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

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

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

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

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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

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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

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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

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

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

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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.

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Flexible Working Memory Through Selective Gating and Attentional Tagging von Kruijne, Wouter, Bohte, Sander M., Roelfsema, Pieter R., Olivers, Christian N. L.

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Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy von Dora, Shirin, Bohte, Sander M., Pennartz, Cyriel M. A.

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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.

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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

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

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Predictive coding with spiking neurons and feedforward gist signaling von Lee, Kwangjun, Dora, Shirin, Mejias, Jorge F, Bohte, Sander M, Pennartz, Cyriel M A

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Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs von Hagenaars, Jesse Jan, Paredes-Valles, Federico, Bohte, Sander, De Croon, Guido

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On the data-driven COS method von Leitao, Álvaro, Oosterlee, Cornelis W., Ortiz-Gracia, Luis, Bohte, Sander M.

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Generalization in fully-connected neural networks for time series forecasting von Borovykh, Anastasia, Oosterlee, Cornelis W., Bohté, Sander M.

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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

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