Guillaume Lajoie

rohan.banerjee@mila.quebec

Biography

Guillaume Lajoie is an Associate professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada-CIFAR AI Research Chair, and a Canada Research Chair (CRC) in Neural Computation and Interfacing.

His research is positioned at the intersection of AI and Neuroscience where he develops tools to better understand mechanisms of intelligence common to both biological and artificial systems. His research group's contributions range from advances in multi-scale learning paradigms for large artificial systems, to applications in neurotechnology. Dr. Lajoie is actively involved in responsible AI development efforts, seeking to identify guidelines and best practices for use of AI in research and beyond.

Current Students

Rohan Banerjee

Collaborating Alumni - Polytechnique Montréal

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

sangnie.bhardwaj@mila.quebec

Colin Bredenberg

Postdoctorate - Université de Montréal

Co-supervisor :

colin.bredenberg@mila.quebec

Leo Choiniere

PhD - Université de Montréal

leo.choiniere@mila.quebec

Claudia Clopath

Independent visiting researcher - University College London

claudia.clopath@mila.quebec

Eric Elmoznino

PhD - Université de Montréal

Principal supervisor :

Yoshua Bengio

eric.elmoznino@mila.quebec

Leo Gagnon

PhD - Université de Montréal

leo.gagnon@mila.quebec

Tom George

Postdoctorate - McGill University

Principal supervisor :

tomgeorge1@btinternet.com

Juan Guerra

Master's Research - Polytechnique Montréal

Principal supervisor :

Marco Bonizzato

juan.guerra@mila.quebec

thomas.jiralerspong@mila.quebec

Nanda Harishankar Krishna

PhD - Université de Montréal

Anna Jahn

Independent visiting researcher - McGill University

anna.jahn1@mila.quebec

Chen Jiang

PhD - McGill University

Principal supervisor :

Paul Masset

chen.jiang@mila.quebec

Thomas Jiralerspong

PhD - Université de Montréal

Co-supervisor :

Yoshua Bengio

Master's Research - Université de Montréal

Co-supervisor :

Dhanya Sridhar

tejaskasetty@gmail.com

PhD - McGill University

Principal supervisor :

pingsheng.li@mila.quebec

mathys.loiselle@mila.quebec

Mathys Loiselle

Research Intern - Concordia University

Co-supervisor :

Matt Perich

Ximeng Mao

PhD - Université de Montréal

Co-supervisor :

Joelle Pineau

ximeng.mao@mila.quebec

Abdel Mfougouon Njupoun

PhD - Université de Montréal

Co-supervisor :

alexandre.payeur@mila.quebec

abdelnjupoun@gmail.com

PhD - Université de Montréal

Co-supervisor :

Independent visiting researcher - Université de Montréal

Julia Price

Master's Research - Université de Montréal

julia.price@mila.quebec

Mauricio Rivera

Master's Research - Université de Montréal

Principal supervisor :

Marco Bonizzato

mauricio.rivera@mila.quebec

Avery Ryoo

PhD - Université de Montréal

Principal supervisor :

Matt Perich

hee-woon.ryoo@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Lune Bellec

sainathp@mila.quebec

ezekiel.williams@mila.quebec

Ryan Vogt

Postdoctorate - Université de Montréal

ryan.vogt@mila.quebec

Ezekiel Williams

PhD - Université de Montréal

Independent visiting researcher - University of Western Ontario

jinkang.xiang@mila.quebec

Jieyu Zhao

Independent visiting researcher - University of South California

jieyu.zhao@mila.quebec

Machine Learning for the Segmentation of Different Nerve Fibre Activations from Brain-to-body Neural Signals

Blog Posts

Représentation graphique d'un nerf vague

May 21, 2025

Param Raval

Olivier Tessier-Larivière

Pascal Fortier-Poisson

Blake Richards

Guillaume Lajoie

Read the article

June 13, 2024

What Do Synaptic Weight Distributions Tell Us About Learning in the Brain ?

