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ORCIDSander M. Bohté
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- affiliation: Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
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2020 – today
- 2024
[j32]Kwangjun Lee, Shirin Dora, Jorge F. Mejías
, Sander M. Bohté, Cyriel M. A. Pennartz:
Predictive coding with spiking neurons and feedforward gist signaling. Frontiers Comput. Neurosci. 18 (2024)- [j31]Sami Mollard, Catherine Wacongne, Sander M. Bohté, Pieter R. Roelfsema:
Recurrent neural networks that learn multi-step visual routines with reinforcement learning. PLoS Comput. Biol. 20(4): 1012030 (2024) - [c40]Robin Weiler, Matthias Brucklacher, Cyriel M. A. Pennartz, Sander M. Bohté:
Masked Image Modeling as a Framework for Self-Supervised Learning Across Eye Movements. ICANN (4) 2024: 17-31 - [c39]Saya Higuchi, Sebastian Kairat, Sander M. Bohté, Sebastian Otte:
Balanced Resonate-and-Fire Neurons. ICML 2024 - [i29]Saya Higuchi, Sebastian Kairat, Sander M. Bohté, Sebastian Otte:
Balanced Resonate-and-Fire Neurons. CoRR abs/2402.14603 (2024) - [i28]Robin Weiler, Matthias Brucklacher, Cyriel M. A. Pennartz, Sander M. Bohté:
Masked Image Modeling as a Framework for Self-Supervised Learning across Eye Movements. CoRR abs/2404.08526 (2024) - [i27]Saya Higuchi, Sander M. Bohté, Sebastian Otte:
Understanding the Convergence in Balanced Resonate-and-Fire Neurons. CoRR abs/2406.00389 (2024) - [i26]Nikolaj Takata Mücke, Sander M. Bohté, Cornelis W. Oosterlee:
The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification. CoRR abs/2406.02204 (2024) - [i25]Tao Sun, Sander M. Bohté:
DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement. CoRR abs/2408.07388 (2024) - [i24]Tao Sun, Sander M. Bohté:
Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression. CoRR abs/2412.00278 (2024) - 2023
- [j30]Nikolaj Takata Mücke, Benjamin Sanderse, Sander M. Bohté, Cornelis W. Oosterlee:
Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications. Comput. Math. Appl. 147: 278-299 (2023) - [j29]Matthias Brucklacher, Sander M. Bohté, Jorge F. Mejías, Cyriel M. A. Pennartz:
Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception. Frontiers Comput. Neurosci. 17 (2023) - [j28]Bojian Yin, Federico Corradi, Sander M. Bohté:
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time. Nat. Mac. Intell. 5(5): 518-527 (2023) - [j27]Lynn K. A. Sörensen, Sander M. Bohté, Dorina De Jong, Heleen A. Slagter, H. Steven Scholte:
Mechanisms of human dynamic object recognition revealed by sequential deep neural networks. PLoS Comput. Biol. 19(6) (2023) - [j26]Nikolaj Takata Mücke, Prerna Pandey, Shashi Jain, Sander M. Bohté, Cornelis W. Oosterlee:
A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning. Sensors 23(13): 6179 (2023) - [c38]Tao Sun, Bojian Yin, Sander M. Bohté:
Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout. ICANN (1) 2023: 393-406 - [i23]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, Gert Cauwenberghs, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Reddy Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Jeremy Forest, Steve B. Furber, Michael Furlong, Aditya Gilra, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Gregor Lenz, Rajit Manohar, Christian Mayr, Konstantinos P. Michmizos, Dylan R. Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayça Özcelikkale, Noah Pacik-Nelson, Priyadarshini Panda, Pao-Sheng Sun, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Catherine D. Schuman, Jae-sun Seo, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Kenneth Michael Stewart, Terrence C. Stewart, Philipp Stratmann, Guangzhi Tang, Jonathan Timcheck, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Biyan Zhou, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi:
