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Turner Lab / Publications
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16 Publications

Showing 1-10 of 16 results
07/18/17 | A connectome of a learning and memory center in the adult Drosophila brain.
Takemura S, Aso Y, Hige T, Wong AM, Lu Z, Xu CS, Rivlin PK, Hess HF, Zhao T, Parag T, Berg S, Huang G, Katz WT, Olbris DJ, Plaza SM, Umayam LA, Aniceto R, Chang L, Lauchie S, et al
eLife. 2017 Jul 18;6:e26975. doi: 10.7554/eLife.26975

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8-nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only six percent of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall.

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05/05/17 | What can tiny mushrooms in fruit flies tell us about learning and memory?
Hige T
Neuroscience Research. 2017 May 05:. doi: 10.1016/j.neures.2017.05.002

Nervous systems have evolved to translate external stimuli into appropriate behavioral responses. In an ever-changing environment, flexible adjustment of behavioral choice by experience-dependent learning is essential for the animal's survival. Associative learning is a simple form of learning that is widely observed from worms to humans. To understand the whole process of learning, we need to know how sensory information is represented and transformed in the brain, how it is changed by experience, and how the changes are reflected on motor output. To tackle these questions, studying numerically simple invertebrate nervous systems has a great advantage. In this review, I will feature the Pavlovian olfactory learning in the fruit fly, Drosophila melanogaster. The mushroom body is a key brain area for the olfactory learning in this organism. Recently, comprehensive anatomical information and the genetic tool sets were made available for the mushroom body circuit. This greatly accelerated the physiological understanding of the learning process. One of the key findings was dopamine-induced long-term synaptic plasticity that can alter the representations of stimulus valence. I will mostly focus on the new studies within these few years and discuss what we can possibly learn about the vertebrate systems from this model organism.

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04/15/16 | Direct neural pathways convey distinct visual information to Drosophila mushroom bodies.
Vogt K, Aso Y, Hige T, Knapek S, Ichinose T, Friedrich AB, Turner GC, Rubin GM, Tanimoto H
eLife. 2016 Apr 15;5:. doi: 10.7554/eLife.14009

Previously, we identified that visual and olfactory associative memories of Drosophila share the mushroom body (MB) circuits (Vogt et al. 2014). Despite well-characterized odor representations in the Drosophila MB, the MB circuit for visual information is totally unknown. Here we show that a small subset of MB Kenyon cells (KCs) selectively responds to visual but not olfactory stimulation. The dendrites of these atypical KCs form a ventral accessory calyx (vAC), distinct from the main calyx that receives olfactory input. We identified two types of visual projection neurons (VPNs) directly connecting the optic lobes and the vAC. Strikingly, these VPNs are differentially required for visual memories of color and brightness. The segregation of visual and olfactory domains in the MB allows independent processing of distinct sensory memories and may be a conserved form of sensory representations among insects.

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12/02/15 | Heterosynaptic plasticity underlies aversive olfactory learning in Drosophila
Hige T, Aso Y, Modi M, Rubin GM, Turner GC
Neuron. 2015 Dec 2;88(5):985-98. doi: 10.1016/j.neuron.2015.11.003

Although associative learning has been localized to specific brain areas in many animals, identifying the underlying synaptic processes in vivo has been difficult. Here, we provide the first demonstration of long-term synaptic plasticity at the output site of the Drosophila mushroom body. Pairing an odor with activation of specific dopamine neurons induces both learning and odor-specific synaptic depression. The plasticity induction strictly depends on the temporal order of the two stimuli, replicating the logical requirement for associative learning. Furthermore, we reveal that dopamine action is confined to and distinct across different anatomical compartments of the mushroom body lobes. Finally, we find that overlap between sparse representations of different odors defines both stimulus specificity of the plasticity and generalizability of associative memories across odors. Thus, the plasticity we find here not only manifests important features of associative learning but also provides general insights into how a sparse sensory code is read out.

