SpikeProp: backpropagation for networks of spiking neurons. - Sander M. Bohté; Joost N. Kok; Johannes A. La Poutré (ESANN, 2000)

Unsupervised Classification of Complex Clusters in Networks of Spiking Neurons. - Sander M. Bohté; Johannes A. La Poutré; Joost N. Kok (IJCNN (3), 2000)

The Effects of Pair-wise and Higher-order Correlations on the Firing Rate of a Postsynaptic Neuron. - Sander M. Bohté; Henk Spekreijse; Pieter R. Roelfsema (Neural Comput., 2000)

Competitive market-based allocation of consumer attention space. - Sander M. Bohté; Enrico H. Gerding; Johannes A. La Poutré (EC, 2001)

An Extensible Agent Architecture for a Competitive Market-Based Allocation of Consumer Attention Space. - Pieter Jan't Hoen; Sander M. Bohté; Enrico H. Gerding; Johannes A. La Poutré (AMEC, 2002)

Modeling efficient conjunction detection with spiking neural networks. - Sander M. Bohté; Joost N. Kok; Johannes A. La Poutré (ESANN, 2002)

Error-backpropagation in temporally encoded networks of spiking neurons. - Sander M. Bohté; Joost N. Kok; Johannes A. La Poutré (Neurocomputing, 2002)

Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. - Sander M. Bohté; Han La Poutré; Joost N. Kok (IEEE Trans. Neural Networks, 2002)

Automated Negotiation and Bundling of Information Goods. - D. J. A. Somefun; Enrico H. Gerding; Sander M. Bohté; Johannes A. La Poutré (AMEC, 2003)

COllective INtelligence with Sequences of Actions - Coordinating Actions in Multi-agent Systems. - Pieter Jan't Hoen; Sander M. Bohté (ECML, 2003)

Nonparametric classification with polynomial MPMC cascades. - Sander M. Bohté; Markus Breitenbach; Gregory Z. Grudic (ICML, 2004)

Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. - Sander M. Bohté; Michael C. Mozer (NIPS, 2004)

The evidence for neural information processing with precise spike-times: A survey. - Sander M. Bohté (Nat. Comput., 2004)

Introduction. - Sander M. Bohté; Michiel C. van Wezel; Joost N. Kok (Nat. Comput., 2004)

Market-based recommendation: Agents that compete for consumer attention. - Sander M. Bohté; Enrico H. Gerding; Johannes A. La Poutré (ACM Trans. Internet Techn., 2004)

Action-Reaction in Multi-Agent Games. - Pieter Jan't Hoen; Sander M. Bohté; Johannes A. La Poutré (EUMAS, 2005)

Applications of spiking neural networks. - Sander M. Bohté; Joost N. Kok (Inf. Process. Lett., 2005)

Learning from induced changes in opponent (re)actions in multi-agent games. - Pieter Jan't Hoen; Sander M. Bohté; Johannes A. La Poutré (AAMAS, 2006)

Strategic Foresighted Learning in Competitive Multi-Agent Games. - Pieter Jan't Hoen; Sander M. Bohté; Han La Poutré (ECAI, 2006)

Improving Patient Activity Schedules by Multi-agent Pareto Appointment Exchanging. - Ivan B. Vermeulen; Sander M. Bohté; D. J. A. Somefun; Han La Poutré (CEC/EEE, 2006)

Adaptive Optimization of Hospital Resource Calendars. - Ivan B. Vermeulen; Sander M. Bohté; Sylvia G. Elkhuizen; J. S. Lameris; Piet J. M. Bakker; Johannes A. La Poutré (AIME, 2007)

Adaptive Patient Scheduling with Dynamic Resource Usage. - Ivan B. Vermeulen; Sander M. Bohté; Han La Poutré (ERCIM News, 2007)

Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity. - Sander M. Bohté; Michael C. Mozer (Neural Comput., 2007)

