Publications

• P. Turishcheva, P. G. Fahey, M. Vystrčilová, L. Hansel, R. Froebe, K. Ponder, Y. Qiu, K. F. Willeke, M. Bashiri, R. Baikulov, Y. Zhu, L. Ma, S. Yu, T. Huang, B. M. Li, W. De Wulf, N. Kudryashova, M. H. Hennig, N. L. Rochefort, A. Onken, E. Wang, Z. Ding, A. S. Tolias, F. H. Sinz, A. S. Ecker (2024). Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos. Advances in Neural Information Processing Systems 38 (NeurIPS 2024).
• S. Esposito, Q. Xu, K. Kania, C. Hewitt, O. Mariotti, L. Petikam, J. Valentin, A. Onken, O. Mac Aodha (2024). GeoGen: Geometry-aware generative modeling via signed distance functions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024), pages 7479-7488.
• B. Li, I. M. Cornacchia, N. L. Rochefort, A. Onken (2023). V1T: large-scale mouse V1 response prediction using a Vision Transformer. Transactions on Machine Learning Research 2835-8856.
• L. Mitskopoulos, A. Onken (2023). Discovering low-dimensional descriptions of multineuronal dependencies. Entropy 25(7):e25071026.
• L. Mitskopoulos, T. Amvrosiadis, A. Onken (2022). Mixed vine copula flows for flexible modeling of neural dependencies. Frontiers in Neuroscience 16:910122.
• N. Kudryashova, T. Amvrosiadis, N. Dupuy, N. Rochefort, A. Onken (2022). Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships. PLoS Computational Biology 18(1):e1009799.
• C. Hurwitz, N. Kudryashova, A. Onken, M. H. Hennig (2021). Building population models for large-scale neural recordings: Opportunities and pitfalls. Current Opinion in Neurobiology 70:64–73.
• S. Jia, Z. Yu, A. Onken, Y. Tian, T. Huang, J. K. Liu (2021). Neural system identification with spike-triggered non-negative matrix factorization. IEEE Transactions on Cybernetics.
• T. Tsunematsu, A. A. Patel, A. Onken, and S. Sakata (2020). State-dependent brainstem ensemble dynamics and their interactions with hippocampus across sleep states. eLife 9:e52244.
• B. Breier and A. Onken (2020). Analysis of video feature learning in two-stream CNNs on the example of zebrafish swim bout classification. In Eighth International Conference on Learning Representations (ICLR 2020).
• A. Onken, J. Xie, S. Panzeri, and C. Padoa-Schioppa (2019). Categorical encoding of decision variables in orbitofrontal cortex. PLoS Computational Biology 15(10): e1006667.
• H. Safaai, A. Onken, C. D. Harvey, and S. Panzeri (2018). Information estimation using nonparametric copulas. Physical Review E 98(5): 053302.
• M. Molano-Mazon, A. Onken, E. Piasini, and S. Panzeri (2018). Synthesizing realistic neural population activity patterns using Generative Adversarial Networks. In Sixth International Conference on Learning Representations (ICLR 2018).
• J. K. Liu, H. M. Schreyer, A. Onken, F. Rozenblit, M. H. Khani, S. Panzeri, and T. Gollisch (2017). Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization. Nature Communications 8: 149.
• I. Delis, A. Onken, and S. Panzeri (2017). Space-by-time tensor decomposition for single-trial analysis of neural signals. In G. Naldi and T. Nieus, editors, Mathematical and Theoretical Neuroscience, Springer INdAM Series, vol 24. Springer, Cham, Cham, pages 223–237.
• V. De Feo, F. Boi, H. Safaai, A. Onken, S. Panzeri, and A. Vato (2017). State-dependent decoding algorithms improve the performance of a bidirectional BMI in anesthetized rats. Frontiers in Neuroscience - Neural Technology 11: 269.
• A. Onken and S. Panzeri (2016). Mixed vine copulas as joint models of spike counts and local field potentials. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon and R. Garnett, editors, Advances in Neural Information Processing Systems 29 (NIPS 2016), pages 1325–1333.
• A. Onken, J. K. Liu, P. P. C. R. Karunasekara, I. Delis, T. Gollisch, and S. Panzeri (2016). Using matrix and tensor factorizations for the single-trial analysis of population spike trains. PLoS Computational Biology 12(11): e1005189.
• I. Delis, A. Onken, P. G. Schyns, S. Panzeri, and M. G. Philiastides (2016). Space-by-time decomposition for single-trial decoding of M/EEG activity. NeuroImage 133: 504–515.
• A. Onken, P. P. C. R. Karunasekara, C. Kayser, and S. Panzeri (2014). Understanding neural population coding: information-theoretic insights from the auditory system. Advances in Neuroscience, vol. 2014, Article ID 907851.
• A. Onken, V. Dragoi, and K. Obermayer (2012). A maximum entropy test for evaluating higher-order correlations in spike counts. PLoS Computational Biology 8(6): e1002539.
• A. Onken, S. Grünewälder, M. H. J. Munk, and K. Obermayer (2009). Analyzing short-term noise dependencies of spike-counts in macaque prefrontal cortex using copulas and the flashlight transformation. PLoS Computational Biology 5(11): e1000577.
• A. Onken and K. Obermayer (2009). A Frank mixture copula for modeling higher-order correlations of neural spike counts. Journal of Physics: Conference Series 197: 012019.
• A. Onken, S. Grünewälder, and K. Obermayer (2009). Correlation coefficients are insufficient for analyzing spike count dependencies. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22 (NIPS 2009), pages 1383–1391.
• A. Onken, S. Grünewälder, M. H. J. Munk, and K. Obermayer (2008). Modeling short-term noise dependence of spike counts in macaque prefrontal cortex. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21 (NIPS 2008), pages 1233–1240.