Neural Computations Research Group
biological and artificial neural dynamics
Guillaume Lajoie (PI)
Version Française en construction !
About Guillaume Lajoie
Assistant Professor
Mila - Québec AI Institute (Core Academic Member)
Research Scholar, Fond de Recherche du Québec en Santé (FRQS)
Research center affiliations:
Departmental Accreditations (UdeM)
Contact & Links
email: g.lajoie 'at' umontreal.ca
snail mail: Dept. de Mathématiques et Statistiques, Université de Montréal, 2920 chemin de la Tour, Montréal, QC, H3T 1J4
some code: github.com/glajoie/
CV: link
this site's sections: people / publications / teaching
I am an applied mathematician interested in the interactions and commonalities of biological and artificial neural computations. My research group works at the intersection of AI and Neuroscience, developing tools to better understand neural networks as well as algorithms for brain-machine interfaces for scientific and clinical use. My work is motivated by the remarkable ability of neural networks (biological and artificial) to learn and support complex, emergent computations. I use tools from dynamical systems, information theory, statistics and machine learning to address a range of problems, in collaboration with experimental neuroscientists and machine intelligence researchers.
News
01/2022: Two papers from the lab accepted at ICLR2022!!!
02/2021 Huge congratulations to lab members Giancarlo Kerg, Ezekiel Williams and Sarthak Mittal for their UNIQUE Center Excellence Scholarships !
01/2021: 5 submissions from the lab accepted at COSYNE 2021 !
PNS-GAN: conditional generation of peripheral nerve signals in the wavelet domain via adversarial networks, Olivier Tessier-Lariviere, Luke Prince, Pascal Fortier-Poisson, Lorenz Wernisch, Emil Hewage, Oli Armitage, Blake Richards*, Guillaume Lajoie*
Urgency as the neural correlate of the opportunity cost of time commitment in de- cision making tasks, Maximilian Puelma Touzel, Paul Cisek, Guillaume Lajoie
Noisy gradient updates drive dimensionality compression in neural networks, Matthew Farrell, Stefano Recanatesi, Madhu Advani, Guillaume Lajoie, Eric Shea-Brown
Learning Function From Structure In Neuromorphic Networks, Laura Suarez, Blake Richards, Guillaume Lajoie, Bratislav Misic
Network-level computational advantages of single-neuron adaptation, Victor Geadah, Giancarlo Kerg, Stefan Horoi, Guy Wolf, Guillaume Lajoie
Keep you eyes out for these !!!!
09/2020: Short opinion piece about the future of automated neurostimualtion optimization , now live on IEEE Brain BrainInsight -- Bayesian optimization for automated neurostimulation: future directions and challenges
09/2020: New paper accepted at NeurIPS2020! See preprint here: Untangling tradeoffs between recurrence and self-attention in neural networks
07/2020: Sarthak's paper accepted at ICML! Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
06/2020: Sam's paper published in IEEE TNSRE! Hierarchical Bayesian Optimization of Spatiotemporal Neurostimulations for Targeted Motor Outputs
Funding
NSERC / CRSNG
FRQNT
FRQS
IVADO
Medteq
Mitacs
Google Research