Multi-agent Pareto appointment exchanging in hospital patient scheduling. - Ivan B. Vermeulen; Sander M. Bohté; Koye Somefun; Johannes A. La Poutré (Serv. Oriented Comput. Appl., 2007)

Decentralized Online Scheduling of Combination-Appointments in Hospitals. - Ivan B. Vermeulen; Sander M. Bohté; Sylvia G. Elkhuizen; Piet J. M. Bakker; Han La Poutré (ICAPS, 2008)

The Complex Dynamics of Sponsored Search Markets. - Valentin Robu; Han La Poutré; Sander M. Bohté (ADMI, 2009)

Optimization of Online Patient Scheduling with Urgencies and Preferences. - Ivan B. Vermeulen; Sander M. Bohté; Peter A. N. Bosman; Sylvia G. Elkhuizen; Piet J. M. Bakker; Johannes A. La Poutré (AIME, 2009)

Adaptive resource allocation for efficient patient scheduling. - Ivan B. Vermeulen; Sander M. Bohté; Sylvia G. Elkhuizen; Han Lameris; Piet J. M. Bakker; Han La Poutré (Artif. Intell. Medicine, 2009)

Fractionally Predictive Spiking Neurons. - Sander M. Bohté; Jaldert O. Rombouts (NIPS, 2010)

Error-Backpropagation in Networks of Fractionally Predictive Spiking Neurons. - Sander M. Bohté (ICANN (1), 2011)

Biologically Plausible Multi-dimensional Reinforcement Learning in Neural Networks. - Jaldert O. Rombouts; Arjen van Ooyen; Pieter R. Roelfsema; Sander M. Bohté (ICANN (1), 2012)

Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model. - Sander M. Bohté (NIPS, 2012)

Neurally Plausible Reinforcement Learning of Working Memory Tasks. - Jaldert O. Rombouts; Sander M. Bohté; Pieter R. Roelfsema (NIPS, 2012)

Learning to Recall. - Jaldert O. Rombouts; Pieter R. Roelfsema; Sander M. Bohté (ERCIM News, 2012)

Computing with Spiking Neuron Networks. - Hélène Paugam-Moisy; Sander M. Bohté (Handbook of Natural Computing, 2012)

Spiking Neural Networks: Principles and Challenges. - André Grüning; Sander M. Bohté (ESANN, 2014)

Learning resets of neural working memory. - Jaldert O. Rombouts; Pieter R. Roelfsema; Sander M. Bohté (ESANN, 2014)

Spiking AGREL. - Davide Zambrano; Jaldert O. Rombouts; Cecilia Laschi; Sander M. Bohté (ESANN, 2014)

Continuous-time on-policy neural Reinforcement Learning of working memory tasks. - Davide Zambrano; Pieter R. Roelfsema; Sander M. Bohté (IJCNN, 2015)

How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks. - Jaldert O. Rombouts; Sander M. Bohté; Pieter R. Roelfsema (PLoS Comput. Biol., 2015)

Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting. - Koen Groenland; Sander M. Bohté (CoRR, 2016)

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks. - Davide Zambrano; Sander M. Bohté (CoRR, 2016)

Modern Machine Learning: More with Less, Cheaper and Better. - Sander M. Bohté; Hung Son Nguyen (ERCIM News, 2016)

Efficient Computation in Adaptive Artificial Spiking Neural Networks. - Davide Zambrano; Roeland Nusselder; H. Steven Scholte; Sander M. Bohté (CoRR, 2017)

Editorial: Artificial Neural Networks as Models of Neural Information Processing. - Marcel van Gerven; Sander M. Bohté (Frontiers Comput. Neurosci., 2017)

A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs. - Shirin Dora; Cyriel M. A. Pennartz; Sander M. Bohté (ICANN (3), 2018)

Continuous-Time Spike-Based Reinforcement Learning for Working Memory Tasks. - Marios Karamanis; Davide Zambrano; Sander M. Bohté (ICANN (2), 2018)

Gating Sensory Noise in a Spiking Subtractive LSTM. - Isabella Pozzi; Roeland Nusselder; Davide Zambrano; Sander M. Bohté (ICANN (1), 2018)

