Portrait de Matt Perich

Matt Perich

Membre académique associé
Professeur adjoint, Université de Montréal, Département de neurosciences
Sujets de recherche
Apprentissage profond
Neurosciences computationnelles
Réseaux de neurones récurrents
Systèmes dynamiques
Site web
Google Scholar
X

Biographie

Matthew G. Perich est professeur adjoint au Département de neurosciences de l’Université de Montréal. Son programme de recherche fusionne l’IA et les neurosciences computationnelles avec la neurophysiologie expérimentale et l’ingénierie neuronale, pour étudier comment les cerveaux biologiques coordonnent les comportements moteurs et, ultimement, pour guider le développement de dispositifs neuroprothétiques de prochaine génération pour la réadaptation.

Étudiants actuels

Margaux Asclipe
Doctorat - UdeM
Reza Asri
Doctorat - UdeM
Github
Google Scholar
Olivier Codol
Postdoctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Lajoie
Github
Google Scholar
Page Hayley
Postdoctorat - UdeM
Github
Anirudh Jamkhandi
Doctorat - UdeM
Site web
Github
Google Scholar
Mathys Loiselle
Stagiaire de recherche - Concordia
Superviseur⋅e principal⋅e :
Guillaume Lajoie
Site web
Github
Avery Ryoo
Doctorat - UdeM
Co-superviseur⋅e :
Guillaume Lajoie
Site web
Github
Google Scholar
Jon Sakata
Collaborateur·rice de recherche - McGill
Site web

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… (voir plus)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 (publié)
doi.org
Misinterpreting the horseshoe effect in neuroscience
Timothée Proix
Matt Perich
Tomislav Milekovic
2022-03-07
bioRxiv (prépublication)
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