A citation-based method for searching scientific literature

Bo Zhao, Ruoxi Ding, Shoushun Chen, Bernabe Linares-Barranco, Huajin Tang. IEEE Trans Neural Netw Learn Syst 2015
Times Cited: 46







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



Times Cited
  Times     Co-cited
Similarity



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
563
39

Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing--application to feedforward ConvNets.
José Antonio Pérez-Carrasco, Bo Zhao, Carmen Serrano, Begoña Acha, Teresa Serrano-Gotarredona, Shouchun Chen, Bernabé Linares-Barranco. IEEE Trans Pattern Anal Mach Intell 2013
56
36

Real-time classification and sensor fusion with a spiking deep belief network.
Peter O'Connor, Daniel Neil, Shih-Chii Liu, Tobi Delbruck, Michael Pfeiffer. Front Neurosci 2013
93
36

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades.
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, Nitish Thakor. Front Neurosci 2015
62
36

HFirst: A Temporal Approach to Object Recognition.
Garrick Orchard, Cedric Meyer, Ralph Etienne-Cummings, Christoph Posch, Nitish Thakor, Ryad Benosman. IEEE Trans Pattern Anal Mach Intell 2015
52
34

Unsupervised learning of visual features through spike timing dependent plasticity.
Timothée Masquelier, Simon J Thorpe. PLoS Comput Biol 2007
159
32

Poker-DVS and MNIST-DVS. Their History, How They Were Made, and Other Details.
Teresa Serrano-Gotarredona, Bernabé Linares-Barranco. Front Neurosci 2015
29
48

Event-driven contrastive divergence for spiking neuromorphic systems.
Emre Neftci, Srinjoy Das, Bruno Pedroni, Kenneth Kreutz-Delgado, Gert Cauwenberghs. Front Neurosci 2014
58
23

Rapid feedforward computation by temporal encoding and learning with spiking neurons.
Qiang Yu, Huajin Tang, Kay Chen Tan, Haizhou Li. IEEE Trans Neural Netw Learn Syst 2013
41
26

Training Deep Spiking Neural Networks Using Backpropagation.
Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer. Front Neurosci 2016
118
23

Robust object recognition with cortex-like mechanisms.
Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, Tomaso Poggio. IEEE Trans Pattern Anal Mach Intell 2007
330
21

CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.
Rafael Serrano-Gotarredona, Matthias Oster, Patrick Lichtsteiner, Alejandro Linares-Barranco, Rafael Paz-Vicente, Francisco Gomez-Rodriguez, Luis Camunas-Mesa, Raphael Berner, Manuel Rivas-Perez, Tobi Delbruck,[...]. IEEE Trans Neural Netw 2009
99
21

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics.
Joseph M Brader, Walter Senn, Stefano Fusi. Neural Comput 2007
114
21

The tempotron: a neuron that learns spike timing-based decisions.
Robert Gütig, Haim Sompolinsky. Nat Neurosci 2006
246
21

HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition.
Xavier Lagorce, Garrick Orchard, Francesco Galluppi, Bertram E Shi, Ryad B Benosman. IEEE Trans Pattern Anal Mach Intell 2017
47
21

Bag of Events: An Efficient Probability-Based Feature Extraction Method for AER Image Sensors.
Xi Peng, Bo Zhao, Rui Yan, Huajin Tang, Zhang Yi. IEEE Trans Neural Netw Learn Syst 2017
13
69

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, Shih-Chii Liu. Front Neurosci 2017
85
19

STDP-based spiking deep convolutional neural networks for object recognition.
Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Simon J Thorpe, Timothée Masquelier. Neural Netw 2018
89
19

Convolutional networks for fast, energy-efficient neuromorphic computing.
Steven K Esser, Paul A Merolla, John V Arthur, Andrew S Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J Berg, Jeffrey L McKinstry, Timothy Melano, Davis R Barch,[...]. Proc Natl Acad Sci U S A 2016
102
17