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking. CoRR abs/2304.04640 (2023) - [i22]Tao Sun, Bojian Yin, Sander M. Bohté:
Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout. CoRR abs/2304.10191 (2023) - 2022
- [j25]Lynn K. A. Sörensen, Davide Zambrano, Heleen A. Slagter, Sander M. Bohté, H. Steven Scholte:
Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention. J. Cogn. Neurosci. 34(4): 655-674 (2022) - [j24]Lynn K. A. Sörensen, Sander M. Bohté, Heleen A. Slagter, H. Steven Scholte:
Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model. PLoS Comput. Biol. 18(4) (2022) - [c37]Guillermo Martín-Sánchez, Sander M. Bohté, Sebastian Otte:
A Taxonomy of Recurrent Learning Rules. ICANN (1) 2022: 478-490 - [c36]Bojian Yin, Qinghai Guo, Federico Corradi, Sander M. Bohté:
Attentive Decision-making and Dynamic Resetting of Continual Running SRNNs for End-to-End Streaming Keyword Spotting. ICONS 2022: 5:1-5:8 - [c35]Anca-Diana Vicol, Bojian Yin, Sander M. Bohté:
Real-time classification of LIDAR data using discrete-time Recurrent Spiking Neural Networks. IJCNN 2022: 1-9 - [i21]Guillermo Martín-Sánchez, Sander M. Bohté, Sebastian Otte:
A Taxonomy of Recurrent Learning Rules. CoRR abs/2207.11439 (2022) - 2021
- [j23]Shirin Dora, Sander M. Bohté, Cyriel M. A. Pennartz:
Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy. Frontiers Comput. Neurosci. 15: 666131 (2021) - [j22]Martin J. Pearson, Shirin Dora, Oliver Struckmeier, Thomas C. Knowles, Ben Mitchinson, Kshitij Tiwari, Ville Kyrki, Sander M. Bohté, Cyriel M. A. Pennartz:
Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding. Frontiers Robotics AI 8: 732023 (2021) - [j21]Davide Zambrano, Pieter R. Roelfsema, Sander M. Bohté:
Learning continuous-time working memory tasks with on-policy neural reinforcement learning. Neurocomputing 461: 635-656 (2021) - [j20]Nikolaj Takata Mücke, Sander M. Bohté, Cornelis W. Oosterlee:
Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning. J. Comput. Sci. 53: 101408 (2021) - [j19]Bojian Yin, Federico Corradi, Sander M. Bohté:
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. Nat. Mach. Intell. 3(10): 905-913 (2021) - [j18]Wouter Kruijne, Sander M. Bohté, Pieter R. Roelfsema, Christian N. L. Olivers:
Flexible Working Memory Through Selective Gating and Attentional Tagging. Neural Comput. 33(1): 1-40 (2021) - [c34]Bojian Yin, H. Steven Scholte, Sander M. Bohté:
LocalNorm: Robust Image Classification Through Dynamically Regularized Normalization. ICANN (4) 2021: 240-252 - [i20]Bojian Yin, Federico Corradi, Sander M. Bohté:
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. CoRR abs/2103.12593 (2021) - [i19]Nikolaj Takata Mücke, Benjamin Sanderse, Sander M. Bohté, Cornelis W. Oosterlee:
Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications. CoRR abs/2111.12408 (2021) - [i18]Bojian Yin, Federico Corradi, Sander M. Bohté:
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time. CoRR abs/2112.11231 (2021) - [i17]Bojian Yin, Federico Corradi, Sander M. Bohté:
Effective and Efficient Spiking Recurrent Neural Networks. ERCIM News 2021(125) (2021) - 2020
- [j17]Noor Seijdel, Nikos Tsakmakidis, Edward H. F. de Haan, Sander M. Bohté, H. Steven Scholte:
