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Results: 26
Number of items: 26
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Sörensen, L. K. A. (2023). Deep neural network models of visual cognition. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Sörensen, L. K. A., Zambrano, D., Slagter, H., Bohte, S., & Scholte, S. (2020, December 16). ModelEvaluation [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.13385471.v1
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