Guillaume Lajoie

matteo.alleman@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

Matteo Alleman

Postdoctorate - Université de Montréal

Rohan Banerjee

Collaborating Alumni - Polytechnique Montréal

rohan.banerjee@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

sangnie.bhardwaj@mila.quebec

Colin Bredenberg

Independent visiting researcher - 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

PhD - Polytechnique Montréal

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

mauriv2010@gmail.com

hee-woon.ryoo@mila.quebec

Avery Ryoo

PhD - Université de Montréal

Principal supervisor :

Matt Perich

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

Blog Posts

Représentation graphique d'un nerf vague

May 21, 2025

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

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

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)

The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Abstract Classical psychedelics induce complex visual hallucinations in humans, generating percepts that are co-herent at a … (see more)low level, but which have 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.

2026-01-12

eLife (accepted)

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning

Seijin Kobayashi

Yanick Schimpf

Maximilian Schlegel

Angelika Steger

Maciej Wolczyk

Johannes Von Oswald

Nino Scherrer

Kaitlin Maile

Blake Aaron Richards

Rif A. Saurous

James Manyika

Blaise Agüera y Arcas

Alexander Meulemans

João Sacramento

Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecede… (see more)nted success on many problem domains. During RL, these models explore by generating new outputs, one token at a time. However, sampling actions token-by-token can result in highly inefficient learning, particularly when rewards are sparse. Here, we show that it is possible to overcome this problem by acting and exploring within the internal representations of an autoregressive model. Specifically, to discover temporally-abstract actions, we introduce a higher-order, non-causal sequence model whose outputs control the residual stream activations of a base autoregressive model. On grid world and MuJoCo-based tasks with hierarchical structure, we find that the higher-order model learns to compress long activation sequence chunks onto internal controllers. Critically, each controller executes a sequence of behaviorally meaningful actions that unfold over long timescales and are accompanied with a learned termination condition, such that composing multiple controllers over time leads to efficient exploration on novel tasks. We show that direct internal controller reinforcement, a process we term "internal RL", enables learning from sparse rewards in cases where standard RL finetuning fails. Our results demonstrate the benefits of latent action generation and reinforcement in autoregressive models, suggesting internal RL as a promising avenue for realizing hierarchical RL within foundation models.

2025-12-22

ArXiv (preprint)

Generalizable, real-time neural decoding with hybrid state-space models

Avery Hee-Woon Ryoo

Nanda H Krishna

Ximeng Mao

Mehdi Azabou

Eva L Dyer

Matthew G Perich

Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-com… (see more)puter interfaces, where models are subject to strict latency constraints. Traditional methods, including simple recurrent neural networks, are fast and lightweight but often struggle to generalize to unseen data. In contrast, recent Transformer-based approaches leverage large-scale pretraining for strong generalization performance, but typically have much larger computational requirements and are not always suitable for low-resource or real-time settings. To address these shortcomings, we present POSSM, a novel hybrid architecture that combines individual spike tokenization via a cross-attention module with a recurrent state-space model (SSM) backbone to enable (1) fast and causal online prediction on neural activity and (2) efficient generalization to new sessions, individuals, and tasks through multi-dataset pretraining. We evaluate POSSM's decoding performance and inference speed on intracortical decoding of monkey motor tasks, and show that it extends to clinical applications, namely handwriting and speech decoding in human subjects. Notably, we demonstrate that pretraining on monkey motor-cortical recordings improves decoding performance on the human handwriting task, highlighting the exciting potential for cross-species transfer. In all of these tasks, we find that POSSM achieves decoding accuracy comparable to state-of-the-art Transformers, at a fraction of the inference cost (up to 9x faster on GPU). These results suggest that hybrid SSMs are a promising approach to bridging the gap between accuracy, inference speed, and generalization when training neural decoders for real-time, closed-loop applications.

