Portrait de Guillaume Dumas

Guillaume Dumas

Membre académique associé
guillaume.dumas@mila.quebec
Professeur agrégé, Université de Montréal, Département de psychiatrie et d’addictologie
Professeur adjoint, McGill University, Département de psychiatrie
Sujets de recherche
Apprentissage automatique médical
Apprentissage par renforcement
Apprentissage profond
Biologie computationnelle
Neurosciences computationnelles
Systèmes dynamiques
Théorie de l'apprentissage automatique
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Biographie

Guillaume Dumas est professeur agrégé de psychiatrie computationnelle à la Faculté de médecine de l'Université de Montréal et chercheur principal du laboratoire de psychiatrie de précision et de physiologie sociale du Centre de recherche du CHU Sainte-Justine. Il est titulaire de la chaire IVADO IA en santé mentale et chercheur-boursier junior 1 du Fonds de recherche du Québec - Santé (FRQS) dans le domaine de l’ IA en santé et de la santé numérique. En 2023, il a été retenu dans le cadre du Programme des chercheurs mondiaux CIFAR-Azrieli pour le programme de recherche Cerveau, esprit et conscience. Il a également été nommé parmi les Futurs leaders canadiens de la recherche sur le cerveau par la Fondation Brain Canada.

Il a auparavant été chercheur permanent en neurosciences et en biologie computationnelle à l'Institut Pasteur (Paris, France), ainsi que chercheur postdoctoral au Center for Complex Systems and Brain Sciences à l’Université Florida Atlantic (FAU), aux États-Unis. Il est titulaire d'un diplôme d'ingénieur en ingénierie avancée et informatique (École centrale Paris), de deux masters (physique théorique, Université Paris-Saclay; sciences cognitives, ENS/EHESS/Paris 5) et d'un doctorat en neurosciences cognitives (Sorbonne Université).

Ses recherches visent à combiner l’intelligence artificielle, les neurosciences cognitives et la médecine numérique à travers un programme interdisciplinaire suivant deux axes principaux :

- L’intelligence artificielle en santé mentale, par la création de nouveaux algorithmes pour étudier le développement de l'architecture cognitive humaine et pour fournir une médecine personnalisée en neuropsychiatrie grâce à des données allant du génome à celles des téléphones intelligents;

- Les neurosciences sociales en intelligence artificielle, par la traduction de la recherche fondamentale sur le cerveau et le formalisme des systèmes dynamiques en des modèles hybrides neurocomputationnels et d’apprentissage automatique (NeuroML) et de nouvelles architectures présentant des capacités d'apprentissage social (NeuroIA Sociale et IHM).

Étudiants actuels

Saskia Helbling
Postdoctorat - UdeM
saskia.helbling@mila.quebec
Site web
Github
Google Scholar
Nadine Mohamed
Maîtrise recherche - UdeM
nadine.mohamed@mila.quebec
Rémy Ramadour
Visiteur de recherche indépendant - CHU Sainte Justine / Université de Montréal
remy.ramadour.hsj@ssss.gouv.qc.ca
Site web
Github
Mahta Ramezanian
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Irina Rish
mahta.ramezanian@mila.quebec
Pascal Tikeng Notsawo
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Rabusseau
pascal.tikeng@mila.quebec
Site web
Github
Google Scholar

