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Results: 26
Number of items: 26
  • Open Access
    Zhang, M., Chitic, R., & Bohté, S. M. (2025). Energy optimization induces predictive-coding properties in a multi-compartment spiking neural network model. PLoS Computational Biology, 21(6), Article e1013112. https://doi.org/10.1371/journal.pcbi.1013112
  • Open Access
    van den Berg, A. R. (2025). Biologically plausible reinforcement learning of deep cognitive processing. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Brucklacher, M. M. (2025). Representation learning with generative neural networks: A biological perspective. [Thesis, fully internal, Universiteit van Amsterdam].
  • Brucklacher, M., Lee, K., Moreni, G., Mejías, J. F., Bohté, S. M., & Pennartz, C. M. A. (2024). Predictive processing in neuroscience, computational modeling and psychology. In J. H. Grafman (Ed.), Encyclopedia of the Human Brain (2nd ed., Vol. 4, pp. 645-667). Elsevier. https://doi.org/10.1016/b978-0-12-820480-1.00201-1
  • Mejías, J. F., Amunts, K., Bjaalie, J. G., Bohté, S. M., Destexhe, A., Muckli, L., Paolucci, P. S., Pearson, M. J., & Pennartz, C. M. A. (2024). Human Brain Project and Beyond. In G. J. Boyle, G. Northoff, A. K. Barbey, F. Fregni, M. Jahanshahi, A. Pascual-Leone, & B. J. Sahakian (Eds.), The Sage Handbook of Cognitive and Systems Neuroscience. - [v. 1]. Cognitive systems, development and applications (pp. 511–532). Sage. https://doi.org/10.4135/9781529616613.n31
  • Open Access
    Mücke, N. T., Bohté, S. M., & Oosterlee, C. W. (2024). The deep latent space particle filter for real-time data assimilation with uncertainty quantification. Scientific Reports, 14, Article 19447. https://doi.org/10.1038/s41598-024-69901-7
  • Open Access
    Lee, K., Dora, S., Mejias, J. F., Bohte, S. M., & Pennartz, C. M. A. (2024). Predictive coding with spiking neurons and feedforward gist signaling. Frontiers in Computational Neuroscience, 18, Article 1338280. https://doi.org/10.3389/fncom.2024.1338280
  • Open Access
    van den Berg, A. R., Roelfsema, P. R., & Bohte, S. M. (2024). Biologically plausible gated recurrent neural networks for working memory and learning-to-learn. PLoS ONE, 19(12), Article e0316453. https://doi.org/10.1371/journal.pone.0316453
  • Open Access
    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
  • Open Access
    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
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