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Read the article

Publications

Leveraging unlabelled data for generalizable neural population decoding

Ximeng Mao

Nanda H. Krishna

Avery Hee-Woon Ryoo

Matthew G. Perich

Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent … (see more)work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance. However, current spike-based models are restricted to supervised learning (SL), limiting training to datasets with paired behavioural labels. To address this limitation, we introduce MOJO (Masked autOencoder-based JOint training), a training framework for spike-tokenizing models that jointly leverages self-supervised learning (SSL) via masked autoencoding and SL objectives. We evaluate MOJO on three spiking datasets spanning monkey motor cortex during reaching tasks and multi-regional mouse recordings during vision and decision making tasks, demonstrating superior performance over purely SL-trained models. This improvement is especially pronounced when training with limited labelled data, particularly in few-shot finetuning, where only a small amount of labelled data from a new session is available. Incorporating SSL also yields more interpretable neuronal representations, improving performance on brain region classification and spike-statistics prediction without explicit optimization for these tasks. We further show that MOJO generalizes beyond spiking data to human electrocorticography during speech, where it continues to outperform purely SL-trained models and achieves performance comparable to neuro-foundation models (NFMs) designed specifically for continuous signals. Overall, augmenting spike-tokenizing models with SSL improves performance in label-impoverished settings and enables the use of unlabelled data across various tasks and species, while generalizing to other neural modalities. These results suggest a path towards more flexible and scalable data usage when training NFMs.

2026-07-14

arXiv (preprint)

Can In-Context Learning Support Intrinsic Curiosity?

Eric Elmoznino

Sangnie Bhardwaj

Johannes Von Oswald

Rajai Nasser

Blaise Agüera y Arcas

João Sacramento

Rif A. Saurous

Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models ha… (see more)ve revolutionized data modeling, the problem of automated data selection, or"intrinsic curiosity", remains a significant challenge. Classic approaches incentivize exploration by rewarding an agent based on its"learning progress", which measures how much a newly acquired observation improves a world model's predictive ability. However, evaluating these rewards traditionally requires expensive inner loops of gradient descent updates within each trajectory, rendering them computationally impractical at scale. In this work, we investigate whether the emergent in-context learning (ICL) capabilities of sequence models can eliminate this bottleneck by serving as immediate, update-free world models. Specifically, we evaluate whether an exploration policy can be trained to maximize learning progress, using solely the prediction errors and counterfactual context manipulations of an in-context learner. We first prove that in general Markov decision processes, this is in fact impossible in an unbiased way: the resulting intrinsic rewards either suffer from nuisance terms that bias their estimation of true learning progress, or they cannot be implemented using an in-context learner's prediction errors. Conversely, we prove a positive result for a broad subclass of non-temporal settings, encompassing active learning and Bayesian Experimental Design: here, ICL-derived rewards successfully bound and asymptotically converge to the true learning progress. We corroborate our theory with controlled experiments across continuous and symbolic environments, demonstrating that our ICL-driven framework successfully trains curious data-collection policies that explore optimally.

2026-06-16

arXiv (preprint)

Simplifying the Modeling of Arbitrary Conditionals in Natural Language

Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right… (see more) decoding and conditional likelihood computation. However, they cannot tractably sample from or evaluate arbitrary conditionals -- e.g., a block of text conditioned on past and future tokens. Recent work aims to solve this problem through novel architectures, but they often lead to sub-optimal modeling of such conditionals and degraded generations. We propose Arbitrary Conditionals GPT (AC-GPT) which introduces a simple modification to standard causal Transformers to enable evaluating and sampling from arbitrary conditionals -- including past, future, and mixed contexts -- within a single forward pass. Unlike prior approaches, our method preserves the standard left-to-right ordering and next-token prediction objective essential for both strong performance and efficient training on natural language. Crucially, this compatibility allows existing LLMs to be fine-tuned for arbitrary conditioning. Our empirical results indicate that our method outperforms baselines on modeling arbitrary conditionals, without degrading standard left-to-right performance.

2026-06-11

arXiv (preprint)

CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs

Joachim Schaeffer

Thomas Jiralerspong

Alexander Panfilov

Jonas Geiping

Yoshua Bengio

Roland S. Zimmermann

AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted mode… (see more)l. This partially tampers with the untrusted model's trajectory. If the trusted model detects such an intervention, it may infer properties of the monitor and adapt to evade control. We introduce \textbf{CIAware-Bench}, a benchmark for measuring \textbf{c}ontrol \textbf{i}ntervention (CI) awareness across frontier models. CIAware-Bench tests whether models can distinguish their own trajectories from those modified by a control intervention. The benchmark is comprised of a suite of four task domains (essay writing, BigCodeBench, Bash Arena, and SHADE-Arena), while varying trajectory watermarking, side-task presence, and the control protocol. Evaluating eleven frontier models, we find low to moderate CI awareness under default settings (up to 0.87; random chance balanced binary classification accuracy is 0.5) with substantial variation across task domains and model pairs. Detection is generally easier across model families, suggesting that models exploit provider-specific differences in style or post-training. Overall, CI awareness is not a fixed model-level property, and should be measured for each new model release and deployment scenario. We release CIAware-Bench to track CI awareness and inform control protocols whose interventions are harder to detect.