Depth in convolutional neural networks solves scene segmentation. PLoS Comput. Biol. 16(7) (2020) - [j16]Jesse J. Hagenaars, Federico Paredes-Vallés, Sander M. Bohté, Guido C. H. E. de Croon:
Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs. IEEE Robotics Autom. Lett. 5(4): 6239-6246 (2020) - [c33]Bojian Yin, Federico Corradi, Sander M. Bohté:
Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks. ICONS 2020: 1:1-1:8 - [c32]Isabella Pozzi, Sander M. Bohté, Pieter R. Roelfsema:
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation. NeurIPS 2020 - [i16]Jesse J. Hagenaars, Federico Paredes-Vallés, Sander M. Bohté, Guido C. H. E. de Croon:
Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs. CoRR abs/2003.03118 (2020) - [i15]Bojian Yin, Federico Corradi, Sander M. Bohté:
Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks. CoRR abs/2005.11633 (2020) - [i14]Nikolaj Takata Mücke, Sander M. Bohté, Cornelis W. Oosterlee:
Reduced Order Modeling for Parameterized Time-Dependent PDEs using Spatially and Memory Aware Deep Learning. CoRR abs/2011.11327 (2020)
2010 – 2019
- 2019
- [j15]Bojian Yin, Marleen Balvert, Rick A. A. van der Spek, Bas E. Dutilh, Sander M. Bohté, Jan Veldink, Alexander Schönhuth:
Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. Bioinform. 35(14): i538-i547 (2019) - [j14]Anastasia Borovykh, Cornelis W. Oosterlee, Sander M. Bohté:
Generalization in fully-connected neural networks for time series forecasting. J. Comput. Sci. 36 (2019) - [i13]Shuaiqiang Liu, Cornelis W. Oosterlee, Sander M. Bohté:
Pricing options and computing implied volatilities using neural networks. CoRR abs/1901.08943 (2019) - [i12]Anastasia Borovykh, Cornelis W. Oosterlee, Sander M. Bohté:
Generalisation in fully-connected neural networks for time series forecasting. CoRR abs/1902.05312 (2019) - [i11]Bojian Yin, Siebren Schaafsma, Henk Corporaal, H. Steven Scholte, Sander M. Bohté:
LocalNorm: Robust Image Classification through Dynamically Regularized Normalization. CoRR abs/1902.06550 (2019) - [i10]Oliver Struckmeier, Kshitij Tiwari, Shirin Dora, Martin J. Pearson, Sander M. Bohté, Cyriel M. A. Pennartz, Ville Kyrki:
MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations. CoRR abs/1909.07201 (2019) - 2018
- [j13]Álvaro Leitao, Cornelis W. Oosterlee, Luis Ortiz-Gracia, Sander M. Bohté:
On the data-driven COS method. Appl. Math. Comput. 317: 68-84 (2018) - [c31]Marios Karamanis, Davide Zambrano, Sander M. Bohté:
Continuous-Time Spike-Based Reinforcement Learning for Working Memory Tasks. ICANN (2) 2018: 250-262 - [c30]Isabella Pozzi, Roeland Nusselder, Davide Zambrano, Sander M. Bohté:
Gating Sensory Noise in a Spiking Subtractive LSTM. ICANN (1) 2018: 284-293 - [c29]Shirin Dora, Cyriel M. A. Pennartz, Sander M. Bohté:
A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs. ICANN (3) 2018: 457-467 - [c28]Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schönhuth, Sander M. Bohté:
An image representation based convolutional network for DNA classification. ICLR (Poster) 2018 - [i9]Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schönhuth, Sander M. Bohté:
An image representation based convolutional network for DNA classification. CoRR abs/1806.04931 (2018) - [i8]Isabella Pozzi, Sander M. Bohté, Pieter R. Roelfsema:
A Biologically Plausible Learning Rule for Deep Learning in the Brain. CoRR abs/1811.01768 (2018) - 2017
- [j12]Marcel van Gerven, Sander M. Bohté:
