Davide Zambrano
Davide Zambrano
Synergy Sports Lab
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Cited by
Cited by
Fast and efficient asynchronous neural computation with adapting spiking neural networks
D Zambrano, SM Bohte
arXiv preprint arXiv:1609.02053, 2016
Sparse computation in adaptive spiking neural networks
D Zambrano, R Nusselder, HS Scholte, SM BohtÚ
Frontiers in neuroscience 12, 987, 2019
Towards a Neuromorphic Vestibular System
F Corradi, D Zambrano, M Raglianti, G Passetti, C Laschi, G Indiveri
IEEE, 2014
An image representation based convolutional network for DNA classification
B Yin, M Balvert, D Zambrano, A Sch÷nhuth, S Bohte
arXiv preprint arXiv:1806.04931, 2018
The Morphological Computation Principles as a New Paradigm for Robotic Design
D Zambrano, C Matteo, C Laschi
Opinions and Outlooks on Morphological Computation, 214–225, 2014
A comparison between two bio-inspired adaptive models of Vestibulo-Ocular Reflex (VOR) implemented on the iCub robot
E Franchi, E Falotico, D Zambrano, GG Muscolo, L Marazzato, P Dario, ...
2010 10th IEEE-RAS International Conference on Humanoid Robots, 251-256, 2010
Implementation of a bio-inspired visual tracking model on the iCub robot
E Falotico, D Zambrano, GG Muscolo, L Marazzato, P Dario, C Laschi
19th International Symposium in Robot and Human Interactive Communicationá…, 2010
A model of the smooth pursuit eye movement with prediction and learning
D Zambrano, E Falotico, L Manfredi, C Laschi
Applied Bionics and Biomechanics 7 (2), 109-118, 2010
Predictive tracking across occlusions in the icub robot
E Falotico, M Taiana, D Zambrano, A Bernardino, J Santos-Victor, P Dario, ...
2009 9th IEEE-RAS International Conference on Humanoid Robots, 486-491, 2009
Continuous-time on-policy neural reinforcement learning of working memory tasks
D Zambrano, PR Roelfsema, SM Bohte
Neural Networks (IJCNN), 2015 International Joint Conference on, 2015
A comparison of human trajectory planning models for implementation on humanoid robot
D Zambrano, D Bernardin, D Bennequin, C Laschi, A Berthoz
2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics andá…, 2012
Gating sensory noise in a spiking subtractive lstm
I Pozzi, R Nusselder, D Zambrano, S BohtÚ
International Conference on Artificial Neural Networks, 284-293, 2018
Implementation of a neuromorphic vestibular sensor with analog VLSI neurons
G Passetti, F Corradi, M Raglianti, D Zambrano, C Laschi, G Indiveri
2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), 174-177, 2013
Learning continuous-time working memory tasks with on-policy neural reinforcement learning
D Zambrano, PR Roelfsema, S Bohte
Neurocomputing, 2021
Leveraging spiking deep neural networks to understand neural mechanisms underlying selective attention
LKA S÷rensen, D Zambrano, HA Slagter, SM BohtÚ, HS Scholte
bioRxiv, 2020.12. 15.422863, 2021
Continuous-time spike-based reinforcement learning for working memory tasks
M Karamanis, D Zambrano, S BohtÚ
International Conference on Artificial Neural Networks, 250-262, 2018
Spiking AGREL
D Zambrano, J Rombouts, C Laschi, S Bohte
signal 2 (3), 3.5, 2014
Seeking quality diversity in evolutionary co-design of morphology and control of soft tensegrity modular robots
E Zardini, D Zappetti, D Zambrano, G Iacca, D Floreano
arXiv preprint arXiv:2104.12175, 2021
Gating out sensory noise in a spike-based Long Short-Term Memory network
D Zambrano, I Pozzi, R Nusselder, S Bohte
Special track on neurocomputing & deep learning and continuos-time computing (NC&DLCC)
C Laschi, E Falotico, M Gewaltig, F R÷hrbein, P Levi, S Ulbrich, ...
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