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van der Grinten, M., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rückauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., & Güçlütürk, Y. (2022). Biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses. bioRxiv 2022.12.23.521749. 🔗

Küçükoğlu, B., Borkent, W., Rueckauer, B., Ahmad, N., Güçlü, U., & van Gerven, M. (2022). Efficient Deep reinforcement learning with predictive processing proximal policy optimization. arXiv:2211.06236 [cs.LG]. 🔗

Thielen, J., Güçlü, U., Güçlütürk, Y., Ambrogioni, L., Bosch, S., & van Gerven, M. (2019). DeepRF: Ultrafast population receptive field mapping with deep learning. bioRxiv 732990. 🔗

Seeliger, K., Sommers, R., Güçlü, U., Bosch, S., & van Gerven, M. (2019). A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time. bioRxiv 687681. 🔗

Geerligs, L., Gözükara, D., Oetringer, D., Campbell, K., van Gerven, M., & Güçlü, U. (2022). A partially nested cortical hierarchy of neural states underlies event segmentation in the human brain. eLife, 11, e77430. 🔗

Le, L., Ambrogioni, L., Seeliger, K., Güçlütürk, Y., van Gerven, M., & Güçlü, U. (2022). Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity. Frontiers in Neuroscience, 16, 940972. 🔗

Küçükoğlu, B., Rueckauer, B., Ahmad, N., de Ruyter van Steveninck, J., Güçlü, U., & van Gerven, M. (2022). Optimization of neuroprosthetic vision via end-to-end deep reinforcement learning. International Journal of Neural Systems, 32(11), 2250052. 🔗

Armeni, K., Güçlü, U., van Gerven, M., & Schoffelen, J. (2022). A 10-hour within-participant magnetoencephalography narrative dataset to test models of language comprehension. Scientific Data, 9, 278. 🔗

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Dado, T., Güçlütürk, Y., Ambrogioni, L., Ras, G., Bosch, S., van Gerven, M., & Güçlü, U. (2022). Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space. Scientific Reports, 12, 141. 🔗

Geerligs, L., van Gerven, M., & Güçlü, U. (2021). Detecting neural state transitions underlying event segmentation. NeuroImage, 236, 118085. 🔗

Seeliger, K., Ambrogioni, L., Güçlü, U., & van Gerven, M. (2021). End-to-end neural system identification with neural information flow. PLOS Computational Biology, 17(2), e1008558. 🔗

Berezutskaya, J., Freudenburg, Z., Ambrogioni, L., Güçlü, U., van Gerven, M., & Ramsey, N. (2020). Cortical network responses map onto data-driven features that capture visual semantics of movie fragments. Scientific Reports, 10, 12077. 🔗

Berezutskaya, J., Freudenburg, Z., Güçlü, U., van Gerven, M., & Ramsey, N. (2020). Brain-optimized extraction of complex sound features that drive continuous auditory perception. PLOS Computational Biology, 16(7), e1007992. 🔗

Escalera, S., Escalante, H., Baró, X., Guyon, I., Madadi, M., Wan, J., Ayache, S., Güçlütürk, Y., & Güçlü, U. (2020). Guest editorial: Image and video inpainting and denoising. IEEE Transactions on Pattern Recognition and Machine Intelligence, 42(5), 1021-1024. 🔗

Escalante, H., Kaya, H., Salah, A., Escalera, S., Güçlütürk, Y., Güçlü, U., Baró, X., Guyon, I., Junior, J., Madadi, M., Ayache, S., Viegas, E., Gürpınar, F., Wicaksana, A., Liem, C., van Gerven, M., & van Lier, R. (2020). Modeling, recognizing, and explaining apparent personality from videos. IEEE Transactions on Affective Computing, 13(2), 894-911. 🔗

Junior, J., Güçlütürk, Y., Pérez, M., Güçlü, U., Andujar, C., Baró, X., Escalante, H., Guyon, I., van Gerven, M., van Lier, R., & Escalera, S. (2019). First impressions: A survey on vision-based apparent personality trait analysis. IEEE Transactions on Affective Computing, 13(1), 75-95. 🔗

Seeliger, K., Güçlü, U., Ambrogioni, L., Güçlütürk, Y., & van Gerven, M. (2018). Generative adversarial networks for reconstructing natural images from brain activity. NeuroImage, 181, 775-785. 🔗

Güçlütürk, Y., Güçlü, U., van Gerven, M., & van Lier, R. (2018). Representations of naturalistic stimulus complexity in early and associative visual and auditory cortices. Scientific Reports, 8, 3439. 🔗

Seeliger, K., Fritsche, M., Güçlü, U., Schoenmakers, S., Schoffelen, J., Bosch, S., & van Gerven, M. (2017). Convolutional neural network-based encoding and decoding of visual object recognition in space and time. NeuroImage, 180(Part A), 253-266. 🔗

