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Understanding animal intelligence
Improving machine intelligence

Research Strategy

We aim to discover new learning rules, architectures and representations from neuroscience and psychology to benefit AI and contribute insights back to these fields.

We are interested in the interactions of brain regions with complementary functions and timescales. For example, left and right hemispheres and slow and fast learning between neocortex and hippocampus.

Our focus is computational descriptions that are implementable.


Deep learning in a bilateral brain with hemispheric specialization

We created an architecture with hemispheric specialization to better understand the brain and uncover new principles for AI

Continual few-shot learning with Hippocampal-inspired replay

We found that replay improves continual few-shot learning significantly, for learning classes as well as specific instances

High-order partially-observable sequences

Recurrent Sparse Memory (RSM) is an unsupervised method for simultaneously learning spatial and sequential structure in partially-observable high Markov order data streams

Fast (One-shot) episodic learning

Our Artificial Hippocampus Algorithm (AHA) can learn to generalize classes from a single instance, while still discriminating between instances of the same class - all without any provided labels



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About Us

Cerenaut (formerly ProjectAGI) is an independent research group that undertakes fundamental research at the intersection of AI, Neuroscience and Psychology. We’re based in Australia. 

Our name reflects humanity’s journey towards higher cognition and intelligence: cere = of the brain, naut = journey

We co-supervise student research projects and collaborate with researchers on topics of shared interest. If you’d like collaborate, get in touch!