Portrait of Matt Perich

Matt Perich

Associate Academic Member
Assistant Professor, Université de Montréal, Department of Neuroscience
Research Topics
Computational Neuroscience
Deep Learning
Dynamical Systems
Recurrent Neural Networks
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Biography

Matthew G. Perich is an assistant professor in the Department of Neuroscience at Université de Montréal. His research program fuses AI and computational neuroscience with experimental neurophysiology and neural engineering to study how biological brains coordinate motor behaviours and ultimately guide the development of next-generation neuroprosthetic devices for rehabilitation.

Current Students

Margaux Asclipe
PhD - Université de Montréal
Reza Asri
PhD - Université de Montréal
Github
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Olivier Codol
Postdoctorate - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Github
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Page Hayley
Postdoctorate - Université de Montréal
Github
Anirudh Jamkhandi
PhD - Université de Montréal
Website
Github
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Mathys Loiselle
Research Intern - Concordia University
Principal supervisor :
Guillaume Lajoie
Website
Github
Avery Ryoo
PhD - Université de Montréal
Co-supervisor :
Guillaume Lajoie
Website
Github
Google Scholar
Jon Sakata
Collaborating researcher - McGill University
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Publications

Motor cortex latent dynamics 1 encode arm movement direction and 2 urgency independently 3
Andrea Colins Rodriguez
Matt Perich
Lee Miller
Mark D. Humphries
10 The fluid movement of an arm is controlled by multiple parameters that can be set 11 independently. Recent studies argue that arm moveme… (see more)nts are generated by the collective 12 dynamics of neurons in motor cortex. But how these collective dynamics simultaneously encode 13 and control multiple parameters of movement is an open question. Using a task where monkeys 14 made sequential, varied arm movements, we show that the direction and urgency of arm 15 movements are simultaneously encoded in the low-dimensional trajectories of population 16 activity: each movement’s direction by a fixed, looped neural trajectory and its urgency by how 17 quickly that trajectory was traversed. Network models showed this latent coding is potentially 18 advantageous as it allows the direction and urgency of arm movement to be independently 19 controlled. Our results suggest how low-dimensional neural dynamics can define multiple 20 parameters of goal-directed movement simultaneously. 21
2023-01-01
(published)
www.semanticscholar.org
Small, correlated changes in synaptic connectivity may facilitate rapid motor learning
Barbara Feulner
Matt Perich
Raeed H. Chowdhury
Lee Miller
Juan A. Gallego
Claudia Clopath
2022-09-02
Nature Communications (published)
doi.org
Misinterpreting the horseshoe effect in neuroscience
Timothée Proix
Matt Perich
Tomislav Milekovic
2022-03-07
bioRxiv (preprint)
doi.org
Small, correlated changes in synaptic connectivity may facilitate rapid motor learning
Barbara Feulner
Matt Perich
Raeed H. Chowdhury
Lee Miller
Juan A. Gallego
Claudia Clopath
2021-10-01
Nature Communications (published)
doi.org