Güçlütürk, Y., Güçlü, U., Baró, X., Escalante, H., Guyon, I., Escalera, S., van Gerven, M., & van Lier, R. (2017). Multimodal first impression analysis with deep residual networks. IEEE Transactions on Affective Computing, 9(3), 316-329. 🔗

Berezutskaya, J., Freudenburg, Z., Güçlü, U., van Gerven, M., & Ramsey, N. (2017). Neural tuning to low-level features of speech throughout the perisylvian cortex. The Journal of Neuroscience, 37(33), 7906-7920. 🔗

Güçlü, U., & van Gerven, M. (2017). Modeling the dynamics of human brain activity with recurrent neural networks. Frontiers in Computational Neuroscience, 11, 7. 🔗

Güçlü, U., & van Gerven, M. (2015). Increasingly complex representations of natural movies across the dorsal stream are shared between subjects. NeuroImage, 145(Part B), 329-336. 🔗

Güçlü, U., & van Gerven, M. (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. The Journal of Neuroscience, 35(27), 10005-10014. 🔗

Schoenmakers, S., Güçlü, U., van Gerven, M., & Heskes, T. (2015). Gaussian mixture models and semantic gating improve reconstructions from human brain activity. Frontiers in Computational Neuroscience, 8, 173. 🔗

Güçlü, U., & van Gerven, M. (2014). Unsupervised feature learning improves prediction of human brain activity in response to natural images. PLOS Computational Biology, 10(8), e1003724. 🔗

Gözükara, D., Oetringer, D., Geerligs, L., & Güçlü, U. (2023). Precision brain encoding under naturalistic conditions. Conference on Cognitive Computational Neuroscience. 🔗

Le, L., Papale, P., Lozano, A., Dado, T., Wang, F., van Gerven, M., Roelfsema, P., Güçlütürk, Y., & Güçlü, U. (2023). End-to-end reconstruction of natural images from multi-unit recordings with Brain2Pix. Conference on Cognitive Computational Neuroscience. 🔗

Dado, T., Papale, P., Lozano, A., Le, L., van Gerven, M., Roelfsema, P., Güçlütürk, Y., & Güçlü, U. (2023). Feature-disentangled reconstruction of perception from multi-unit recording. Conference on Cognitive Computational Neuroscience. 🔗

Mahner, F., Muttenthaler, L., Güçlü, U., & Hebart, M. (2023). Dimensions that matter: Interpretable object dimensions in humans and deep neural networks. Conference on Cognitive Computational Neuroscience. 🔗

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Dallaire, P., Ambrogioni, L., Trottier, L., Güçlü, U., Hinne, M., Giguère, P., Chaib-Draa, B., van Gerven, M., & Laviolette, F. (2020). The Indian chefs process. Uncertainty in Artificial Intelligence. 🔗

Seeliger, K., Ambrogioni, L., Güçlü, U., & van Gerven, M. (2019). Neural information flow: Learning neural information processing systems from brain activity. Conference on Cognitive Computational Neuroscience. 🔗

Bokkers, L., Ambrogioni, L., & Güçlü, U. (2019). Segmentation of photovoltaic panels in aerial photography using group equivariant FCNs. Benelux Conference on Machine Learning. 🔗

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Bollen, C., Güçlü, U., van Wezel, R., van Gerven, M., & Güçlütürk, Y. (2019). Simulating neuroprosthetic vision for emotion recognition. International Conference on Affective Computing and Intelligent Interaction Workshops. 🔗

Ambrogioni, L., Güçlü, U., Berezutskaya, J., van den Borne, E., Güçlütürk, Y., Hinne, M., Maris, E., & van Gerven, M. (2019). Forward amortized inference for likelihood-free variational marginalization. International Conference on Artificial Intelligence and Statistics. 🔗

Ambrogioni, L., Ebel, P., Hinne, M., Güçlü, U., van Gerven, M., & Maris, E. (2019). SpikeCaKe: Semi-analytic nonparametric Bayesian inference for spike-spike neuronal connectivity. International Conference on Artificial Intelligence and Statistics. 🔗

Ambrogioni, L., Güçlü, U., Güçlütürk, Y., Hinne, M., van Gerven, M., & Maris, E. (2018). Wasserstein variational inference. Neural Information Processing Systems. 🔗

Güçlütürk, Y., Güçlü, U., Seeliger, K., Bosch, S., van Lier, R., & van Gerven, M. (2017). Reconstructing perceived faces from brain activations with deep adversarial neural decoding. Neural Information Processing Systems. 🔗

Ambrogioni, L., Berezutskaya, J., Güçlü, U., van den Borne, E., Güçlütürk, Y., van Gerven, M., & Maris, E. (2017). Bayesian model ensembling using meta-trained recurrent neural networks. Neural Information Processing Systems Workshops.