2026-06-08

arXiv (preprint)

Human learning of noninvasive brain–computer interfaces via manifold geometry

Erica L. Busch

E. Chandra Fincke

Smita Krishnaswamy

Nicholas B. Turk‐Browne

2026-06-08

Nature Neuroscience (published)

Compositional Flow Matching with Factored Velocity Fields

Avery Hee-Woon Ryoo

Dane Malenfant

Matthew G Perich

Conditional generative models can have difficulty generating attribute combinations absent from training, even when each individual factor i… (see more)s densely covered, otherwise known as a failure to compositionally generalize. We propose a factored conditional flow matching architecture that uses a shared base velocity augmented by per-factor heads, summed at the bottleneck. We show that on the Shapes3D and MPI3D-real datasets, the factored architecture matches or beats a parameter-matched monolithic baseline under three structured zero-shot holdout strengths over a two-attribute lattice, notably lowering heldout FID by

2026-05-29

SPIGM @ International Conference on Machine Learning (poster)

openreview.net

Dynamics and Representation Structure of Local Approximations to Gradient-Based Learning in Linear Recurrent Neural Networks

Ezekiel Williams

Alexandre Payeur

Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information tha… (see more)t can plausibly be used during learning. A common strategy to satisfy these constraints is to modify gradient descent by neglecting non-local terms to varying degrees, as in random feedback local online (RFLO) learning and truncated backpropagation through time (tBPTT). However, the learning dynamics of these algorithms, and how they compare with BPTT, remain poorly understood. We apply dynamical systems theory to data-aligned linear RNNs -- whose dynamics can be separated into orthogonal modes -- to compare stationary solutions, stability properties, and convergence rates, finding qualitatively distinct behaviour for RFLO versus BPTT and one-step tBPTT. We further observe that the solutions learned by RFLO are restricted to low-rank perturbations of initial parameters, a result which holds beyond the data-aligned setting. Our work provides analytical insight into how locality constraints shape learning dynamics, with implications for neuroscientific models of learning and alternative optimization approaches for RNNs.

2026-05-28

arXiv (preprint)

Modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are coherent at a low level, but which have … (see more)surreal, dream-like qualities at a high level. While there are many hypotheses as to how classical psychedelics could induce these effects, there are no concrete mechanistic models that capture the variety of observed effects in humans, while remaining consistent with the known pharmacological effects of classical psychedelics on neural circuits. In this work, we propose the ‘oneirogen hypothesis,’ which posits that the perceptual effects of classical psychedelics are a result of their pharmacological actions inducing neural activity states that truly are more similar to dream-like states. We simulate classical psychedelics’ effects via manipulating neural network models trained on perceptual tasks with the Wake-Sleep algorithm. This established machine learning algorithm leverages two activity phases: a perceptual phase (wake) where sensory inputs are encoded, and a generative phase (dream) where the network internally generates activity consistent with stimulus-evoked responses. We simulate the action of psychedelics by partially shifting the model to the ‘Sleep’ state, which entails a greater influence of top-down connections, in line with the impact of psychedelics on apical dendrites. The effects resulting from this manipulation capture a number of experimentally observed phenomena, including the emergence of hallucinations, increases in stimulus-conditioned variability, and large increases in synaptic plasticity. We further provide a number of testable predictions which could be used to validate or invalidate our oneirogen hypothesis.

2026-04-20

eLife (published)

Beyond Distribution Sharpening: The Importance of Task Rewards

Sarthak Mittal

Leo Gagnon

Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into t… (see more)heir training pipelines, enabling systems to evolve from pure reasoning models into sophisticated agents. However, debate persists regarding whether RL genuinely instills new skills within a base model or merely sharpens its existing distribution to elicit latent capabilities. To address this dichotomy, we present an explicit comparison between distribution sharpening and task-reward-based learning, utilizing RL as a tool to implement both paradigms. Our analysis reveals the inherent limitations of distribution sharpening, demonstrating from first principles how and why the optima can be unfavorable and the approach fundamentally unstable. Furthermore, our experiments using Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct and Qwen3-4B-Instruct-2507 on math datasets confirm that sharpening yields limited gains, whereas incorporating task-based reward signal can greatly help achieve robust performance improvements and stable learning.

