Guillaume Lajoie

Biographie

Guillaume Lajoie est professeur agrégé au Département de mathématiques et de statistiques (DMS) de l'Université de Montréal et membre académique principal de Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire CIFAR (CCAI Canada) ainsi que d'une chaire de recherche du Canada (CRC) en calcul et interfaçage neuronaux.

Auparavant, Guillaume Lajoie a été chercheur postdoctoral à l'Institut de dynamique et d'auto-organisation Max-Planck et à l'Institut de neuro-ingénierie de l'Université de Washington. Il a obtenu son doctorat à l'Université de Washington (Seattle), au Département de mathématiques appliquées.

Ses recherches sont positionnées à l'intersection de l'IA et des neurosciences où il développe des outils pour mieux comprendre les mécanismes d'intelligence communs aux systèmes biologiques et artificiels. Les contributions de son groupe de recherche vont des progrès des paradigmes d'apprentissage à plusieurs échelles pour les grands systèmes artificiels aux applications en neurotechnologie. Dr. Lajoie participe activement aux efforts de développement responsables de l'IA, cherchant à identifier les lignes directrices et les meilleures pratiques pour l'utilisation de l'IA dans la recherche et au-delà.

Ses récentes recherches se concentrent sur le développement de biais inductifs architecturaux pour la propagation de l'information dans les réseaux récurrents, ainsi que le développement d'algorithmes et de modèles pour l'optimisation de l'interface bidirectionnelle cerveau-machine.

Étudiants actuels

Stefan Bauer

Visiteur de recherche indépendant

Superviseur⋅e principal⋅e :

Yoshua Bengio

Sangnie Bhardwaj

Doctorat - UdeM

Co-superviseur⋅e :

Hugo Larochelle

Colin Bredenberg

Postdoctorat - UdeM

Co-superviseur⋅e :

Leo Choiniere

Doctorat - UdeM

Olivier Codol

Postdoctorat - UdeM

Co-superviseur⋅e :

Matt Perich

Eric Elmoznino

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Leo Gagnon

Doctorat - UdeM

Rose Guay Hottin

Maîtrise recherche - Polytechnique

Superviseur⋅e principal⋅e :

Marco Bonizzato

Juan Guerra

Maîtrise recherche - Polytechnique

Superviseur⋅e principal⋅e :

Marco Bonizzato

Site web

Nanda Harishankar Krishna

Doctorat - UdeM

Collaborateur·rice de recherche - Western Washington University (faculty; assistant prof))

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Collaborateur·rice de recherche - UdeM

Ximeng Mao

Doctorat - UdeM

Co-superviseur⋅e :

Joelle Pineau

Abdel Mfougouon Njupoun

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Amine Natik

Doctorat - UdeM

Co-superviseur⋅e :

François Paugam

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Lune Bellec

Alexandre Payeur

Collaborateur·rice de recherche - UdeM

Mohammad Pezeshki

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Param Raval

Collaborateur·rice alumni - UdeM

Que nous apprennent les distributions des coefficients synaptiques au sujet de l’apprentissage dans le cerveau ?

Avery Ryoo

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - McGill

Stagiaire de recherche - Western Washington University

Co-superviseur⋅e :

Doctorat - UdeM

Billets de blogue

13 juin 2024

par

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Lire l'article

Publications

A connectomics-based taxonomy of mammals

Laura E. Suárez

Yossi Yovel

Martijn P. van den Heuvel

Olaf Sporns

Yaniv Assaf

Bratislav Mišić

2022-03-12

bioRxiv (prépublication)

A connectomics-based taxonomy of mammals

Laura E. Suárez

Yossi Yovel

M. P. van den Heuvel

Olaf Sporns

Yaniv Assaf

Bratislav Mišić

Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whe… (voir plus)ther inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle for the comparison of neural architectures have been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyze the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion magnetic resonance imaging (MRI) scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a single protocol on a single scanner. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting the connectome organization recapitulates traditional taxonomies defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.

