Guillaume Lajoie, Ph.D.

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Williams E, Ryoo AH, Jiralerspong T, et al. (2024) Expressivity of Neural Networks with Random Weights and Learned Biases. Arxiv
Suárez LE, Mihalik A, Milisav F, et al. (2024) Connectome-based reservoir computing with the conn2res toolbox. Nature Communications. 15: 656
Azabou M, Arora V, Ganesh V, et al. (2023) A Unified, Scalable Framework for Neural Population Decoding. Arxiv
Liu YH, Baratin A, Cornford J, et al. (2023) How connectivity structure shapes rich and lazy learning in neural circuits. Arxiv
Abrevaya G, Dumas G, Aravkin AY, et al. (2021) Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. Neural Computation. 33: 2087-2127
Recanatesi S, Farrell M, Lajoie G, et al. (2021) Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nature Communications. 12: 1417
Laferriere S, Bonizzato M, Cote SL, et al. (2020) Hierarchical Bayesian optimization of spatiotemporal neurostimulations for targeted motor outputs. Ieee Transactions On Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society
Lajoie G, Krouchev NI, Kalaska JF, et al. (2017) Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface. Plos Computational Biology. 13: e1005343
Lajoie G, Lin KK, Thivierge JP, et al. (2016) Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems. Plos Computational Biology. 12: e1005258
Vincent-Lamarre P, Lajoie G, Thivierge JP. (2016) Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks. Journal of Computational Neuroscience