SNSF-funded research ETHZ-funded research

I'm a principal investigator at the Department of Computer Science of ETH Zurich hosted at Angelika Steger's group. My research interests lie at the intersection of neuroscience and machine learning. 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

Simon Schug — PhD student

Nicolas Zucchet — PhD student

Johannes von Oswald — PhD student (co-supervised with Angelika Steger)

Dominic Zhao — External collaborator at Common Sense Machines (previously: BSc student)

Alexandra Proca — Research assistant

Alexander Meulemans — Collaborator at the Computer Science Department of ETH Zürich

Seijin Kobayashi — Collaborator at the Computer Science Department of ETH Zürich

Angelika Steger — Collaborator at the Computer Science Department of ETH Zürich

Maciej Wołczyk — Visiting student

Anja Šurina — Master's student

News

Nicolas's talk for the Brain & AI group at Meta AI: Nicolas gave a talk presenting our least-control principle for learning to Jean-Rémi King's Brain & AI group at Meta AI on November 16.

Mathematics, Physics & Machine Learning seminar talk: I gave an IST Mathematics, Physics & Machine Learning seminar talk on November 10.

Panel discussion on lifelong learning: I will participate on a panel discussion on Lifelong Learning Machines at NeurIPS 2022.

Visit to Mila: Simon, Alexander, Nicolas and I will be visiting Blake Richards's lab at Mila.

Doctoral symposium at EPIA: Together with Fernando P. Santos and Henrique Lopes Cardoso I'm organizing a doctoral symposium at EPIA, the Portuguese conference on artificial intelligence which will be held in Lisbon.

DeepMind talk by Johannes: Johannes gave a talk at DeepMind, London presenting our models and algorithms for continual learning and meta-learning.

MLSSN 2022 lecture (video on YouTube): I gave a lecture with Alexander, Simon and Nicolas for the MLSSN 2022 summer school in Krakow, Poland where we discussed bilevel optimization problems involving neural networks. We covered how to solve them with recurrent backpropagation, equilibrium propagation, and some of our own work on learning and meta-learning without error backpropagation. The lecture is now on YouTube.

Oxford seminar talk: I gave an Oxford NeuroTheory Forum seminar presenting our work on biologically-plausible meta-learning.

NAISys 2022 poster: Alexander presented ongoing work on our new principle for learning at the NAISys 2022 conference in Cold Spring Harbor, NY.

Swiss Computational Neuroscience Retreat: Nicolas presented our work on biologically-plausible meta-learning at the Swiss Computational Neuroscience Retreat in Crans Montana.

Recent papers

The least-control principle for local learning at equilibrium Alexander Meulemans*, Nicolas Zucchet*, Seijin Kobayashi*, Johannes von Oswald, João Sacramento (2022). The least-control principle for local learning at equilibrium.
NeurIPS 2022 (Oral)
[ paper ]
* — equal contributions

 

A contrastive rule for meta-learning Nicolas Zucchet*, Simon Schug*, Johannes von Oswald*, Dominic Zhao, João Sacramento (2021). A contrastive rule for meta-learning.
NeurIPS 2022
[ paper ]
* — equal contributions

 

Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation Nicolas Zucchet, João Sacramento (2022). Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation.
Neural Computation
[ link to journal | paper pdf ]

 

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

 

Credit assignment in neural networks through deep feedback control 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

 

Posterior meta-replay for continual learning 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

 

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

 

Neural networks with late-phase weights 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

 

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

 

A theoretical framework for target propagation 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 ]

 

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

 

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

 

Cortical backprop Milton Llera, João Sacramento, Rui P. Costa (2019). Computational roles of plastic probabilistic synapses.
Current Opinion in Neurobiology
[ link to journal ]

 

Cortical backprop 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

From 2019 to 2021, I was a guest lecturer for the Learning in Deep Artificial and Biological Neuronal Networks course offered at ETH Zürich.

Before that 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.