Guillaume Dumas

Irina Rish

Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (voir plus) due to the limited availability of data and the high cost of recordings. The existence of large hospital datasets and laboratory collections partially mitigates this issue. However, the lack of standardized recording protocols, varying numbers of channels, diverse setups, scenarios, and recording devices further complicate the task. This work addresses these challenges by introducing the Brain Foundation Model (BFM), a suite of open-source models trained on brain signals. These models serve as foundational tools for various types of time-series neuroimaging tasks. This work presents the first model of the BFM series, which is trained on electroencephalogram signal data. Our results demonstrate that BFM-EEG can generate signals more accurately than other models. Upon acceptance, we will release the model weights and pipeline.

2024-10-09

NeurIPS.cc/2024/Workshop/TSALM (publié)

LLMs and Personalities: Inconsistencies Across Scales

Tosato Tommaso

Mahmood Hegazy

This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (voir plus)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.

2024-10-08

NeurIPS.cc/2024/Workshop/Behavioral_ML (présentation orale)

Long-term outcomes of critically ill patients with hematological malignancies: what is the impact of the coronavirus disease 2019 pandemic? Author's reply

Laveena Munshi

Sangeeta Mehta

2024-09-30

Intensive Care Medicine (publié)

Diagnostic tests for infections in critically ill immunocompromised patients

Adrien Joseph

Lara Zafrani

2024-09-18

Intensive Care Medicine (publié)

Non-invasive electroencephalography in awake cats: Feasibility and application to sensory processing in chronic pain

Aliénor Delsart

Aude Castel

Colombe Otis

Mathieu Lachance

Maude Barbeau-Grégoire

Bertrand Lussier

Franck Péron

Marc Hébert

Nicolas Lapointe

Maxim Moreau

Johanne Martel-Pelletier

Jean-Pierre Pelletier

Eric Troncy

2024-08-21

Journal of Neuroscience Methods (publié)

Oxygen thresholds in critically ill patients: need for personalized targets. Author's reply.

Laveena Munshi

2024-08-11

Intensive Care Medicine (publié)

Scalable Approaches for a Theory of Many Minds

Maximilian Puelma Touzel

Amin Memarian

Matthew D Riemer

Andrei Mircea

Andrew Robert Williams

Elin Ahlstrand

A major challenge as we move towards building agents for real-world problems, which could involve a massive number of human and/or machine a… (voir plus)gents, is that we must learn to reason about the behavior of these many other agents. In this paper, we consider the problem of scaling a predictive Theory of Mind (ToM) model to a very large number of interacting agents with a fixed computational budget. Motivated by the limited diversity of agent types, existing approaches to scalable TOM learn versatile single-agent representations for quickly adapting to new agents encountered sequentially. We consider the more general setting that many agents are observed in parallel and formulate the corresponding Theory of Many Minds (ToMM) problem of estimating the joint policy. We frame the scaling behavior of solutions in terms of parameter sharing schemes and in particular propose two parameter-free architectural features that endow models with the ability to exploit action correlations: encoding a multi-agent context, and decoding through an abstracted joint action space. The increased predictive capabilities that have come with foundation models have made it easier to imagine the possibility of using these models to make simulations that imitate the behavior of many agents within complex real-world systems. Being able to perform these simulations in a general-purpose way would not only help make more capable agents, it also would be a very useful capability for applications in social science, political science, and economics.

2024-06-17

ICML.cc/2024/Workshop/Agentic_Markets (poster)

Lost in Translation: The Algorithmic Gap Between LMs and the Brain

Tosato Tommaso

Tikeng Notsawo Pascal Junior

Helbling Saskia

Irina Rish

Language Models (LMs) have achieved impressive performance on various linguistic tasks, but their relationship to human language processing … (voir plus)in the brain remains unclear. This paper examines the gaps and overlaps between LMs and the brain at different levels of analysis, emphasizing the importance of looking beyond input-output behavior to examine and compare the internal processes of these systems. We discuss how insights from neuroscience, such as sparsity, modularity, internal states, and interactive learning, can inform the development of more biologically plausible language models. Furthermore, we explore the role of scaling laws in bridging the gap between LMs and human cognition, highlighting the need for efficiency constraints analogous to those in biological systems. By developing LMs that more closely mimic brain function, we aim to advance both artificial intelligence and our understanding of human cognition.

