Prof. dr. S.M. (Sander) Bohte
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Swammerdam Institute for Life Sciences
Bezoekadres
- Science Park 904
Postadres
-
Postbus 94246
1090 GE Amsterdam
Contactgegevens
-
Publicaties
2023
Brucklacher, M., Bohté, S. M., Mejias, J. F., & Pennartz, C. M. A. (2023). Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception. Frontiers in Computational Neuroscience, 17, Article 1207361. https://doi.org/10.3389/fncom.2023.1207361 [details]- Mücke, N. T., Pandey, P., Jain, S., Bohté, S. M., & Oosterlee, C. W. (2023). A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning. Sensors, 23(13), Article 6179. https://doi.org/10.3390/s23136179
- Mücke, N. T., Sanderse, B., Bohté, S. M., & Oosterlee, C. W. (2023). Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications. Computers and Mathematics with Applications, 147, 278-299. https://doi.org/10.1016/j.camwa.2023.07.028 [details]
- Sörensen, L. K. A., Bohté, S. M., de Jong, D., Slagter, H. A., & Scholte, H. S. (2023). Mechanisms of human dynamic object recognition revealed by sequential deep neural networks. PLoS Computational Biology, 19(6), Article e1011169. https://doi.org/10.1371/journal.pcbi.1011169
- Yin, B., Corradi, F., & Bohté, S. M. (2023). Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time. Nature Machine Intelligence, 5(5), 518-527. https://doi.org/10.48550/arXiv.2112.11231, https://doi.org/10.1038/S42256-023-00650-4 [details]
2022
- Sörensen, L. K. A., Bohté, S. M., Slagter, H. A., & Scholte, H. S. (2022). Arousal state affects perceptual decisionmaking by modulating hierarchical sensory processing in a large-scale visual system model. PLoS Computational Biology, 18(4), Article e1009976. https://doi.org/10.1371/journal.pcbi.1009976 [details]
- Sörensen, L. K. A., Zambrano, D., Slagter, H. A., Bohté, S. M., & Scholte, H. S. (2022). Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention. Journal of Cognitive Neuroscience, 34(4), 655-674. https://doi.org/10.1162/jocn_a_01819 [details]
2021
- Dora, S., Bohte, S. M., & Pennartz, C. M. A. (2021). Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy. Frontiers in Computational Neuroscience, 15, Article 666131. https://doi.org/10.3389/fncom.2021.666131 [details]
- Pearson, M. J., Dora, S., Struckmeier, O., Knowles, T. C., Mitchinson, B., Tiwari, K., Kyrki, V., Bohte, S., & Pennartz, C. M. A. (2021). Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding. Frontiers in Robotics and AI, 8, Article 732023. https://doi.org/10.3389/frobt.2021.732023 [details]
- Yin, B., Scholte, H. S., & Bohté, S. (2021). LocalNorm: Robust Image Classification Through Dynamically Regularized Normalization. In I. Farkaš, P. Masulli, S. Otte, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021 : proceedings (Vol. IV, pp. 240-252). (Lecture Notes in Computer Science; Vol. 12894). Springer. https://doi.org/10.1007/978-3-030-86380-7_20 [details]
2020
- Seijdel, N., Tsakmakidis, N., de Haan, E. H. F., Bohte, S. M., & Scholte, H. S. (2020). Depth in convolutional neural networks solves scene segmentation. PLoS Computational Biology, 16(7), Article e1008022. https://doi.org/10.1371/journal.pcbi.1008022 [details]
2019
- Zambrano, D., Nusselder, R., Scholte, H. S., & Bohté, S. M. (2019). Sparse Computation in Adaptive Spiking Neural Networks. Frontiers in Neuroscience, 12, Article 987. https://doi.org/10.3389/fnins.2018.00987 [details]
2018
- Scholte, H. S., Losch, M. M., Ramakrishnan, K., de Haan, E. H. F., & Bohte, S. M. (2018). Visual pathways from the perspective of cost functions and multi-task deep neural networks. Cortex, 98, 249-261. Advance online publication. https://doi.org/10.1016/j.cortex.2017.09.019 [details]
2019
- Seijdel, N., Tsakmakidis, N., De Haan, E. H. F., Bohte, S. M., & Scholte, H. S. (2019). Depth in convolutional neural networks solves scene segmentation. (v1 ed.) BioRxiv. https://doi.org/10.1101/2019.12.16.877753 [details]
2012
- Bohte, S. M., Scholte, H. S., & Ghebreab, S. (2012). Information-Maximizing Local Spatial Scale Selection in Early Visual Processing. Abstract from NIPS Workshop on Information in Perception and Action, Lake Tahoe, December 2012. http://www.montefiore.ulg.ac.be/~tjung/nips12workshop
2020
- Sörensen, L. K. A., Zambrano, D., Slagter, H., Bohte, S. & Scholte, S. (16-12-2020). ModelAnalysis. Universiteit van Amsterdam. https://doi.org/10.21942/uva.13386377.v1
- Sörensen, L. K. A., Zambrano, D., Slagter, H., Bohte, S. & Scholte, S. (16-12-2020). ModelTraining. Universiteit van Amsterdam. https://doi.org/10.21942/uva.13386395.v1
- Sörensen, L. K. A., Zambrano, D., Slagter, H., Bohte, S. & Scholte, S. (16-12-2020). ModelEvaluation. Universiteit van Amsterdam. https://doi.org/10.21942/uva.13385471.v1
This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list. -
Nevenwerkzaamheden
-
CWI
Senior onderzoeker
-
CWI
Brucklacher, M., Bohté, S. M., Mejias, J. F., & Pennartz, C. M. A. (2023). Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception. Frontiers in Computational Neuroscience, 17, Article 1207361.