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      The connectome of the adult Drosophila mushroom body provides insights into function

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          Abstract

          Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory, and activity regulation. Here, we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

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          Most cited references216

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          Normalized cuts and image segmentation

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            Synaptic plasticity and memory: an evaluation of the hypothesis.

            Changing the strength of connections between neurons is widely assumed to be the mechanism by which memory traces are encoded and stored in the central nervous system. In its most general form, the synaptic plasticity and memory hypothesis states that "activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the information storage underlying the type of memory mediated by the brain area in which that plasticity is observed." We outline a set of criteria by which this hypothesis can be judged and describe a range of experimental strategies used to investigate it. We review both classical and newly discovered properties of synaptic plasticity and stress the importance of the neural architecture and synaptic learning rules of the network in which it is embedded. The greater part of the article focuses on types of memory mediated by the hippocampus, amygdala, and cortex. We conclude that a wealth of data supports the notion that synaptic plasticity is necessary for learning and memory, but that little data currently supports the notion of sufficiency.
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              dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering

              Tal Galili (2015)
              Summary: dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing trees to one another (both statistically and visually). As such, dendextend offers a flexible framework for enhancing R's rich ecosystem of packages for performing hierarchical clustering of items. Availability and implementation: The dendextend R package (including detailed introductory vignettes) is available under the GPL-2 Open Source license and is freely available to download from CRAN at: (http://cran.r-project.org/package=dendextend) Contact: Tal.Galili@math.tau.ac.il
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                14 December 2020
                2020
                : 9
                : e62576
                Affiliations
                [1 ]Janelia Research Campus, Howard Hughes Medical Institute AshburnUnited States
                [2 ]Department of Neuroscience, Columbia University, Zuckerman Institute New YorkUnited States
                [3 ]Drosophila Connectomics Group, Department of Zoology, University of Cambridge CambridgeUnited Kingdom
                [4 ]Centre for Neural Circuits & Behaviour, University of Oxford OxfordUnited Kingdom
                [5 ]Neurobiology Division, MRC Laboratory of Molecular Biology CambridgeUnited Kingdom
                Brandeis University United States
                Brandeis University United States
                Brandeis University United States
                Brandeis University United States
                Brandeis University United States
                Howard Hughes Medical Institute, University of Oregon United States
                Author information
                https://orcid.org/0000-0002-6658-9175
                https://orcid.org/0000-0003-0930-7327
                http://orcid.org/0000-0001-6333-0072
                https://orcid.org/0000-0001-9713-4088
                http://orcid.org/0000-0002-0041-9229
                http://orcid.org/0000-0002-1854-8336
                http://orcid.org/0000-0002-1195-0445
                http://orcid.org/0000-0002-7297-4547
                http://orcid.org/0000-0002-5633-1314
                http://orcid.org/0000-0002-3822-489X
                http://orcid.org/0000-0003-2400-6426
                http://orcid.org/0000-0003-1131-0410
                http://orcid.org/0000-0003-1974-7174
                http://orcid.org/0000-0002-8060-2807
                http://orcid.org/0000-0001-5948-3092
                http://orcid.org/0000-0002-3289-6564
                https://orcid.org/0000-0002-2939-1688
                http://orcid.org/0000-0002-0587-9355
                http://orcid.org/0000-0003-2422-6576
                http://orcid.org/0000-0003-4503-6229
                https://orcid.org/0000-0001-8762-8703
                Article
                62576
                10.7554/eLife.62576
                7909955
                33315010
                38f7d163-77e2-475b-855c-9c365f4e5907
                © 2020, Li et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 28 August 2020
                : 11 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award ID: Internal funding
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 203261/Z/16/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 200846/Z/16/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: NeuroNex DBI-1707398
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000015, Department of Energy;
                Award ID: DE-SC0020347
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC-U105188491
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: SCGB
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000861, Burroughs Wellcome Fund;
                Award ID: 1017109
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01EB029858
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                Analysis of neuronal circuit architecture provides mechanistic insights into what an adult fly can learn, remember, and use to guide its behavior.

                Life sciences
                dopamine,learning,memory,neuronal circuits,behavior,d. melanogaster
                Life sciences
                dopamine, learning, memory, neuronal circuits, behavior, d. melanogaster

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