João Sacramento

Research

I'm a principal investigator at the Institute of Neuroinformatics and a guest researcher at Angelika Steger's group in the Computer Science Department of ETH Zürich. My research interests lie at the intersection of neuroscience and machine learning. I try to understand the principles that enable humans to learn so efficiently, when compared to artificial neural networks. My goal is to develop better neuroscience-inspired machine learning algorithms, and in turn to use insights gained from designing these to understand learning in the brain.

My research is supported by a SNSF Ambizione Fellowship and an ETH Zurich Research Grant.

Previously, from 2015 to 2018, I was a postdoc with Walter Senn at the University of Bern. Together with Rui P. Costa and Yoshua Bengio we developed a model for error backpropagation in the cortex.

I received my PhD in computer science from IST (University of Lisbon, 2014) where I studied neural network models of memory with Andreas Wichert. Still at IST, in 2015, I was awarded a short research fellowship to work with Francisco C. Santos. During this period I studied energy-efficient synaptic plasticity rules with Mark van Rossum.

Current students and collaborators

Upcoming and recent presentations

Recent papers

	Nicolas Zucchet, João Sacramento (2022). Beyond backpropagation: implicit gradients for bilevel optimization. Preprint: arXiv:2205.03076 [ paper ]

 

	Nicolas Zucchet*, Simon Schug*, Johannes von Oswald*, Dominic Zhao, João Sacramento (2021). A contrastive rule for meta-learning. Preprint: arXiv:2104.01677 [ paper ] * — equal contributions

 

	Alexander Meulemans*, Matilde T. Farinha*, Maria R. Cervera*, João Sacramento, Benjamin F. Grewe (2022). Minimizing control for credit assignment with strong feedback. ICML 2022 [ paper ] * — equal contributions

 

	Johannes von Oswald*, Dominic Zhao*, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, João Sacramento (2021). Learning where to learn: Gradient sparsity in meta and continual learning. NeurIPS 2021 [ paper ] * — equal contributions

 

	Alexander Meulemans*, Matilde T. Farinha*, Javier G. Ordóñez, Pau V. Aceituno, João Sacramento, Benjamin F. Grewe (2021). Credit assignment in neural networks through deep feedback control. NeurIPS 2021 (Spotlight) [ paper ] * — equal contributions

 

	Christian Henning*, Maria R. Cervera*, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento (2021). Posterior meta-replay for continual learning. NeurIPS 2021 [ paper ] * — equal contributions

 

	Jakob Jordan, João Sacramento, Willem A. M. Wybo, Mihai A. Petrovici*, Walter Senn* (2021). Learning Bayes-optimal dendritic opinion pooling. Preprint: arXiv:2104.13238 [ paper ] * — equal contributions

 

	Johannes von Oswald*, Seijin Kobayashi*, Alexander Meulemans, Christian Henning, Benjamin F. Grewe, João Sacramento (2020). Neural networks with late-phase weights. ICLR 2021 [ paper | code ] * — equal contributions

 

	Dominic Zhao, Seijin Kobayashi, João Sacramento*, Johannes von Oswald* (2020). Meta-learning via hypernetworks. NeurIPS Workshop on Meta-Learning 2020 [ paper ] * — equal contributions

 

	Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe (2020). A theoretical framework for target propagation. NeurIPS 2020 (Spotlight) [ paper | code ]

 

	Johannes von Oswald*, Christian Henning*, Benjamin F. Grewe, João Sacramento (2019). Continual learning with hypernetworks. ICLR 2020 (Spotlight) [ paper | talk video | code ] * — equal contributions

 

	Blake Richards*, Timothy P. Lillicrap*, ..., João Sacramento, ..., Denis Therien*, Konrad P. Körding* (2019). A deep learning framework for neuroscience. Nature Neuroscience 22:1761-1770 [ link to journal ] * — equal contributions

 

	Milton Llera, João Sacramento, Rui P. Costa (2019). Computational roles of plastic probabilistic synapses. Current Opinion in Neurobiology 54:90-97 [ link to journal ]

 

	João Sacramento, Rui P. Costa, Yoshua Bengio, Walter Senn (2018). Dendritic cortical microcircuits approximate the backpropagation algorithm. NeurIPS 2018 (Oral) [ paper | talk video ]

If my articles are behind a paywall you can't get through please send me an e-mail.

A complete list of publications is here.

Teaching

Currently, I'm a guest lecturer for the Learning in Deep Artificial and Biological Neuronal Networks course offered at ETH Zürich.

In the past I served as a teaching assistant at the Department of Computer Science and Engineering of IST, where I lectured practical classes on computer programming and basic algorithms.