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Socio-Economic Objective : Emerging Defence Technologies
Research Topic : Sensory function
Australian State/Territory : SA
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT180100466

    Funder
    Australian Research Council
    Funding Amount
    $857,189.00
    Summary
    From insects to robots: how brains make predictions and ignore distractions. This project aims to address fundamental questions in neuroscience and to integrate this biological understanding with the development of leading-edge robotics. Whether a human catching a ball or a dragonfly feeding in a swarm, brains have the remarkable ability to predict the future location of moving targets. The brain predicts in the presence of distractions and even if the target disappears, for example, when hidden .... From insects to robots: how brains make predictions and ignore distractions. This project aims to address fundamental questions in neuroscience and to integrate this biological understanding with the development of leading-edge robotics. Whether a human catching a ball or a dragonfly feeding in a swarm, brains have the remarkable ability to predict the future location of moving targets. The brain predicts in the presence of distractions and even if the target disappears, for example, when hidden behind another object. This project will investigate how brains use both environmental and internal information to select a target and predict its future location. By implementing bio-inspired computations in hardware, this project aims to provide significant benefits such as improving autonomous systems for defence, health and transportation.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240101673

    Funder
    Australian Research Council
    Funding Amount
    $540,834.00
    Summary
    Comparative analysis of sensor noise for target detection in dragonfly eyes. Dragonflies hunt tiny prey in the low-light conditions of late dusk, a signal-to-noise problem that challenges any engineered system. Using a comparative approach across dragonfly species, we aim to use novel optical and physiological measures to determine how sensors with noise underlie target-detection, in varying scene brightness. The project outcomes will be a comparative characterisation of signal-to-noise measures .... Comparative analysis of sensor noise for target detection in dragonfly eyes. Dragonflies hunt tiny prey in the low-light conditions of late dusk, a signal-to-noise problem that challenges any engineered system. Using a comparative approach across dragonfly species, we aim to use novel optical and physiological measures to determine how sensors with noise underlie target-detection, in varying scene brightness. The project outcomes will be a comparative characterisation of signal-to-noise measures of dragonfly eye optics (including eye size) and early sensory neurons. We will match detection thresholds with downstream target-detecting neurons and dragonfly behaviour. This will provide insight into signal detection, which is a ubiquitous problem across information processing, computer vision and autonomous systems.
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    Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE150100548

    Funder
    Australian Research Council
    Funding Amount
    $359,000.00
    Summary
    Neural and robotic correlates of predictive coding and selective attention. Whether a human catching a ball, a dog leaping at a frisbee or a dragonfly hunting prey amidst a swarm, brains both large and small have evolved the ability to focus attention on one moving target, even in the presence of distracters. This project aims to investigate how brains solve this challenging problem by recording the activity of dragonfly neurons that selectively attend to one target whilst ignoring others. The p .... Neural and robotic correlates of predictive coding and selective attention. Whether a human catching a ball, a dog leaping at a frisbee or a dragonfly hunting prey amidst a swarm, brains both large and small have evolved the ability to focus attention on one moving target, even in the presence of distracters. This project aims to investigate how brains solve this challenging problem by recording the activity of dragonfly neurons that selectively attend to one target whilst ignoring others. The project aims to examine how expectation and attention are encoded in the brain and will build an autonomous robot using computational models bio-inspired from this neuronal processing. Robots capable of visually perceiving and interacting with targets in natural environments have applications in health, surveillance and defence.
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