Guillaume Lajoie
Guillaume Lajoie
Assistant Professor, Applied Mathematics, Université de Montréal
Verified email at - Homepage
Cited by
Cited by
Chaos and reliability in balanced spiking networks with temporal drive
G Lajoie, KK Lin, E Shea-Brown
Physical Review E 87 (5), 052901, 2013
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
G Kerg, K Goyette, MP Touzel, G Gidel, E Vorontsov, Y Bengio, G Lajoie
arXiv preprint arXiv:1905.12080, 2019
Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks
P Vincent-Lamarre, G Lajoie, JP Thivierge
Journal of computational neuroscience 41 (3), 305-322, 2016
Dimensionality compression and expansion in Deep Neural Networks
S Recanatesi, M Farrell, M Advani, T Moore, G Lajoie, E Shea-Brown
arXiv preprint arXiv:1906.00443, 2019
Encoding in balanced networks: Revisiting spike patterns and chaos in stimulus-driven systems
G Lajoie, KK Lin, JP Thivierge, E Shea-Brown
PLoS computational biology 12 (12), e1005258, 2016
Structured chaos shapes spike-response noise entropy in balanced neural networks
G Lajoie, JP Thivierge, E Shea-Brown
Frontiers in computational neuroscience 8, 123, 2014
Shared inputs, entrainment, and desynchrony in elliptic bursters: From slow passage to discontinuous circle maps
G Lajoie, E Shea-Brown
SIAM Journal on Applied Dynamical Systems 10 (4), 1232-1271, 2011
Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface
G Lajoie, NI Krouchev, JF Kalaska, AL Fairhall, EE Fetz
PLoS computational biology 13 (2), e1005343, 2017
Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules
S Mittal, A Lamb, A Goyal, V Voleti, M Shanahan, G Lajoie, M Mozer, ...
International Conference on Machine Learning, 6972-6986, 2020
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion
M Farrell, S Recanatesi, T Moore, G Lajoie, E Shea-Brown
bioRxiv, 564476, 2019
Dynamic compression and expansion in a classifying recurrent network
M Farrell, S Recanatesi, G Lajoie, E Shea-Brown
bioRxiv, 564476, 2019
Gradient Starvation: A Learning Proclivity in Neural Networks
M Pezeshki, SO Kaba, Y Bengio, A Courville, D Precup, G Lajoie
arXiv preprint arXiv:2011.09468, 2020
Predictive learning extracts latent space representations from sensory observations
S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown
bioRxiv, 471987, 2019
Cortical network mechanisms of anodal and cathodal transcranial direct current stimulation in awake primates
AR Bogaard, G Lajoie, H Boyd, A Morse, S Zanos, EE Fetz
bioRxiv, 516260, 2019
Signatures and mechanisms of low-dimensional neural predictive manifolds
S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown
bioRxiv, 471987, 2018
On lyapunov exponents for rnns: Understanding information propagation using dynamical systems tools
R Vogt, MP Touzel, E Shlizerman, G Lajoie
arXiv preprint arXiv:2006.14123, 2020
Untangling tradeoffs between recurrence and self-attention in neural networks
G Kerg, B Kanuparthi, A Goyal, K Goyette, Y Bengio, G Lajoie
arXiv preprint arXiv:2006.09471, 2020
Low-dimensional dynamics of encoding and learning in recurrent neural networks
S Horoi, V Geadah, G Wolf, G Lajoie
Canadian Conference on Artificial Intelligence, 276-282, 2020
Learning function from structure in neuromorphic networks
LE Suarez, BA Richards, G Lajoie, B Misic
bioRxiv, 2020
Dynamic signal tracking in a simple V1 spiking model
G Lajoie, LS Young
Neural computation 28 (9), 1985-2010, 2016
The system can't perform the operation now. Try again later.
Articles 1–20