Güçlü, U., Güçlütürk, Y., Ambrogioni, L., Maris, E., van Lier, R., & van Gerven, M. (2017). Algorithmic composition of polyphonic music with the WaveCRF. Neural Information Processing Systems Workshops. 🔗

Güçlütürk, Y., Güçlü, U., Pérez, M., Escalante, H., Baró, X., Andujar, C., Guyon, I., Junior, J., Madadi, M., Escalera, S., Van Gerven, M., & Van Lier, R. (2017). Visualizing apparent personality analysis with deep residual networks. International Conference on Computer Vision Workshops. 🔗

Berezutskaya, J., Freudenburg, Z., Ramsey, N., Güçlü, U., & van Gerven, M. (2017). Modeling brain responses to perceived speech with LSTM networks. Benelux Conference on Machine Learning. 🔗

Escalante, H., Guyon, I., Escalera, S., Junior, J., Madadi, M., Baró, X., Ayache, S., Viegas, E., Güçlütürk, Y., Güçlü, U., van Gerven, M., & van Lier, R. (2017). Design of an explainable machine learning challenge for video interviews. International Joint Conference on Neural Networks. 🔗

Güçlü, U., Thielen, J., Hanke, M., & van Gerven, M. (2016). Brains on beats. Neural Information Processing Systems. 🔗

Güçlütürk, Y., Güçlü, U., van Lier, R., & van Gerven, M. (2016). Convolutional sketch inversion. European Conference on Computer Vision Workshops. 🔗

Güçlütürk, Y., Güçlü, U., van Gerven, M., & van Lier, R. (2016). Deep impression: Audiovisual deep residual networks for multimodal apparent personality trait recognition. European Conference on Computer Vision Workshops. 🔗

Güçlü, U., & van Gerven, M. (2015). Semantic vector space models predict neural responses to complex visual stimuli. Neural Information Processing Systems Workshops. 🔗

Güçlü, U., & van Gerven, M. (2013). Unsupervised learning of features for Bayesian decoding in functional magnetic resonance imaging. Benelux Conference on Machine Learning. 🔗

Güçlütürk, Y., Güçlü, U., & Samraj, A. (2010). An online single trial analysis of the P300 event related potential for the disabled. Convention of Electrical and Electronics Engineers in Israel. 🔗

Güçlü, U., Güçlütürk, Y., & Loo, C. (2010). Evaluation of fractal dimension estimation methods for feature extraction in motor imagery based brain computer interface. World Conference on Information Technology. 🔗

Güçlü, U., Güçlütürk, Y., & Samraj, A. (2010). A novel approach to improve the performance of the P300 speller paradigm. International Conference on Systems, Man and Cybernetics. 🔗

van Gerven, M., Seeliger, K., Güçlü, U., & Güçlütürk, Y. (2019). Current advances in neural decoding. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (pp. 379-394). Springer. 🔗

Güçlü, U., & van Gerven, M. (2017). Probing human brain function with artificial neural networks. In Computational Models of Brain and Behavior (pp. 413-423). Wiley. 🔗

Escalera, S., Ayache, S., Wan, J., Madadi, M., Güçlü, U., & Baró, X. (2019). Inpainting and Denoising Challenges. Springer. 🔗

Escalante, H., Escalera, S., Guyon, I., Baró, X., Güçlütürk, Y., Güçlü, U., & van Gerven, M. (2019). Explainable and Interpretable Models in Computer Vision and Machine Learning. Springer. 🔗

Güçlü, U. (2018). Neural coding with deep learning [Doctoral Thesis, Radboud University]. 🔗

Ras, G., Ambrogioni, L., Haselager, P., van Gerven, M., & Güçlü, U. (2020). Explainable 3D convolutional neural networks by learning temporal transformations. arXiv:2006.15983 [cs.CV]. 🔗

Ras, G., Dotsch, R., Ambrogioni, L., Güçlü, U., & van Gerven, M. (2019). Background hardly matters: Understanding personality attribution in deep residual networks. arXiv:1912.09831 [cs.LG]. 🔗

Ambrogioni, L., Güçlü, U., & van Gerven, M. (2019). k-GANs: Ensemble of generative models with semi-discrete optimal transport. arXiv:1907.04050 [stat.ML]. 🔗

Ambrogioni, L., Güçlü, U., Güçlütürk, Y., & van Gerven, M. (2018). Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference. arXiv:1811.02827 [stat.ML]. 🔗

Ambrogioni, L., Güçlü, U., van Gerven, M., & Maris, E. (2017). The kernel mixture network: A nonparametric method for conditional density estimation of continuous random variables. arXiv:1705.07111 [stat.ML]. 🔗

Güçlü, U., Güçlütürk, Y., Madadi, M., Escalera, S., Baró, X., González, J., van Lier, R., & van Gerven, M. (2017). End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks. arXiv:1703.03305 [cs.CV]. 🔗

Ambrogioni, L., Güçlü, U., Maris, E., & van Gerven, M. (2017). Estimating nonlinear dynamics with the ConvNet smoother. arXiv:1702.05243 [stat.ML]. 🔗