Visualizing a joint future of neuroscience and neuromorphic engineering von Zenke, Friedemann, Bohté, Sander M, Clopath, Claudia, Comşa, Iulia M, Göltz, Julian, Maass, Wolfgang, Masquelier, Timothée, Naud, Richard, Neftci, Emre O, Petrovici, Mihai A, Scherr, Franz, Goodman, Dan F M

Volltext

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks von Sörensen, Lynn K A, Bohté, Sander M, de Jong, Dorina, Slagter, Heleen A, Scholte, H Steven

Volltext

Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time von Yin, Bojian, Corradi, Federico, Bohté, Sander M.

Volltext

A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning von Mücke, Nikolaj T, Pandey, Prerna, Jain, Shashi, Bohté, Sander M, Oosterlee, Cornelis W

Volltext

Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception von Brucklacher, Matthias, Bohté, Sander M, Mejias, Jorge F, Pennartz, Cyriel M A

Volltext

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model von Sörensen, Lynn K A, Bohté, Sander M, Slagter, Heleen A, Scholte, H Steven

Volltext

Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention von Sörensen, Lynn K. A, Zambrano, Davide, Slagter, Heleen A, Bohté, Sander M, Scholte, H. Steven

Volltext

Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks von Yin, Bojian, Corradi, Federico, Bohté, Sander M.

Volltext

Editorial: Artificial Neural Networks as Models of Neural Information Processing von van Gerven, Marcel, Bohte, Sander

Volltext

The deep latent space particle filter for real-time data assimilation with uncertainty quantification von Mücke, Nikolaj T., Bohté, Sander M., Oosterlee, Cornelis W.

Volltext

DPSNN: spiking neural network for low-latency streaming speech enhancement von Sun, Tao, Bohté, Sander

Volltext

Sparse Computation in Adaptive Spiking Neural Networks von Zambrano, Davide, Nusselder, Roeland, Scholte, H Steven, Bohté, Sander M

Volltext

Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning von Mücke, Nikolaj T., Bohté, Sander M., Oosterlee, Cornelis W.

Volltext

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

Volltext

Recurrent neural networks that learn multi-step visual routines with reinforcement learning von Mollard, Sami, Wacongne, Catherine, Bohte, Sander M, Roelfsema, Pieter R

Volltext

Pricing options and computing implied volatilities using neural networks von Liu, Shuaiqiang, Oosterlee, Cornelis Willebrordus, Bohte, Sander M

Volltext

Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications von Mücke, Nikolaj T., Sanderse, Benjamin, Bohté, Sander M., Oosterlee, Cornelis W.

Volltext

How attention can create synaptic tags for the learning of working memories in sequential tasks von Rombouts, Jaldert O, Bohte, Sander M, Roelfsema, Pieter R

Volltext

Biologically plausible gated recurrent neural networks for working memory and learning-to-learn von van den Berg, Alexandra R, Roelfsema, Pieter R, Bohte, Sander M

Volltext

Depth in convolutional neural networks solves scene segmentation von Seijdel, Noor, Tsakmakidis, Nikos, de Haan, Edward H. F, Bohte, Sander M, Scholte, H. Steven, Gershman, Samuel J, Isik, Leyla

Volltext