examine reconstructions of the mushroom bodies from the first instar larva and adult Drosophila melanogaster. We perform a Bayesian model comparison for two constraint models and a random wiring null model. Overall, we find that flexibility under a homeostatically fixed total synaptic weight describes Kenyon cell connectivity better than other models, suggesting a principle shaping the apparently random structure of Kenyon cell wiring. Furthermore, we find evidence that larval Kenyon cells are more flexible earlier in development, suggesting a mechanism whereby neural circuits begin as flexible systems that develop into specialized computational circuits. High-throughput electron microscopic anatomical experiments have begun to yield detailed maps of neural circuit connectivity. Uncovering the principles that govern these circuit structures is a major challenge for systems neuroscience. Healthy neural circuits must be able to perform computational tasks while satisfying physiological constraints. Those constraints can restrict a neuron’s possible connectivity, and thus potentially restrict its computation. Here we examine simple models of constraints on total synaptic weights, and calculate the number of circuit configurations they allow: a simple measure of their computational flexibility. We propose probabilistic models of connectivity that weight the number of synaptic partners according to computational flexibility under a constraint and test them using recent wiring diagrams from a learning center, the mushroom body, in the fly brain. We compare constraints that fix or bound a neuron’s total connection strength to a simple random wiring null model. Of these models, the fixed total connection strength matched the overall connectivity best in mushroom bodies from both larval and adult flies. We also provide evidence suggesting that neural circuits are more flexible in early stages of development and lose this flexibility as they grow towards specialized function.
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Affiliation(s)
- Gabriel Koch Ocker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- * E-mail:
| | - Michael A. Buice
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
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160
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Schleyer M, Weiglein A, Thoener J, Strauch M, Hartenstein V, Kantar Weigelt M, Schuller S, Saumweber T, Eichler K, Rohwedder A, Merhof D, Zlatic M, Thum AS, Gerber B. Identification of Dopaminergic Neurons That Can Both Establish Associative Memory and Acutely Terminate Its Behavioral Expression. J Neurosci 2020; 40:5990-6006. [PMID: 32586949 PMCID: PMC7392503 DOI: 10.1523/jneurosci.0290-20.2020] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/14/2020] [Accepted: 05/19/2020] [Indexed: 02/01/2023] Open
Abstract
An adaptive transition from exploring the environment in search of vital resources to exploiting these resources once the search was successful is important to all animals. Here we study the neuronal circuitry that allows larval Drosophila melanogaster of either sex to negotiate this exploration-exploitation transition. We do so by combining Pavlovian conditioning with high-resolution behavioral tracking, optogenetic manipulation of individually identified neurons, and EM data-based analyses of synaptic organization. We find that optogenetic activation of the dopaminergic neuron DAN-i1 can both establish memory during training and acutely terminate learned search behavior in a subsequent recall test. Its activation leaves innate behavior unaffected, however. Specifically, DAN-i1 activation can establish associative memories of opposite valence after paired and unpaired training with odor, and its activation during the recall test can terminate the search behavior resulting from either of these memories. Our results further suggest that in its behavioral significance DAN-i1 activation resembles, but does not equal, sugar reward. Dendrogram analyses of all the synaptic connections between DAN-i1 and its two main targets, the Kenyon cells and the mushroom body output neuron MBON-i1, further suggest that the DAN-i1 signals during training and during the recall test could be delivered to the Kenyon cells and to MBON-i1, respectively, within previously unrecognized, locally confined branching structures. This would provide an elegant circuit motif to terminate search on its successful completion.SIGNIFICANCE STATEMENT In the struggle for survival, animals have to explore their environment in search of food. Once food is found, however, it is adaptive to prioritize exploiting it over continuing a search that would now be as pointless as searching for the glasses you are wearing. This exploration-exploitation trade-off is important for animals and humans, as well as for technical search devices. We investigate which of the only 10,000 neurons of a fruit fly larva can tip the balance in this trade-off, and identify a single dopamine neuron called DAN-i1 that can do so. Given the similarities in dopamine neuron function across the animal kingdom, this may reflect a general principle of how search is terminated once it is successful.
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Affiliation(s)
- Michael Schleyer
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Aliće Weiglein
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Juliane Thoener
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Martin Strauch
- Institute of Imaging & Computer Vision, RWTH Aachen University, 52056 Aachen, Germany
| | - Volker Hartenstein
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California 90095-1606
| | - Melisa Kantar Weigelt
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Sarah Schuller
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Timo Saumweber
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
| | - Katharina Eichler
- University of Konstanz, Institute for Biology, 78464 Konstanz, Germany
- HHMI Janelia Research Campus, Ashburn, Virginia 20147
- Institute of Neurobiology, University of Puerto Rico Medical Science Campus, Old San Juan, Puerto Rico 00901
| | - Astrid Rohwedder
- University of Konstanz, Institute for Biology, 78464 Konstanz, Germany
- Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, United Kingdom
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, 52056 Aachen, Germany
| | - Marta Zlatic
- HHMI Janelia Research Campus, Ashburn, Virginia 20147
- Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, United Kingdom
| | - Andreas S Thum
- University of Konstanz, Institute for Biology, 78464 Konstanz, Germany
- University Leipzig, Institute for Biology, 04103 Leipzig, Germany
| | - Bertram Gerber
- Leibniz Institute for Neurobiology, Department Genetics of Learning and Memory, 39118 Magdeburg, Germany
- Centre for Behavioural Brain Sciences, 39108 Magdeburg, Germany
- Institute for Biology, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
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161
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Mariano V, Achsel T, Bagni C, Kanellopoulos AK. Modelling Learning and Memory in Drosophila to Understand Intellectual Disabilities. Neuroscience 2020; 445:12-30. [PMID: 32730949 DOI: 10.1016/j.neuroscience.2020.07.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 07/19/2020] [Accepted: 07/20/2020] [Indexed: 12/24/2022]
Abstract
Neurodevelopmental disorders (NDDs) include a large number of conditions such as Fragile X syndrome, autism spectrum disorders and Down syndrome, among others. They are characterized by limitations in adaptive and social behaviors, as well as intellectual disability (ID). Whole-exome and whole-genome sequencing studies have highlighted a large number of NDD/ID risk genes. To dissect the genetic causes and underlying biological pathways, in vivo experimental validation of the effects of these mutations is needed. The fruit fly, Drosophila melanogaster, is an ideal model to study NDDs, with highly tractable genetics, combined with simple behavioral and circuit assays, permitting rapid medium-throughput screening of NDD/ID risk genes. Here, we review studies where the use of well-established assays to study mechanisms of learning and memory in Drosophila has permitted insights into molecular mechanisms underlying IDs. We discuss how technologies in the fly model, combined with a high degree of molecular and physiological conservation between flies and mammals, highlight the Drosophila system as an ideal model to study neurodevelopmental disorders, from genetics to behavior.
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Affiliation(s)
- Vittoria Mariano
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne 1005, Switzerland; Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Tilmann Achsel
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne 1005, Switzerland
| | - Claudia Bagni
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne 1005, Switzerland; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome 00133, Italy.
