Portrait de Marco Bonizzato

Marco Bonizzato

Membre académique associé
marco.bonizzato@mila.quebec
Professeur adjoint, Polytechnique Montréal, Département de génie électrique
Professeur adjoint, Université de Montréal, Département de neurosciences
Sujets de recherche
Neurosciences computationnelles
Optimisation en boîte noire
Systèmes dynamiques
Google Scholar

Biographie

Marco Bonizzato est ingénieur en contrôle, en électricité et en sciences de la vie, et cumule plus de 10 ans d'expérience dans le domaine des interfaces cerveau-ordinateur implantables et de la technologie de neuromodulation. Il possède une double expertise unique en prothèses neurales et en intelligence et optimisation des machines.

Il est professeur adjoint de génie électrique à Polytechnique Montréal et professeur auxiliaire de neurosciences à l'Université de Montréal.

Il dirige également le laboratoire sciNeurotech. L'objectif de la recherche qui y est menée est de développer l'arc translationnel complet de nouvelles thérapies de neurostimulation afin de restaurer la fonction sensorimotrice après un neurotraumatisme, depuis la découverte chez le rongeur jusqu'à l'application dans la technologie médicale humaine, adaptée et personnalisée à chaque utilisateur par l'intelligence artificielle.

Étudiants actuels

Publications

Uncovering and leveraging the return of voluntary motor programs after paralysis using a bi-cortical neuroprosthesis
Maude Duguay
Marco Bonizzato
Hugo Delivet-Mongrain
Nicolas Fortier-Lebel
Marina Martinez
2023-08-31
Progress in Neurobiology (publié)
doi.org
Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations
David Bergeron
Christian Iorio-Morin
Marco Bonizzato
Guillaume Lajoie
Nathalie Orr Gaucher
Éric Racine
Alexander G. Weil
2023-04-27
Journal of Child Neurology (publié)
doi.org
Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys
Marco Bonizzato
Rose Guay Hottin
Sandrine L. Côté
Elena Massai
Leo Choiniere
Uzay Macar
Samuel Laferrière
Parikshat Sirpal
Stephan Quessy
Guillaume Lajoie
Marina Martinez
Numa Dancause
Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged ne… (voir plus)urostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve “prior” expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.
2023-03-31
Cell Reports Medicine (publié)
doi.org
Implementing automation in deep brain stimulation: has the time come?
Marco Bonizzato
Alfonso Fasano
2022-11-30
The Lancet Digital Health (publié)
doi.org
Learning to evoke complex motor outputs with spatiotemporal neurostimulation using a hierarchical and adaptive optimization algorithm.
Samuel Laferrière
Marco Bonizzato
Numa Dancause
Guillaume Lajoie
The development of neurostimulation techniques to evoke motor patterns is an active area of research. It serves as a crucial experimental to… (voir plus)ol to probe computation in neural circuits, and it has applications in neuroprostheses used to aid recovery of function after stroke or injury. There are two important challenges when designing algorithms to unveil and control neurostimulation-to-motor mappings, thereby linking spatiotemporal patterns of neural stimulation to muscle activation: (1) the exploration of motor maps needs to be fast and efficient (exhaustive search is to be avoided for clinical and experimental reasons) (2) online learning needs to be flexible enough to track ongoing changes in these maps. We propose a stimulation search algorithm to address these issues, and demonstrate its efficacy with experiments in non-human primate models. Our solution is a novel iterative process using Bayesian Optimization via Gaussian Processes on increasingly complex signal spaces. We show that our algorithm can successfully and rapidly learn mappings between complex stimulation patterns and evoked muscle activation patterns, where standard approaches fail. Importantly, we uncover nonlinear circuit-level computations in M1 that would not have been possible to identify using conventional mapping techniques.
2021-12-31
Conference on Cognitive Computational Neuroscience (publié)
doi.org