Editorial: Artificial Neural Networks as Models of Neural Information Processing. Frontiers Comput. Neurosci. 11: 114 (2017) - [i7]Davide Zambrano, Roeland Nusselder, H. Steven Scholte, Sander M. Bohté:
Efficient Computation in Adaptive Artificial Spiking Neural Networks. CoRR abs/1710.04838 (2017) - 2016
- [i6]Koen Groenland, Sander M. Bohté:
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting. CoRR abs/1603.03657 (2016) - [i5]Davide Zambrano, Sander M. Bohté:
Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks. CoRR abs/1609.02053 (2016) - [i4]Sander M. Bohté, Hung Son Nguyen:
Modern Machine Learning: More with Less, Cheaper and Better. ERCIM News 2016(107) (2016) - 2015
- [j11]Jaldert O. Rombouts, Sander M. Bohté, Pieter R. Roelfsema:
How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks. PLoS Comput. Biol. 11(3) (2015) - [c27]Davide Zambrano, Pieter R. Roelfsema, Sander M. Bohté:
Continuous-time on-policy neural Reinforcement Learning of working memory tasks. IJCNN 2015: 1-8 - 2014
- [c26]André Grüning, Sander M. Bohté:
Spiking Neural Networks: Principles and Challenges. ESANN 2014 - [c25]Jaldert O. Rombouts, Pieter R. Roelfsema, Sander M. Bohté:
Learning resets of neural working memory. ESANN 2014 - [c24]Davide Zambrano, Jaldert O. Rombouts, Cecilia Laschi, Sander M. Bohté:
Spiking AGREL. ESANN 2014 - 2012
- [c23]Jaldert O. Rombouts, Arjen van Ooyen, Pieter R. Roelfsema, Sander M. Bohté:
Biologically Plausible Multi-dimensional Reinforcement Learning in Neural Networks. ICANN (1) 2012: 443-450 - [c22]Sander M. Bohté:
Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model. NIPS 2012: 1844-1852 - [c21]Jaldert O. Rombouts, Sander M. Bohté, Pieter R. Roelfsema:
Neurally Plausible Reinforcement Learning of Working Memory Tasks. NIPS 2012: 1880-1888 - [p1]Hélène Paugam-Moisy, Sander M. Bohté:
Computing with Spiking Neuron Networks. Handbook of Natural Computing 2012: 335-376 - [i3]Jaldert O. Rombouts, Pieter R. Roelfsema, Sander M. Bohté:
Learning to Recall. ERCIM News 2012(91) (2012) - 2011
- [c20]Sander M. Bohté:
Error-Backpropagation in Networks of Fractionally Predictive Spiking Neurons. ICANN (1) 2011: 60-68 - 2010
- [c19]Sander M. Bohté, Jaldert O. Rombouts:
Fractionally Predictive Spiking Neurons. NIPS 2010: 253-261 - [i2]Sander M. Bohté, Jaldert O. Rombouts:
Fractionally Predictive Spiking Neurons. CoRR abs/1010.6178 (2010)
2000 – 2009
- 2009
- [j10]Ivan B. Vermeulen, Sander M. Bohté, Sylvia G. Elkhuizen, Han Lameris, Piet J. M. Bakker, Han La Poutré:
Adaptive resource allocation for efficient patient scheduling. Artif. Intell. Medicine 46(1): 67-80 (2009) - [c18]Valentin Robu, Han La Poutré, Sander M. Bohté:
The Complex Dynamics of Sponsored Search Markets. ADMI 2009: 183-198 - [c17]Ivan B. Vermeulen, Sander M. Bohté, Peter A. N. Bosman, Sylvia G. Elkhuizen, Piet J. M. Bakker, Johannes A. La Poutré:
Optimization of Online Patient Scheduling with Urgencies and Preferences. AIME 2009: 71-80 - 2008
- [c16]Ivan B. Vermeulen, Sander M. Bohté, Sylvia G. Elkhuizen, Piet J. M. Bakker, Han La Poutré:
Decentralized Online Scheduling of Combination-Appointments in Hospitals. ICAPS 2008: 372-379 - 2007
- [j9]Sander M. Bohté, Michael C. Mozer:
Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity. Neural Comput. 19(2): 371-403 (2007) - [j8]Ivan B. Vermeulen, Sander M. Bohté, Koye Somefun, Johannes A. La Poutré:
Multi-agent Pareto appointment exchanging in hospital patient scheduling. Serv. Oriented Comput. Appl. 1(3): 185-196 (2007) - [c15]Ivan B. Vermeulen, Sander M. Bohté, Sylvia G. Elkhuizen, J. S. Lameris, Piet J. M. Bakker, Johannes A. La Poutré:
