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

Federico Arangath Joseph

Collaborating researcher - ETH Zurich

Stefan Bauer

Independent visiting researcher

Principal supervisor :

Yoshua Bengio

Sangnie Bhardwaj

PhD - Université de Montréal

Co-supervisor :

Hugo Larochelle

Colin Bredenberg

Postdoctorate - Université de Montréal

Co-supervisor :

Leo Choiniere

PhD - Université de Montréal

Olivier Codol

Postdoctorate - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Leo Gagnon

PhD - Université de Montréal

Juan Guerra

Master's Research - Polytechnique Montréal

Principal supervisor :

Marco Bonizzato

Website

Nanda Harishankar Krishna

PhD - Université de Montréal

Collaborating researcher - Western Washington University (faculty; assistant prof))

Principal supervisor :

Master's Research - Université de Montréal

Co-supervisor :

Collaborating researcher - Université de Montréal

Ximeng Mao

PhD - Université de Montréal

Co-supervisor :

Joelle Pineau

Abdel Mfougouon Njupoun

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Co-supervisor :

Amine Natik

PhD - Université de Montréal

Co-supervisor :

Guy Wolf

François Paugam

PhD - Université de Montréal

Principal supervisor :

Lune Bellec

Alexandre Payeur

Collaborating researcher - Université de Montréal

Mohammad Pezeshki

Collaborating researcher

Principal supervisor :

Postdoctorate - McGill University

Principal supervisor :

Param Raval

Collaborating Alumni - Université de Montréal

Avery Ryoo

Master's Research - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Co-supervisor :

Lune Bellec

Laura Suarez

PhD - McGill University

Ryan Vogt

Postdoctorate - Université de Montréal

Vivian White

Research Intern - Western Washington University

Co-supervisor :

PhD - Université de Montréal

Website

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

Blog Posts

June 13, 2024

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Read the article

Publications

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.

2024-01-16

ICLR.cc/2024/Conference (poster)

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

2024-01-16

ICLR.cc/2024/Conference (spotlight)

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.

2024-01-16

ICLR.cc/2024/Conference (poster)

Synaptic Weight Distributions Depend on the Geometry of Plasticity

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic … (see more)plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the choice of distance for synaptic changes - i.e. the geometry of synaptic plasticity. Gradient descent assumes that the distance is Euclidean, but many other distances are possible, and there is no reason that biology necessarily uses Euclidean geometry. Here, using the theoretical tools provided by mirror descent, we show that the distribution of synaptic weights will depend on the geometry of synaptic plasticity. We use these results to show that experimentally-observed log-normal weight distributions found in several brain areas are not consistent with standard gradient descent (i.e. a Euclidean geometry), but rather with non-Euclidean distances. Finally, we show that it should be possible to experimentally test for different synaptic geometries by comparing synaptic weight distributions before and after learning. Overall, our work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.

2024-01-16

ICLR.cc/2024/Conference (spotlight)

Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation

Ximeng Mao

Yao-Chuan Chang

Stavros Zanos

2024-01-12

Journal of Neural Engineering (published)

A benchmark of individual auto-regressive models in a massive fMRI dataset

Fraçois Paugam

Basile Pinsard

Lune Bellec

Dense functional magnetic resonance imaging datasets open new avenues to create auto-regressive models of brain activity. Individual idiosyn… (see more)crasies are obscured by group models, but can be captured by purely individual models given sufficient amounts of training data. In this study, we compared several deep and shallow individual models on the temporal auto-regression of BOLD time series recorded during a natural video watching task. The best performing models were then analyzed in terms of their data requirements and scaling, subject specificity and the space-time structure of their predicted dynamics. We found the Chebnets, a type of graph convolutional neural network, to be best suited for temporal BOLD auto-regression, closely followed by linear models. Chebnets demonstrated an increase in performance with increasing amounts of data, with no complete saturation at 9 h of training data. Good generalization to other kinds of video stimuli and to resting state data marked the Chebnets’ ability to capture intrinsic brain dynamics rather than only stimulus-specific autocorrelation patterns. Significant subject specificity was found at short prediction time lags. The Chebnets were found to capture lower frequencies at longer prediction time lags, and the spatial correlations in predicted dynamics were found to match traditional functional connectivity networks. Overall, these results demonstrate that large individual fMRI datasets can be used to efficiently train purely individual auto-regressive models of brain activity, and that massive amounts of individual data are required to do so. The excellent performance of the Chebnets likely reflects their ability to combine spatial and temporal interactions on large time scales at a low complexity cost. The non-linearities of the models did not appear as a key advantage. In fact, surprisingly, linear versions of the Chebnets appeared to outperform the original nonlinear ones. Individual temporal auto-regressive models have the potential to improve the predictability of the BOLD signal. This study is based on a massive, publicly-available dataset, which can serve for future benchmarks of individual auto-regressive modeling.

2024-01-01

Imaging Neuroscience (published)

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 are 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.

2024-01-01

International Conference on Learning Representations (published)

Neural manifolds and learning regimes in neural-interface tasks

Alexandre Payeur

Amy L. Orsborn

2023-12-23

bioRxiv (preprint)

Large language models: What could they do for neurology?

2023-12-01

Journal of the Neurological Sciences (published)

Discrete, compositional, and symbolic representations through attractor dynamics

Andrew Nam

Eric Elmoznino

Nikolay Malkin

Chen Sun

Yoshua Bengio

Compositionality is an important feature of discrete symbolic systems, such as language and programs, as it enables them to have infinite ca… (see more)pacity despite a finite symbol set. It serves as a useful abstraction for reasoning in both cognitive science and in AI, yet the interface between continuous and symbolic processing is often imposed by fiat at the algorithmic level, such as by means of quantization or a softmax sampling step. In this work, we explore how discretization could be implemented in a more neurally plausible manner through the modeling of attractor dynamics that partition the continuous representation space into basins that correspond to sequences of symbols. Building on established work in attractor networks and introducing novel training methods, we show that imposing structure in the symbolic space can produce compositionality in the attractor-supported representation space of rich sensory inputs. Lastly, we argue that our model exhibits the process of an information bottleneck that is thought to play a role in conscious experience, decomposing the rich information of a sensory input into stable components encoding symbolic information.

2023-10-27

NeurIPS.cc/2023/Workshop/InfoCog (oral)

Amortizing intractable inference in large language models

Edward J Hu

Moksh J. Jain

Eric Elmoznino

Younesse Kaddar

Yoshua Bengio

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.

2023-10-06

ArXiv (preprint)

arxiv.org

A Unified, Scalable Framework for Neural Population Decoding

Mehdi Azabou

Vinam Arora

Venkataramana Ganesh

Ximeng Mao

Santosh B Nachimuthu

Michael Jacob Mendelson

Matt Perich

Eva L Dyer

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both the model … (see more)size and the datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale for neural decoding models.