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162
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Input Connectivity Reveals Additional Heterogeneity of Dopaminergic Reinforcement in Drosophila. Curr Biol 2020; 30:3200-3211.e8. [PMID: 32619479 PMCID: PMC7443709 DOI: 10.1016/j.cub.2020.05.077] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/14/2020] [Accepted: 05/22/2020] [Indexed: 11/23/2022]
Abstract
Different types of Drosophila dopaminergic neurons (DANs) reinforce memories of unique valence and provide state-dependent motivational control [1]. Prior studies suggest that the compartment architecture of the mushroom body (MB) is the relevant resolution for distinct DAN functions [2, 3]. Here we used a recent electron microscope volume of the fly brain [4] to reconstruct the fine anatomy of individual DANs within three MB compartments. We find the 20 DANs of the γ5 compartment, at least some of which provide reward teaching signals, can be clustered into 5 anatomical subtypes that innervate different regions within γ5. Reconstructing 821 upstream neurons reveals input selectivity, supporting the functional relevance of DAN sub-classification. Only one PAM-γ5 DAN subtype γ5(fb) receives direct recurrent feedback from γ5β′2a mushroom body output neurons (MBONs) and behavioral experiments distinguish a role for these DANs in memory revaluation from those reinforcing sugar memory. Other DAN subtypes receive major, and potentially reinforcing, inputs from putative gustatory interneurons or lateral horn neurons, which can also relay indirect feedback from MBONs. We similarly reconstructed the single aversively reinforcing PPL1-γ1pedc DAN. The γ1pedc DAN inputs mostly differ from those of γ5 DANs and they cluster onto distinct dendritic branches, presumably separating its established roles in aversive reinforcement and appetitive motivation [5, 6]. Tracing also identified neurons that provide broad input to γ5, β′2a, and γ1pedc DANs, suggesting that distributed DAN populations can be coordinately regulated. These connectomic and behavioral analyses therefore reveal further complexity of dopaminergic reinforcement circuits between and within MB compartments. Nanoscale anatomy reveals additional subtypes of rewarding dopaminergic neurons Connectomics reveals input specificity to subtypes of dopaminergic neurons Axon morphology implies dopaminergic neurons provide subcompartment-level function Unique dopaminergic subtypes serve aversive memory extinction and sugar learning
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163
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Miroschnikow A, Schlegel P, Pankratz MJ. Making Feeding Decisions in the Drosophila Nervous System. Curr Biol 2020; 30:R831-R840. [DOI: 10.1016/j.cub.2020.06.036] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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164
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Salazar JL, Yang SA, Yamamoto S. Post-Developmental Roles of Notch Signaling in the Nervous System. Biomolecules 2020; 10:biom10070985. [PMID: 32630239 PMCID: PMC7408554 DOI: 10.3390/biom10070985] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 12/14/2022] Open
Abstract
Since its discovery in Drosophila, the Notch signaling pathway has been studied in numerous developmental contexts in diverse multicellular organisms. The role of Notch signaling in nervous system development has been extensively investigated by numerous scientists, partially because many of the core Notch signaling components were initially identified through their dramatic ‘neurogenic’ phenotype of developing fruit fly embryos. Components of the Notch signaling pathway continue to be expressed in mature neurons and glia cells, which is suggestive of a role in the post-developmental nervous system. The Notch pathway has been, so far, implicated in learning and memory, social behavior, addiction, and other complex behaviors using genetic model organisms including Drosophila and mice. Additionally, Notch signaling has been shown to play a modulatory role in several neurodegenerative disease model animals and in mediating neural toxicity of several environmental factors. In this paper, we summarize the knowledge pertaining to the post-developmental roles of Notch signaling in the nervous system with a focus on discoveries made using the fruit fly as a model system as well as relevant studies in C elegans, mouse, rat, and cellular models. Since components of this pathway have been implicated in the pathogenesis of numerous psychiatric and neurodegenerative disorders in human, understanding the role of Notch signaling in the mature brain using model organisms will likely provide novel insights into the mechanisms underlying these diseases.
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Affiliation(s)
- Jose L. Salazar
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA; (J.L.S.); (S.-A.Y.)
| | - Sheng-An Yang
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA; (J.L.S.); (S.-A.Y.)
| | - Shinya Yamamoto
- Department of Molecular and Human Genetics, Baylor College of Medicine (BCM), Houston, TX 77030, USA; (J.L.S.); (S.-A.Y.)
- Department of Neuroscience, BCM, Houston, TX 77030, USA
- Program in Developmental Biology, BCM, Houston, TX 77030, USA
- Development, Disease Models & Therapeutics Graduate Program, BCM, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
- Correspondence: ; Tel.: +1-832-824-8119
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165
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Yuan J, Zhang J, Shen L, Zhang D, Yu W, Han H. Massive Data Management and Sharing Module for Connectome Reconstruction. Brain Sci 2020; 10:brainsci10050314. [PMID: 32455914 PMCID: PMC7288162 DOI: 10.3390/brainsci10050314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/19/2020] [Accepted: 05/20/2020] [Indexed: 11/16/2022] Open
Abstract
Recently, with the rapid development of electron microscopy (EM) technology and the increasing demand of neuron circuit reconstruction, the scale of reconstruction data grows significantly. This brings many challenges, one of which is how to effectively manage large-scale data so that researchers can mine valuable information. For this purpose, we developed a data management module equipped with two parts, a storage and retrieval module on the server-side and an image cache module on the client-side. On the server-side, Hadoop and HBase are introduced to resolve massive data storage and retrieval. The pyramid model is adopted to store electron microscope images, which represent multiresolution data of the image. A block storage method is proposed to store volume segmentation results. We design a spatial location-based retrieval method for fast obtaining images and segments by layers rapidly, which achieves a constant time complexity. On the client-side, a three-level image cache module is designed to reduce latency when acquiring data. Through theoretical analysis and practical tests, our tool shows excellent real-time performance when handling large-scale data. Additionally, the server-side can be used as a backend of other similar software or a public database to manage shared datasets, showing strong scalability.
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Affiliation(s)
- Jingbin Yuan
- School of Automation, Harbin University of Science and Technology, Harbin 150080, China; (J.Y.); (J.Z.)
| | - Jing Zhang
- School of Automation, Harbin University of Science and Technology, Harbin 150080, China; (J.Y.); (J.Z.)
| | - Lijun Shen
- Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (D.Z.); (W.Y.)
- Correspondence: (L.S.); (H.H); Tel.: +86-010-82544385 (L.S.); +86-010-82544710 (H.H)
| | - Dandan Zhang
- Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (D.Z.); (W.Y.)
| | - Wenhuan Yu
- Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (D.Z.); (W.Y.)
| | - Hua Han
- The National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- The School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (L.S.); (H.H); Tel.: +86-010-82544385 (L.S.); +86-010-82544710 (H.H)
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166
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Siju KP, Štih V, Aimon S, Gjorgjieva J, Portugues R, Grunwald Kadow IC. Valence and State-Dependent Population Coding in Dopaminergic Neurons in the Fly Mushroom Body. Curr Biol 2020; 30:2104-2115.e4. [PMID: 32386530 DOI: 10.1016/j.cub.2020.04.037] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 03/13/2020] [Accepted: 04/16/2020] [Indexed: 11/26/2022]
Abstract
Neuromodulation permits flexibility of synapses, neural circuits, and ultimately behavior. One neuromodulator, dopamine, has been studied extensively in its role as a reward signal during learning and memory across animal species. Newer evidence suggests that dopaminergic neurons (DANs) can modulate sensory perception acutely, thereby allowing an animal to adapt its behavior and decision making to its internal and behavioral state. In addition, some data indicate that DANs are not homogeneous but rather convey different types of information as a heterogeneous population. We have investigated DAN population activity and how it could encode relevant information about sensory stimuli and state by taking advantage of the confined anatomy of DANs innervating the mushroom body (MB) of the fly Drosophila melanogaster. Using in vivo calcium imaging and a custom 3D image registration method, we found that the activity of the population of MB DANs encodes innate valence information of an odor or taste as well as the physiological state of the animal. Furthermore, DAN population activity is strongly correlated with movement, consistent with a role of dopamine in conveying behavioral state to the MB. Altogether, our data and analysis suggest that DAN population activities encode innate odor and taste valence, movement, and physiological state in a MB-compartment-specific manner. We propose that dopamine shapes innate perception through combinatorial population coding of sensory valence, physiological, and behavioral context.
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Affiliation(s)
- K P Siju
- TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Vilim Štih
- Sensorimotor Control Group, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Sophie Aimon
- TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Julijana Gjorgjieva
- TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438 Frankfurt am Main, Germany
| | - Ruben Portugues
- Sensorimotor Control Group, Max Planck Institute of Neurobiology, 82152 Martinsried, Germany; Institute of Neuroscience, Technical University of Munich, 80802 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 80802 Munich, Germany
| | - Ilona C Grunwald Kadow
- TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; ZIEL-Institute of Food and Health, Technical University of Munich, 85354 Freising, Germany.