2026-04-16

arXiv (preprint)

A Compression Perspective on Simplicity Bias

Mizu Nishikawa-Toomey

Sarthak Mittal

Dhanya Sridhar

Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cas… (see more)t new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing supervised learning as a problem of optimal two-part lossless compression. Our theory explains how simplicity bias governs feature selection in neural networks through a fundamental trade-off between model complexity (the cost of describing the hypothesis) and predictive power (the cost of describing the data). Our framework predicts that as the amount of available training data increases, learners transition through qualitatively different features -- from simple spurious shortcuts to complex features -- only when the reduction in data encoding cost justifies the increased model complexity. Consequently, we identify distinct data regimes where increasing data promotes robustness by ruling out trivial shortcuts, and conversely, regimes where limiting data can act as a form of complexity-based regularization, preventing the learning of unreliable complex environmental cues. We validate our theory on a semi-synthetic benchmark showing that the feature selection of neural networks follows the same trajectory of solutions as optimal two-part compressors.

2026-03-25

arXiv (preprint)

JEDI: Jointly Embedded Inference of Neural Dynamics

Matthew G. Perich

Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to … (see more)map the mechanisms of the brain's flexibility onto the dynamics underlying neural populations. However, identifying task-specific dynamical rules from limited, noisy, and high-dimensional experimental neural recordings remains a major challenge, as experimental data often provide only partial access to brain states and dynamical mechanisms. While recurrent neural networks (RNNs) directly constrained neural data have been effective in inferring underlying dynamical mechanisms, they are typically limited to single-task domains and struggle to generalize across behavioral conditions. Here, we introduce JEDI, a hierarchical model that captures neural dynamics across tasks and contexts by learning a shared embedding space over RNN weights. This model recapitulates individual samples of neural dynamics while scaling to arbitrarily large and complex datasets, uncovering shared structure across conditions in a single, unified model. Using simulated RNN datasets, we demonstrate that JEDI accurately learns robust, generalizable, condition-specific embeddings. By reverse-engineering the weights learned by JEDI, we show that it recovers ground truth fixed point structures and unveils key features of the underlying neural dynamics in the eigenspectra. Finally, we apply JEDI to motor cortex recordings during monkey reaching to extract mechanistic insight into the neural dynamics of motor control. Our work shows that joint learning of contextual embeddings and recurrent weights provides scalable and generalizable inference of brain dynamics from recordings alone.

2026-03-10

arXiv (preprint)

Evolutionarily conserved neural dynamics across mice, monkeys, and humans

Olivier Codol

Margaux Asclipe

Anton R Sobinov

Z. Jeffrey Chen

Junchol Park

Nicholas G. Hatsopoulos

Joshua T. Dudman

Juan Álvaro Gallego

Matthew G. Perich

Zihao Chen

On evolutionary timescales, brain circuits adapt to support survival in each species' ecological niche. While some anatomical aspects of neu… (see more)ral circuitry are conserved across species with distant evolutionary origins, each species also exhibits specific circuit adaptations that enable its behavioral repertoire. It remains unclear whether homologous brain regions leverage analogous neural computations as different species perform common behaviors such as reaching and manipulating objects. Here, we directly assessed conservation of neural computations using intracortical recordings from mouse, monkey, and human motor cortex-a homologous region across many mammals-during motor behaviors crucial for survival. We hypothesized that, despite their phylogenetic distance, rodents and primates produce movements through conserved neural computations implemented by motor cortical population dynamics. Remarkably, we found that movement-related neural dynamics were highly conserved across species, while variations in behavioral output were uniquely captured in neural trajectory geometries. Strikingly, neural dynamics during movement across species were more conserved than those across brain regions in the same human and between motor preparation and execution in the same monkeys. Lastly, through manipulation of neural network models trained to perform reaching movements, we reinforce that conservation of neural dynamics across species likely stems from shared circuit constraints. We thus assert that evolution maintains neural computations across phylogeny even as behavioral repertoires expand.

2026-03-06

bioRxiv (preprint)