2024-06-16

ICML.cc/2024/Workshop/LLMs_and_Cognition (poster)

Bringing together multimodal and multilevel approaches to study the emergence of social bonds between children and improve social AI

Julie Bonnaire

Justine Cassell

This protocol paper outlines an innovative multimodal and multilevel approach to studying the emergence and evolution of how children build … (voir plus)social bonds with their peers, and its potential application to improving social artificial intelligence (AI). We detail a unique hyperscanning experimental framework utilizing functional near-infrared spectroscopy (fNIRS) to observe inter-brain synchrony in child dyads during collaborative tasks and social interactions. Our proposed longitudinal study spans middle childhood, aiming to capture the dynamic development of social connections and cognitive engagement in naturalistic settings. To do so we bring together four kinds of data: the multimodal conversational behaviors that dyads of children engage in, evidence of their state of interpersonal rapport, collaborative performance on educational tasks, and inter-brain synchrony. Preliminary pilot data provide foundational support for our approach, indicating promising directions for identifying neural patterns associated with productive social interactions. The planned research will explore the neural correlates of social bond formation, informing the creation of a virtual peer learning partner in the field of Social Neuroergonomics. This protocol promises significant contributions to understanding the neural basis of social connectivity in children, while also offering a blueprint for designing empathetic and effective social AI tools, particularly for educational contexts.

2024-05-16

Frontiers in Neuroergonomics (publié)

295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population

Kuldeep Kumar

Sayeh Kazem

Zhijie Liao

Jakub Kopal

Guillaume Huguet

Thomas Renne

Martineau Jean‐Louis

Zhe Xie

Zohra Saci

Laura Almasy

David C. Glahn

Tomáš Paus

Carrie E. Bearden

Paul M. Thompson

Richard A. I. Bethlehem

Varun Warrier

Sébastien Jacquemont

2024-04-28

Biological Psychiatry (inconnu)

Association between arterial oxygen and mortality across critically ill patients with hematologic malignancies: results from an international collaborative network

Idunn S. Morris

Tamishta Hensman

Alexandre Demoule

Achille Kouatchet

Virginie Lemiale

Djamel Mokart

Frédéric Pène

Elie Azoulay

Laveena Munshi

Laurent François Dominique Naike Fabrice Emmanuel Yves Mic Argaud Barbier Benoit Bigé Bruneel Canet Cohen Dar

Laurent Argaud

François Barbier

Dominique Benoit

Naike Bigé

Fabrice Bruneel

Emmanuel Canet

Yves Cohen

Michael Darmon

Didier Gruson … (voir 31 de plus)

Kada Klouche

Loay Kontar

Alexandre Lautrette

Christine Lebert

Guillaume Louis

Julien Mayaux

Anne-Pascale Meert

Anne-Sophie Moreau

Martine Nyunga

Vincent Peigne

Pierre Perez

Jean Herlé Raphalen

Carole Schwebel

Jean-Marie Tonnelier

Florent Wallet

Lara Zafrani

Bram Rochwerg

Farah Shoukat

Dean Fergusson

Bruno Ferreyro

Paul Heffernan

Margaret Herridge

Sheldon Magder

Mark Minden

Rakesh Patel

Salman Qureshi

Aaron Schimmer

Santhosh Thyagu

Han Ting Wang

Sangeeta Mehta

Sean M. Bagshaw

2024-04-09

Intensive Care Medicine (publié)

On quasi-homomorphism rigidity for lattices in simple algebraic groups