Portrait of Guillaume Lajoie

Guillaume Lajoie

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Mathematics and Statistics
Consultant

Biography

Guillaume Lajoie is an assistant professor in the Department of Mathematics and Statistics at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute. He is also a Fonds de recherche du Québec - Health Research Scholar and holds a Tier 2 Canada Research Chair in Neural Computation and Interfacing.

Previously, Lajoie was a postdoctoral fellow at the Max Planck Institute for Dynamics and Self-Organization in Germany and at the University of Washington’s Institute for Neuroengineering. He obtained his PhD from the Department of Applied Mathematics at the University of Washington (Seattle).

Lying at the intersection of AI and neuroscience, Lajoie’s research pursues questions surrounding neural network dynamics and computations, which has potential applications to neuroengineering.

Recent work has focused on the development of architectural inductive biases for information propagation in recurrent networks, as well as the development of algorithms and models for bidirectional brain-machine interface optimization.

Current Students

Collaborating researcher - Université de Montréal
PhD - Université de Montréal
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Postdoctorate - Université de Montréal
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PhD - Université de Montréal
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PhD - Université de Montréal
Master's Research - Université de Montréal
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Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
PhD - McGill University
PhD - Université de Montréal
PhD - Université de Montréal
Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Postdoctorate - McGill University
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Master's Research - Polytechnique Montréal
Principal supervisor :
PhD - Université de Montréal
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PhD - Université de Montréal
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Independent visiting researcher
Principal supervisor :
Collaborating researcher - Polytechnique Montréal
Principal supervisor :
Research Intern - Western Washington University
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PhD - Université de Montréal
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Publications

Online Bayesian optimization of vagus nerve stimulation.
Lorenz Wernisch
Tristan Edwards
Antonin Berthon
Olivier Tessier-Lariviere
Elvijs Sarkans
Myrta Stoukidi
Pascal Fortier-Poisson
Max Pinkney
Michael Thornton
Catherine Hanley
Susannah Lee
Joel Jennings
Ben Appleton
Philip Garsed
Bret Patterson
Buttinger Will
Samuel Gonshaw
Matjaž Jakopec
Sudhakaran Shunmugam
Jorin Mamen … (see 4 more)
Aleksi Tukiainen
Oliver Armitage
Emil Hewage
OBJECTIVE In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimu… (see more)lation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects. Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate, making it an ideal candidate for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to vagus nerve stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target heart rates and optimizing neural B-fiber activations despite high intersubject variability. Significance. An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing. .
Assistive sensory-motor perturbations influence learned neural representations
Pavithra Rajeswaran
Alexandre Payeur
Amy L. Orsborn
Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using B… (see more)rain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.
Learning and Aligning Structured Random Feature Networks
Vivian White
Muawiz Sajjad Chaudhary
Kameron Decker Harris
Artificial neural networks (ANNs) are considered ``black boxes'' due to the difficulty of interpreting their learned weights. While choosin… (see more)g the best features is not well understood, random feature networks (RFNs) and wavelet scattering ground some ANN learning mechanisms in function space with tractable mathematics. Meanwhile, the genetic code has evolved over millions of years, shaping the brain to devlop variable neural circuits with reliable structure that resemble RFNs. We explore a similar approach, embedding neuro-inspired, wavelet-like weights into multilayer RFNs. These can outperform scattering and have kernels that describe their function space at large width. We build learnable and deeper versions of these models where we can optimize separate spatial and channel covariances of the convolutional weight distributions. We find that these networks can perform comparatively with conventional ANNs while dramatically reducing the number of trainable parameters. Channel covariances are most influential, and both weight and activation alignment are needed for classification performance. Our work outlines how neuro-inspired configurations may lead to better performance in key cases and offers a potentially tractable reduced model for ANN learning.
Learning and Aligning Structured Random Feature Networks
Vivian White
Muawiz Sajjad Chaudhary
Kameron Decker Harris
Artificial neural networks (ANNs) are considered "black boxes'' due to the difficulty of interpreting their learned weights. While choosing… (see more) the best features is not well understood, random feature networks (RFNs) and wavelet scattering ground some ANN learning mechanisms in function space with tractable mathematics. Meanwhile, the genetic code has evolved over millions of years, shaping the brain to develop variable neural circuits with reliable structure that resemble RFNs. We explore a similar approach, embedding neuro-inspired, wavelet-like weights into multilayer RFNs. These can outperform scattering and have kernels that describe their function space at large width. We build learnable and deeper versions of these models where we can optimize separate spatial and channel covariances of the convolutional weight distributions. We find that these networks can perform comparatively with conventional ANNs while dramatically reducing the number of trainable parameters. Channel covariances are most influential, and both weight and activation alignment are needed for classification performance. Our work outlines how neuro-inspired configurations may lead to better performance in key cases and offers a potentially tractable reduced model for ANN learning.
Sources of richness and ineffability for phenomenally conscious states
Xu Ji
Eric Elmoznino
George Deane
Axel Constant
Jonathan Simon
Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats
Léo Choinière
Rose Guay-Hottin
Rémi Picard
Numa Dancause
Connectome-based reservoir computing with the conn2res toolbox
Laura E. Suárez
Agoston Mihalik
Filip Milisav
Kenji Marshall
Mingze Li
Petra E. Vértes
Bratislav Mišić
Amortizing intractable inference in large language models
Edward J Hu
Moksh J. Jain
Eric Elmoznino
Younesse Kaddar
Nikolay Malkin
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This l… (see more)imits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.
Delta-AI: Local objectives for amortized inference in sparse graphical models
Jean-Pierre R. Falet
Hae Beom Lee
Nikolay Malkin
Chen Sun
Dragos Secrieru
Dinghuai Zhang
We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call …
How connectivity structure shapes rich and lazy learning in neural circuits
Yuhan Helena Liu
Aristide Baratin
Jonathan Cornford
Stefan Mihalas
Eric Todd SheaBrown
In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learn… (see more)ing dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity generally has a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights — in particular their effective rank — influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Tianhong Li
Sangnie Bhardwaj
Yonglong Tian
Han Zhang
Jarred Barber
Dina Katabi
Huiwen Chang
Dilip Krishnan
Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalizati… (see more)on capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce
Sufficient conditions for offline reactivation in recurrent neural networks
Nanda H Krishna
Colin Bredenberg
Daniel Levenstein
During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (see more)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.