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167
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Bates AS, Manton JD, Jagannathan SR, Costa M, Schlegel P, Rohlfing T, Jefferis GSXE. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 2020; 9:e53350. [PMID: 32286229 PMCID: PMC7242028 DOI: 10.7554/elife.53350] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 04/11/2020] [Indexed: 11/18/2022] Open
Abstract
To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.
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Affiliation(s)
| | - James D Manton
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Sridhar R Jagannathan
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Marta Costa
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Torsten Rohlfing
- SRI International, Neuroscience Program, Center for Health SciencesMenlo ParkUnited States
| | - Gregory SXE Jefferis
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Drosophila Connectomics Group, Department of Zoology, University of CambridgeCambridgeUnited Kingdom
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168
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Charles AS, Falk B, Turner N, Pereira TD, Tward D, Pedigo BD, Chung J, Burns R, Ghosh SS, Kebschull JM, Silversmith W, Vogelstein JT. Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics. Annu Rev Neurosci 2020; 43:441-464. [PMID: 32283996 DOI: 10.1146/annurev-neuro-100119-110036] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.
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Affiliation(s)
- Adam S Charles
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; .,Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Benjamin Falk
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Nicholas Turner
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Talmo D Pereira
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA
| | - Daniel Tward
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Benjamin D Pedigo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Jaewon Chung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Randal Burns
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Justus M Kebschull
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; .,Stanford University, Palo Alto, California 94305, USA
| | - William Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; .,Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
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169
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Modi MN, Shuai Y, Turner GC. The Drosophila Mushroom Body: From Architecture to Algorithm in a Learning Circuit. Annu Rev Neurosci 2020; 43:465-484. [PMID: 32283995 DOI: 10.1146/annurev-neuro-080317-0621333] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The Drosophila brain contains a relatively simple circuit for forming Pavlovian associations, yet it achieves many operations common across memory systems. Recent advances have established a clear framework for Drosophila learning and revealed the following key operations: a) pattern separation, whereby dense combinatorial representations of odors are preprocessed to generate highly specific, nonoverlapping odor patterns used for learning; b) convergence, in which sensory information is funneled to a small set of output neurons that guide behavioral actions; c) plasticity, where changing the mapping of sensory input to behavioral output requires a strong reinforcement signal, which is also modulated by internal state and environmental context; and d) modularization, in which a memory consists of multiple parallel traces, which are distinct in stability and flexibility and exist in anatomically well-defined modules within the network. Cross-module interactions allow for higher-order effects where past experience influences future learning. Many of these operations have parallels with processes of memory formation and action selection in more complex brains.
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Affiliation(s)
- Mehrab N Modi
- Janelia Research Campus, Ashburn, Virginia 20147, USA;
| | - Yichun Shuai
- Janelia Research Campus, Ashburn, Virginia 20147, USA;
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170
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Bilz F, Geurten BRH, Hancock CE, Widmann A, Fiala A. Visualization of a Distributed Synaptic Memory Code in the Drosophila Brain. Neuron 2020; 106:963-976.e4. [PMID: 32268119 DOI: 10.1016/j.neuron.2020.03.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 11/19/2019] [Accepted: 03/13/2020] [Indexed: 10/24/2022]
Abstract
During associative conditioning, animals learn which sensory cues are predictive for positive or negative conditions. Because sensory cues are encoded by distributed neurons, one has to monitor plasticity across many synapses to capture how learned information is encoded. We analyzed synaptic boutons of Kenyon cells of the Drosophila mushroom body γ lobe, a brain structure that mediates olfactory learning. A fluorescent Ca2+ sensor was expressed in single Kenyon cells so that axonal boutons could be assigned to distinct cells and Ca2+ could be measured across many animals. Learning induced directed synaptic plasticity in specific compartments along the axons. Moreover, we show that odor-evoked Ca2+ dynamics across boutons decorrelate as a result of associative learning. Information theory indicates that learning renders the stimulus representation more distinct compared with naive stimuli. These data reveal that synaptic boutons rather than cells act as individually modifiable units, and coherence among them is a memory-encoding parameter.
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Affiliation(s)
- Florian Bilz
- Department of Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute for Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
| | - Bart R H Geurten
- Department of Cellular Neurobiology, Johann-Friedrich-Blumenbach-Institute for Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
| | - Clare E Hancock
- Department of Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute for Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
| | - Annekathrin Widmann
- Department of Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute for Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany
| | - André Fiala
- Department of Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute for Zoology and Anthropology, University of Göttingen, Julia-Lermontowa-Weg 3, 37077 Göttingen, Germany.
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171
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Eschbach C, Fushiki A, Winding M, Schneider-Mizell CM, Shao M, Arruda R, Eichler K, Valdes-Aleman J, Ohyama T, Thum AS, Gerber B, Fetter RD, Truman JW, Litwin-Kumar A, Cardona A, Zlatic M. Recurrent architecture for adaptive regulation of learning in the insect brain. Nat Neurosci 2020; 23:544-555. [PMID: 32203499 PMCID: PMC7145459 DOI: 10.1038/s41593-020-0607-9] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 02/06/2020] [Indexed: 11/09/2022]
Abstract
Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.
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Affiliation(s)
- Claire Eschbach
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Akira Fushiki
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Departments of Neuroscience and Neurology, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Michael Winding
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Casey M Schneider-Mizell
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Mei Shao
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | | | - Katharina Eichler
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Institute of Neurobiology, University of Puerto Rico Medical Science Campus, San Juan, Puerto Rico, USA
| | | | - Tomoko Ohyama
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Department of Biology, McGill University, Montreal, Quebec, Canada
| | - Andreas S Thum
- Department of Genetics, Institute for Biology, University of Leipzig, Leipzig, Germany
| | - Bertram Gerber
- Abteilung Genetik von Lernen & Gedächtnis, Leibniz Institut für Neurobiologie, Otto von Guericke University Magdeburg, Institut für Biologie, Verhaltensgenetik, & Center for Behavioral Brain Sciences, Magdeburg, Germany
| | | | - James W Truman
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Ashok Litwin-Kumar
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
| | - Albert Cardona
- HHMI Janelia Research Campus, Ashburn, VA, USA.
- MRC Laboratory of Molecular Biology, Cambridge, UK.
- Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK.
| | - Marta Zlatic
- HHMI Janelia Research Campus, Ashburn, VA, USA.
- Department of Zoology, University of Cambridge, Cambridge, UK.
- MRC Laboratory of Molecular Biology, Cambridge, UK.
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172
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Krüppel S, Tetzlaff C. The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity. Netw Neurosci 2020; 4:174-199. [PMID: 32166207 PMCID: PMC7055647 DOI: 10.1162/netn_a_00118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 12/09/2019] [Indexed: 11/25/2022] Open
Abstract
Along sensory pathways, representations of environmental stimuli become increasingly sparse and expanded. If additionally the feed-forward synaptic weights are structured according to the inherent organization of stimuli, the increase in sparseness and expansion leads to a reduction of sensory noise. However, it is unknown how the synapses in the brain form the required structure, especially given the omnipresent noise of environmental stimuli. Here, we employ a combination of synaptic plasticity and intrinsic plasticity—adapting the excitability of each neuron individually—and present stimuli with an inherent organization to a feed-forward network. We observe that intrinsic plasticity maintains the sparseness of the neural code and thereby allows synaptic plasticity to learn the organization of stimuli in low-noise environments. Nevertheless, even high levels of noise can be handled after a subsequent phase of readaptation of the neuronal excitabilities by intrinsic plasticity. Interestingly, during this phase the synaptic structure has to be maintained. These results demonstrate that learning and recalling in the presence of noise requires the coordinated interplay between plasticity mechanisms adapting different properties of the neuronal circuit. Everyday life requires living beings to continuously recognize and categorize perceived stimuli from the environment. To master this task, the representations of these stimuli become increasingly sparse and expanded along the sensory pathways of the brain. In addition, the underlying neuronal network has to be structured according to the inherent organization of the environmental stimuli. However, how the neuronal network learns the required structure even in the presence of noise remains unknown. In this theoretical study, we show that the interplay between synaptic plasticity—controlling the synaptic efficacies—and intrinsic plasticity—adapting the neuronal excitabilities—enables the network to encode the organization of environmental stimuli. It thereby structures the network to correctly categorize stimuli even in the presence of noise. After having encoded the stimuli’s organization, consolidating the synaptic structure while keeping the neuronal excitabilities dynamic enables the neuronal system to readapt to arbitrary levels of noise resulting in a near-optimal classification performance for all noise levels. These results provide new insights into the interplay between different plasticity mechanisms and how this interplay enables sensory systems to reliably learn and categorize stimuli from the surrounding environment.
