Code resources - Publications - Teaching
Postdoc at the Laboratory of Computational Neuroscience
Ecole Polytechnique Fédérale de Lausanne (EPFL)
guillaume . bellec @ epfl . ch
CV
I develop computational theories of brains and intelligent machines. I studied machine learning during my Master in Paris and I completed my PhD in the Institute for Theoretical Computer Science of TU Graz in Austria. Currently, I am a postdoc in the Laboratory of Computational Neuroscience at EPFL in Switzerland. My work is most well-known for showing that a competitive artificial intelligence can emerge from simple mathematical models of biologically realistic neural networks. Multiple of my publications are published in selective computer science conferences like NeurIPS or ICLR and more generalist journals like Nature Communications.
In 2019 I created Chord ai with another AI researcher Vivien Seguy. Chord ai is a mobile application using deep learning and artificial neural networks to recognize musical chords in real-time. The application and Chord ai has had more than 2,000,000 users on iOS and Android platforms in 2023. The technical achievement has been to bring state-of-the-art artificial intelligence technology to any popular power-limited mobile device. Besides the scientific challenge, I hope that it will help amateur musicians like me to improve their musical skills.
Trial matching in PyTorch: biological network model of electrophysiology data with optimal transport NeurIPS 2023.
Sample-and-measure in TensorFlow 2: fitting neural data with a differentiable spiking simulator NeurIPS 2021.
CLAPP in PyTorch: local (layer-wise) self-supervised learning NeurIPS 2021.
E-prop in Tensorflow: a local alternative to back-prop through time Nature Communications 2020 paper.
LSNN in Tensorflow: Long short-term memory and meta-learning in spiking neural networks NeurIPS 2018.
Deep Rewiring in Tensorflow: training sparse deep networks from scratch ICLR 2018.
For a complete list of publications, visit my google scholar profile.
Spiking Music: Audio Compression with Event Based Auto-encoders
M Lisboa, G Bellec
Arxiv 2024
High-performance deep spiking neural networks with 0.3 spikes per neuron
A Stanojevic, S Woźniak, G Bellec, G Cherubini, A Pantazi, W Gerstner
Nature Communications 2024 - Arxiv
Trial matching: capturing variability with data-constrained spiking neural networks
C Sourmpis, CCH Petersen, W Gerstner, G Bellec
NeurIPS 2023 - Code - Arxiv
Fitting summary statistics of neural data with a differentiable spiking network simulator
G Bellec*, S Wang*, A Modirshanechi, J Brea, W Gerstner
NeurIPS 2021 - Code
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
B Illing, J Ventura, G Bellec*, W Gerstner*
NeurIPS 2021 - Code
A solution to the learning dilemma for recurrent networks of spiking neurons
G Bellec*, F Scherr*, A Subramoney, E Hajek, D Salaj, R Legenstein, W Maass
Nature Communications - Code
Biologically inspired alternatives to backpropagation through time for
learning in recurrent neural nets
G Bellec*, F Scherr*, E Hajek, D Salaj, R Legenstein, W Maass
TL;DR: Three chapters on eligibility propagation, the first became the Nature Communications paper
Arxiv 2019
Long short-term memory and Learning-to-learn in networks of spiking neurons
G Bellec*, D Salaj*, A Subramoney*, R Legenstein, W Maass
NIPS 2018 - Code - Arxiv
Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype
C Liu*, G Bellec* … R Legenstein and C Mayr
Frontiers in Neuroscience 2018
Deep Rewiring: Training very sparse deep networks
G Bellec, D Kappel, W Maass, R Legenstein
ICLR 2018 - Code
Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system
S Schmitt, J Klähn, G Bellec … R Legenstein, W Maass, J Schemmel, K Meier
(IJCNN 2017) International Joint Conference on Neural Networks
Slow feature analysis with spiking neurons and its application to audio stimuli
G Bellec, M Galtier, R Brette, P Yger
(JCNS 2016) Journal of Computational Neuroscience
Creating audio-based experiments as social web games with the casimir framework
D Wolff, G Bellec, A Friberg, A MacFarlane, T Weyde
(AES 2014) Audio Engineering Society Conference
A social network integrated game experiment to relate tapping to speed perception and explore rhythm reproduction
G Bellec, A Elowsson, A Friberg, D Wolff, T Weyde
(SMS 2013) Sound and Music Computing Conference
(*: comparable contributions and teamwork)
Machine Learning at TU Graz in 2019 (practicals at master level)
Registration page
Introduction to machine learning at TU Graz from 2016 to 2019
(aka computational intelligence, lectures, and practicals at bachelor level)
Registration page
Reinforcement learning at TU Graz in 2017
(aka autonomously learning systems, practicals at master level)