* – equal contributions

2021

Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
S Schug*, F Benzing*, A Steger
eLife 10: e69884
Paper Preprint Code

Learning where to learn: Gradient sparsity in meta and continual learning
J von Oswald*, D Zhao*, S Kobayashi, S Schug, M Caccia, N Zucchet, J Sacramento
NeurIPS 2021
Paper Preprint Code

A contrastive rule for meta-learning
N Zucchet*, S Schug*, J von Oswald*, D Zhao, J Sacramento
Preprint: arXiv:2104.01677
Preprint

2020

Task-Agnostic Continual Learning via Stochastic Synapses
S Schug, F Benzing, A Steger
Workshop on Continual Learning at ICML 2020
Workshop Paper

Evolving instinctive behaviour in resource-constrained autonomous agents using grammatical evolution
A Hallawa, S Schug, G Iacca, G Ascheid
EvoStar 2020
Paper