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      Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila

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          Abstract

          Animals discriminate stimuli, learn their predictive value and use this knowledge to modify their behavior. In Drosophila, the mushroom body (MB) plays a key role in these processes. Sensory stimuli are sparsely represented by ∼2000 Kenyon cells, which converge onto 34 output neurons (MBONs) of 21 types. We studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network. We also show that optogenetic activation of MBONs can, depending on cell type, induce repulsion or attraction in flies. The behavioral effects of MBON perturbation are combinatorial, suggesting that the MBON ensemble collectively represents valence. We propose that local, stimulus-specific dopaminergic modulation selectively alters the balance within the MBON network for those stimuli. Our results suggest that valence encoded by the MBON ensemble biases memory-based action selection.

          DOI: http://dx.doi.org/10.7554/eLife.04580.001

          eLife digest

          An animal's survival depends on its ability to respond appropriately to its environment, approaching stimuli that signal rewards and avoiding any that warn of potential threats. In fruit flies, this behavior requires activity in a region of the brain called the mushroom body, which processes sensory information and uses that information to influence responses to stimuli.

          Aso et al. recently mapped the mushroom body of the fruit fly in its entirety. This work showed, among other things, that the mushroom body contained 21 different types of output neurons. Building on this work, Aso et al. have started to work out how this circuitry enables flies to learn to associate a stimulus, such as an odor, with an outcome, such as the presence of food.

          Two complementary techniques—the use of molecular genetics to block neuronal activity, and the use of light to activate neurons (a technique called optogenetics)—were employed to study the roles performed by the output neurons in the mushroom body. Results revealed that distinct groups of output cells must be activated for flies to avoid—as opposed to approach—odors. Moreover, the same output neurons are used to avoid both odors and colors that have been associated with punishment. Together, these results indicate that the output cells do not encode the identity of stimuli: rather, they signal whether a stimulus should be approached or avoided. The output cells also regulate the amount of sleep taken by the fly, which is consistent with the mushroom body having a broader role in regulating the fly's internal state.

          The results of these experiments—combined with new knowledge about the detailed structure of the mushroom body—lay the foundations for new studies that explore associative learning at the level of individual circuits and their component cells. Given that the organization of the mushroom body has much in common with that of the mammalian brain, these studies should provide insights into the fundamental principles that underpin learning and memory in other species, including humans.

          DOI: http://dx.doi.org/10.7554/eLife.04580.002

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            High-throughput Ethomics in Large Groups of Drosophila

            We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting within a planar arena. Our system includes machine vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly, or as a vector that concisely captures the statistical properties of all behaviors displayed within a given period. We found that behavioral differences between individuals are consistent over time and are sufficient to accurately predict gender and genotype. In addition, we show that the relative positions of flies during social interactions vary according to gender, genotype, and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.
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              Neural correlates, computation and behavioural impact of decision confidence.

              Humans and other animals must often make decisions on the basis of imperfect evidence. Statisticians use measures such as P values to assign degrees of confidence to propositions, but little is known about how the brain computes confidence estimates about decisions. We explored this issue using behavioural analysis and neural recordings in rats in combination with computational modelling. Subjects were trained to perform an odour categorization task that allowed decision confidence to be manipulated by varying the distance of the test stimulus to the category boundary. To understand how confidence could be computed along with the choice itself, using standard models of decision-making, we defined a simple measure that quantified the quality of the evidence contributing to a particular decision. Here we show that the firing rates of many single neurons in the orbitofrontal cortex match closely to the predictions of confidence models and cannot be readily explained by alternative mechanisms, such as learning stimulus-outcome associations. Moreover, when tested using a delayed reward version of the task, we found that rats' willingness to wait for rewards increased with confidence, as predicted by the theoretical model. These results indicate that confidence estimates, previously suggested to require 'metacognition' and conscious awareness are available even in the rodent brain, can be computed with relatively simple operations, and can drive adaptive behaviour. We suggest that confidence estimation may be a fundamental and ubiquitous component of decision-making.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                2050-084X
                23 December 2014
                2014
                : 3
                : e04580
                Affiliations
                [1 ]Janelia Research Campus, Howard Hughes Medical Institute , Ashburn, United States
                [2 ]deptDepartment of Cellular and Molecular Physiology , Yale School of Medicine , New Haven, United States
                [3 ]Max Planck Institute of Neurobiology , Martinsried, Germany
                [4 ]deptGraduate School of Life Sciences , Tohoku University , Sendai, Japan
                [5 ]deptGenes and Dynamics of Memory Systems, Brain Plasticity Unit , Centre National de la Recherche Scientifique, ESPCI , Paris, France
                [6 ]deptDepartment of Genetics , Yale School of Medicine , New Haven, United States
                [7 ]deptProgram in Cellular Neuroscience, Neurodegeneration and Repair , Yale School of Medicine , New Haven, United States
                Howard Hughes Medical Institute, Stanford University , United States
                Howard Hughes Medical Institute, Stanford University , United States
                Author notes
                [* ]For correspondence: asoy@ 123456janelia.hhmi.org (YA);
                [†]

                Department of Psychological Sciences, University of San Diego, San Diego, United States.

                [‡]

                Department of Neuroscience, Brown University, Providence, United States.

                [§]

                Department of Neurobiologie and Tierphysiologie, Institute of Biology 1, Albert Ludwig University of Freiburg, Freiburg, Germany.

                Author information
                http://orcid.org/0000-0001-8396-1533
                Article
                04580
                10.7554/eLife.04580
                4273436
                25535794
                60a3755d-4790-4625-b90a-49bd10bff2d2
                © 2014, Aso 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
                : 03 September 2014
                : 07 November 2014
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, universityHoward Hughes Medical Institute;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, universityHoward Hughes Medical Institute;
                Award ID: Janelia Visiting Scientist Program
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award Recipient :
                Funded by: Labex MemoLife;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: Bernstein Focus Neurobiology of Learning 01GQ0932
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004189, Max-Planck-Gesellschaft;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: TA 552/5-1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: MEXT/JSPS KAKENHI 25890003, 26120705, 26119503 and 26250001
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007428, Naito Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, universityNational Institute of Neurological Disorders and Stroke;
                Award ID: R01NS055035, R01NS056443, R01NS091070
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, universityNational Institute of General Medical Sciences;
                Award ID: R01GM098931
                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
                2.0
                Output neurons in the mushroom body of the fruit fly brain encode the positive or negative survival value of stimuli, enabling insects to choose adaptive approach and avoidance behaviors through associative learning.

                Life sciences
                mushroom body,memory,behavioral valence,sleep,population code,action selection,d. melanogaster

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