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10/08/15 | Plasticity-driven individualization of olfactory coding in mushroom body output neurons.
Hige T, Aso Y, Rubin GM, Turner GC
Nature. 2015 Oct 8;526(7572):258-62. doi: 10.1038/nature15396

Although all sensory circuits ascend to higher brain areas where stimuli are represented in sparse, stimulus-specific activity patterns, relatively little is known about sensory coding on the descending side of neural circuits, as a network converges. In insects, mushroom bodies have been an important model system for studying sparse coding in the olfactory system, where this format is important for accurate memory formation. In Drosophila, it has recently been shown that the 2,000 Kenyon cells of the mushroom body converge onto a population of only 34 mushroom body output neurons (MBONs), which fall into 21 anatomically distinct cell types. Here we provide the first, to our knowledge, comprehensive view of olfactory representations at the fourth layer of the circuit, where we find a clear transition in the principles of sensory coding. We show that MBON tuning curves are highly correlated with one another. This is in sharp contrast to the process of progressive decorrelation of tuning in the earlier layers of the circuit. Instead, at the population level, odour representations are reformatted so that positive and negative correlations arise between representations of different odours. At the single-cell level, we show that uniquely identifiable MBONs display profoundly different tuning across different animals, but that tuning of the same neuron across the two hemispheres of an individual fly was nearly identical. Thus, individualized coordination of tuning arises at this level of the olfactory circuit. Furthermore, we find that this individualization is an active process that requires a learning-related gene, rutabaga. Ultimately, neural circuits have to flexibly map highly stimulus-specific information in sparse layers onto a limited number of different motor outputs. The reformatting of sensory representations we observe here may mark the beginning of this sensory-motor transition in the olfactory system.

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04/22/15 | Learning: the good, the bad, and the fly.
Hige T, Turner G
Neuron. 2015 Apr 22;86(2):343-5. doi: 10.1016/j.neuron.2015.04.012

Olfactory memories can be very good-your mother's baking-or very bad-your father's cooking. We go through life forming these different associations with the smells we encounter. But what makes one association pleasant and another repulsive? Work in deep areas of the Drosophila brain has revealed the beginnings of an answer, as reported in this issue of Neuron by Owald et al. (2015).

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02/26/14 | OpenStage: a low-cost motorized microscope stage with sub-micron positioning accuracy.
Campbell RA, Eifert RW, Turner GC
PloS One. 2014 Feb 26;9(2):e88977. doi: 10.1371/journal.pone.0088977

Recent progress in intracellular calcium sensors and other fluorophores has promoted the widespread adoption of functional optical imaging in the life sciences. Home-built multiphoton microscopes are easy to build, highly customizable, and cost effective. For many imaging applications a 3-axis motorized stage is critical, but commercially available motorization hardware (motorized translators, controller boxes, etc) are often very expensive. Furthermore, the firmware on commercial motor controllers cannot easily be altered and is not usually designed with a microscope stage in mind. Here we describe an open-source motorization solution that is simple to construct, yet far cheaper and more customizable than commercial offerings. The cost of the controller and motorization hardware are under $1000. Hardware costs are kept low by replacing linear actuators with high quality stepper motors. Electronics are assembled from commonly available hobby components, which are easy to work with. Here we describe assembly of the system and quantify the positioning accuracy of all three axes. We obtain positioning repeatability of the order of 1 μm in X/Y and 0.1 μm in Z. A hand-held control-pad allows the user to direct stage motion precisely over a wide range of speeds (10(-1) to 10(2) μm·s(-1)), rapidly store and return to different locations, and execute "jumps" of a fixed size. In addition, the system can be controlled from a PC serial port. Our "OpenStage" controller is sufficiently flexible that it could be used to drive other devices, such as micro-manipulators, with minimal modifications.