Publications

From inter‐brain connectivity to inter‐personal psychiatry
Guillaume Dumas
2022-05-06
World Psychiatry (publié)
doi.org
Social Neuro AI: Social Interaction as the "Dark Matter" of AI
Samuele Bolotta
Guillaume Dumas
This article introduces a three-axis framework indicating how AI can be informed by biological examples of social learning mechanisms. We ar… (voir plus)gue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. However, the field of AI has mostly embraced a solipsistic perspective on intelligence. We thus argue that social interactions not only are largely unexplored in this field but also are an essential element of advanced cognitive ability, and therefore constitute metaphorically the dark matter of AI. In the first section, we discuss how social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and empirical findings from social neuroscience. Then, we discuss three lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by dynamical systems. Third, embodiment has been demonstrated to provide more sophisticated array of communicative signals. To conclude, we discuss the example of active inference, which offers powerful insights for developing agents that possess biological realism, can self-organize in time, and are socially embodied.
2022-05-05
Frontiers in Computer Science (publié)
doi.org
arxiv.org
Neurobiological Correlates of Change in Adaptive Behavior in Autism.
Charlotte M. Pretzsch
Tim Schäfer
Michael V. Lombardo
Varun Warrier
Caroline Mann
Anke Bletsch
Chris H. Chatham
Dorothea L. Floris
Julian Tillmann
Afsheen Yousaf
Emily J. H. Jones
Tony Charman
Sara Ambrosino
Thomas Bourgeron
Guillaume Dumas
Eva Loth
Beth Oakley
Jan K. Buitelaar
Freddy Cliquet
Claire Leblond … (voir 7 de plus)
Simon Baron-Cohen
Christian Beckmann
Tobias Banaschewski
Sarah Durston
Christine M. Freitag
Declan Murphy
Christine Ecker
2022-04-30
American Journal of Psychiatry (publié)
doi.org
P397. Genomic Deletions and Duplications Show Mirror Effects on Cognitive Ability According to Spatial Patterns of Gene Expression in the Human Brain
Kuldeep Kumar
Sayeh Kazem
Guillaume Huguet
Élise Douard
Zohra Saci
Laura Almasy
David Glahn
Guillaume Dumas
Sébastien Jacquemont
2022-04-30
Biological Psychiatry (publié)
doi.org
Exploring age-related changes in inter-brain synchrony during verbal communication
Suzanne Dikker
Emily N. Mech
Laura Gwilliams
Tessa West
Guillaume Dumas
Kara D. Federmeier

Successful communication is key to health in older age. This is true in the narrow sense of being able to gain critical information e.g.,… (voir plus) from health care providers, but also more broadly in being able to maintain social ties and pursue meaningful activities, which, in turn, are central to maintaining health and well-being. Compared to younger adults, older adults show both quantitative and qualitative changes in how information is processed and used over time to achieve comprehension. Such systematic age-related neural dissimilarities in processing dynamics and strategies raise fundamental questions about how the human brain supports cross-generational communication, especially in light of accumulating evidence linking interpersonal similarities in brain responses to communicative success. Yet despite its prevalence and tangible health-related importance, naturalistic intergenerational communication involving older adults is understudied. In this paper, we lay out why filling this research gap is critical in advancing our understanding of naturalistic communication, with implications for both science and practice.