An image representation based convolutional network for DNA classification. - Bojian Yin; Marleen Balvert; Davide Zambrano; Alexander Schönhuth; Sander M. Bohté (ICLR (Poster), 2018)

On the data-driven COS method. - Álvaro Leitao; Cornelis W. Oosterlee; Luis Ortiz-Gracia; Sander M. Bohté (Appl. Math. Comput., 2018)

A Biologically Plausible Learning Rule for Deep Learning in the Brain. - Isabella Pozzi; Sander M. Bohté; Pieter R. Roelfsema (CoRR, 2018)

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. - Bojian Yin; Marleen Balvert; Rick A. A. van der Spek; Bas E. Dutilh; Sander M. Bohté; Jan Veldink; Alexander Schönhuth (Bioinform., 2019)

Pricing options and computing implied volatilities using neural networks. - Shuaiqiang Liu; Cornelis W. Oosterlee; Sander M. Bohté (CoRR, 2019)

Generalisation in fully-connected neural networks for time series forecasting. - Anastasia Borovykh; Cornelis W. Oosterlee; Sander M. Bohté (CoRR, 2019)

LocalNorm: Robust Image Classification through Dynamically Regularized Normalization. - Bojian Yin; Siebren Schaafsma; Henk Corporaal; H. Steven Scholte; Sander M. Bohté (CoRR, 2019)

MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations. - Oliver Struckmeier; Kshitij Tiwari; Shirin Dora; Martin J. Pearson; Sander M. Bohté; Cyriel M. A. Pennartz; Ville Kyrki (CoRR, 2019)

Generalization in fully-connected neural networks for time series forecasting. - Anastasia Borovykh; Cornelis W. Oosterlee; Sander M. Bohté (J. Comput. Sci., 2019)

Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks. - Bojian Yin; Federico Corradi; Sander M. Bohté (ICONS, 2020)

Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation. - Isabella Pozzi; Sander M. Bohté; Pieter R. Roelfsema (NeurIPS, 2020)

Depth in convolutional neural networks solves scene segmentation. - Noor Seijdel; Nikos Tsakmakidis; Edward H. F. de Haan; Sander M. Bohté; H. Steven Scholte (PLoS Comput. Biol., 2020)

Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs. - Jesse J. Hagenaars; Federico Paredes-Vallés; Sander M. Bohté; Guido C. H. E. de Croon (IEEE Robotics Autom. Lett., 2020)

LocalNorm: Robust Image Classification Through Dynamically Regularized Normalization. - Bojian Yin; H. Steven Scholte; Sander M. Bohté (ICANN (4), 2021)

Effective and Efficient Spiking Recurrent Neural Networks. - Bojian Yin; Federico Corradi; Sander M. Bohté (ERCIM News, 2021)

Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy. - Shirin Dora; Sander M. Bohté; Cyriel M. A. Pennartz (Frontiers Comput. Neurosci., 2021)

Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding. - Martin J. Pearson; Shirin Dora; Oliver Struckmeier; Thomas C. Knowles; Ben Mitchinson; Kshitij Tiwari; Ville Kyrki; Sander M. Bohté; Cyriel M. A. Pennartz (Frontiers Robotics AI, 2021)

Learning continuous-time working memory tasks with on-policy neural reinforcement learning. - Davide Zambrano; Pieter R. Roelfsema; Sander M. Bohté (Neurocomputing, 2021)

Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning. - Nikolaj Takata Mücke; Sander M. Bohté; Cornelis W. Oosterlee (J. Comput. Sci., 2021)

Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. - Bojian Yin; Federico Corradi; Sander M. Bohté (Nat. Mach. Intell., 2021)

Flexible Working Memory Through Selective Gating and Attentional Tagging. - Wouter Kruijne; Sander M. Bohté; Pieter R. Roelfsema; Christian N. L. Olivers (Neural Comput., 2021)

