Discovery Early Career Researcher Award - Grant ID: DE170101514
Funder
Australian Research Council
Funding Amount
$372,000.00
Summary
The control of neuroplasticity in the brain. This project aims to determine how neuroplasticity – the brain’s ability to remodel and make new circuits – is controlled in both excitatory and inhibitory neurons. This capacity, vital for all cognitive functions, diminishes as people age. It is imperative to determine neuroplasticity’s mechanisms and how and why they change, but it is not known how both excitatory and inhibitory neurons contribute to neuroplasticity and how these dynamic alterations ....The control of neuroplasticity in the brain. This project aims to determine how neuroplasticity – the brain’s ability to remodel and make new circuits – is controlled in both excitatory and inhibitory neurons. This capacity, vital for all cognitive functions, diminishes as people age. It is imperative to determine neuroplasticity’s mechanisms and how and why they change, but it is not known how both excitatory and inhibitory neurons contribute to neuroplasticity and how these dynamic alterations are controlled. Understanding neuroplasticity is vital for learning, memory and healthy ageing throughout life.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less