A citation-based method for searching scientific literature


List of co-cited articles
178 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


A theory of cerebellar cortex.
D Marr. J Physiol 1969
48

Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation.
Jesús A Garrido, Niceto R Luque, Egidio D'Angelo, Eduardo Ros. Front Neural Circuits 2013
40
44


Distributed synergistic plasticity and cerebellar learning.
Zhenyu Gao, Boeke J van Beugen, Chris I De Zeeuw. Nat Rev Neurosci 2012
289
36

The cerebellum in action: a simulation and robotics study.
Constanze Hofstötter, Matti Mintz, Paul F M J Verschure. Eur J Neurosci 2002
33
28


Computer simulation of cerebellar information processing.
J F Medina, M D Mauk. Nat Neurosci 2000
216
28

Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks.
Claudia Casellato, Alberto Antonietti, Jesus A Garrido, Giancarlo Ferrigno, Egidio D'Angelo, Alessandra Pedrocchi. Front Comput Neurosci 2015
24
29

Large-scale model of mammalian thalamocortical systems.
Eugene M Izhikevich, Gerald M Edelman. Proc Natl Acad Sci U S A 2008
394
24

Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.
Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, Andrew S Cassidy, Jun Sawada, Filipp Akopyan, Bryan L Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura,[...]. Science 2014
466
24

A real-time spiking cerebellum model for learning robot control.
Richard R Carrillo, Eduardo Ros, Christian Boucheny, Olivier J-M D Coenen. Biosystems 2008
39
24

A spiking network model for passage-of-time representation in the cerebellum.
Tadashi Yamazaki, Shigeru Tanaka. Eur J Neurosci 2007
45
24

The cerebellum as a liquid state machine.
Tadashi Yamazaki, Shigeru Tanaka. Neural Netw 2007
66
20

Adaptive cerebellar spiking model embedded in the control loop: context switching and robustness against noise.
N R Luque, J A Garrido, R R Carrillo, S Tolu, E Ros. Int J Neural Syst 2011
37
20

Adaptive robotic control driven by a versatile spiking cerebellar network.
Claudia Casellato, Alberto Antonietti, Jesus A Garrido, Richard R Carrillo, Niceto R Luque, Eduardo Ros, Alessandra Pedrocchi, Egidio D'Angelo. PLoS One 2014
29
20

Using a million cell simulation of the cerebellum: network scaling and task generality.
Wen-Ke Li, Matthew J Hausknecht, Peter Stone, Michael D Mauk. Neural Netw 2013
19
26

Mechanisms of cerebellar learning suggested by eyelid conditioning.
J F Medina, W L Nores, T Ohyama, M D Mauk. Curr Opin Neurobiol 2000
135
16

Sensory prediction or motor control? Application of marr-albus type models of cerebellar function to classical conditioning.
Nathan F Lepora, John Porrill, Christopher H Yeo, Paul Dean. Front Comput Neurosci 2010
37
16

A mechanism for savings in the cerebellum.
J F Medina, K S Garcia, M D Mauk. J Neurosci 2001
143
16

Cerebellar-inspired adaptive control of a robot eye actuated by pneumatic artificial muscles.
Alexander Lenz, Sean R Anderson, A G Pipe, Chris Melhuish, Paul Dean, John Porrill. IEEE Trans Syst Man Cybern B Cybern 2009
10
40

A cerebellar model for predictive motor control tested in a brain-based device.
Jeffrey L McKinstry, Gerald M Edelman, Jeffrey L Krichmar. Proc Natl Acad Sci U S A 2006
18
22

Cerebellar cortex: its simulation and the relevance of Marr's theory.
T Tyrrell, D Willshaw. Philos Trans R Soc Lond B Biol Sci 1992
78
16







Timing mechanisms in the cerebellum: testing predictions of a large-scale computer simulation.
J F Medina, K S Garcia, W L Nores, N M Taylor, M D Mauk. J Neurosci 2000
196
16

Neural modeling of an internal clock.
Tadashi Yamazaki, Shigeru Tanaka. Neural Comput 2005
37
16


A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.
Francisco Naveros, Niceto R Luque, Jesús A Garrido, Richard R Carrillo, Mancia Anguita, Eduardo Ros. IEEE Trans Neural Netw Learn Syst 2015
10
40

Reconstruction and Simulation of Neocortical Microcircuitry.
Henry Markram, Eilif Muller, Srikanth Ramaswamy, Michael W Reimann, Marwan Abdellah, Carlos Aguado Sanchez, Anastasia Ailamaki, Lidia Alonso-Nanclares, Nicolas Antille, Selim Arsever,[...]. Cell 2015
492
16


The human brain in numbers: a linearly scaled-up primate brain.
Suzana Herculano-Houzel. Front Hum Neurosci 2009
501
16

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.
Jakob Jordan, Tammo Ippen, Moritz Helias, Itaru Kitayama, Mitsuhisa Sato, Jun Igarashi, Markus Diesmann, Susanne Kunkel. Front Neuroinform 2018
25
16

4
100

A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.
Jayram Moorkanikara Nageswaran, Nikil Dutt, Jeffrey L Krichmar, Alex Nicolau, Alexander V Veidenbaum. Neural Netw 2009
54
12


The blue brain project.
Henry Markram. Nat Rev Neurosci 2006
385
12

Simple model of spiking neurons.
E M Izhikevich. IEEE Trans Neural Netw 2003
927
12

A large-scale model of the functioning brain.
Chris Eliasmith, Terrence C Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Yichuan Tang, Daniel Rasmussen. Science 2012
233
12

PyNN: A Common Interface for Neuronal Network Simulators.
Andrew P Davison, Daniel Brüderle, Jochen Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent Perrinet, Pierre Yger. Front Neuroinform 2009
201
12



Cerebellar microcomplexes.
M Ito. Int Rev Neurobiol 1997
46
12

Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics.
Eduardo Ros, Richard Carrillo, Eva M Ortigosa, Boris Barbour, Rodrigo Agís. Neural Comput 2006
39
12

Cerebellar motor learning: when is cortical plasticity not enough?
John Porrill, Paul Dean. PLoS Comput Biol 2007
43
12




Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.