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Associative learning allows animals to use past experience to predict future events. The circuits underlying memory formation support immediate and sustained changes in function, often in response to a single example. Larval Drosophila is a genetic model for memory formation that can be accessed at molecular, synaptic, cellular, and circuit levels, often simultaneously, but existing behavioral assays for larval learning and memory do not address individual animals, and it has been difficult to form long-lasting memories, especially those requiring synaptic reorganization. We demonstrate a new assay for learning and memory capable of tracking the changing preferences of individual larvae. We use this assay to explore how activation of a pair of reward neurons changes the response to the innately aversive gas carbon dioxide (CO2). We confirm that when coupled to CO2 presentation in appropriate temporal sequence, optogenetic reward reduces avoidance of CO2. We find that learning is switch-like: all-or-none and quantized in two states. Memories can be extinguished by repeated unrewarded exposure to CO2 but are stabilized against extinction by repeated training or overnight consolidation. Finally, we demonstrate long-lasting protein synthesis dependent and independent memory formation., eLife digest Brains learn from experience. They take events from the past, link them together, and use them to predict the future. This is true for fruit flies, Drosophila melanogaster, as well as for humans. One of the main questions in the field of neuroscience is, how does this kind of associative learning happen? Fruit fly larvae can learn to associate a certain smell with a sugar reward. When a group of larvae learn to associate a smell with sugar, most but not all of them will approach that smell in the future. This shows associative learning in action, but it raises a big question. Did the larvae that failed to approach the smell fail to learn, or did they just happen to make a mistake finding the smell? Given another chance, would exactly the same larvae approach the smell as the first time? In other words, did all the larvae learn a little, or did some larvae learn completely and others learn nothing? To find out, Lesar et al. built a computer-controlled maze to test whether individual fruit fly larvae liked or avoided a smell. Whenever a larva reached the middle of the Y-shaped maze, it could choose to go down one of two remaining corridors. One corridor contained air and the other carbon dioxide, a gas they would naturally avoid. Lesar et al. taught each larva to like carbon dioxide by activating reward neurons in its brain while filling the maze with carbon dioxide gas. Studying each larva as it navigated the maze revealed that they learn in a single jump, a 'lightbulb moment'. When Lesar et al. activated the reward neurons, the larva either ‘got it’ and stopped avoiding carbon dioxide altogether, or it did not. In the second case, it behaved as if it had received no training at all. Classic and modern experiments on people suggest that humans might also learn in jumps, but research on our own brains is challenging. Fruit flies are an excellent model organism to study memory formation because they are easy to breed, and it is easy to manipulate their genetic code. Work in flies has already revealed many of the genes and cells responsible for learning and memory. But, to find the specific brain changes that explain learning, researchers need to know whether the animals they are examining have actually learned something. This new maze could help researchers to identify those individuals, making it easier to find out exactly how associative learning works.

Animal behavior is shaped both by evolution and by individual experience. In many species parallel brain pathways are thought to encode innate and learnt behavior drives and as a result may link the same sensory cue to different actions if innate and learnt drives are in opposition. How these opposing drives are integrated into a single coherent action is not well understood. In insects, the Mushroom Body Output Neurons (MBONs) and the Lateral Horn Neurons (LHNs) are thought to provide the learnt and innate drives, respectively. However their patterns of convergence and the mechanisms by which their outputs are used to select actions are not well understood. We used electron microscopy reconstruction to comprehensively map the downstream targets of all MBONs in Drosophila larva and characterise their patterns of convergence with LHNs. We discovered convergence neurons that receive direct input from MBONs and LHNs and compare opposite behaviour drives. Functional imaging and optogenetic manipulation suggest these convergence neurons compute the overall predicted value of approaching or avoiding an odor and mediate action selection. Our study describes the circuit mechanisms allowing integration of opposing drives from parallel olfactory pathways.

Drosophila Transmembrane channel-like (Tmc) is a protein that functions in larval proprioception. The closely related TMC1 protein is required for mammalian hearing, and is a pore forming subunit of the hair cell mechanotransduction channel. In hair cells, TMC1 is gated by small deflections of microvilli that produce tension on extracellular tip-links that connect adjacent villi. How Tmc might be gated in larval proprioceptors, which are neurons having a morphology that is completely distinct from hair cells, is unknown. Here, we have used high-speed confocal microscopy to both measure displacements of proprioceptive sensory dendrites during larval movement, and to optically measure neural activity of the moving proprioceptors. Unexpectedly, the pattern of dendrite deformation for distinct neurons was unique and differed depending on the direction of locomotion: ddaE neuron dendrites were strongly curved by forward locomotion while the dendrites of ddaD were more strongly deformed by backward locomotion. Furthermore, GCaMP6f calcium signals recorded in the proprioceptive neurons during locomotion indicated tuning to the direction of movement. ddaE showed strong activation during forward locomotion while ddaD showed responses that were strongest during backwards locomotion. Peripheral proprioceptive neurons in animals mutant for Tmc showed a near complete loss of movement related calcium signals. As the strength of the responses of wild type animals was correlated with dendrite curvature, we propose that Tmc channels may be activated by membrane curvature in dendrites that are exposed to strain. Our findings begin to explain how distinct cellular systems rely on a common molecular pathway for mechanosensory responses.

Small animals use sensory information to navigate their environment in order to reach more favorable conditions. in gradients of light, temperature, odors and CO2,Drosophilalarvae alternate periods of runs and turns, regulating the frequency size and direction of turns, to move in a favorable direction. Whether larvae use the same strategies when navigating in response to somatosensory input is unknown. Further, while many of the sensory neurons that mediate navigation behaviors have been described, where and how these navigational strategies are implemented in the central nervous system and controlled by neuronal circuit elements is not well known. Here we characterize for the first time the navigational strategies of Drosophila larvae in gradients of air-current speeds using high-throughput behavioral assays and quantitative behavioral analysis. We find that larvae extend runs towards favorable directions and shorten runs in unfavorable directions, and that larvae regulate both the direction and amplitudes of turns. These results suggest similar central decision-making mechanisms underlie navigation behaviors in somatosensory and other sensory modalities. By silencing the activity of individual neurons and very sparse expression patterns (2 or 3 neuron types), we further identify the sensory neurons and circuit elements in the ventral nerve cord and brain of the larva required for navigational decisions during anemotaxis. The phenotypes of these central neurons are consistent with a mechanism where the increase of the turning rate in unfavorable conditions and decrease in turning rate in favorable conditions are independently controlled. In addition, we find phenotypes that suggest that the decisions of whether and which way to turn are controlled independently. Our study reveals that different neuronal modules in the nerve cord and brain mediate different aspects of navigational decision making. The neurons identified in our screen provide a basis for future detailed mechanistic understanding of the circuit principles of navigational decisionmaking.

Optical recordings of neural activity in behaving animals can reveal the neural correlates of decision making, but such recordings are compromised by brain motion that often accompanies behavior. Two-photon point scanning microscopy is especially sensitive to motion artifacts, and to date, two-photon recording of activity has required rigid mechanical coupling between the brain and microscope. To overcome these difficulties, we developed a two-photon tracking microscope with extremely low latency (360 μs) feedback implemented in hardware. We maintained continuous focus on neurons moving with velocities of 3 mm/s and accelerations of 1 m/s2 both in-plane and axially, allowing high-bandwidth measurements with modest excitation power. We recorded from motor- and inter-neurons in unrestrained freely behaving fruit fly larvae, correlating neural activity with stimulus presentation and behavioral outputs. Our technique can be extended to stabilize recordings in a variety of moving substrates.