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Affiliation(s)
- Steffen Krüppel
- Department of Computational Neuroscience, Third Institute of Physics - Biophysics, Georg-August-University, Göttingen, Germany
| | - Christian Tetzlaff
- Department of Computational Neuroscience, Third Institute of Physics - Biophysics, Georg-August-University, Göttingen, Germany
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173
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Ray S, Aldworth ZN, Stopfer MA. Feedback inhibition and its control in an insect olfactory circuit. eLife 2020; 9:53281. [PMID: 32163034 PMCID: PMC7145415 DOI: 10.7554/elife.53281] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 03/09/2020] [Indexed: 01/20/2023] Open
Abstract
Inhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Our simulations suggest that depolarizing GGN at its input branch can globally inhibit KCs several hundred microns away. Our in vivorecordings show that GGN responds to odors with complex temporal patterns of depolarization and hyperpolarization that can vary with odors and across animals, leading our model to predict the existence of a yet-undiscovered olfactory pathway. Our analysis reveals basic new features of GGN and the olfactory network surrounding it.
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Affiliation(s)
- Subhasis Ray
- Section on Sensory Coding and Neural Ensembles, NICHD, NIH, Bethesda, United States
| | - Zane N Aldworth
- Section on Sensory Coding and Neural Ensembles, NICHD, NIH, Bethesda, United States
| | - Mark A Stopfer
- Section on Sensory Coding and Neural Ensembles, NICHD, NIH, Bethesda, United States
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174
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Multiple network properties overcome random connectivity to enable stereotypic sensory responses. Nat Commun 2020; 11:1023. [PMID: 32094345 PMCID: PMC7039968 DOI: 10.1038/s41467-020-14836-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 02/03/2020] [Indexed: 02/08/2023] Open
Abstract
Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our results reveal the fundamental mechanisms and constraints with which convergence enables stereotypy in sensory responses despite random connectivity.
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175
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Johnston WJ, Palmer SE, Freedman DJ. Nonlinear mixed selectivity supports reliable neural computation. PLoS Comput Biol 2020; 16:e1007544. [PMID: 32069273 PMCID: PMC7048320 DOI: 10.1371/journal.pcbi.1007544] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 02/28/2020] [Accepted: 11/12/2019] [Indexed: 12/17/2022] Open
Abstract
Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinearly mixed feature representations have been observed throughout primary sensory, decision-making, and motor brain areas. In these areas, different features are almost always nonlinearly mixed to some degree, rather than represented separately or with only additive (linear) mixing, which we refer to as pure selectivity. Mixed selectivity has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that it has another important benefit: in many cases, it makes orders of magnitude fewer decoding errors than pure selectivity even when both forms of selectivity use the same number of spikes. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that mixed selectivity exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that nonlinear mixed selectivity may be a general coding scheme exploited by the brain for reliable and efficient neural computation. Neurons in the brain are unreliable, while both perception and behavior are generally reliable. In this work, we study how the neural population response to sensory, motor, and cognitive features can produce this reliability. Across the brain, single neurons have been shown to respond to particular conjunctions of multiple features, termed nonlinear mixed selectivity. In this work, we show that populations of these mixed selective neurons lead to many fewer decoding errors than populations without mixed selectivity, even when both neural codes are given the same number of spikes. We show that the reliability benefits from mixed selectivity are quite general, holding under different assumptions about metabolic costs and neural noise as well as for both categorical and sensory errors. Further, previous theoretical work has shown that mixed selectivity enables the learning of complex behaviors with simple decoders. Through the analysis of neural data, we show that the brain implements mixed selectivity even when it would not serve this purpose. Thus, we argue that the brain also implements mixed selectivity to exploit its general benefits for reliable and efficient neural computation.
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Affiliation(s)
- W. Jeffrey Johnston
- Graduate Program in Computational Neuroscience, The University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, The University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
| | - Stephanie E. Palmer
- Graduate Program in Computational Neuroscience, The University of Chicago, Chicago, Illinois, United States of America
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, The University of Chicago, Chicago, Illinois, United States of America
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, Illinois, United States of America
- Department of Physics, The University of Chicago, Chicago, Illinois, United States of America
| | - David J. Freedman
- Graduate Program in Computational Neuroscience, The University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, The University of Chicago, Chicago, Illinois, United States of America
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, The University of Chicago, Chicago, Illinois, United States of America
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176
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tpHusion: An efficient tool for clonal pH determination in Drosophila. PLoS One 2020; 15:e0228995. [PMID: 32059043 PMCID: PMC7021318 DOI: 10.1371/journal.pone.0228995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 01/27/2020] [Indexed: 02/04/2023] Open
Abstract
Genetically encoded pH indicators (GEpHI) have emerged as important tools for investigating intracellular pH (pHi) dynamics in Drosophila. However, most of the indicators are based on the Gal4/UAS binary expression system. Here, we report the generation of a ubiquitously-expressed GEpHI. The fusion protein of super ecliptic pHluorin and FusionRed was cloned under the tubulin promoter (tpHusion) to drive it independently of the Gal4/UAS system. The function of tpHusion was validated in various tissues from different developmental stages of Drosophila. Differences in pHi were also indicated correctly in fixed tissues. Finally, we describe the use of tpHusion for comparative analysis of pHi in manipulated clones and the surrounding cells in epithelial tissues. Our findings establish tpHusion as a robust tool for studying pHi in Drosophila.
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177
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Jovanic T. Studying neural circuits of decision-making in Drosophila larva. J Neurogenet 2020; 34:162-170. [PMID: 32054384 DOI: 10.1080/01677063.2020.1719407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
To study neural circuits underlying decisions, the model organism used for that purpose has to be simple enough to be able to dissect the circuitry neuron by neuron across the nervous system and in the same time complex enough to be able to perform different types of decisions. Here, I lay out the case: (1) that Drosophila larva is an advantageous model system that balances well these two requirements and (2) the insights gained from this model, assuming that circuit principles may be shared across species, can be used to advance our knowledge of neural circuit implementation of decision-making in general, including in more complex brains.
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Affiliation(s)
- Tihana Jovanic
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris Saclay, Gif-sur-Yvette, France.,Decision and Bayesian Computation, UMR 3571 Neuroscience Department & USR 3756 (C3BI/DBC), Institut Pasteur & CNRS, Paris, France
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178
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Boto T, Stahl A, Tomchik SM. Cellular and circuit mechanisms of olfactory associative learning in Drosophila. J Neurogenet 2020; 34:36-46. [PMID: 32043414 DOI: 10.1080/01677063.2020.1715971] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Recent years have witnessed significant progress in understanding how memories are encoded, from the molecular to the cellular and the circuit/systems levels. With a good compromise between brain complexity and behavioral sophistication, the fruit fly Drosophila melanogaster is one of the preeminent animal models of learning and memory. Here we review how memories are encoded in Drosophila, with a focus on short-term memory and an eye toward future directions. Forward genetic screens have revealed a large number of genes and transcripts necessary for learning and memory, some acting cell-autonomously. Further, the relative numerical simplicity of the fly brain has enabled the reverse engineering of learning circuits with remarkable precision, in some cases ascribing behavioral phenotypes to single neurons. Functional imaging and physiological studies have localized and parsed the plasticity that occurs during learning at some of the major loci. Connectomics projects are significantly expanding anatomical knowledge of the nervous system, filling out the roadmap for ongoing functional/physiological and behavioral studies, which are being accelerated by simultaneous tool development. These developments have provided unprecedented insight into the fundamental neural principles of learning, and lay the groundwork for deep understanding in the near future.
