Neural Computations Research Group

biological and artificial neural dynamics

Guillaume Lajoie (PI)

Version Française en construction !

About Guillaume Lajoie

Assistant Professor

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

  • 06/2022

Paper on opportunity cost of time and urgency signals in the brain is out in PloS Comp Biol.! Amazing work lead by Max Puelma Touzel

Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost, Max Puelma Touzel, Paul Cisek, Guillaume Lajoiem PloS CB (2022)


  • 01/2022: Four abstracts from the lab accepted at COSYNE22

    • Top-down optimization recovers biological coding principles of single-neuron adaptation in RNNs, V. Geadah, G. Kerg, S. Horoi, G. Wolf, G. Lajoie

    • Deliberation gated by opportunity cost adapts to context with urgency in non-human primates, M. Puelma Touzel, P. Cisek, G. Lajoie

    • Dissecting emergent network noise compensation mechanisms in working mem- ory tasks, C. Bredenberg, M. Puelma Touzel, R. Engelken, G. Lajoie

    • Beyond accuracy: robustness and generalization properties of biologically plausible learning rules, Y. H. Liu, G. Lajoie

  • 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 !!!!

Funding

NSERC / CRSNG

FRQNT

FRQS

IVADO

Medteq

Mitacs

Google Research