2025-12-02

Advances in Neural Information Processing Systems (Accept (poster))

openreview.net

Tracing the Representation Geometry of Language Models from Pretraining to Post-training

Melody Zixuan Li

Kumar Krishna Agrawal

Arna Ghosh

Komal Kumar Teru

Adam Santoro

Blake A. Richards

Standard training metrics like loss fail to explain the emergence of complex capabilities in large language models. We take a spectral appro… (see more)ach to investigate the geometry of learned representations across pretraining and post-training, measuring effective rank (RankMe) and eigenspectrum decay (

2025-12-02

Conference on Neural Information Processing Systems (Accept (poster))

openreview.net

Training neural networks from scratch in a videogame leads to brittle brain encoding

Basile Pinsard

Recent brain-encoding studies using videogame tasks suggest that the training objective of an artificial neural network plays a central role… (see more) in how well the network’s representations align with brain activity. This study investigates the alignment of artificial neural network activations with brain activity elicited by a video game task using models trained from scratch in controlled settings. We specifically compared three model training objectives: reinforcement learning, imitation learning, and a vision task, while accounting for other potential factors which may impact performance such as training data and model architecture. We tested models on brain encoding, i.e. their ability to predict functional magnetic resonance imaging (fMRI) signals acquired while human subjects played different levels of the video game Super Mario Bros. When tested on new playthroughs from the game levels seen at training, the reinforcement learning objective had a small but significant advantage in brain encoding, followed by the imitation learning and vision models. We hypothesized that brain-aligned representations would emerge only in task-competent models, and that the specific brain regions well encoded by a model would depend on the nature of the task it was trained on. While brain encoding did improve during model training, even an untrained model with matching architecture approached the performance of the best models. Contrary to our hypotheses, no model layers or specific training objectives aligned preferentially with specific brain areas. Large performance gaps also persisted in fully trained models across game levels, both those seen during training and entirely novel ones. Overall, even though reinforcement learning presented a small advantage to train brain encoding models for videogame data, all tested brain encoding models exhibited brittle performance with limited generalization both within- and out-of-distribution. Overall, our results suggest that training small artificial models from scratch is not sufficiently reliable, and that incorporating pretrained models such as foundation vision–action models may ultimately be necessary to support robust inferences about brain representations.

2025-12-01

bioRxiv (preprint)

Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning

Alexander Meulemans

Rajai Nasser

Maciej Wolczyk

Marissa A. Weis

Seijin Kobayashi

Blake Aaron Richards

Angelika Steger

Marcus Hutter

James Manyika

Rif A. Saurous

João Sacramento

Blaise Agüera y Arcas

2025-11-26

ArXiv (preprint)

Brain-like neural dynamics for behavioral control develop through reinforcement learning

Olivier Codol

Nanda H. Krishna

Matthew G. Perich

During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produ… (see more)ce neural dynamics that enable the rich repertoire of behaviors we perform. What begins as a series of “babbles” coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories—supervised learning (SL) and reinforcement learning (RL)—that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that this similarity with biological brains depends critically on matching biomechanical properties of the limb. Dynamical analysis on network activity revealed that our RL-trained networks operate at the “edge of chaos”, a dynamical regime known for its computational richness, greater memory capacity, and robust plasticity properties. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.

2025-11-01

bioRxiv (preprint)

Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers

2025-10-12

ArXiv (preprint)

In-context learning and Occam's razor

A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees fo… (see more)r generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best: a principle called Occam's razor. Despite the need for simple models, most current approaches in machine learning only minimize the training error, and at best indirectly promote simplicity through regularization or architecture design. Here, we draw a connection between Occam's razor and in-context learning: an emergent ability of certain sequence models like Transformers to learn at inference time from past observations in a sequence. In particular, we show that the next-token prediction loss used to train in-context learners is directly equivalent to a data compression technique called prequential coding, and that minimizing this loss amounts to jointly minimizing both the training error and the complexity of the model that was implicitly learned from context. Our theory and the empirical experiments we use to support it not only provide a normative account of in-context learning, but also elucidate the shortcomings of current in-context learning methods, suggesting ways in which they can be improved. We make our code available at https://github.com/3rdCore/PrequentialCode.

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)