Back to the Team Page
Team
Postdocs
Pau Vilimelis Aceituno
Postdoc
Bio
I am a theoretician at the interface between Neuroscience, Machine Learning and Neuromorphic Computing. Using Information Theory, Signal Processing, Random Matrices and Graph Theory, I look for theories and principles that explain how neural networks learn and compute in brains and machines. I also have a general interest (and occasional projects) in other fields such as ecology or economics. Previously I have worked on designing satellites, software for error-prone hardware, data mining for airline IT, and time-series processing for microbiomes.
Publications
Continual Learning through Control Minimization
2026, ICML
Biologically inspired memristive neuron capable of on-chip learning
2026, Neuromorphic Computing and Engineering
A Combination of Noise and Bilateral Filters Achieve Supralinear and Scalable Adversarial Robustness in CNNs
2026, CVPR
The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training
2025, ICML
Temporal horizons in forecasting: a performance-learnability trade-off
2025, Transactions in Machine Learning Research
Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit
2024, Science
Theoretical principles explain the structure of the insect head direction circuit
2024, eLife
Bio-inspired, task-free continual learning through activity regularization.
2023, Biological Cybernetics
Learning Cortical Hierarchies with mporal Hebbian Updates.
2023, Frontiers. Comp. Neurosc
Mini-batching ecological data to improve ecosystem models with machine learning
2022, Methods in Ecology and Evolution
Credit Assignment in Neural Networks through Deep Feedback Control
2021, NeurIPS
Inhibition tunes prefrontal circuit dynamics to promote sociosexual behavior in female mice
2026, eLife