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6 Janelia Publications

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    08/19/22 | Flexible control of behavioral variability mediated by an internal representation of head direction
    Chuntao Dan , Brad K. Hulse , Vivek Jayaraman , Ann M. Hermundstad
    bioRxiv. 2022 Aug 19:. doi: 10.1101/2021.08.18.456004

    Internal representations are thought to support the generation of flexible, long-timescale behavioral patterns in both animals and artificial agents. Here, we present a novel conceptual framework for how Drosophila use their internal representation of head direction to maintain preferred headings in their surroundings, and how they learn to modify these preferences in the presence of selective thermal reinforcement. To develop the framework, we analyzed flies’ behavior in a classical operant visual learning paradigm and found that they use stochastically generated fixations and directed turns to express their heading preferences. Symmetries in the visual scene used in the paradigm allowed us to expose how flies’ probabilistic behavior in this setting is tethered to their head direction representation. We describe how flies’ ability to quickly adapt their behavior to the rules of their environment may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in the structure of their circuits. Many of the mechanisms we outline may also be relevant for rapidly adaptive behavior driven by internal representations in other animals, including mammals.

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    08/17/22 | Homeodomain proteins hierarchically specify neuronal diversity and synaptic connectivity
    Chundi Xu , Tyler B. Ramos , Ed M. Rogers , Michael B. Reiser , Chris Q. Doe
    bioRxiv. 2022 Aug 17:. doi: 10.1101/2021.10.01.462699

    The brain generates diverse neuron types which express unique homeodomain transcription factors (TFs) and assemble into precise neural circuits. Yet a mechanistic framework is lacking for how homeodomain TFs specify both neuronal fate and synaptic connectivity. We use Drosophila lamina neurons (L1-L5) to show the homeodomain TF Brain-specific homeobox (Bsh) is initiated in lamina precursor cells (LPCs) where it specifies L4/L5 fate and suppresses homeodomain TF Zfh1 to prevent L1/L3 fate. Subsequently, Bsh activates the homeodomain TF Apterous (Ap) in L4 in a feedforward loop to express the synapse recognition molecule DIP-β, in part by Bsh direct binding a DIP-β intron. Thus, homeodomain TFs function hierarchically: primary homeodomain TF (Bsh) first specifies neuronal fate, and subsequently acts with secondary homeodomain TF (Ap) to activate DIP-β, thereby generating precise synaptic connectivity. We speculate that hierarchical homeodomain TF function may represent a general principle for coordinating neuronal fate specification and circuit assembly.

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    05/26/22 | One engram two readouts: stimulus dynamics switch a learned behavior in Drosophila
    Mehrab N Modi , Adithya Rajagopalan , Hervé Rouault , Yoshinori Aso , Glenn C Turner
    bioRxiv. 2022 May 26:. doi: 10.1101/2022.05.24.492551

    Memory guides the choices an animal makes across widely varying conditions in dynamic environments. Consequently, the most adaptive choice depends on the options available. How can a single memory support optimal behavior across different sets of choice options? We address this using olfactory learning in Drosophila. Even when we restrict an odor-punishment association to a single set of synapses using optogenetics, we find that flies still show choice behavior that depends on the options it encounters. Here we show that how the odor choices are presented to the animal influences memory recall itself. Presenting two similar odors in sequence enabled flies to not only discriminate them behaviorally but also at the level of neural activity. However, when the same odors were encountered as solitary stimuli, no such differences were detectable. These results show that memory recall is not simply a comparison to a static learned template, but can be adaptively modulated by stimulus dynamics.

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    05/25/22 | Expectation-based learning rules underlie dynamic foraging in Drosophila
    Adithya E. Rajagopalan , Ran Darshan , James E. Fitzgerald , Glenn C. Turner
    bioRxiv. 2022 May 25:. doi: 10.1101/2022.05.24.493252

    Foraging animals must use decision-making strategies that dynamically account for uncertainty in the world. To cope with this uncertainty, animals have developed strikingly convergent strategies that use information about multiple past choices and reward to learn representations of the current state of the world. However, the underlying learning rules that drive the required learning have remained unclear. Here, working in the relatively simple nervous system of Drosophila, we combine behavioral measurements, mathematical modeling, and neural circuit perturbations to show that dynamic foraging depends on a learning rule incorporating reward expectation. Using a novel olfactory dynamic foraging task, we characterize the behavioral strategies used by individual flies when faced with unpredictable rewards and show, for the first time, that they perform operant matching. We build on past theoretical work and demonstrate that this strategy requires the existence of a covariance-based learning rule in the mushroom body - a hub for learning in the fly. In particular, the behavioral consequences of optogenetic perturbation experiments suggest that this learning rule incorporates reward expectation. Our results identify a key element of the algorithm underlying dynamic foraging in flies and suggest a comprehensive mechanism that could be fundamental to these behaviors across species.

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    03/14/22 | A population of descending neurons that regulates the flight motor of Drosophila.
    Namiki S, Ros IG, Morrow C, Rowell WJ, Card GM, Korff W, Dickinson MH
    Current Biology. 2022 Mar 14;32(5):1189-1196. doi: 10.1016/j.cub.2022.01.008

    Similar to many insect species, Drosophila melanogaster is capable of maintaining a stable flight trajectory for periods lasting up to several hours. Because aerodynamic torque is roughly proportional to the fifth power of wing length, even small asymmetries in wing size require the maintenance of subtle bilateral differences in flapping motion to maintain a stable path. Flies can even fly straight after losing half of a wing, a feat they accomplish via very large, sustained kinematic changes to both the damaged and intact wings. Thus, the neural network responsible for stable flight must be capable of sustaining fine-scaled control over wing motion across a large dynamic range. In this study, we describe an unusual type of descending neuron (DNg02) that projects directly from visual output regions of the brain to the dorsal flight neuropil of the ventral nerve cord. Unlike many descending neurons, which exist as single bilateral pairs with unique morphology, there is a population of at least 15 DNg02 cell pairs with nearly identical shape. By optogenetically activating different numbers of DNg02 cells, we demonstrate that these neurons regulate wingbeat amplitude over a wide dynamic range via a population code. Using two-photon functional imaging, we show that DNg02 cells are responsive to visual motion during flight in a manner that would make them well suited to continuously regulate bilateral changes in wing kinematics. Collectively, we have identified a critical set of descending neurons that provides the sensitivity and dynamic range required for flight control.

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    02/01/22 | A neural circuit linking learning and sleep in Drosophila long-term memory.
    Lei Z, Henderson K, Keleman K
    Nature Communications. 2022 Feb 01;13(1):609. doi: 10.1038/s41467-022-28256-1

    Animals retain some but not all experiences in long-term memory (LTM). Sleep supports LTM retention across animal species. It is well established that learning experiences enhance post-learning sleep. However, the underlying mechanisms of how learning mediates sleep for memory retention are not clear. Drosophila males display increased amounts of sleep after courtship learning. Courtship learning depends on Mushroom Body (MB) neurons, and post-learning sleep is mediated by the sleep-promoting ventral Fan-Shaped Body neurons (vFBs). We show that post-learning sleep is regulated by two opposing output neurons (MBONs) from the MB, which encode a measure of learning. Excitatory MBONs-γ2α'1 becomes increasingly active upon increasing time of learning, whereas inhibitory MBONs-β'2mp is activated only by a short learning experience. These MB outputs are integrated by SFS neurons, which excite vFBs to promote sleep after prolonged but not short training. This circuit may ensure that only longer or more intense learning experiences induce sleep and are thereby consolidated into LTM.

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