A Taxonomy of Recurrent Learning Rules. - Guillermo Martín-Sánchez; Sander M. Bohté; Sebastian Otte (ICANN (1), 2022)

Attentive Decision-making and Dynamic Resetting of Continual Running SRNNs for End-to-End Streaming Keyword Spotting. - Bojian Yin; Qinghai Guo; Federico Corradi; Sander M. Bohté (ICONS, 2022)

Real-time classification of LIDAR data using discrete-time Recurrent Spiking Neural Networks. - Anca-Diana Vicol; Bojian Yin; Sander M. Bohté (IJCNN, 2022)

Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention. - Lynn K. A. Sörensen; Davide Zambrano; Heleen A. Slagter; Sander M. Bohté; H. Steven Scholte (J. Cogn. Neurosci., 2022)

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model. - Lynn K. A. Sörensen; Sander M. Bohté; Heleen A. Slagter; H. Steven Scholte (PLoS Comput. Biol., 2022)

Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout. - Tao Sun; Bojian Yin; Sander M. Bohté (ICANN (1), 2023)

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking. - Jason Yik; Soikat Hasan Ahmed; Zergham Ahmed; Brian Anderson; Andreas G. Andreou; Chiara Bartolozzi; Arindam Basu; Douwe den Blanken; Petrut Bogdan; Sander M. Bohté; Younes Bouhadjar; Sonia M. Buckley; et al. (CoRR, 2023)

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks. - Lynn K. A. Sörensen; Sander M. Bohté; Dorina De Jong; Heleen A. Slagter; H. Steven Scholte (PLoS Comput. Biol., 2023)

Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception. - Matthias Brucklacher; Sander M. Bohté; Jorge F. Mejías; Cyriel M. A. Pennartz (Frontiers Comput. Neurosci., 2023)

Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time. - Bojian Yin; Federico Corradi; Sander M. Bohté (Nat. Mac. Intell., 2023)

A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning. - Nikolaj Takata Mücke; Prerna Pandey; Shashi Jain; Sander M. Bohté; Cornelis W. Oosterlee (Sensors, 2023)

Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications. - Nikolaj Takata Mücke; Benjamin Sanderse; Sander M. Bohté; Cornelis W. Oosterlee (Comput. Math. Appl., 2023)

Masked Image Modeling as a Framework for Self-Supervised Learning Across Eye Movements. - Robin Weiler; Matthias Brucklacher; Cyriel M. A. Pennartz; Sander M. Bohté (ICANN (4), 2024)

Balanced Resonate-and-Fire Neurons. - Saya Higuchi; Sebastian Kairat; Sander M. Bohté; Sebastian Otte (ICML, 2024)

Understanding the Convergence in Balanced Resonate-and-Fire Neurons. - Saya Higuchi; Sander M. Bohté; Sebastian Otte (CoRR, 2024)

The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification. - Nikolaj Takata Mücke; Sander M. Bohté; Cornelis W. Oosterlee (CoRR, 2024)

DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement. - Tao Sun; Sander M. Bohté (CoRR, 2024)

Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression. - Tao Sun; Sander M. Bohté (CoRR, 2024)

Predictive coding with spiking neurons and feedforward gist signaling. - Kwangjun Lee; Shirin Dora; Jorge F. Mejías; Sander M. Bohté; Cyriel M. A. Pennartz (Frontiers Comput. Neurosci., 2024)

DPSNN: spiking neural network for low-latency streaming speech enhancement. - Tao Sun 026; Sander M. Bohté (Neuromorph. Comput. Eng., 2024)

Recurrent neural networks that learn multi-step visual routines with reinforcement learning. - Sami Mollard; Catherine Wacongne; Sander M. Bohté; Pieter R. Roelfsema (PLoS Comput. Biol., 2024)

All Eyes, no IMU: Learning Flight Attitude from Vision Alone. - Jesse J. Hagenaars; Stein Stroobants; Sander M. Bohté; Guido C. H. E. de Croon (CoRR, 2025)