Projets novateurs réalisés

Impact of Pulvinar on cortical visual processing

The perception of external stimuli is traditionally considered to result solely from the processing of thalamic signals through direct cortico-cortical connections between areas organized in a hierarchical manner1. In this corticocentric view, the potential role of higher-order (HO) thalamic nuclei that are reciprocally connected to cortical areas is not considered.
The main objective of this project is to test this hypothesis at the visual system level by examining the impact of modulating the neural activity of the pulvinar, the HO visual thalamic nucleus, on the processing of information within and across areas of the visual cortex. We postulate that the pulvinar will modulate activity in the primary visual cortex and will both contribute to specific RF properties and exert contextual modulation in higher-order areas. To test our hypotheses, we will determine the impact of inactivating sub-regions of the pulvinar on the cortical activity of areas of different hierarchical levels measured by optical brain imaging.

Paediatric Evaluation of Emotions Relationships and Socialisation (PEERS): L’adaptation et la normalisation d’une batterie d’évaluation de la cognition sociale

Socio-emotional skills are crucial for developing satisfying relationships; failure in this domain can have significant consequences for well-being across the lifespan such as bullying, social anxiety and isolation. However, no well-validated socio-emotional assessments exist, limiting our ability to effectively intervene. The Paediatric Evaluation of Emotions Relationships and Socialisation (PEERS) battery was developed by the Australian Centre for Child Neuropsychology Studies team to address this gap. Embedded in the SOCIAL (Socio-Cognitive Integration of Abilities model, Beauchamp & Anderson, 2010) biopsychosocial theoretical framework, PEERS Clinical is a child-direct, interactive, individually administered assessment of social skills delivered via iPad by a trained assessor. Our aim is to collaborate to produce a French language adaptation to better evaluate and intervene with regard to social cognition and competence with French Canadian children. Work on normative data will also be done on typically developing children aged 4-18 years and on children with clinical diagnoses such as autism spectrum disorder and ADHD.

Interpretation and Characterization of Recurrent Neural Networks through Lyapunov Exponent Methodology

Neuroscience-inspired AI has emerged as state-of-the-art in many machine learning applications. Recurrent Neural Networks (RNNs) are a machine learning tool used to learn patterns in sequential (time-dependent) data which have also been used to model neural dynamics in the brain. Various frameworks have been developed to create RNNs capable of learning from data which have long-term dependencies. Architectures such as the LSTM and GRU have been shown to successfully learn long-term dependencies, but the underlying mechanisms which lead to their success and failure are not well understood. By incorporating tools from dynamical systems, we will examine the learning trajectories of different networks as they learn different tasks. This will create a general and formal structure in which the dynamics of different networks and models can be compared. Understanding the dynamic properties of these systems will support and broaden the performance and range of RNN-based applications of machine learning to complex, dynamic processes.

High-Performance, High Assurance Software

Optimal Computational Algorithms, Inc. (OCA) provides the highest quality

mathematical software possible. Our goal is to produce scientific software with nearoptimal

performance on increasingly parallel systems, while assuring software

correctness by construction. This research project continues a partnership with

McMaster University, and individual internship projects will focus on extending OCA’s

tools to target new computations involving the design and analysis of Magnetic

Resonance experiments, and to automatically generate proofs of software correctness

that can be checked independently by third-party software.

Analyzing noise compensation properties of trained recurrent neural networks

Reliability is a fundamental requirement for computational systems, brains and artificial models alike: a system should respond the same way for repeated presentations of the same stimulus. However, the brain has two features that can threaten its reliability: intrinsic stochasticity and chaos. Stochasticity takes the form of random fluctuations affecting the reliability of components of the system, whereas chaos is an emergent property of the entire system that causes similar inputs or initial conditions to produce totally different outputs. The brain must have mechanisms to compensate for its noisy and unreliable machinery, and the goal of our project is to characterize these mechanisms. To this end, we will first develop tools for quantifying the reliability of models of neural circuits, and we will subsequently extend the capabilities of these tools to analyze data collected from real neuroscience experiments.

Translation for educational change: Translator identity and the Global Storybooks project

Our research seeks to better understand challenges that translators encounter in translating stories for the diverse sites on the Global Storybooks portal (https://globalstorybooks.net/), and how they resolve these challenges. We will draw on our existing database of translation data to investigate how translators draw on a range of linguistic resources to achieve a satisfactory representation of the target translation. Practically, this data will help us to develop improved guidelines for translators, as well as proofreaders, editors, and coordinators. More theoretically, the data will enable us to develop a model of translator identity that will legitimize the diverse participants who are active in translation projects. We hope this model will provide a useful framework for further research on translation in diverse global contexts. The sub-objectives of the project are as follows: Sub-objective 1: Review existing database of translation data in the Global Storybooks project and identify common translation challenges and practices of translators. Sub-objective 2: Identify two translation case studies for comprehensive analysis. Sub-objective 3: Based on the case studies, (i) develop a comprehensive set of guidelines for translators working on new Global Storybooks sites and (ii) develop a model of translator identity that would be useful for further research purposes.

