Publications

Imagining and building wise machines: The centrality of AI metacognition

Samuel G. B. Johnson

Amir-Hossein Karimi

Yoshua Bengio

Nick Chater

Tobias Gerstenberg

Kate Larson

Sydney Levine

Melanie Mitchell

Iyad Rahwan

Bernhard Schölkopf

Igor Grossmann

2024-11-03

ArXiv (preprint)

doi.org

arxiv.org

PP259 Topic: AS25–Sedation/Analgesia/Delirium/Withdrawal Syndrome/Other: PSYCHOMOTOR AGITATION DETECTION USING DEEP LEARNING

S. Dastani

A. Harakeh

A. Wiedemann

Philippe Jouvet

S. Ebrahimi Kahou

2024-11-03

Pediatric Critical Care Medicine (published)

doi.org

Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach

Prashant Govindarajan

Mathieu Reymond

Santiago Miret

Mariano Phielipp

A. Chandar

2024-11-02

NeurIPS.cc/2024/Workshop/AI4Mat (published)

openreview.net

ImmunoStruct: Integration of protein sequence, structure, and biochemical properties for immunogenicity prediction and interpretation

Kevin Bijan Givechian

João Felipe Rocha

Edward Yang

Chen Liu

Kerrie Greene

Rex Ying

Etienne Caron

Akiko Iwasaki

Smita Krishnaswamy

2024-11-02

bioRxiv (preprint)

doi.org

Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Luke Marks

Alasdair Paren

David M. Krueger

Fazl Barez

Sparse Autoencoders (SAEs) have shown promise in improving the interpretability of neural network activations, but can learn features that a… (see more)re not features of the input, limiting their effectiveness. We propose \textsc{Mutual Feature Regularization} \textbf{(MFR)}, a regularization technique for improving feature learning by encouraging SAEs trained in parallel to learn similar features. We motivate \textsc{MFR} by showing that features learned by multiple SAEs are more likely to correlate with features of the input. By training on synthetic data with known features of the input, we show that \textsc{MFR} can help SAEs learn those features, as we can directly compare the features learned by the SAE with the input features for the synthetic data. We then scale \textsc{MFR} to SAEs that are trained to denoise electroencephalography (EEG) data and SAEs that are trained to reconstruct GPT-2 Small activations. We show that \textsc{MFR} can improve the reconstruction loss of SAEs by up to 21.21\% on GPT-2 Small, and 6.67\% on EEG data. Our results suggest that the similarity between features learned by different SAEs can be leveraged to improve SAE training, thereby enhancing performance and the usefulness of SAEs for model interpretability.

2024-11-01

ArXiv (preprint)

doi.org

openreview.net

AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces

S. Gowaikar

Hugo Berard

Rashid A. Mushkani

Emmanuel Beaudry Marchand

Toumadher Ammar

Shin Koseki

Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding… (see more) the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.

2024-10-31

ArXiv (preprint)

doi.org

arxiv.org

Association Between Circulating Vitamin K Levels, Gut Microbiome, and Type 1 Diabetes: A Mendelian Randomization Study

Samuel De La Barrera

Benjamin De La Barrera

Marc-André Legault

Isabel Gamache

Despoina Manousaki

Background/Objectives: Nutritional deficiencies have been proposed as possible etiological causes for autoimmune diseases, among which type … (see more)1 diabetes (T1D). Vitamin K (VK) has potentially positive effects on type 2 diabetes, but its role on T1D in humans remains largely unknown. We aimed to examine the presence of a causal association between VK and T1D using a Mendelian randomization (MR) approach. Methods: Genetic variants from a genome-wide association study (GWAS) for VK (N = 2138 Europeans) were used as instruments in our two-sample MR study to investigate whether circulating VK levels are causally associated with the risk of T1D in a large European T1D GWAS cohort (18,942 cases/520,580 controls). Through a multivariable MR (MVMR), the effects of both VK and specific gut microbiota on T1D were investigated given that the gut microbiome synthesizes VK. Results: We found that changes in levels of circulating VK did not affect T1D risk in our univariate two-sample MR, but this study had limited power to detect small effects of VK (OR for T1D of less than 0.8). However, our MVMR indicated a suggestive association of VK with the risk of T1D adjusting for two different gut microbiome populations. Conclusions: In conclusion, VK levels are unlikely to significantly affect the risk of T1D, but small effects cannot be excluded, and the role of gut microbiome in this association should be further investigated.

2024-10-31

Nutrients (published)

doi.org

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region

Armando Romani

Alberto Antonietti

Davide Bella

Julian Budd

Elisabetta Giacalone

Kerem Kurban

Sára Sáray

Marwan Abdellah

Alexis Arnaudon

Elvis Boci

Cristina Colangelo

Jean-Denis Courcol

Thomas Delemontex

András Ecker

Joanne Falck

Cyrille Favreau

Michael Gevaert

Juan B. Hernando

Joni Herttuainen

Genrich Ivaska … (see 28 more)

Lida Kanari

Anna-Kristin Kaufmann

James Gonzalo King

Pramod Kumbhar

Sigrun Lange

Huanxiang Lu

Carmen Alina Lupascu

Rosanna Migliore

Fabien Petitjean

Judit Planas

Pranav Rai

Srikanth Ramaswamy

Michael W. Reimann

Juan Luis Riquelme

Nadir Román Guerrero

Ying Shi

Vishal Sood

Mohameth François Sy

Werner Van Geit

Liesbeth Vanherpe

Tamás F. Freund

Audrey Mercer

Eilif Muller

Felix Schürmann

Alex M. Thomson

Michele Migliore

Szabolcs Káli

Henry Markram

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive fun… (see more)ctions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions. In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings. Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

2024-10-31

PLoS Biology (published)

doi.org

Evaluating the effectiveness of the Smart About Meds (SAM) mobile application among patients discharged from hospital: protocol of a randomised controlled trial

Robyn Tamblyn

Bettina Habib

David L Buckeridge

Daniala L Weir

Elizaveta Frolova

Rolan Alattar

Jessica Rogozinsky

Caroline Beauchamp

Rosalba Pupo

Susan J Bartlett

Emily McDonald

NCT05371548.

2024-10-31

BMJ Open (published)

doi.org

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

Kathleen A. Williams

Ole Numssen

Juan David Guerra

Jakub Kopal

Danilo Bzdok

Gesa Hartwigsen

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in dai… (see more)ly life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

2024-10-31

Heliyon (published)

doi.org

Predictive Modeling of Body Image Dissatisfaction in People With Type 1 Diabetes

COURTNEY SOUTH

SHAHRYAR EBRAHIMI

ANNE-SOPHIE BRAZEAU

Maria Cutumisu

2024-10-31

Canadian Journal of Diabetes (published)

doi.org

SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals