| 2025 | IJCNN | Curriculum Design for Scalable Biologically Plausible Deep Reinforcement Learning. | Alexandra R. Van Den Berg, Pieter R. Roelfsema, Sander M. Boht |
| 2024 | ICANN | Masked Image Modeling as a Framework for Self-Supervised Learning Across Eye Movements. | Robin Weiler, Matthias Brucklacher, Cyriel M. A. Pennartz, Sander M. Boht |
| 2024 | ICML | Balanced Resonate-and-Fire Neurons. | Saya Higuchi, Sebastian Kairat, Sander M. Boht, Sebastian Otte |
| 2023 | ICANN | Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout. | Tao Sun, Bojian Yin, Sander M. Boht |
| 2022 | ICANN | A Taxonomy of Recurrent Learning Rules. | Guillermo Martn-Snchez, Sander M. Boht, Sebastian Otte |
| 2022 | IJCNN | Real-time classification of LIDAR data using discrete-time Recurrent Spiking Neural Networks. | Anca-Diana Vicol, Bojian Yin, Sander M. Boht |
| 2021 | ICANN | LocalNorm: Robust Image Classification Through Dynamically Regularized Normalization. | Bojian Yin, H. Steven Scholte, Sander M. Boht |
| 2018 | ICANN | A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs. | Shirin Dora, Cyriel M. A. Pennartz, Sander M. Boht |
| 2018 | ICANN | Continuous-Time Spike-Based Reinforcement Learning for Working Memory Tasks. | Marios Karamanis, Davide Zambrano, Sander M. Boht |
| 2018 | ICANN | Gating Sensory Noise in a Spiking Subtractive LSTM. | Isabella Pozzi, Roeland Nusselder, Davide Zambrano, Sander M. Boht |
| 2018 | ICLR | An image representation based convolutional network for DNA classification. | Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schnhuth, Sander M. Boht |
| 2015 | IJCNN | Continuous-time on-policy neural Reinforcement Learning of working memory tasks. | Davide Zambrano, Pieter R. Roelfsema, Sander M. Boht |
| 2014 | ESANN | Spiking Neural Networks: Principles and Challenges. | Andr Grning, Sander M. Boht |
| 2014 | ESANN | Learning resets of neural working memory. | Jaldert O. Rombouts, Pieter R. Roelfsema, Sander M. Boht |
| 2014 | ESANN | Spiking AGREL. | Davide Zambrano, Jaldert O. Rombouts, Cecilia Laschi, Sander M. Boht |
| 2012 | ICANN | Biologically Plausible Multi-dimensional Reinforcement Learning in Neural Networks. | Jaldert O. Rombouts, Arjen van Ooyen, Pieter R. Roelfsema, Sander M. Boht |
| 2011 | ICANN | Error-Backpropagation in Networks of Fractionally Predictive Spiking Neurons. | Sander M. Boht |
| 2009 | AIME | Optimization of Online Patient Scheduling with Urgencies and Preferences. | Ivan B. Vermeulen, Sander M. Boht, Peter A. N. Bosman, Sylvia G. Elkhuizen, Piet J. M. Bakker, Johannes A. La Poutr |
| 2007 | AIME | Adaptive Optimization of Hospital Resource Calendars. | Ivan B. Vermeulen, Sander M. Boht, Sylvia G. Elkhuizen, J. S. Lameris, Piet J. M. Bakker, Johannes A. La Poutr |
| 2006 | ECAI | Strategic Foresighted Learning in Competitive Multi-Agent Games. | Pieter Jan't Hoen, Sander M. Boht, Han La Poutr |
| 2005 | EUMAS | Action-Reaction in Multi-Agent Games. | Pieter Jan't Hoen, Sander M. Boht, Johannes A. La Poutr |
| 2004 | ICML | Nonparametric classification with polynomial MPMC cascades. | Sander M. Boht, Markus Breitenbach, Gregory Z. Grudic |
| 2002 | ESANN | Modeling efficient conjunction detection with spiking neural networks. | Sander M. Boht, Joost N. Kok, Johannes A. La Poutr |
| 2000 | ESANN | SpikeProp: backpropagation for networks of spiking neurons. | Sander M. Boht, Joost N. Kok, Johannes A. La Poutr |
| 2000 | IJCNN | Unsupervised Classification of Complex Clusters in Networks of Spiking Neurons. | Sander M. Boht, Johannes A. La Poutr, Joost N. Kok |