Adaptive Optimization of Hospital Resource Calendars. AIME 2007: 305-315 - [i1]Ivan B. Vermeulen, Sander M. Bohté, Han La Poutré:
Adaptive Patient Scheduling with Dynamic Resource Usage. ERCIM News 2007(69) (2007) - 2006
- [c14]Pieter Jan't Hoen, Sander M. Bohté, Johannes A. La Poutré:
Learning from induced changes in opponent (re)actions in multi-agent games. AAMAS 2006: 728-735 - [c13]Pieter Jan't Hoen, Sander M. Bohté, Han La Poutré:
Strategic Foresighted Learning in Competitive Multi-Agent Games. ECAI 2006: 536- - [c12]Ivan B. Vermeulen, Sander M. Bohté, D. J. A. Somefun, Han La Poutré:
Improving Patient Activity Schedules by Multi-agent Pareto Appointment Exchanging. CEC/EEE 2006: 9 - 2005
- [j7]Sander M. Bohté, Joost N. Kok:
Applications of spiking neural networks. Inf. Process. Lett. 95(6): 519-520 (2005) - [c11]Sander M. Bohté, Michael C. Mozer:
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. BNAIC 2005: 319-320 - [c10]Pieter Jan't Hoen, Sander M. Bohté, Johannes A. La Poutré:
Action-Reaction in Multi-Agent Games. EUMAS 2005: 18-24 - 2004
- [j6]Sander M. Bohté, Michiel C. van Wezel, Joost N. Kok:
Introduction. Nat. Comput. 3(2): 133-134 (2004) - [j5]Sander M. Bohté:
The evidence for neural information processing with precise spike-times: A survey. Nat. Comput. 3(2): 195-206 (2004) - [j4]Sander M. Bohté, Enrico H. Gerding, Johannes A. La Poutré:
Market-based recommendation: Agents that compete for consumer attention. ACM Trans. Internet Techn. 4(4): 420-448 (2004) - [c9]Sander M. Bohté, Markus Breitenbach, Gregory Z. Grudic:
Nonparametric classification with polynomial MPMC cascades. ICML 2004 - [c8]Sander M. Bohté, Michael C. Mozer:
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. NIPS 2004: 201-208 - 2003
- [c7]D. J. A. Somefun, Enrico H. Gerding, Sander M. Bohté, Johannes A. La Poutré:
Automated Negotiation and Bundling of Information Goods. AMEC 2003: 1-17 - [c6]Pieter Jan't Hoen, Sander M. Bohté:
COllective INtelligence with Sequences of Actions - Coordinating Actions in Multi-agent Systems. ECML 2003: 181-192 - 2002
- [j3]Sander M. Bohté, Joost N. Kok, Johannes A. La Poutré:
Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1-4): 17-37 (2002) - [j2]Sander M. Bohté, Han La Poutré, Joost N. Kok:
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Trans. Neural Networks 13(2): 426-435 (2002) - [c5]Pieter Jan't Hoen, Sander M. Bohté, Enrico H. Gerding, Johannes A. La Poutré:
An Extensible Agent Architecture for a Competitive Market-Based Allocation of Consumer Attention Space. AMEC 2002: 273-288 - [c4]Sander M. Bohté, Joost N. Kok, Johannes A. La Poutré:
Modeling efficient conjunction detection with spiking neural networks. ESANN 2002: 263-268 - 2001
- [c3]Sander M. Bohté, Enrico H. Gerding, Johannes A. La Poutré:
Competitive market-based allocation of consumer attention space. EC 2001: 202-205 - 2000
- [j1]Sander M. Bohté, Henk Spekreijse, Pieter R. Roelfsema:
The Effects of Pair-wise and Higher-order Correlations on the Firing Rate of a Postsynaptic Neuron. Neural Comput. 12(1): 153-179 (2000) - [c2]Sander M. Bohté, Joost N. Kok, Johannes A. La Poutré:
SpikeProp: backpropagation for networks of spiking neurons. ESANN 2000: 419-424 - [c1]Sander M. Bohté, Johannes A. La Poutré, Joost N. Kok:
Unsupervised Classification of Complex Clusters in Networks of Spiking Neurons. IJCNN (3) 2000: 279-284
Coauthor Index
Han La Poutré
aka: Johannes A. La Poutré
aka: Johannes A. La Poutré
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