Marco Bonizzato

alireza.aminian@mila.quebec

Biography

Marco Bonizzato is a control, electrical and life sciences engineer with over ten years’ experience in implantable brain-computer interfaces and neuromodulation technology. He has unique double expertise in (a) neural prostheses, and (b) machine intelligence and optimization.

Bonizzato is an assistant professor of electrical engineering at Polytechnique Montréal and an adjunct professor of neuroscience at Université de Montréal.

He also directs the Polytechnique’s sciNeurotech Lab, whose research goal is to develop the entire translational arc of new neurostimulation therapies aimed at restoring sensorimotor function after neurotrauma—from discovery in rodents to application in human medical technologies tailored and personalized to the user using AI.

Current Students

Alireza Aminian

PhD - Polytechnique Montréal

Website

davide.burchielli@mila.quebec

Davide Burchielli

PhD - Polytechnique Montréal

claire.dussard@mila.quebec

Radu Darie

Postdoctorate - Polytechnique Montréal

radu.darie@mila.quebec

Postdoctorate - Polytechnique Montréal

Jean-Sébastien Giroux

Master's Research - Polytechnique Montréal

jean-sebastien.giroux@mila.quebec

Website

Juan Guerra

Master's Research - Polytechnique Montréal

Co-supervisor :

lison.kardassevitch@mila.quebec

juan.guerra@mila.quebec

PhD - Université de Montréal

Github

Mauricio Rivera

Master's Research - Université de Montréal

Co-supervisor :

mauriv2010@gmail.com

Publications

Uncovering and leveraging the return of voluntary motor programs after paralysis using a bi-cortical neuroprosthesis

Maude Duguay

Hugo Delivet-Mongrain

Nicolas Fortier-Lebel

Marina Martinez

2023-08-31

Progress in Neurobiology (published)

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

David Bergeron

Christian Iorio-Morin

Nathalie Orr Gaucher

Éric Racine

Alexander G. Weil

2023-04-27

Journal of Child Neurology (published)

Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys

Rose Guay Hottin

Sandrine L. Côté

Elena Massai

Leo Choiniere

Uzay Macar

Samuel Laferrière

Parikshat Sirpal

Stephan Quessy

Marina Martinez

Numa Dancause

Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged ne… (see more)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 (published)

Implementing automation in deep brain stimulation: has the time come?

Alfonso Fasano

2022-11-30

The Lancet Digital Health (published)

Learning to evoke complex motor outputs with spatiotemporal neurostimulation using a hierarchical and adaptive optimization algorithm.

Samuel Laferrière

Numa Dancause

The development of neurostimulation techniques to evoke motor patterns is an active area of research. It serves as a crucial experimental to… (see more)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 (published)