activity
20172021
most citedLearning offline: memory replay in biological and artificial reinforcement learning

39 citations · 110 across the 4 of their papers we have counts for

collaborators

5 papers

q-bio.NC20212 cited

Cortico-cerebellar networks as decoupling neural interfaces

Joseph Pemberton, Ellen Boven, Richard Apps +1

The brain solves the credit assignment problem remarkably well. For credit to be assigned across neural networks they must, in principle, wait for specific neural computations to f…

cs.LG202139 cited

Learning offline: memory replay in biological and artificial reinforcement learning

Emma L. Roscow, Raymond Chua, Rui Ponte Costa +2

Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning,…

q-bio.NC2018

Dendritic cortical microcircuits approximate the backpropagation algorithm

João Sacramento, Rui Ponte Costa, Yoshua Bengio +1

Deep learning has seen remarkable developments over the last years, many of them inspired by neuroscience. However, the main learning mechanism behind these advances - error backpr…

q-bio.NC201738 cited

Dendritic error backpropagation in deep cortical microcircuits

João Sacramento, Rui Ponte Costa, Yoshua Bengio +1

Animal behaviour depends on learning to associate sensory stimuli with the desired motor command. Understanding how the brain orchestrates the necessary synaptic modifications acro…

q-bio.NC201731 cited

Cortical microcircuits as gated-recurrent neural networks

Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford +2

Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas. Such stereotyped structure suggests the existence of common co…