Research groups
DPAG Collaborators
-
Randy Bruno
Professor of Neuroscience
-
Armin Lak
Associate Professor of Neuroscience
-
Andrew Peters
Sir Henry Dale Fellow
Rui Ponte Costa
Group Leader
Rui leads the Neural & Machine Learning group that leverages AI principles to develop a new generation of computational models of learning in the brain. In particular, the group focuses on understanding how (i) cortical circuits, (ii) neuromodulation and (iii) subcortical regions enable efficient credit assignment in the brain.
During his undergraduate studies in computer science at the University of Coimbra (Portugal), Rui became fascinated by our ability to solve and learn complex problems. This led him to pursue a PhD in Computational Neuroscience and Machine Learning at the University of Edinburgh (UK) where he established close collaborations between theoretical (Mark van Rossum) and experimental groups (P. Jesper Sjöström). During this time Rui was also a visiting PhD student at the University College London (UK) and McGill University (Canada). Next, Rui conducted postdoctoral research in computational neuroscience & machine learning at the University of Oxford (UK) with Tim Vogels where he established collaborations with the groups of Nando de Freitas (Oxford/Google Deepmind) and Nigel Emptage (Oxford). Next, Rui did a short postdoc with the group of Walter Senn (Bern, Switzerland) in collaboration with Yoshua Bengio (MILA, Canada). In 2018 Rui moved to the University of Bristol to start his own group (Neural & Machine Learning group), which in 2023 moved to the University of Oxford.
Rui co-organises the Oxford NeuroTheory Forum.
Key publications
-
Cerebro-cerebellar networks facilitate learning through feedback decoupling.
Journal article
Boven E. et al, (2023), Nat Commun, 14
-
Single-phase deep learning in cortico-cortical networks
Conference paper
Greedy W. et al, (2022), Advances in Neural Information Processing Systems 35
-
Developmental depression-to-facilitation shift controls excitation-inhibition balance.
Journal article
Jia DW. et al, (2022), Commun Biol, 5
-
A deep learning framework for neuroscience.
Journal article
Richards BA. et al, (2019), Nat Neurosci, 22, 1761 - 1770
Recent publications
-
A theory of temporal self-supervised learning in neocortical layers
Preprint
Kermani Nejad K. et al, (2024)
-
Distributional coding of associative learning in discrete populations of midbrain dopamine neurons.
Journal article
Avvisati R. et al, (2024), Cell Rep, 43
-
Hippocampal networks support reinforcement learning in partially observable environments
Preprint
Pedamonti D. et al, (2023)
-
Cerebro-cerebellar networks facilitate learning through feedback decoupling.
Journal article
Boven E. et al, (2023), Nat Commun, 14
-
Single-phase deep learning in cortico-cortical networks
Conference paper
Greedy W. et al, (2022), Advances in Neural Information Processing Systems 35