Daniel Ben Dayan Rubin
Daniel Ben Dayan Rubin
Intel Labs
Bestätigte E-Mail-Adresse bei
Zitiert von
Zitiert von
Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores
AS Cassidy, P Merolla, JV Arthur, SK Esser, B Jackson, R Alvarez-Icaza, ...
Neural Networks (IJCNN), The 2013 International Joint Conference on, 1-10, 2013
Building block of a programmable neuromorphic substrate: A digital neurosynaptic core
JV Arthur, PA Merolla, F Akopyan, R Alvarez, A Cassidy, S Chandra, ...
Neural Networks (IJCNN), The 2012 International Joint Conference on, 1-8, 2012
Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses
M Rigotti, DD Ben Dayan Rubin, XJ Wang, S Fusi
Frontiers in computational neuroscience 4, 24, 2010
Neuromorphic accelerators: a comparison between neuroscience and machine-learning approaches
Z Du, DD Ben-Dayan Rubin, Y Chen, L He, T Chen, L Zhang, C Wu, ...
Proceedings of the 48th International Symposium on Microarchitecture, 494-507, 2015
Attractor concretion as a mechanism for the formation of context representations
M Rigotti, D Ben Dayan Rubin, SE Morrison, CD Salzman, S Fusi
Neuroimage 52 (3), 833-847, 2010
Long memory lifetimes require complex synapses and limited sparseness
DD Ben Dayan Rubin, S Fusi
Frontiers in computational neuroscience 1, 7, 2007
An adaptive neuro-fuzzy method (ANFIS) for estimating single-trial movement-related potentials
DD Ben Dayan Rubin, G Baselli, GF Inbar, S Cerutti
Biological cybernetics 91 (2), 63-75, 2004
Characterizing the firing properties of an adaptive analog VLSI neuron
D Ben Dayan Rubin, E Chicca, G Indiveri
International Workshop on Biologically Inspired Approaches to Advanced …, 2004
Image difference based segmentation using recursive neural networks
DD Ben-Dayan Rubin, E Hoffer
US Patent 10,148,872, 2018
Frontoparietal cortical networks revealed by structural equation modeling and high resolution EEG during a short term memory task
F Babiloni, F Cincotti, A Basilisco, E Maso, M Bufano, C Babiloni, ...
First International IEEE EMBS Conference on Neural Engineering, 2003 …, 2003
Instruction, circuits, and logic for piecewise linear approximation
DD Ben-Dayan Rubin, Y Glesner
US Patent 9,990,196, 2018
The importance of neural diversity in complex cognitive tasks
M Rigotti, DD Ben Dayan Rubin, XJ Wang, S Fusi
Program No. 929.3. Abstract viewer/Itinerary planner, 2007
Peak self-normalization gain control based on hopf resonators cascade signal spectral decomposition
DDBD Rubin
US Patent App. 17/643,652, 2022
Binary Vector Factorization
DD Ben-Dayan Rubin
US Patent 10,394,930, 2019
Binary Multiplier for Binary Vector Factorization
E Cohen, DD Ben-Dayan Rubin, M Behar, D Vainbrand
US Patent US10210137B2, 2019
An adaptive spiking neural network for decision making in partially observable environments
M Rigotti, D Ben Dayan Rubin, ND Daw, S Fusi
Cosyne: Computational and Systems Neuroscience Conference, 2012, 2012
Models of neural networks of spiking neurons performing complex cognitive tasks
D Ben Dayan Rubin, M Rigotti, S Fusi
A theory for the formation of the neural representation of context in reinforcement learning
M Rigotti, D Ben Dayan Rubin, S Fusi
CNS 2009, 2009
Mixed neuronal selectivity is important in recurrent neural networks implementing context dependent tasks
D Rigotti, Mattia, Ben Dayan Rubin, XJ Wang, S Fusi
CNS 2008, 2008
Complex synapses resolve the paradox of less sparse memories in longer memory lifetime areas of the brain
D Ben Dayan Rubin, S Fusi
SFN 2008, 2008
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