Neuro-AI 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'

  • snail mail: Dept. de Mathématiques et Statistiques, Université de Montréal, 2920 chemin de la Tour, Montréal, QC, H3T 1J4

  • some code:

  • 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, or Neuro-AI, 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.


  • 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 !!!!








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