Decoding the neural dynamics of emotion-related human memory optimization using AI-informed multivariate techniques

Episodic memory, our fascinating ability to encode and mentally relive past experiences, lies at the core of human cognition. It allows individuals not only to recall past events, but it is crucial in planning and guiding future behavior. However, among all of our daily-life experiences, only some events will be transformed into lasting memories, particularly because of their emotional salience. From the brain perspective’s, emotions are thought to sustainably recruit the amygdala, thus facilitating memory processes occurring in the hippocampus. In addition, while the first step in forming durable memories is the initial learning, it has become increasingly clear that memory reinstatement of encoding neural patterns at retrieval also plays an important role in remembering. Yet, we know very little about the precise brain mechanisms supporting emotion-related memory enhancement via neural pattern reinstatement. Here, we propose to use machine learning to establish an AI-informed multivariate approach to isolate the neural processes that are boosted by emotions and which mediate learning and retrieval enhancement.

Decoding the neural correlates of dynamic decision-making in humans

This research project will combine computational modeling, machine learning (ML) algorithms and whole-brain neural recordings (magnetoencephalography, MEG) to shed light on how the mechanisms underlying dynamic decision making are implemented in the human brain. Specifically, we will use the statistical framework of information theory to characterize inter-areal neural coupling and the direction of information flow when weighing sensory evidence and committing to a specific choice during dynamic decision-making. By providing a quantitative link between the behavioral and neural dynamics subserving how decisions are continuously formed in the brain, this project will contribute to expose mechanisms that are likely to figure prominently in human cognition, in health and disease.

Sliammon Carbon Interests: Background research on feasibility of acarbon offset project application.

This research project will explore the costs and benefits of putting a carbon offset project into

application for Tla’ Amin Timber Products. This project will prepare the background and inventory

documents for a carbon offset application according to the two chosen Standards, Protocol for tbe

Creation of Forest Carbon Offsets in British Columbia Version 1.0 for ImproVed Forest

Management and Pacific Carbon Standard. Simultaneously, I will develop a community

engagement program so that I can provide basic carbon offset and forest management terminology and

knowledge. I hope to provide enough information throughout the life of the project so that

stakeholders understand the project findings; and stakeholders are able to make a fair decision about

project acceptance or refusal. Up to 4 carbon offset project scenarios will be presented to Tla’Amin

stakeholders at a community presentation at the end of 6 months. I will use a number of softwares and

programs…

Biosensing JFET platform with printed graphene gate and customizable functionalization

The goal of this research project is to create a novel type of biosensor by combining two complimentary microfabrication techniques. First, a silicon chip containing JFET transistors with an open gate will be fabricated using traditional microfabrication techniques that are highly reliable and give good performance. Second, a graphene layer will be inkjet printed onto the open gate of the transistor. The graphene will act as a sensor transducer to sense pH or biological species such as insulin. Graphene has a large surface area and good electrical properties, which makes it the ideal transducer material. The graphene can be functionalized to detect various different biological species. Combined with the flexibility of inkjet printing, this will allow different biosensors to be fabricated easily next to each other on the same silicon platform.

Définir les objectifs et indicateurs d’adaptation aux changements climatiques

Puisque les changements climatiques sont déjà ressentis et le seront de plus en plus, il est nécessaire de s’adapter à de conditions environnementales sans précédent. Une quantité grandissante de programmes de financement vise maintenant à soutenir les communautés dans leurs démarches d’adaptation; par conséquent, le besoin d’évaluer l’atteinte des cibles d’adaptation devient de plus en plus important. Étant donné que le concept de l’adaptation est en évolution constante, et que les mesures d’adaptation sont généralement plus difficiles à quantifier que celles de lutte (par ex., réduction des émissions carboniques), il est essentiel de définir des indicateur quantifiables pouvant être utilisés par différents paliers de gouvernements (municipalités, régions, etc.) pour mesurer l’atteinte des cibles d’adaptation. L’objectif principal du présent projet est donc de recenser les cibles et les indicateurs d’adaptation aux changements climatiques utilisés à l’échelle mondiale afin de déterminer lesquels pourraient être applicables au contexte québécois. Ce projet aura permettra d’avancer la recherche dans ce domaine et d’interagir avec les acteurs du milieu, et donc permettra de faire rayonner l’organisme partenaire en ce sens.

Analyse et recherche d’optimisation d’un processus d’adaptation de contenu issu de la numérisation 3D à un contenu photo-réaliste et interactif

Le récents progrès dans le domaine de la numérisation 3D (SLAM, Lidar, lumière structurée, photogrammétrie, etc.) ont ouvert de nouvelles portes quant à la possibilité de réaliser des maquettes numériques interactive. Cependant, les outils traditionnels pour traiter les données brutes (nuage de point) de ces capteurs ne permettent pas de générer un modèle numérique adapté à l’utilisation d’une maquette 3D numérique photoréaliste et interactive. PreVu3D désire donc mettre en place son propre processus de traitement de donnée en adaptant les outils actuels et, dans le cas où ces outils n’existent pas, développer ces propres outils adaptés à ce cas particulier.