
Cerenaut is an independent research group based in Australia, focusing on fundamental research at the intersection of AI, Neuroscience, and Psychology. Their core strategy is to discover new learning rules, architectures, and representations inspired by neuroscience and psychology to advance AI. They are interested in the interactions between brain regions and emphasize computational descriptions that are implementable. Key research themes include biological plausibility, mental simulation, sparse coding, disentangled representations, attention, embodiment, credit assignment, continual learning, sparse rewards, unsupervised learning, semi-symbolic representations, and hierarchical planning. Cerenaut also collaborates with other researchers and institutions on projects, inviting external researchers to contribute to their Requests for Research (RFRs).

Cerenaut is an independent research group based in Australia, focusing on fundamental research at the intersection of AI, Neuroscience, and Psychology. Their core strategy is to discover new learning rules, architectures, and representations inspired by neuroscience and psychology to advance AI. They are interested in the interactions between brain regions and emphasize computational descriptions that are implementable. Key research themes include biological plausibility, mental simulation, sparse coding, disentangled representations, attention, embodiment, credit assignment, continual learning, sparse rewards, unsupervised learning, semi-symbolic representations, and hierarchical planning. Cerenaut also collaborates with other researchers and institutions on projects, inviting external researchers to contribute to their Requests for Research (RFRs).