Search Results - "Bohté, Sander M"

Visualizing a joint future of neuroscience and neuromorphic engineering by 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

“…Recent research resolves the challenging problem of building biophysically plausible spiking neural models that are also capable of complex information…”
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A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning by Mücke, Nikolaj T, Pandey, Prerna, Jain, Shashi, Bohté, Sander M, Oosterlee, Cornelis W

“…Localizing leakages in large water distribution systems is an important and ever-present problem. Due to the complexity originating from water pipeline…”
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Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time by Yin, Bojian, Corradi, Federico, Bohté, Sander M.

“…With recent advances in learning algorithms, recurrent networks of spiking neurons are achieving performance that is competitive with vanilla recurrent neural…”
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Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model by Sörensen, Lynn K A, Bohté, Sander M, Slagter, Heleen A, Scholte, H Steven

“…Arousal levels strongly affect task performance. Yet, what arousal level is optimal for a task depends on its difficulty. Easy task performance peaks at higher…”
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Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception by Brucklacher, Matthias, Bohté, Sander M, Mejias, Jorge F, Pennartz, Cyriel M A

“…The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: perceived objects must be mapped to high-level…”
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Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention by Sörensen, Lynn K A, Zambrano, Davide, Slagter, Heleen A, Bohté, Sander M, Scholte, H Steven

“…Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying…”
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Mechanisms of human dynamic object recognition revealed by sequential deep neural networks by Sörensen, Lynn K A, Bohté, Sander M, de Jong, Dorina, Slagter, Heleen A, Scholte, H Steven

“…Humans can quickly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in…”
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Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks by Yin, Bojian, Corradi, Federico, Bohté, Sander M.

“…Inspired by detailed modelling of biological neurons, spiking neural networks (SNNs) are investigated as biologically plausible and high-performance models of…”
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The deep latent space particle filter for real-time data assimilation with uncertainty quantification by Mücke, Nikolaj T., Bohté, Sander M., Oosterlee, Cornelis W.

“…In data assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining…”
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Recurrent neural networks that learn multi-step visual routines with reinforcement learning by Mollard, Sami, Wacongne, Catherine, Bohte, Sander M, Roelfsema, Pieter R

“…Many cognitive problems can be decomposed into series of subproblems that are solved sequentially by the brain. When subproblems are solved, relevant…”
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Sparse Computation in Adaptive Spiking Neural Networks by Zambrano, Davide, Nusselder, Roeland, Scholte, H Steven, Bohté, Sander M

“…Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time,…”
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Pricing options and computing implied volatilities using neural networks by Liu, Shuaiqiang, Oosterlee, Cornelis Willebrordus, Bohte, Sander M

“…This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities…”
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How attention can create synaptic tags for the learning of working memories in sequential tasks by Rombouts, Jaldert O, Bohte, Sander M, Roelfsema, Pieter R

“…Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this…”
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Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy by Dora, Shirin, Bohte, Sander M., Pennartz, Cyriel M. A.

“…Predictive coding provides a computational paradigm for modeling perceptual processing as the construction of representations accounting for causes of sensory…”
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Depth in convolutional neural networks solves scene segmentation by Seijdel, Noor, Tsakmakidis, Nikos, de Haan, Edward H. F, Bohte, Sander M, Scholte, H. Steven, Gershman, Samuel J, Isik, Leyla

“…Feed-forward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in…”
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Predictive coding with spiking neurons and feedforward gist signaling by Lee, Kwangjun, Dora, Shirin, Mejias, Jorge F, Bohte, Sander M, Pennartz, Cyriel M A

“…Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and…”
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Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs by Hagenaars, Jesse Jan, Paredes-Valles, Federico, Bohte, Sander, De Croon, Guido

“…Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very…”
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Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications by Mücke, Nikolaj T., Sanderse, Benjamin, Bohté, Sander M., Oosterlee, Cornelis W.

“…In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, the Markov Chain Generative…”
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Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning by Mücke, Nikolaj T., Bohté, Sander M., Oosterlee, Cornelis W.

“…•Non-intrusive reduced order model for parameterized dynamic PDEs using deep learning.•Dimensionality reduction using convolutional autoencoders.•Time stepping…”
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Generalization in fully-connected neural networks for time series forecasting by Borovykh, Anastasia, Oosterlee, Cornelis W., Bohté, Sander M.

“…•We study the generalisation ability of neural networks in time series forecasting.•We propose metrics that quantify if a network will perform well on unseen…”
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