Guillaume Bellec

Assistant Professor at the Machine Learning Research Unit
Technical University of Vienna (TU Wien, Austria)
guillaume . bellec @ tuwien . ac . at

Office FB 02 14, Second floor
Erzherzog-Johann-Platz 1
1040 Vienna

+43 664 14 00 987

CV

Google scholar

I study the principles of brain computation using machine learning and artificial intelligence (AI). My work is most well-known for showing how a functional AI can emerge from bio-inspired or biophysically realistic neuron models. I studied machine learning during my Master’s in Paris and completed my PhD in 2019 at the Institute for Theoretical Computer Science of TU Graz in Austria. After a postdoc in the Laboratory of Computational Neuroscience at EPFL in Switzerland, I started my lab in 2025 at TU Wien (Vienna, Austria). Multiple of my publications are published in selective computer science conferences like NeurIPS, ICLR, ICML 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 2025. 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.

Since 2025, the work of my research group at TU Wien is funded in part by the WWTF Vienna Research Group leader award Our project is called “AI & Neuroscience”. We are very grateful to the Vienna Science and Technology Funds (WWTF) for their support.

Publications & open codes

For a complete list of publications, visit my google scholar profile.

• Can local learning match self-supervised backpropagation?
  W Zihan, A Delrocq, W Gerstner, G Bellec
  ICML 2026 - Arxiv - Code

• Biologically informed cortical models predict optogenetic perturbations
  C Sourmpis, CH Petersen, W Gerstner, G Bellec
  TL;DR: Developing deep learning methods for cortical neuron recordings
  eLife 2026 - Biorxiv 2024 - Code

• 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 - Code

• Spiking Music: Audio Compression with Event Based Auto-encoders
  M Lisboa, G Bellec
  TL;DR: Never published elsewhere but I really like this paper, check it out
  Arxiv 2024

• 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)

Teaching

• AI4all: The essentials of AI (Introduction to AI for all engineers at TU Wien)
  Registration page for 2026W

• Machine Learning (masters of data science and logic and AI) at TU Wien since 2026
  Registration page for 2027S

• 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)