2022-04-18
PsyArXiv (OSF Preprints) (prépublication)
doi.org
From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology
Maxwell J. D. Ramstead
Anil K. Seth
Casper Hesp
Lars Sandved-Smith
Jonas Mago
Michael Lifshitz
Giuseppe Pagnoni
Ryan Smith
Guillaume Dumas
Antoine Lutz
Karl Friston
Axel Constant
This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and bio… (voir plus)logy. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.
2022-03-17
Review of Philosophy and Psychology (publié)
doi.org
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks
Jean-christophe Gagnon-audet
Kartik Ahuja
Mohammad Javad Darvishi Bayazi
Guillaume Dumas
Irina Rish
2022-03-17
arXiv (prépublication)
doi.org
arxiv.org
Interindividual Differences in Cortical Thickness and Their Genomic Underpinnings in Autism Spectrum Disorder.
Christine Ecker
Charlotte M. Pretzsch
Anke Bletsch
Caroline Mann
Tim Schaefer
Sara Ambrosino
Julian Tillmann
Afsheen Yousaf
Andreas Chiocchetti
Michael V. Lombardo
Varun Warrier
Nico Bast
Carolin Moessnang
Sarah Baumeister
Flavio Dell’Acqua
Dorothea L. Floris
Mariam Zabihi
Andre Marquand
Freddy Cliquet
Claire Leblond … (voir 19 de plus)
Clara A. Moreau
Nick Puts
Tobias Banaschewski
Emily J. H. Jones
Luke Mason
Sven Bölte
Andreas Meyer-Lindenberg
Antonio Persico
Sarah Durston
Simon Baron-Cohen
Will Spooren
Eva Loth
Christine M. Freitag
Tony Charman
Guillaume Dumas
Thomas Bourgeron
Christian Beckmann
Jan K. Buitelaar
Declan Murphy
2022-02-28
American Journal of Psychiatry (publié)
doi.org
Generative Models of Brain Dynamics -- A review
Mahta Ramezanian Panahi
Germán Abrevaya
Jean-christophe Gagnon-audet
Vikram Voleti
Irina Rish
Guillaume Dumas
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-tw… (voir plus)entieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
2021-12-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Preference for biological motion is reduced in ASD: implications for clinical trials and the search for biomarkers
Luke Mason
F. Shic
T. Falck-Ytter
Bhismadev Chakrabarti
Tony Charman
Eva Loth
Julian Tillmann
Tobias Banaschewski
Simon Baron-Cohen
Sven Bölte
J. Buitelaar
Sarah Durston
Bob Oranje
Antonio Persico
C. Beckmann
Thomas Bougeron
Flavio Dell’Acqua
Christine Ecker
Carolin Moessnang
D. Murphy … (voir 49 de plus)
M. H. Johnson
Emily J. H. Jones
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette Chris Ineke Daisy Guillaume Jessica Vincent Pilar David Lindsay Joerg Rosemary Meng-Chuan Xavier Liogier Michael V. David J. René Andre Maarten Andreas Nico Bethany Laurence Marianne Gahan Barbara Amber Jessica Roberto Antonia San José Emily Will Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Jumana Sara Sarah Carsten Michael Daniel Claudia Yvette C Ahmad Ambrosino Baumeister Bours Brammer Brandeis
Jumana Ahmad
Sara Ambrosino
Sarah Baumeister
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna
Yvette de Bruijn
Christopher H. Chatham
Ineke Cornelissen
Daisy Crawley
Guillaume Dumas
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Lindsay Ham
Joerg F. Hipp
Rosemary Holt
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Andre Marquand
Maarten Mennes
Andreas Meyer-Lindenberg
Nico Bast
Beth Oakley
Larry O’Dwyer
Marianne Oldehinkel
Gahan Pandina
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Antonia San José Cáceres
Emily Simonoff
Will Spooren
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2021-12-14
Molecular Autism (publié)
doi.org
Early Transcriptional Changes in Rabies Virus-Infected Neurons and Their Impact on Neuronal Functions
Seonhee Kim
Florence Larrous
Hugo Varet
Rachel Legendre
Lena Feige
Guillaume Dumas
Rebecca Matsas
Georgia Kouroupi
Regis Grailhe
Hervé Bourhy
Rabies is a zoonotic disease caused by rabies virus (RABV). As rabies advances, patients develop a variety of severe neurological symptoms t… (voir plus)hat inevitably lead to coma and death. Unlike other neurotropic viruses that can induce symptoms of a similar range, RABV-infected post-mortem brains do not show significant signs of inflammation nor the structural damages on neurons. This suggests that the observed neurological symptoms possibly originate from dysfunctions of neurons. However, many aspects of neuronal dysfunctions in the context of RABV infection are only partially understood, and therefore require further investigation. In this study, we used differentiated neurons to characterize the RABV-induced transcriptomic changes at the early time-points of infection. We found that the genes modulated in response to the infection are particularly involved in cell cycle, gene expression, immune response, and neuronal function-associated processes. Comparing a wild-type RABV to a mutant virus harboring altered matrix proteins, we found that the RABV matrix protein plays an important role in the early down-regulation of host genes, of which a significant number is involved in neuronal functions. The kinetics of differentially expressed genes (DEGs) are also different between the wild type and mutant virus datasets. The number of modulated genes remained constant upon wild-type RABV infection up to 24 h post-infection, but dramatically increased in the mutant condition. This result suggests that the intact viral matrix protein is important to control the size of host gene modulation. We then examined the signaling pathways previously studied in relation to the innate immune responses against RABV, and found that these pathways contribute to the changes in neuronal function-associated processes. We further examined a set of regulated genes that could impact neuronal functions collectively, and demonstrated in calcium imaging that indeed the spontaneous activity of neurons is influenced by RABV infection. Overall, our findings suggest that neuronal function-associated genes are modulated by RABV early on, potentially through the viral matrix protein-interacting signaling molecules and their downstream pathways.
2021-12-12
Frontiers in Microbiology (publié)
doi.org
Maternal chemosignals enhance infant-adult brain-to-brain synchrony
Yaara Endevelt-Shapira
Amir Djalovski
Guillaume Dumas
Ruth Feldman
2021-12-09
Science Advances (publié)
doi.org