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12/01/13 | Integration of the olfactory code across dendritic claws of single mushroom body neurons.
Gruntman E, Turner GC
Nature Neuroscience. 2013 Dec;16(12):1821-9. doi: 10.1038/nn.3547

In the olfactory system, sensory inputs are arranged in different glomerular channels, which respond in combinatorial ensembles to the various chemical features of an odor. We investigated where and how this combinatorial code is read out deeper in the brain. We exploited the unique morphology of neurons in the Drosophila mushroom body, which receive input on large dendritic claws. Imaging odor responses of these dendritic claws revealed that input channels with distinct odor tuning converge on individual mushroom body neurons. We determined how these inputs interact to drive the cell to spike threshold using intracellular recordings to examine mushroom body responses to optogenetically controlled input. Our results provide an elegant explanation for the characteristic selectivity of mushroom body neurons: these cells receive different types of input and require those inputs to be coactive to spike. These results establish the mushroom body as an important site of integration in the fly olfactory system.

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06/19/13 | Imaging a population code for odor identity in the Drosophila mushroom body.
Campbell RA, Honegger KS, Qin H, Li W, Demir E, Turner GC
The Journal of Neuroscience : the official journal of the Society for Neuroscience. 2013 Jun 19;33(25):10568-81. doi: 10.1523/JNEUROSCI.0682-12.2013

The brain represents sensory information in the coordinated activity of neuronal ensembles. Although the microcircuits underlying olfactory processing are well characterized in Drosophila, no studies to date have examined the encoding of odor identity by populations of neurons and related it to the odor specificity of olfactory behavior. Here we used two-photon Ca(2+) imaging to record odor-evoked responses from >100 neurons simultaneously in the Drosophila mushroom body (MB). For the first time, we demonstrate quantitatively that MB population responses contain substantial information on odor identity. Using a series of increasingly similar odor blends, we identified conditions in which odor discrimination is difficult behaviorally. We found that MB ensemble responses accounted well for olfactory acuity in this task. Kenyon cell ensembles with as few as 25 cells were sufficient to match behavioral discrimination accuracy. Using a generalization task, we demonstrated that the MB population code could predict the flies' responses to novel odors. The degree to which flies generalized a learned aversive association to unfamiliar test odors depended upon the relative similarity between the odors' evoked MB activity patterns. Discrimination and generalization place different demands on the animal, yet the flies' choices in these tasks were reliably predicted based on the amount of overlap between MB activity patterns. Therefore, these different behaviors can be understood in the context of a single physiological framework.

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08/17/11 | Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body.
Honegger KS, Campbell RA, Turner GC
The Journal of Neuroscience : the official journal of the Society for Neuroscience. 2011 Aug 17;31(33):11772-85. doi: 10.1523/JNEUROSCI.1099-11.2011

Sensory stimuli are represented in the brain by the activity of populations of neurons. In most biological systems, studying population coding is challenging since only a tiny proportion of cells can be recorded simultaneously. Here we used two-photon imaging to record neural activity in the relatively simple Drosophila mushroom body (MB), an area involved in olfactory learning and memory. Using the highly sensitive calcium indicator GCaMP3, we simultaneously monitored the activity of >100 MB neurons in vivo (∼5% of the total population). The MB is thought to encode odors in sparse patterns of activity, but the code has yet to be explored either on a population level or with a wide variety of stimuli. We therefore imaged responses to odors chosen to evaluate the robustness of sparse representations. Different odors activated distinct patterns of MB neurons; however, we found no evidence for spatial organization of neurons by either response probability or odor tuning within the cell body layer. The degree of sparseness was consistent across a wide range of stimuli, from monomolecular odors to artificial blends and even complex natural smells. Sparseness was mainly invariant across concentrations, largely because of the influence of recent odor experience. Finally, in contrast to sensory processing in other systems, no response features distinguished natural stimuli from monomolecular odors. Our results indicate that the fundamental feature of odor processing in the MB is to create sparse stimulus representations in a format that facilitates arbitrary associations between odor and punishment or reward.

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