Publications

Top-down feedback matters: Functional impact of brainlike connectivity motifs on audiovisual integration
Mashbayar Tugsbayar
Mingze Li
Eilif B Muller
Blake Richards
Artificial neural networks (ANNs) are an important tool for studying neural computation, but many features of the brain are not captured by … (voir plus)standard ANN architectures. One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and it has a unique modulatory impact on activity in neocortical pyramidal neurons. However, we still do not understand its computational role. Here we develop a deep neural network model that captures the core functional properties of top-down feedback in the neocortex, allowing us to construct hierarchical recurrent ANN models that more closely reflect the architecture of the brain. We use this to explore the impact of different hierarchical recurrent architectures on an audiovisual integration task. We find that certain hierarchies, namely those that mimic the architecture of the human brain, impart ANN models with a light visual bias similar to that seen in humans. This bias does not impair performance on the audiovisual tasks. The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other, and from traditional feedforward and laterally recurrent models. Altogether our findings demonstrate that modulatory top-down feedback is a computationally relevant feature of biological brains, and that incorporating it into ANNs affects their behavior and constrains the solutions it’s likely to discover.
2025-11-09
bioRxiv (prépublication)
doi.org
Understanding the role of depth in the neural tangent kernel for overparameterized neural networks
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2025-11-09
ArXiv (prépublication)
doi.org
arxiv.org
Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages
Oluwadara Kalejaiye
Luel Hagos Beyene
David Ifeoluwa Adelani
Mmekut-Mfon Gabriel Edet
A. D. Akpan
Eno-Abasi Urua
Anietie U Andy
2025-11-08
ArXiv (prépublication)
doi.org
arxiv.org
Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models
Zhanke Zhou
Zhaocheng Zhu
Xuan Li
Mikhail Galkin
Xiao Feng
Sanmi Koyejo
Jian Tang
Bo Han
2025-11-07
NeurIPS.cc/2025/Workshop_Mexico_City/ResponsibleFM (publié)
doi.org
openreview.net
Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models
Gabriel Missael Barco
Ronan Legin
Connor Stone
Yashar Hezaveh
Laurence Perreault-Levasseur
2025-11-05
ArXiv (prépublication)
doi.org
arxiv.org
LLM-as-a-Judge: Toward World Models for Slate Recommendation Systems
Baptiste Bonin
Maxime Heuillet
Audrey Durand
Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) re… (voir plus)search. We investigate how Large Language Models (LLM) can effectively act as world models of user preferences through pairwise reasoning over slates. We conduct an empirical study involving several LLMs on three tasks spanning different datasets. Our results reveal relationships between task performance and properties of the preference function captured by LLMs, hinting towards areas for improvement and highlighting the potential of LLMs as world models in recommender systems.
2025-11-05
ArXiv (prépublication)
doi.org
arxiv.org
Medication-based mortality prediction in COPD using machine learning and conventional statistical methods.
Ana Paula Pena-Gralle
Amélie Forget
Yohann Chiu
Marc-André Legault
M. Beauchesne
Lucie Blais
2025-11-04
International Journal of Medical Informatics (publié)
doi.org
Oneiris: An AI-augmented Brain-Computer Interface for Exploring Personal and Collective Dreamscapes
Antoine Bellemare‐Pepin
Philipp Thölke
Karim Jerbi CoCo Lab
Suzanne Kite
2025-11-04
Proceedings of the Conference on Animation and Interactive Art (publié)
doi.org
RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring
Khouloud Oueslati
Maxime Lamothe
Foutse Khomh
2025-11-04
ArXiv (prépublication)
doi.org
arxiv.org
Why Less is More (Sometimes): A Theory of Data Curation
Elvis Dopgima Dohmatob
Mohammad Pezeshki
Reyhane Askari Hemmat
2025-11-04
ArXiv (prépublication)
doi.org
arxiv.org
DeLLMphi: A Multi-Turn Method for Multi-Agent Forecasting
Andrew Robert Williams
Martin Weiss
Victoria Feere
Nasim Rahaman
Hugo Larochelle
The Delphi method is a structured forecasting process that engages experts in iterative prediction and reflection. Each round, experts submi… (voir plus)t forecasts to a mediator, receive an aggregated and synthesized response highlighting key arguments, and update their forecasts based on collective insight. However, Delphi panels are labour intensive, slow and hard to reproduce, requiring diverse knowledgeable participants to engage periodically across weeks or months. To address these constraints, we propose **DeLLMphi**, a forecasting method that replaces human experts and mediators with LLMs. We show (i) that providing example superforecaster reasoning traces and predictions helps to elicit more accurate forecasts from LLM experts, (ii) that the mediator plays the crucial role of surfacing different lines of reasoning and points of disagreement, and (iii) that multiple rounds and experts lead to better forecasts, showing that multi-turn interaction is key to DeLLMphi.
2025-11-03
NeurIPS.cc/2025/Workshop/MTI-LLM (poster)
openreview.net
openreview.net
Longitudinal functional connectivity during rest and task is differentially related to Alzheimer's pathology and episodic memory in older adults
Larissa Fischer
Jenna N. Adams
Eóin N. Molloy
Jennifer Tremblay-Mercier
Jordana Remz
Alexa Pichet Binette
M. Natasha Rajah
Sylvia Villeneuve
Anne Maass
PREVENT-AD Research Group
Changes in functional connectivity (FC) strength involving the medial temporal lobe (MTL) and posteromedial cortex (PMC) are related to earl… (voir plus)y Alzheimer’s pathology and alterations in episodic memory performance in cognitively unimpaired older adults, but their dynamics remain unclear. We examined how longitudinal changes in FC involving MTL and PMC during resting-state, episodic memory encoding, and retrieval relate to subsequent amyloid- and tau-PET burden, longitudinal episodic memory performance, and the APOE4 genotype in 152 cognitively unimpaired older adults from the PREVENT-AD cohort. We found APOE4- and fMRI paradigm-dependent associations of change in FC strength with pathology burden and change in episodic memory performance. Decreasing FC over time, or “hypoconnectivity”, within PMC during rest in APOE4 carriers and during retrieval in APOE4 non-carriers was related to more amyloid and tau, respectively. Conversely, increasing FC over time, or “hyperconnectivity”, within MTL during encoding in APOE4 carriers and between MTL and PMC during retrieval independent of APOE4 status was related to more tau. Further, increasing FC between MTL and PMC during rest, unlike during encoding, was beneficial for episodic memory. Our study highlights that pathology-related episodic memory network changes manifest differently during rest and task and have differential implications for episodic memory trajectories. The online version contains supplementary material available at 10.1038/s41598-025-21596-0.
2025-11-03
Scientific Reports (publié)
doi.org