| Generative adversarial networks for reconstructing natural images from brain activity K Seeliger, U Güçlü, L Ambrogioni, Y Güçlütürk, MAJ van Gerven NeuroImage 181, 775-785, 2018 | 150 | 2018 |
| Theta oscillations locked to intended actions rhythmically modulate perception A Tomassini, L Ambrogioni, WP Medendorp, E Maris Elife 6, e25618, 2017 | 99 | 2017 |
| Neural dynamics of perceptual inference and its reversal during imagery N Dijkstra, L Ambrogioni, D Vidaurre, M van Gerven elife 9, e53588, 2020 | 61 | 2020 |
| Structurally-informed Bayesian functional connectivity analysis M Hinne, L Ambrogioni, RJ Janssen, T Heskes, MAJ van Gerven NeuroImage 86, 294-305, 2014 | 54 | 2014 |
| Wasserstein variational inference L Ambrogioni, U Güçlü, Y Güçlütürk, M Hinne, E Maris, MAJ van Gerven Neural Information Processing Systems 2018, 2018 | 47 | 2018 |
| The kernel mixture network: A nonparametric method for conditional density estimation of continuous random variables L Ambrogioni, U Güçlü, MAJ van Gerven, E Maris arXiv preprint arXiv:1705.07111, 2017 | 41 | 2017 |
| End-to-end neural system identification with neural information flow K Seeliger, L Ambrogioni, Y Güçlütürk, LM van den Bulk, U Güçlü, ... PLoS computational biology 17 (2), e1008558, 2021 | 35 | 2021 |
| Hyperrealistic Neural Decoding: Reconstruction of Face Stimuli From FMRI Measurements via the GAN Latent Space T Dado, Y Güçlütürk, L Ambrogioni, G Ras, SE Bosch, M van Gerven, ... | 31* | 2020 |
| Gait-prop: A biologically plausible learning rule derived from backpropagation of error N Ahmad, MAJ van Gerven, L Ambrogioni Neural Information Processing Systems, 2020 | 29 | 2020 |
| Automatic structured variational inference L Ambrogioni, K Lin, E Fertig, S Vikram, M Hinne, D Moore, M van Gerven International Conference on Artificial Intelligence and Statistics, 676-684, 2021 | 24 | 2021 |
| GP CaKe: Effective brain connectivity with causal kernels L Ambrogioni, M Hinne, M Van Gerven, E Maris Neural Information Processing Systems 2017, 2017 | 15 | 2017 |
| Complex-valued Gaussian process regression for time series analysis L Ambrogioni, E Maris Signal Processing 160, 215-228, 2019 | 14 | 2019 |
| Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference L Ambrogioni, U Guclu, M van Gerven Bayesian Deep Learning workshop. NeurIPS, 2018 | 13 | 2018 |
| Forward amortized inference for likelihood-free variational marginalization L Ambrogioni, U Güçlü, J Berezutskaya, E van den Borne, Y Güçlütürk, ... The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 12 | 2019 |
| Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity L Le, L Ambrogioni, K Seeliger, Y Güç Lütürk, M Van Gerven, U Güç Lü Frontiers in Neuroscience, 1684, 0 | 12* | |
| Cortical network responses map onto data-driven features that capture visual semantics of movie fragments J Berezutskaya, ZV Freudenburg, L Ambrogioni, U Güçlü, ... Scientific reports 10 (1), 12077, 2020 | 10 | 2020 |
| Dynamic Decomposition of Spatiotemporal Neural Signals L Ambrogioni, van Gerven Marcel A.J., E Maris PLoS Computational Biology, 2016 | 10 | 2016 |
| Integral transforms from finite data: An application of gaussian process regression to fourier analysis L Ambrogioni, E Maris International Conference on Artificial Intelligence and Statistics, 217-225, 2018 | 9 | 2018 |
| Automatic variational inference with cascading flows L Ambrogioni, G Silvestri, M van Gerven International Conference on Machine Learning, 2021 | 8 | 2021 |
| DeepRF: Ultrafast population receptive field mapping with deep learning J Thielen, U Güçlü, Y Güçlütürk, L Ambrogioni, SE Bosch, ... bioRxiv, 732990, 2019 | 8 | 2019 |
Marcel van GervenProfessor of Artificial Cognitive Systems, Donders Institute for Brain, Cognition and BehaviourVerified email at donders.ru.nl