2022-03-12

bioRxiv (preprint)

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

Yuhan Helena Liu

Arna Ghosh

Eric Todd SheaBrown

openreview.net

Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal

Sharath Chandra Raparthy

Irina Rish

Yoshua Bengio

Multi-head, key-value attention is the backbone of transformer-like model architectures which have proven to be widely successful in recent … (voir plus)years. This attention mechanism uses multiple parallel key-value attention blocks (called heads), each performing two fundamental computations: (1) search - selection of a relevant entity from a set via query-key interaction, and (2) retrieval - extraction of relevant features from the selected entity via a value matrix. Standard attention heads learn a rigid mapping between search and retrieval. In this work, we first highlight how this static nature of the pairing can potentially: (a) lead to learning of redundant parameters in certain tasks, and (b) hinder generalization. To alleviate this problem, we propose a novel attention mechanism, called Compositional Attention, that replaces the standard head structure. The proposed mechanism disentangles search and retrieval and composes them in a dynamic, flexible and context-dependent manner. Through a series of numerical experiments, we show that it outperforms standard multi-head attention on a variety of tasks, including some out-of-distribution settings. Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed. Our proposed mechanism generalizes multi-head attention, allows independent scaling of search and retrieval and is easy to implement in a variety of established network architectures.

2022-01-01

International Conference on Learning Representations (publié)

openreview.net

Goal-driven optimization of single-neuron properties in artiﬁcial networks reveals regularization role of neural diversity and adaptation in the brain

Victor Geadah

Stefan Horoi

Giancarlo Kerg

Neurons in the brain have rich and adaptive input-output properties. Features such as diverse f-I curves and spike frequency adaptation are … (voir plus)known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single neuron ﬂexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and of neural circuits must be considered as a complete system. In this work, we use artiﬁcial neural networks to systematically investigate single neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We ﬁnd that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we ﬁnd that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single neuron properties and argue that neural diversity and adaptation plays an active regularization role that enables neural circuits to optimally propagate information across time.

2022-01-01

(published)

www.semanticscholar.org

Goal-driven optimization of single-neuron properties in artiﬁcial networks reveals regularization role of neural diversity and adaptation in the brain

Victor Geadah

Stefan Horoi

Giancarlo Kerg

2022-01-01

(publié)

www.semanticscholar.org

Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty

Thomas George

Aristide Baratin

Among attempts at giving a theoretical account of the success of deep neural networks, a recent line of work has identified a so-called `laz… (voir plus)y' training regime in which the network can be well approximated by its linearization around initialization. Here we investigate the comparative effect of the lazy (linear) and feature learning (non-linear) regimes on subgroups of examples based on their difficulty. Specifically, we show that easier examples are given more weight in feature learning mode, resulting in faster training compared to more difficult ones. In other words, the non-linear dynamics tends to sequentialize the learning of examples of increasing difficulty. We illustrate this phenomenon across different ways to quantify example difficulty, including c-score, label noise, and in the presence of easy-to-learn spurious correlations. Our results reveal a new understanding of how deep networks prioritize resources across example difficulty.

2022-01-01

Trans. Mach. Learn. Res. (publié)

openreview.net

Learning function from structure in neuromorphic networks

Laura E. Suárez

Bratislav Mišić

2021-08-09

Nature Machine Intelligence (publié)

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

Germán Abrevaya

Guillaume Dumas

Aleksandr Y. Aravkin

Peng Zheng

Jean-Christophe Gagnon-Audet

James Kozloski

Pablo Polosecki

David Cox

Silvina Ponce Dawson

Guillermo Cecchi

Irina Rish

Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by … (voir plus)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.

2021-05-26

Neural Computation (publié)

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

Olivier Tessier-Lariviere

Luke Y. Prince

Pascal Fortier-Poisson

Lorenz Wernisch

Oliver Armitage

Emil Hewage

Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover k… (voir plus)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.

2021-05-04

International IEEE/EMBS Conference on Neural Engineering (publié)

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

Alexander Tong

Guillaume Huguet

Dennis L. Shung

Amine Natik

Manik Kuchroo

Smita Krishnaswamy

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further

2021-01-01

arXiv.org (preprint)

dblp.uni-trier.de

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

Alexander Tong

Guillaume Huguet

Dennis Shung

Amine Natik

Manik Kuchroo