Publications

Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks
Ramnath Kumar
Tristan Deleu
Yoshua Bengio
2022-01-26
ArXiv (prépublication)
arxiv.org
arxiv.org
Lacking social support is associated with structural divergences in hippocampus-default network co-variation patterns
Chris Zajner
R. Nathan Spreng
Danilo Bzdok
Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominatin… (voir plus)g factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40 000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3 and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC–DN co-variation followed a clear segregation into the left and right brain hemispheres. Separable regimes of structural HC–DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.
2022-01-24
Social Cognitive and Affective Neuroscience (publié)
doi.org
Multilevel development of cognitive abilities in an artificial neural network
Konstantin Volzhenin
Jean-Pierre Changeux
Guillaume Dumas
Multiple biological mechanisms support the unique ability of the brain to develop complex cognitive abilities. Nevertheless, it remains uncl… (voir plus)ear which mechanisms are necessary and sufficient. We propose a neurocomputational model of the developing brain spanning sensorimotor, cognitive, and conscious levels. The model solves three tasks of increasing complexity: from visual recognition to cognitive manipulation and maintenance of conscious percepts. Results highlight two fundamental mechanisms for the multilevel development of cognitive abilities in biological neural networks: 1) synaptic epigenesis, with Hebbian learning at the local scale and reinforcement learning at the global scale; and 2) self-organized dynamics, through spontaneous activity and balanced excitatory/inhibitory ratio of neurons. We emphasize how these core features of human intelligence could guide future development in artificial intelligence.
2022-01-24
bioRxiv (prépublication)
doi.org
The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning
Andrei Cristian Nica
Khimya Khetarpal
Doina Precup
2022-01-23
ArXiv (prépublication)
arxiv.org
arxiv.org
Neural correlates of local parallelism during naturalistic vision
John Wilder
Morteza Rezanejad
Sven Dickinson
Kaleem Siddiqi
Allan Jepson
Dirk B. Walther
Human observers can rapidly perceive complex real-world scenes. Grouping visual elements into meaningful units is an integral part of this p… (voir plus)rocess. Yet, so far, the neural underpinnings of perceptual grouping have only been studied with simple lab stimuli. We here uncover the neural mechanisms of one important perceptual grouping cue, local parallelism. Using a new, image-computable algorithm for detecting local symmetry in line drawings and photographs, we manipulated the local parallelism content of real-world scenes. We decoded scene categories from patterns of brain activity obtained via functional magnetic resonance imaging (fMRI) in 38 human observers while they viewed the manipulated scenes. Decoding was significantly more accurate for scenes containing strong local parallelism compared to weak local parallelism in the parahippocampal place area (PPA), indicating a central role of parallelism in scene perception. To investigate the origin of the parallelism signal we performed a model-based fMRI analysis of the public BOLD5000 dataset, looking for voxels whose activation time course matches that of the locally parallel content of the 4916 photographs viewed by the participants in the experiment. We found a strong relationship with average local symmetry in visual areas V1-4, PPA, and retrosplenial cortex (RSC). Notably, the parallelism-related signal peaked first in V4, suggesting V4 as the site for extracting paralleism from the visual input. We conclude that local parallelism is a perceptual grouping cue that influences neuronal activity throughout the visual hierarchy, presumably starting at V4. Parallelism plays a key role in the representation of scene categories in PPA.
2022-01-20
PLoS ONE (publié)
doi.org
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
Peyman Bateni
Jarred Barber
Raghav Goyal
Vaden Masrani
Jan-Willem van de Meent
Leonid Sigal
Frank N. Wood
2022-01-12
ArXiv (prépublication)
doi.org
arxiv.org
Biomedical Research & Informatics Living Laboratory for Innovative Advances of New Technologies in Community Mobility Rehabilitation: Protocol for a longitudinal evaluation of mobility outcomes (Preprint)
Sara Ahmed
Philippe Archambault
Claudine Auger
Audrey Durand
Joyce Fung
Eva Kehayia
Anouk Lamontagne
Annette Majnemer
Sylvie Nadeau
Joelle Pineau
Alain Ptito
Bonnie Swaine
2022-01-12
(publié)
doi.org
Magnetoencephalography resting-state correlates of executive and language components of verbal fluency
Victor Oswald
Younes Zerouali
Aubrée Boulet-Craig
Maja Krajinovic
Caroline Laverdière
Daniel Sinnett
Pierre Jolicoeur
Sarah Lippé
Karim Jerbi CoCo Lab
Philippe Robaey
2022-01-09
Scientific Reports (publié)
doi.org
Automated, interactive, and traceable domain modelling empowered by artificial intelligence
Rijul Saini
Gunter Mussbacher
Jin L.C. Guo
Jörg Kienzle
2022-01-07
Software and Systems Modeling (publié)
doi.org
Attention Option-Critic
Raviteja Chunduru
Doina Precup
2022-01-06
ArXiv (prépublication)
openreview.net
openreview.net
Global variation in event-based surveillance for disease outbreak detection: A time series analysis (Preprint)
Iris Ganser
Rodolphe Thiébaut
David L Buckeridge
BACKGROUND Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed t… (voir plus)o allow timely detection of infectious disease outbreaks using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. OBJECTIVE To assess the variation in timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability, using the example of seasonal influenza epidemics in 24 countries. METHODS We obtained influenza-related reports from two EBS systems, HealthMap and the WHO Epidemic Intelligence from Open Sources (EIOS), and weekly virologic influenza counts from FluNet as a gold standard. Epidemic influenza periods were detected based on report frequency using Bayesian change point analysis. Timely sensitivity, i.e., outbreak detection within the first two weeks before or after an outbreak onset, was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. RESULTS Overall, monitoring the frequency of EBS reports detected 73.5% of outbreaks, but only 9.2% within two weeks of onset; in the best case, monitoring the frequency of health-related reports identified 29% of outbreaks within two weeks of onset. We observed a large degree of variability in all evaluation metrics across countries. The number of EBS reports available within a country, the human development index, and the country’s geographical location partially explained this variability. CONCLUSIONS Monitoring the frequency of EBS reports allowed just under 10% of seasonal influenza outbreaks to be detected in a timely manner in a worldwide analysis, with a large variability in detection capabilities. This article documents the global variation of EBS performance and demonstrates that monitoring report frequency alone in EBS may be insufficient for timely detection of outbreaks. Moreover, factors such as human development index and geographical location of a country may influence the performance of EBS and should be considered in future evaluations.
2022-01-04
(publié)
doi.org
Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis
Iris Ganser
Rodolphe Thiébaut
David L Buckeridge
2022-01-04
JMIR Public Health and Surveillance (publié)
doi.org