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Affiliation(s)
- Tamara Boto
- Department of Neuroscience, The Scripps Research Institute, Jupiter, FL, USA
| | - Aaron Stahl
- Department of Neuroscience, The Scripps Research Institute, Jupiter, FL, USA
| | - Seth M Tomchik
- Department of Neuroscience, The Scripps Research Institute, Jupiter, FL, USA
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179
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Barabási DL, Barabási AL. A Genetic Model of the Connectome. Neuron 2020; 105:435-445.e5. [PMID: 31806491 PMCID: PMC7007360 DOI: 10.1016/j.neuron.2019.10.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/07/2019] [Accepted: 10/24/2019] [Indexed: 11/18/2022]
Abstract
The connectomes of organisms of the same species show remarkable architectural and often local wiring similarity, raising the question: where and how is neuronal connectivity encoded? Here, we start from the hypothesis that the genetic identity of neurons guides synapse and gap-junction formation and show that such genetically driven wiring predicts the existence of specific biclique motifs in the connectome. We identify a family of large, statistically significant biclique subgraphs in the connectomes of three species and show that within many of the observed bicliques the neurons share statistically significant expression patterns and morphological characteristics, supporting our expectation of common genetic factors that drive the synapse formation within these subgraphs. The proposed connectome model offers a self-consistent framework to link the genetics of an organism to the reproducible architecture of its connectome, offering experimentally falsifiable predictions on the genetic factors that drive the formation of individual neuronal circuits.
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Affiliation(s)
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Data and Network Science, Central European University, Budapest 1051, Hungary.
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180
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Le Möel F, Wystrach A. Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies. PLoS Comput Biol 2020; 16:e1007631. [PMID: 32023241 PMCID: PMC7034919 DOI: 10.1371/journal.pcbi.1007631] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 02/21/2020] [Accepted: 01/04/2020] [Indexed: 11/19/2022] Open
Abstract
Solitary foraging insects display stunning navigational behaviours in visually complex natural environments. Current literature assumes that these insects are mostly driven by attractive visual memories, which are learnt when the insect's gaze is precisely oriented toward the goal direction, typically along its familiar route or towards its nest. That way, an insect could return home by simply moving in the direction that appears most familiar. Here we show using virtual reconstructions of natural environments that this principle suffers from fundamental drawbacks, notably, a given view of the world does not provide information about whether the agent should turn or not to reach its goal. We propose a simple model where the agent continuously compares its current view with both goal and anti-goal visual memories, which are treated as attractive and repulsive respectively. We show that this strategy effectively results in an opponent process, albeit not at the perceptual level-such as those proposed for colour vision or polarisation detection-but at the level of the environmental space. This opponent process results in a signal that strongly correlates with the angular error of the current body orientation so that a single view of the world now suffices to indicate whether the agent should turn or not. By incorporating this principle into a simple agent navigating in reconstructed natural environments, we show that it overcomes the usual shortcomings and produces a step-increase in navigation effectiveness and robustness. Our findings provide a functional explanation to recent behavioural observations in ants and why and how so-called aversive and appetitive memories must be combined. We propose a likely neural implementation based on insects' mushroom bodies' circuitry that produces behavioural and neural predictions contrasting with previous models.
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Affiliation(s)
- Florent Le Möel
- Research Centre on Animal Cognition, University Paul Sabatier/CNRS, Toulouse, France
| | - Antoine Wystrach
- Research Centre on Animal Cognition, University Paul Sabatier/CNRS, Toulouse, France
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181
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Masson JB, Laurent F, Cardona A, Barré C, Skatchkovsky N, Zlatic M, Jovanic T. Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila. PLoS Genet 2020; 16:e1008589. [PMID: 32059010 PMCID: PMC7173939 DOI: 10.1371/journal.pgen.1008589] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 04/21/2020] [Accepted: 12/30/2019] [Indexed: 11/21/2022] Open
Abstract
Nervous systems have the ability to select appropriate actions and action sequences in response to sensory cues. The circuit mechanisms by which nervous systems achieve choice, stability and transitions between behaviors are still incompletely understood. To identify neurons and brain areas involved in controlling these processes, we combined a large-scale neuronal inactivation screen with automated action detection in response to a mechanosensory cue in Drosophila larva. We analyzed behaviors from 2.9x105 larvae and identified 66 candidate lines for mechanosensory responses out of which 25 for competitive interactions between actions. We further characterize in detail the neurons in these lines and analyzed their connectivity using electron microscopy. We found the neurons in the mechanosensory network are located in different regions of the nervous system consistent with a distributed model of sensorimotor decision-making. These findings provide the basis for understanding how selection and transition between behaviors are controlled by the nervous system.
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Affiliation(s)
- Jean-Baptiste Masson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department, Institut Pasteur & CNRS, Paris, France
| | - François Laurent
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department, Institut Pasteur & CNRS, Paris, France
| | - Albert Cardona
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
- Department of Physiology, Development, and Neuroscience, Cambridge University, Cambridge, United Kingdom
- MRC Laboratory of Molecular Biology, Trumpington, Cambridge, United Kingdom
| | - Chloé Barré
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department, Institut Pasteur & CNRS, Paris, France
| | - Nicolas Skatchkovsky
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department, Institut Pasteur & CNRS, Paris, France
| | - Marta Zlatic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
- MRC Laboratory of Molecular Biology, Trumpington, Cambridge, United Kingdom
- Department of Zoology, Cambridge University, Cambridge, United Kingdom
| | - Tihana Jovanic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
- Decision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department, Institut Pasteur & CNRS, Paris, France
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris Saclay, Gif-sur-Yvette, France
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182
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Abstract
The Mushroom Body (MB) is the primary location of stored associative memories in the Drosophila brain. We discuss recent advances in understanding the MB's neuronal circuits made using advanced light microscopic methods and cell-type-specific genetic tools. We also review how the compartmentalized nature of the MB's organization allows this brain area to form and store memories with widely different dynamics.
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Affiliation(s)
- Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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183
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Brünner B, Saumweber J, Samur M, Weber D, Schumann I, Mahishi D, Rohwedder A, Thum AS. Food restriction reconfigures naïve and learned choice behavior in Drosophila larvae. J Neurogenet 2020; 34:123-132. [PMID: 31975653 DOI: 10.1080/01677063.2020.1714612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
In many animals, the establishment and expression of food-related memory is limited by the presence of food and promoted by its absence, implying that this behavior is driven by motivation. In the past, this has already been demonstrated in various insects including honeybees and adult Drosophila. For Drosophila larvae, which are characterized by an immense growth and the resulting need for constant food intake, however, knowledge is rather limited. Accordingly, we have analyzed whether starvation modulates larval memory formation or expression after appetitive classical olfactory conditioning, in which an odor is associated with a sugar reward. We show that odor-sugar memory of starved larvae lasts longer than in fed larvae, although the initial performance is comparable. 80 minutes after odor fructose conditioning, only starved but not fed larvae show a reliable odor-fructose memory. This is likely due to a specific increase in the stability of anesthesia-resistant memory (ARM). Furthermore, we observe that starved larvae, in contrast to fed ones, prefer sugars that offer a nutritional benefit in addition to their sweetness. Taken together our work shows that Drosophila larvae adjust the expression of learned and naïve choice behaviors in the absence of food. These effects are only short-lasting probably due to their lifestyle and their higher internal motivation to feed. In the future, the extensive use of established genetic tools will allow us to identify development-specific differences arising at the neuronal and molecular level.
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Affiliation(s)
- Benita Brünner
- Department of Genetics, University of Leipzig, Leipzig, Germany
| | | | - Merve Samur
- Department of Genetics, University of Leipzig, Leipzig, Germany.,Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
| | - Denise Weber
- Department of Genetics, University of Leipzig, Leipzig, Germany
| | | | - Deepthi Mahishi
- Department of Genetics, University of Leipzig, Leipzig, Germany
| | | | - Andreas S Thum
- Department of Genetics, University of Leipzig, Leipzig, Germany
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184
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Elkahlah NA, Rogow JA, Ahmed M, Clowney EJ. Presynaptic developmental plasticity allows robust sparse wiring of the Drosophila mushroom body. eLife 2020; 9:e52278. [PMID: 31913123 PMCID: PMC7028369 DOI: 10.7554/elife.52278] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 01/07/2020] [Indexed: 01/29/2023] Open
Abstract
In order to represent complex stimuli, principle neurons of associative learning regions receive combinatorial sensory inputs. Density of combinatorial innervation is theorized to determine the number of distinct stimuli that can be represented and distinguished from one another, with sparse innervation thought to optimize the complexity of representations in networks of limited size. How the convergence of combinatorial inputs to principle neurons of associative brain regions is established during development is unknown. Here, we explore the developmental patterning of sparse olfactory inputs to Kenyon cells of the Drosophila melanogaster mushroom body. By manipulating the ratio between pre- and post-synaptic cells, we find that postsynaptic Kenyon cells set convergence ratio: Kenyon cells produce fixed distributions of dendritic claws while presynaptic processes are plastic. Moreover, we show that sparse odor responses are preserved in mushroom bodies with reduced cellular repertoires, suggesting that developmental specification of convergence ratio allows functional robustness.
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Affiliation(s)
- Najia A Elkahlah
- Department of Molecular, Cellular and Developmental BiologyThe University of MichiganAnn ArborUnited States
| | - Jackson A Rogow
- Laboratory of Neurophysiology and BehaviorThe Rockefeller UniversityNew YorkUnited States
| | - Maria Ahmed
- Department of Molecular, Cellular and Developmental BiologyThe University of MichiganAnn ArborUnited States
| | - E Josephine Clowney
- Department of Molecular, Cellular and Developmental BiologyThe University of MichiganAnn ArborUnited States
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185
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Affiliation(s)
- Nadine Ehmann
- Department of Animal Physiology, Institute of Biology, Leipzig University, Leipzig, Germany
| | - Dennis Pauls
- Department of Animal Physiology, Institute of Biology, Leipzig University, Leipzig, Germany
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186
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Neurochemical Organization of the Drosophila Brain Visualized by Endogenously Tagged Neurotransmitter Receptors. Cell Rep 2020; 30:284-297.e5. [DOI: 10.1016/j.celrep.2019.12.018] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 10/19/2019] [Accepted: 12/06/2019] [Indexed: 02/08/2023] Open
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187
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Zarin AA, Mark B, Cardona A, Litwin-Kumar A, Doe CQ. A multilayer circuit architecture for the generation of distinct locomotor behaviors in Drosophila. eLife 2019; 8:e51781. [PMID: 31868582 PMCID: PMC6994239 DOI: 10.7554/elife.51781] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 12/22/2019] [Indexed: 12/22/2022] Open
Abstract
Animals generate diverse motor behaviors, yet how the same motor neurons (MNs) generate two distinct or antagonistic behaviors remains an open question. Here, we characterize Drosophila larval muscle activity patterns and premotor/motor circuits to understand how they generate forward and backward locomotion. We show that all body wall MNs are activated during both behaviors, but a subset of MNs change recruitment timing for each behavior. We used TEM to reconstruct a full segment of all 60 MNs and 236 premotor neurons (PMNs), including differentially-recruited MNs. Analysis of this comprehensive connectome identified PMN-MN 'labeled line' connectivity; PMN-MN combinatorial connectivity; asymmetric neuronal morphology; and PMN-MN circuit motifs that could all contribute to generating distinct behaviors. We generated a recurrent network model that reproduced the observed behaviors, and used functional optogenetics to validate selected model predictions. This PMN-MN connectome will provide a foundation for analyzing the full suite of larval behaviors.
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Affiliation(s)
- Aref Arzan Zarin
- Institute of NeuroscienceHoward Hughes Medical Institute, University of OregonEugeneUnited States
| | - Brandon Mark
- Institute of NeuroscienceHoward Hughes Medical Institute, University of OregonEugeneUnited States
| | - Albert Cardona
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ashok Litwin-Kumar
- Mortimer B Zuckerman Mind Brain Behavior Institute, Department of NeuroscienceColumbia UniversityNew YorkUnited States
| | - Chris Q Doe
- Institute of NeuroscienceHoward Hughes Medical Institute, University of OregonEugeneUnited States
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188
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Collins LT. The case for emulating insect brains using anatomical "wiring diagrams" equipped with biophysical models of neuronal activity. BIOLOGICAL CYBERNETICS 2019; 113:465-474. [PMID: 31696303 DOI: 10.1007/s00422-019-00810-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 10/29/2019] [Indexed: 06/10/2023]
Abstract
Developing whole-brain emulation (WBE) technology would provide immense benefits across neuroscience, biomedicine, artificial intelligence, and robotics. At this time, constructing a simulated human brain lacks feasibility due to limited experimental data and limited computational resources. However, I suggest that progress toward this goal might be accelerated by working toward an intermediate objective, namely insect brain emulation (IBE). More specifically, this would entail creating biologically realistic simulations of entire insect nervous systems along with more approximate simulations of non-neuronal insect physiology to make "virtual insects." I argue that this could be realistically achievable within the next 20 years. I propose that developing emulations of insect brains will galvanize the global community of scientists, businesspeople, and policymakers toward pursuing the loftier goal of emulating the human brain. By demonstrating that WBE is possible via IBE, simulating mammalian brains and eventually the human brain may no longer be viewed as too radically ambitious to deserve substantial funding and resources. Furthermore, IBE will facilitate dramatic advances in cognitive neuroscience, artificial intelligence, and robotics through studies performed using virtual insects.
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Affiliation(s)
- Logan T Collins
- Department of Psychology and Neuroscience, University of Colorado, Boulder, 2860 Wilderness Place, Boulder, CO, 80301, USA.
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189
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Crews ST. Drosophila Embryonic CNS Development: Neurogenesis, Gliogenesis, Cell Fate, and Differentiation. Genetics 2019; 213:1111-1144. [PMID: 31796551 PMCID: PMC6893389 DOI: 10.1534/genetics.119.300974] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 09/26/2019] [Indexed: 01/04/2023] Open
Abstract
The Drosophila embryonic central nervous system (CNS) is a complex organ consisting of ∼15,000 neurons and glia that is generated in ∼1 day of development. For the past 40 years, Drosophila developmental neuroscientists have described each step of CNS development in precise molecular genetic detail. This has led to an understanding of how an intricate nervous system emerges from a single cell. These studies have also provided important, new concepts in developmental biology, and provided an essential model for understanding similar processes in other organisms. In this article, the key genes that guide Drosophila CNS development and how they function is reviewed. Features of CNS development covered in this review are neurogenesis, gliogenesis, cell fate specification, and differentiation.
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Affiliation(s)
- Stephen T Crews
- Department of Biochemistry and Biophysics, Integrative Program for Biological and Genome Sciences, School of Medicine, The University of North Carolina at Chapel Hill, North Carolina 27599
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190
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Amin H, Lin AC. Neuronal mechanisms underlying innate and learned olfactory processing in Drosophila. CURRENT OPINION IN INSECT SCIENCE 2019; 36:9-17. [PMID: 31280185 DOI: 10.1016/j.cois.2019.06.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 06/03/2019] [Indexed: 06/09/2023]
Abstract
Olfaction allows animals to adapt their behavior in response to different chemical cues in their environment. How does the brain efficiently discriminate different odors to drive appropriate behavior, and how does it flexibly assign value to odors to adjust behavior according to experience? This review traces neuronal mechanisms underlying these processes in adult Drosophila melanogaster from olfactory receptors to higher brain centers. We highlight neural circuit principles such as lateral inhibition, segregation and integration of olfactory channels, temporal accumulation of sensory evidence, and compartmentalized synaptic plasticity underlying associative memory.
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Affiliation(s)
- Hoger Amin
- Department of Biomedical Science, University of Sheffield, Firth Court, Western Bank, Sheffield, S10 2TN, United Kingdom
| | - Andrew C Lin
- Department of Biomedical Science, University of Sheffield, Firth Court, Western Bank, Sheffield, S10 2TN, United Kingdom.
