Life or death decisions: making fast, accurate choices in a complex world. This project aims to understand how hoverflies and honey bees, with tiny brains and sensory systems, excel at making fast and accurate decisions while on the fly in a complex world. The project will combine brain recordings with flight analyses and computational modelling to generate new knowledge on how animals may utilize movements to simplify information sampling. Expected outcomes are a novel, comprehensive understand ....Life or death decisions: making fast, accurate choices in a complex world. This project aims to understand how hoverflies and honey bees, with tiny brains and sensory systems, excel at making fast and accurate decisions while on the fly in a complex world. The project will combine brain recordings with flight analyses and computational modelling to generate new knowledge on how animals may utilize movements to simplify information sampling. Expected outcomes are a novel, comprehensive understanding of how animal movements could enhance decision speed and accuracy. This should provide substantial benefits for neuroscience, and for enhancing performance of autonomous robotic systems operating in challenging environments, such as disaster relief, mining and remote exploration. Read moreRead less
Closing the loop between salience and brain activity. This project aims to understand how animals exposed to an abundance of highly complex information decide what to attend to, that is, how they determine visual saliency. The project will approach this question by systematically tracking visual decision-making in the smallest animal brains, in closed-loop virtual reality environment. This approach will uncover basic working principles applicable to any system that needs to pay attention in a vi ....Closing the loop between salience and brain activity. This project aims to understand how animals exposed to an abundance of highly complex information decide what to attend to, that is, how they determine visual saliency. The project will approach this question by systematically tracking visual decision-making in the smallest animal brains, in closed-loop virtual reality environment. This approach will uncover basic working principles applicable to any system that needs to pay attention in a visually cluttered world, from insects to humans or autonomous vehicles.Read moreRead less
Hierarchical information processing in the primate visual cortex. This project aims to understand how visual information is transformed across hierarchical levels in the brain. Neuroscientists have long recognised that the visual cortex can be conceptualised as a hierarchical processing network. This became apparent when learning algorithms based on hierarchical networks ("deep learning") changed artificial intelligence. This project will combine high-throughput electrophysiology with analytical ....Hierarchical information processing in the primate visual cortex. This project aims to understand how visual information is transformed across hierarchical levels in the brain. Neuroscientists have long recognised that the visual cortex can be conceptualised as a hierarchical processing network. This became apparent when learning algorithms based on hierarchical networks ("deep learning") changed artificial intelligence. This project will combine high-throughput electrophysiology with analytical tools adopted from deep learning. By explaining the physiological properties of higher-level neurons in terms of hierarchical networks, the project expects to address long standing questions in neuroscience, and provide insights on biological hierarchical computation.Read moreRead less
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.Read moreRead less