Guillaume Lajoie

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

Sangnie Bhardwaj

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

Colin Bredenberg

Postdoctorate - Université de Montréal

Co-supervisor :

Blake Richards

Leo Choiniere

PhD - Université de Montréal

Eric Elmoznino

PhD - Université de Montréal

Principal supervisor :

Leo Gagnon

PhD - Université de Montréal

Tom George

Postdoctorate - McGill University

Principal supervisor :

Juan Guerra

Master's Research - Polytechnique Montréal

Principal supervisor :

Nanda Harishankar Krishna

PhD - Université de Montréal

Anna Jahn

Independent visiting researcher - McGill University

Chen Jiang

PhD - McGill University

Principal supervisor :

Paul Masset

Thomas Jiralerspong

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

Co-supervisor :

PhD - McGill University

Principal supervisor :

Blake Richards

Mathys Loiselle

Research Intern - Concordia University

Co-supervisor :

Ximeng Mao

PhD - Université de Montréal

Co-supervisor :

Abdel Mfougouon Njupoun

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Co-supervisor :

Independent visiting researcher - Université de Montréal

Julia Price

Master's Research - Université de Montréal

Mauricio Rivera

Master's Research - Université de Montréal

Principal supervisor :

Marco Bonizzato

Avery Ryoo

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

Lune Bellec

Ryan Vogt

Postdoctorate - Université de Montréal

Ezekiel Williams

PhD - Université de Montréal

Jieyu Zhao

Independent visiting researcher - University of South California

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

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

Nan Rosemary Ke

Danilo Rezende

Michael Mozer

Christopher Pal

Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise tha… (see more)t the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.

2021-10-10

Datasets and Benchmarks Track @ Neural Information Processing Systems (accepted)

openreview.net

Learning function from structure in neuromorphic networks

Laura E. Suárez

Blake A. Richards

Guillaume Lajoie

Bratislav Misic

The connection patterns of neural circuits in the brain form a complex network. Collective signaling within the network manifests as pattern… (see more)ed neural activity, and is thought to support human cognition and adaptive behavior. Recent technological advances permit macro-scale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging, and use reservoir computing to implement these connectomes as artificial neural networks. We then train these neuromorphic networks to learn a cognitive task. We show that biologically realistic neural architectures perform optimally when they display critical dynamics. We find that performance is driven by network topology, and that the modular organization of large-scale functional systems is computationally relevant. Throughout, we observe a prominent interaction between network structure and dynamics, such that the same underlying architecture can support a wide range of learning capacities across dynamical regimes. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity, conceptually bridging neuroscience and artificial intelligence.

2021-08-08

Nature Machine Intelligence (unknown)

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance

Dennis Shung

Manik Kuchroo

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (see more)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an

2021-07-25

arXiv (preprint)

Jean-christophe Gagnon-audet

arxiv.org

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.

Germán Abrevaya

Guillaume Dumas

Aleksandr Y. Aravkin

Peng Zheng

James R. Kozloski

Pablo Polosecki

Guillaume Lajoie

David D. Cox

Silvina Ponce Dawson

Guillermo A. Cecchi

Irina Rish

Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by … (see more)linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.

2021-06-06

Neural Computation (unknown)

Olivier Tessier-Larivière

PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks

Luke Y. Prince

Pascal Fortier-Poisson

Lorenz Wernisch

Oliver Armitage

Emil Hewage

Guillaume Lajoie

Blake Aaron Richards

Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover k… (see more)nown ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.

2021-05-03

International IEEE/EMBS Conference on Neural Engineering (published)

Untangling tradeoffs between recurrence and self-attention in neural networks

2020-06-15

arXiv (preprint)

arxiv.org

Implicit Regularization via Neural Feature Alignment

R Devon Hjelm

We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a regularization effect induc… (see more)ed by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. This can be interpreted as a combined mechanism of feature selection and compression. By extrapolating a new analysis of Rademacher complexity bounds for linear models, we motivate and study a heuristic complexity measure that captures this phenomenon, in terms of sequences of tangent kernel classes along optimization paths.

2019-12-31

arXiv (preprint)

proceedings.mlr.press

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

Murray Shanahan

Michael Mozer

Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. To… (see more)p-down signals consist of beliefs and expectations based on past experience and short-term memory, such as how the phrase `peanut butter and~...' will be completed. The optimal combination of bottom-up and top-down information remains an open question, but the manner of combination must be dynamic and both context and task dependent. To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow. We explore deep recurrent neural net architectures in which bottom-up and top-down signals are dynamically combined using attention. Modularity of the architecture further restricts the sharing and communication of information. Together, attention and modularity direct information flow, which leads to reliable performance improvements in perceptual and language tasks, and in particular improves robustness to distractions and noisy data. We demonstrate on a variety of benchmarks in language modeling, sequential image classification, video prediction and reinforcement learning that the \emph{bidirectional} information flow can improve results over strong baselines.

2019-12-31

ICML (published)

proceedings.mlr.press

Learning Long-term Dependencies Using Cognitive Inductive Biases in Self-attention RNNs

Attention and self-attention mechanisms, inspired by cognitive processes, are now central to state-of-the-art deep learning on sequential ta… (see more)sks. However, most recent progress hinges on heuristic approaches that rely on considerable memory and computational resources that scale poorly. In this work, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence. We use simple numerical experiments to demonstrate that this mechanism helps enable recurrent systems on generalization and transfer learning tasks. Based on our results, we propose a concrete direction of research to improve scalability and generalization of attentive recurrent networks.

2019-12-31

(published)

www.semanticscholar.org

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

Giancarlo Kerg

Kyle Goyette

Maximilian Puelma Touzel

A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over lo… (see more)ng time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences.

2019-09-05

NeurIPS.cc/2019/Reproducibility_Challenge (unknown)