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191
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Selcho M, Pauls D. Linking physiological processes and feeding behaviors by octopamine. CURRENT OPINION IN INSECT SCIENCE 2019; 36:125-130. [PMID: 31606580 DOI: 10.1016/j.cois.2019.09.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 09/09/2019] [Indexed: 05/21/2023]
Abstract
The biogenic amine octopamine and to some extent its precursor tyramine function as an alerting signal in insects. Octopaminergic/tyraminergic neurons arborize in most parts of the central nervous system and additionally reach almost all peripheral organs, tissues, and muscles. Indeed, octopamine is involved in motivation, arousal, and the initiation of different behaviors reflecting its function as an alerting signal. A well-studied example of octopamine function is feeding behavior in Drosophila. Here, the amine is involved in food search, sugar/bitter sensitivity, food intake, and starvation-induced hyperactivity. Thereby octopamine modulates feeding initiation in response to internal needs and external stimuli. Additionally, it seems that octopamine/tyramine orchestrate behaviors such as locomotion and feeding or flight and song production to adapt the behavioral outcome of an animal to physiological and environmental conditions. There is a possibility that octopamine and tyramine are required in the selection of behaviors in insects.
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Affiliation(s)
- Mareike Selcho
- Neurobiology and Genetics, Theodor-Boveri-Institute Biocenter, University of Würzburg, Würzburg, Germany; Department of Animal Physiology, Institute of Biology, Leipzig University, Leipzig, Germany.
| | - Dennis Pauls
- Neurobiology and Genetics, Theodor-Boveri-Institute Biocenter, University of Würzburg, Würzburg, Germany; Department of Animal Physiology, Institute of Biology, Leipzig University, Leipzig, Germany.
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192
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Toshima N, Kantar Weigelt M, Weiglein A, Boetzl FA, Gerber B. An amino-acid mixture can be both rewarding and punishing to larval Drosophila melanogaster. ACTA ACUST UNITED AC 2019; 222:jeb.209486. [PMID: 31672727 DOI: 10.1242/jeb.209486] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 10/24/2019] [Indexed: 12/17/2022]
Abstract
Amino acids are important nutrients for animals because they are necessary for protein synthesis in particular during growth, as well as for neurotransmission. However, little is known about how animals use past experience to guide their search for amino-acid-rich food. We reasoned that the larvae of Drosophila melanogaster are suitable for investigating this topic because they are the feeding and growth stages in the life cycle of these holometabolous insects. Specifically, we investigated whether experiencing an odour with a 20 amino-acid mixture as a semi-natural tastant during training establishes odour-tastant associative memories. Across a broad concentration range (0.01-20 mmol l-1), such an amino-acid mixture was found to have a rewarding effect, establishing appetitive memory for the odour. To our surprise, however, manipulation of the test conditions revealed that relatively high concentrations of the amino-acid mixture (3.3 mmol l-1 and higher) in addition establish aversive memory for the odour. We then characterized both of these oppositely valenced memories in terms of their dependency on the number of training trials, their temporal stability, their modulation through starvation and the specific changes in locomotion underlying them. Collectively, and in the light of what is known about the neuronal organization of odour-food memory in larval D . melanogaster, our data suggest that these memories are established in parallel. We discuss the similarity of our results to what has been reported for sodium chloride, and the possible neurogenetic bases for concentration-dependent changes in valence when these tastants are used as reinforcers.
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Affiliation(s)
- Naoko Toshima
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology (LIN), Brenneckestrasse 6, 39118 Magdeburg, Germany
| | - Melisa Kantar Weigelt
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology (LIN), Brenneckestrasse 6, 39118 Magdeburg, Germany
| | - Aliće Weiglein
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology (LIN), Brenneckestrasse 6, 39118 Magdeburg, Germany
| | - Fabian A Boetzl
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
| | - Bertram Gerber
- Department Genetics of Learning and Memory, Leibniz Institute for Neurobiology (LIN), Brenneckestrasse 6, 39118 Magdeburg, Germany.,Institute for Biology, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.,Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany
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193
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Aso Y, Ray RP, Long X, Bushey D, Cichewicz K, Ngo TT, Sharp B, Christoforou C, Hu A, Lemire AL, Tillberg P, Hirsh J, Litwin-Kumar A, Rubin GM. Nitric oxide acts as a cotransmitter in a subset of dopaminergic neurons to diversify memory dynamics. eLife 2019; 8:49257. [PMID: 31724947 PMCID: PMC6948953 DOI: 10.7554/elife.49257] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022] Open
Abstract
Animals employ diverse learning rules and synaptic plasticity dynamics to record temporal and statistical information about the world. However, the molecular mechanisms underlying this diversity are poorly understood. The anatomically defined compartments of the insect mushroom body function as parallel units of associative learning, with different learning rates, memory decay dynamics and flexibility (Aso and Rubin, 2016). Here, we show that nitric oxide (NO) acts as a neurotransmitter in a subset of dopaminergic neurons in Drosophila. NO's effects develop more slowly than those of dopamine and depend on soluble guanylate cyclase in postsynaptic Kenyon cells. NO acts antagonistically to dopamine; it shortens memory retention and facilitates the rapid updating of memories. The interplay of NO and dopamine enables memories stored in local domains along Kenyon cell axons to be specialized for predicting the value of odors based only on recent events. Our results provide key mechanistic insights into how diverse memory dynamics are established in parallel memory systems.
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Affiliation(s)
- Yoshinori Aso
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Robert P Ray
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Xi Long
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Karol Cichewicz
- Department of Biology, University of Virginia, Charlottesville, United States
| | - Teri-Tb Ngo
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Brandi Sharp
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | | | - Amy Hu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Andrew L Lemire
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Paul Tillberg
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Jay Hirsh
- Department of Biology, University of Virginia, Charlottesville, United States
| | - Ashok Litwin-Kumar
- Department of Neuroscience, Columbia University, New York, United States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
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194
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Mancini N, Hranova S, Weber J, Weiglein A, Schleyer M, Weber D, Thum AS, Gerber B. Reversal learning in Drosophila larvae. Learn Mem 2019; 26:424-435. [PMID: 31615854 PMCID: PMC6796787 DOI: 10.1101/lm.049510.119] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 08/09/2019] [Indexed: 01/08/2023]
Abstract
Adjusting behavior to changed environmental contingencies is critical for survival, and reversal learning provides an experimental handle on such cognitive flexibility. Here, we investigate reversal learning in larval Drosophila Using odor-taste associations, we establish olfactory reversal learning in the appetitive and the aversive domain, using either fructose as a reward or high-concentration sodium chloride as a punishment, respectively. Reversal learning is demonstrated both in differential and in absolute conditioning, in either valence domain. In differential conditioning, the animals are first trained such that an odor A is paired, for example, with the reward whereas odor B is not (A+/B); this is followed by a second training phase with reversed contingencies (A/B+). In absolute conditioning, odor B is omitted, such that the animals are first trained with paired presentations of A and reward, followed by unpaired training in the second training phase. Our results reveal "true" reversal learning in that the opposite associative effects of both the first and the second training phase are detectable after reversed-contingency training. In what is a surprisingly quick, one-trial contingency adjustment in the Drosophila larva, the present study establishes a simple and genetically easy accessible study case of cognitive flexibility.
