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

Reza Asri
PhD - Université de Montréal
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Nerea Carbonell Munoz
Research Intern - EPFL
Github
Olivier Codol
Postdoctorate - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Page Hayley
Postdoctorate - Université de Montréal
Github
Anirudh Jamkhandi
PhD - Université de Montréal
Website
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Ali Korojy
Master's Research - Université de Montréal
Avery Ryoo
Master's Research - Université de Montréal
Co-supervisor :
Guillaume Lajoie
Website
Github
Google Scholar

Publications

Expressivity of Neural Networks with Random Weights and Learned Biases
Ezekiel Williams
Avery Hee-Woon Ryoo
Thomas Jiralerspong
Alexandre Payeur
Matt Perich
Luca Mazzucato
Guillaume Lajoie
2024-07-01
ArXiv (preprint)
doi.org
arxiv.org
Expressivity of Neural Networks with Fixed Weights and Learned Biases
Ezekiel Williams
Avery Hee-Woon Ryoo
Thomas Jiralerspong
Alexandre Payeur
Matt Perich
Luca Mazzucato
Guillaume Lajoie
2024-06-16
ICML.cc/2024/Workshop/HiLD (poster)
openreview.net
openreview.net
A Unified, Scalable Framework for Neural Population Decoding
Mehdi Azabou
Vinam Arora
Venkataramana Ganesh
Ximeng Mao
Santosh B Nachimuthu
Michael Jacob Mendelson
Blake Richards
Matt Perich
Guillaume Lajoie
Eva L Dyer
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both the model … (see more)size and the datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large-scale multi-session model trained on large datasets from seven nonhuman primates, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale for neural decoding models.
openreview.net
Nonlinear manifolds underlie neural population activity during behaviour
Cátia Fortunato
Jorge Bennasar-Vázquez
Junchol Park
Joanna C. Chang
Lee Miller
Joshua T. Dudman
Matt Perich
Juan A. Gallego
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well de… (see more)scribed by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat “neural manifold”. Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey motor cortex, mouse motor cortex, mouse striatum, and human motor cortex, we show that: 1) neural manifolds are intrinsically nonlinear; 2) the degree of their nonlinearity varies across architecturally distinct brain regions; and 3) manifold nonlinearity becomes more evident during complex tasks that require more varied activity patterns. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
2023-07-19
bioRxiv (preprint)
doi.org
Motor cortex latent dynamics encode arm movement direction and urgency independently
Andrea Colins Rodriguez
Matt Perich
Lee Miller
Mark D. Humphries
2023-05-26
bioRxiv (preprint)
doi.org
De novo motor learning creates structure in neural activity space that shapes adaptation
Joanna C. Chang
Matt Perich
Lee Miller
Juan A. Gallego
Claudia Clopath
2023-05-24
bioRxiv (preprint)
doi.org
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
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