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Gaëtan Vignoud
Gaëtan Vignoud
PhD student, INRIA & Collège de France
Bestätigte E-Mail-Adresse bei inria.fr
Titel
Zitiert von
Zitiert von
Jahr
A variational inequality perspective on generative adversarial networks
G Gidel, H Berard, G Vignoud, P Vincent, S Lacoste-Julien
arXiv preprint arXiv:1802.10551, 2018
4082018
Structured odorant response patterns across a complete olfactory receptor neuron population
G Si, JK Kanwal, Y Hu, CJ Tabone, J Baron, M Berck, G Vignoud, ...
Neuron 101 (5), 950-962. e7, 2019
722019
Concurrent Thalamostriatal and Corticostriatal Spike-Timing-Dependent Plasticity and Heterosynaptic Interactions Shape Striatal Plasticity Map
A Mendes, G Vignoud, S Perez, E Perrin, J Touboul, L Venance
Cerebral Cortex, 2020
162020
Interplay of multiple pathways and activity-dependent rules in STDP
G Vignoud, L Venance, JD Touboul
PLoS computational biology 14 (8), e1006184, 2018
142018
Video-Based Automated Assessment of Movement Parameters Consistent with MDS-UPDRS III in Parkinson’s Disease
G Vignoud, C Desjardins, Q Salardaine, M Mongin, B Garcin, L Venance, ...
Journal of Parkinson's Disease, 1-12, 2022
132022
Stochastic models of neural synaptic plasticity
P Robert, G Vignoud
SIAM Journal on Applied Mathematics 81 (5), 1821-1846, 2021
132021
Striatum expresses region-specific plasticity consistent with distinct memory abilities
S Perez, Y Cui, G Vignoud, E Perrin, A Mendes, Z Zheng, J Touboul, ...
Cell Reports 38 (11), 2022
122022
Environmental enrichment shapes striatal spike-timing-dependent plasticity in vivo
T Morera-Herreras, Y Gioanni, S Perez, G Vignoud, L Venance
Scientific reports 9 (1), 19451, 2019
92019
Stochastic Models of Neural Plasticity: A Scaling Approach
P Robert, G Vignoud
arXiv preprint arXiv:2106.04845, 2021
7*2021
Video-based automated analysis of MDS-UPDRS III parameters in Parkinson disease
G Vignoud, C Desjardins, Q Salardaine, M Mongin, B Garcin, L Venance, ...
bioRxiv, 2022.05. 23.493047, 2022
62022
Averaging Principles for Markovian Models of Plasticity
P Robert, G Vignoud
Journal of Statistical Physics 183 (3), 1-43, 2021
62021
Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks
T Mesnard, G Vignoud, J Sacramento, W Senn, Y Bengio
arXiv preprint arXiv:1911.08585, 2019
52019
Invariances in a combinatorial olfactory receptor code
G Si, JK Kanwal, Y Hu, CJ Tabone, J Baron, M Berck, G Vignoud, ...
bioRxiv, 208538, 2017
32017
Spontaneous dynamics of synaptic weights in stochastic models with pair-based spike-timing-dependent plasticity
G Vignoud, P Robert
Physical Review E 105 (5), 054405, 2022
12022
On the Spontaneous Dynamics of Synaptic Weights in Stochastic Models with Pair-Based STDP
P Robert, G Vignoud
arXiv preprint arXiv:2111.07919, 2021
12021
A Palm space approach to non-linear Hawkes processes
P Robert, G Vignoud
Electronic Journal of Probability 29, 1-37, 2024
2024
Synaptic plasticity in stochastic neuronal networks
G Vignoud
Sorbonne Université, 2022
2022
Analyse du syndrome parkinsonien par une approche informatique de deep learning
C Desjardins, Q Salardaine, B Degos, G Vignoud
Revue Neurologique 177, S30-S31, 2021
2021
Movement Disorders Analysis Using a Deep Learning Approach
C Desjardins, Q Salardaine, G Vignoud, B Degos
Movement Disorders 35, 2020
2020
Fully discretized training of neural networks through direct feedback
T Mesnard, G Vignoud, J Binas, Y Bengio
2018
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