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Affiliation(s)
- Nino Mancini
- Department of Genetics, Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany
| | - Sia Hranova
- Institute for Biology, University of Leipzig, 04103 Leipzig, Germany
| | - Julia Weber
- Department of Genetics, Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany
| | - Aliće Weiglein
- Department of Genetics, Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany
| | - Michael Schleyer
- Department of Genetics, Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany
| | - Denise Weber
- Institute for Biology, University of Leipzig, 04103 Leipzig, Germany
| | - Andreas S Thum
- Institute for Biology, University of Leipzig, 04103 Leipzig, Germany
| | - Bertram Gerber
- Department of Genetics, Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany
- Institute for Biology, Otto von Guericke University, 39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany
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195
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Pape C, Matskevych A, Wolny A, Hennies J, Mizzon G, Louveaux M, Musser J, Maizel A, Arendt D, Kreshuk A. Leveraging Domain Knowledge to Improve Microscopy Image Segmentation With Lifted Multicuts. FRONTIERS IN COMPUTER SCIENCE 2019. [DOI: 10.3389/fcomp.2019.00006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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196
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Motta A, Berning M, Boergens KM, Staffler B, Beining M, Loomba S, Hennig P, Wissler H, Helmstaedter M. Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science 2019; 366:science.aay3134. [PMID: 31649140 DOI: 10.1126/science.aay3134] [Citation(s) in RCA: 137] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022]
Abstract
The dense circuit structure of mammalian cerebral cortex is still unknown. With developments in three-dimensional electron microscopy, the imaging of sizable volumes of neuropil has become possible, but dense reconstruction of connectomes is the limiting step. We reconstructed a volume of ~500,000 cubic micrometers from layer 4 of mouse barrel cortex, ~300 times larger than previous dense reconstructions from the mammalian cerebral cortex. The connectomic data allowed the extraction of inhibitory and excitatory neuron subtypes that were not predictable from geometric information. We quantified connectomic imprints consistent with Hebbian synaptic weight adaptation, which yielded upper bounds for the fraction of the circuit consistent with saturated long-term potentiation. These data establish an approach for the locally dense connectomic phenotyping of neuronal circuitry in the mammalian cortex.
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Affiliation(s)
- Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Manuel Berning
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Kevin M Boergens
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Benedikt Staffler
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Marcel Beining
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Sahil Loomba
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Philipp Hennig
- Probabilistic Numerics Group, Max Planck Institute for Intelligent Systems, D-72076 Tübingen, Germany
| | - Heiko Wissler
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany.
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197
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Thane M, Viswanathan V, Meyer TC, Paisios E, Schleyer M. Modulations of microbehaviour by associative memory strength in Drosophila larvae. PLoS One 2019; 14:e0224154. [PMID: 31634372 PMCID: PMC6802848 DOI: 10.1371/journal.pone.0224154] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 10/07/2019] [Indexed: 11/19/2022] Open
Abstract
Finding food is a vital skill and a constant task for any animal, and associative learning of food-predicting cues gives an advantage in this daily struggle. The strength of the associations between cues and food depends on a number of parameters, such as the salience of the cue, the strength of the food reward and the number of joint cue-food experiences. We investigate what impact the strength of an associative odour-sugar memory has on the microbehaviour of Drosophila melanogaster larvae. We find that larvae form stronger memories with increasing concentrations of sugar or odour, and that these stronger memories manifest themselves in stronger modulations of two aspects of larval microbehaviour, the rate and the direction of lateral reorientation manoeuvres (so-called head casts). These two modulations of larval behaviour are found to be correlated to each other in every experiment performed, which is in line with a model that assumes that both modulations are controlled by a common motor output. Given that the Drosophila larva is a genetically tractable model organism that is well suited to the study of simple circuits at the single-cell level, these analyses can guide future research into the neuronal circuits underlying the translation of associative memories of different strength into behaviour, and may help to understand how these processes are organised in more complex systems.
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Affiliation(s)
- Michael Thane
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, Magdeburg, Germany
| | - Vignesh Viswanathan
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, Magdeburg, Germany
| | - Tessa Christin Meyer
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, Magdeburg, Germany
| | - Emmanouil Paisios
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, Magdeburg, Germany
| | - Michael Schleyer
- Leibniz Institute for Neurobiology (LIN), Department Genetics of Learning and Memory, Magdeburg, Germany
- * E-mail:
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198
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Doiron B, Lengyel M. Editorial overview: Computational neuroscience. Curr Opin Neurobiol 2019; 58:iii-vii. [DOI: 10.1016/j.conb.2019.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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199
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Aversive Training Induces Both Presynaptic and Postsynaptic Suppression in Drosophila. J Neurosci 2019; 39:9164-9172. [PMID: 31558620 DOI: 10.1523/jneurosci.1420-19.2019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 09/18/2019] [Accepted: 09/22/2019] [Indexed: 11/21/2022] Open
Abstract
The α'β' subtype of Drosophila mushroom body neurons (MBn) is required for memory acquisition, consolidation and early memory retrieval after aversive olfactory conditioning. However, in vivo functional imaging studies have failed to detect an early forming memory trace in these neurons as reflected by an enhanced G-CaMP signal in response to presentation of the learned odor. Moreover, whether cellular memory traces form early after conditioning in the mushroom body output neurons (MBOn) downstream of the α'β' MBn remains unknown. Here, we show that aversive olfactory conditioning suppresses the calcium responses to the learned odor in both α'3 and α'2 axon segments of α'β' MBn and in the dendrites of α'3 MBOn immediately after conditioning using female flies. Notably, the cellular memory traces in both α'3 MBn and α'3 MBOn are short-lived and persist for <30 min. The suppressed response in α'3 MBn is accompanied by a reduction of acetylcholine (ACh) release, suggesting that the memory trace in postsynaptic α'3 MBOn may simply reflect the suppression in presynaptic α'3 MBn. Furthermore, we show that the α'3 MBn memory trace does not occur from the inhibition of GABAergic neurons via GABAA receptor activation. Because activation of the α'3 MBOn drives approach behavior of adult flies, our results demonstrate that aversive conditioning promotes avoidance behavior through suppression of the α'3 MBn-MBOn circuit.SIGNIFICANCE STATEMENT Drosophila learn to avoid an odor if that odor is repeatedly paired with electric shock. Mushroom body neurons (MBns) are known to be major cell types that mediate this form of aversive conditioning. Here we show that aversive conditioning causes a reduced response to the conditioned odor in an axon branch of one subtype of the MBn for no more than 30 min after conditioning, and in the dendrites of postsynaptic, MB output neurons (MBOns). Because experimenter-induced activation of the MBOn induces approach behavior by the fly, our data support a model that aversive learning promotes avoidance by suppressing the MBn-MBOn synapses that normally promote attraction.
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200
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Molina-Obando S, Vargas-Fique JF, Henning M, Gür B, Schladt TM, Akhtar J, Berger TK, Silies M. ON selectivity in the Drosophila visual system is a multisynaptic process involving both glutamatergic and GABAergic inhibition. eLife 2019; 8:e49373. [PMID: 31535971 PMCID: PMC6845231 DOI: 10.7554/elife.49373] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 09/18/2019] [Indexed: 01/06/2023] Open
Abstract
Sensory systems sequentially extract increasingly complex features. ON and OFF pathways, for example, encode increases or decreases of a stimulus from a common input. This ON/OFF pathway split is thought to occur at individual synaptic connections through a sign-inverting synapse in one of the pathways. Here, we show that ON selectivity is a multisynaptic process in the Drosophila visual system. A pharmacogenetics approach demonstrates that both glutamatergic inhibition through GluClα and GABAergic inhibition through Rdl mediate ON responses. Although neurons postsynaptic to the glutamatergic ON pathway input L1 lose all responses in GluClα mutants, they are resistant to a cell-type-specific loss of GluClα. This shows that ON selectivity is distributed across multiple synapses, and raises the possibility that cell-type-specific manipulations might reveal similar strategies in other sensory systems. Thus, sensory coding is more distributed than predicted by simple circuit motifs, allowing for robust neural processing.
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Affiliation(s)
- Sebastian Molina-Obando
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Juan Felipe Vargas-Fique
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - Miriam Henning
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
| | - Burak Gür
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
- International Max Planck Research School and Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences (GGNB) at the University of GöttingenGöttingenGermany
| | - T Moritz Schladt
- Department of Molecular Sensory SystemsCenter of Advanced European Studies and Research (caesar)BonnGermany
| | - Junaid Akhtar
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
| | - Thomas K Berger
- Department of Molecular Sensory SystemsCenter of Advanced European Studies and Research (caesar)BonnGermany
- Institute of Physiology and PathophysiologyPhilipps-Universität MarburgMarburgGermany
| | - Marion Silies
- Institute of Developmental Biology and NeurobiologyJohannes Gutenberg-Universität MainzMainzGermany
- European Neuroscience Institute Göttingen – A Joint Initiative of the University Medical Center Göttingen and the Max-Planck-SocietyGöttingenGermany
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