Efficient feedforward categorization of objects and human postures with address-event image sensors.
Shoushun Chen, Polina Akselrod, Bo Zhao, Jose Antonio Perez Carrasco, Bernabe Linares-Barranco, Eugenio Culurciello. IEEE Trans Pattern Anal Mach Intell 2012
13
53

Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking.
Xavier Lagorce, Cédric Meyer, Sio-Hoi Ieng, David Filliat, Ryad Benosman. IEEE Trans Neural Netw Learn Syst 2015
34
20

Simple model of spiking neurons.
E M Izhikevich. IEEE Trans Neural Netw 2003
15


Hierarchical models of object recognition in cortex.
M Riesenhuber, T Poggio. Nat Neurosci 1999
13



Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
13

Comparison between Frame-Constrained Fix-Pixel-Value and Frame-Free Spiking-Dynamic-Pixel ConvNets for Visual Processing.
Clément Farabet, Rafael Paz, Jose Pérez-Carrasco, Carlos Zamarreño-Ramos, Alejandro Linares-Barranco, Yann Lecun, Eugenio Culurciello, Teresa Serrano-Gotarredona, Bernabe Linares-Barranco. Front Neurosci 2012
19
31



Benchmarking neuromorphic vision: lessons learnt from computer vision.
Cheston Tan, Stephane Lallee, Garrick Orchard. Front Neurosci 2015
12
41

Triplets of spikes in a model of spike timing-dependent plasticity.
Jean-Pascal Pfister, Wulfram Gerstner. J Neurosci 2006
259
10

A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.
Ning Qiao, Hesham Mostafa, Federico Corradi, Marc Osswald, Fabio Stefanini, Dora Sumislawska, Giacomo Indiveri. Front Neurosci 2015
120
10

A fast learning algorithm for deep belief nets.
Geoffrey E Hinton, Simon Osindero, Yee-Whye Teh. Neural Comput 2006
10

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images.
Gregory K Cohen, Garrick Orchard, Sio-Hoi Leng, Jonathan Tapson, Ryad B Benosman, André van Schaik. Front Neurosci 2016
15
33

SWAT: a spiking neural network training algorithm for classification problems.
John J Wade, Liam J McDaid, Jose A Santos, Heather M Sayers. IEEE Trans Neural Netw 2010
43
11

Speed of processing in the human visual system.
S Thorpe, D Fize, C Marlot. Nature 1996
10

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.
Nikola Kasabov, Kshitij Dhoble, Nuttapod Nuntalid, Giacomo Indiveri. Neural Netw 2013
67
10

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.
Emre O Neftci, Charles Augustine, Somnath Paul, Georgios Detorakis. Front Neurosci 2017
48
10

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass. PLoS Comput Biol 2013
101
8

Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.
Olivier Bichler, Damien Querlioz, Simon J Thorpe, Jean-Philippe Bourgoin, Christian Gamrat. Neural Netw 2012
29
13

The brian simulator.
Dan F M Goodman, Romain Brette. Front Neurosci 2009
194
8

Neuromorphic silicon neuron circuits.
Giacomo Indiveri, Bernabé Linares-Barranco, Tara Julia Hamilton, André van Schaik, Ralph Etienne-Cummings, Tobi Delbruck, Shih-Chii Liu, Piotr Dudek, Philipp Häfliger, Sylvie Renaud,[...]. Front Neurosci 2011
267
8

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.
Evangelos Stromatias, Daniel Neil, Michael Pfeiffer, Francesco Galluppi, Steve B Furber, Shih-Chii Liu. Front Neurosci 2015
22
18


Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs.
H Markram, J Lübke, M Frotscher, B Sakmann. Science 1997
8

An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking.
David Reverter Valeiras, Xavier Lagorce, Xavier Clady, Chiara Bartolozzi, Sio-Hoi Ieng, Ryad Benosman. IEEE Trans Neural Netw Learn Syst 2015
16
25



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.