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Sander Bohte
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Cited by
Year
Error-backpropagation in temporally encoded networks of spiking neurons
SM Bohte, JN Kok, H La Poutre
Neurocomputing 48 (1), 17-37, 2002
1414*2002
Conditional time series forecasting with convolutional neural networks
A Borovykh, S Bohte, CW Oosterlee
arXiv preprint arXiv:1703.04691, 2017
6272017
Handbook of natural computing
T Bäck, JN Kok, G Rozenberg
Springer, Heidelberg, 2012
538*2012
Artificial neural networks as models of neural information processing
M Van Gerven, S Bohte
Frontiers in computational neuroscience 11, 114, 2017
5142017
Computing with spiking neuron networks
H Paugam-Moisy, S Bohte
Handbook of Natural Computing, 40p. Springer, Heidelberg, 2009
4052009
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
SM Bohte, H La Poutré, JN Kok
IEEE Transactions on neural networks 13 (2), 426-435, 2002
3012002
The evidence for neural information processing with precise spike-times: A survey
SM Bohte
Natural Computing 3, 195-206, 2004
2142004
Adaptive resource allocation for efficient patient scheduling
IB Vermeulen, SM Bohte, SG Elkhuizen, H Lameris, PJM Bakker, ...
Artificial intelligence in medicine 46 (1), 67-80, 2009
1752009
Method and system for automated marketing of attention area content
L Poutre, J Antonius, SM Bohte, EH Gerding, FW Bomhof, J Jonker, ...
US Patent App. 20,030/018,539, 2002
1652002
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
B Yin, F Corradi, SM Bohté
Nature Machine Intelligence 3 (10), 905-913, 2021
1592021
Pricing options and computing implied volatilities using neural networks
S Liu, CW Oosterlee, SM Bohte
Risks 7 (1), 16, 2019
1582019
Sparse computation in adaptive spiking neural networks
D Zambrano, R Nusselder, HS Scholte, SM Bohté
Frontiers in neuroscience 12, 414117, 2019
131*2019
Dilated convolutional neural networks for time series forecasting
A Borovykh, S Bohte, CW Oosterlee
Journal of Computational Finance 29, 73-101, 2018
1182018
Spiking neural networks: Principles and challenges.
A Grüning, SM Bohte
ESANN, 2014
1142014
Effective and efficient computation with multiple-timescale spiking recurrent neural networks
B Yin, F Corradi, SM Bohté
International Conference on Neuromorphic Systems 2020, 1-8, 2020
1002020
Spike-prop: error-backpropagation in multi-layer networks of spiking neurons
SM Bohte, JN Kok, H La Poutre
Neurocomputing 48 (1-4), 17-37, 2002
842002
Spiking neural networks
SM Bohte
792003
How attention can create synaptic tags for the learning of working memories in sequential tasks
JO Rombouts, SM Bohte, PR Roelfsema
PLoS computational biology 11 (3), e1004060, 2015
752015
Visualizing a joint future of neuroscience and neuromorphic engineering
F Zenke, SM Bohté, C Clopath, IM Comşa, J Göltz, W Maass, ...
Neuron 109 (4), 571-575, 2021
702021
The effects of pair-wise and higher-order correlations on the firing rate of a postsynaptic neuron
SM Bohté, H Spekreijse, PR Roelfsema
Neural Computation 12 (1), 153-179, 2000
692000
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Articles 1–20