| Year |
Citation |
Score |
| 2020 |
Kheradpisheh SR, Masquelier T. Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron. International Journal of Neural Systems. 2050027. PMID 32466691 DOI: 10.1142/S0129065720500276 |
0.806 |
|
| 2019 |
Mozafari M, Ganjtabesh M, Nowzari-Dalini A, Masquelier T. SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron. Frontiers in Neuroscience. 13: 625. PMID 31354403 DOI: 10.3389/Fnins.2019.00625 |
0.71 |
|
| 2019 |
Tavanaei A, Ghodrati M, Kheradpisheh SR, Masquelier T, Maida A. Deep learning in spiking neural networks. Neural Networks : the Official Journal of the International Neural Network Society. 111: 47-63. PMID 30682710 DOI: 10.1016/J.Neunet.2018.12.002 |
0.812 |
|
| 2019 |
Mozafari M, Ganjtabesh M, Nowzari-Dalini A, Thorpe SJ, Masquelier T. Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks Pattern Recognition. 94: 87-95. DOI: 10.1016/J.Patcog.2019.05.015 |
0.733 |
|
| 2018 |
Masquelier T, Kheradpisheh SR. Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection. Frontiers in Computational Neuroscience. 12: 74. PMID 30279653 DOI: 10.3389/Fncom.2018.00074 |
0.799 |
|
| 2018 |
Mozafari M, Kheradpisheh SR, Masquelier T, Nowzari-Dalini A, Ganjtabesh M. First-Spike-Based Visual Categorization Using Reward-Modulated STDP. Ieee Transactions On Neural Networks and Learning Systems. PMID 29993898 DOI: 10.1109/Tnnls.2018.2826721 |
0.804 |
|
| 2018 |
Huth J, Masquelier T, Arleo A. Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing. Frontiers in Neuroinformatics. 12: 9. PMID 29563867 DOI: 10.3389/Fninf.2018.00009 |
0.609 |
|
| 2017 |
Kheradpisheh SR, Ganjtabesh M, Thorpe SJ, Masquelier T. STDP-based spiking deep convolutional neural networks for object recognition. Neural Networks : the Official Journal of the International Neural Network Society. 99: 56-67. PMID 29328958 DOI: 10.1016/J.Neunet.2017.12.005 |
0.816 |
|
| 2017 |
Ashtiani MN, Kheradpisheh SR, Masquelier T, Ganjtabesh M. Object Categorization in Finer Levels Relies More on Higher Spatial Frequencies and Takes Longer. Frontiers in Psychology. 8: 1261. PMID 28790954 DOI: 10.3389/Fpsyg.2017.01261 |
0.748 |
|
| 2017 |
Deneux T, Masquelier T, Bermudez MA, Masson GS, Deco G, Vanzetta I. Visual stimulation quenches global alpha range activity in awake primate V4: a case study. Neurophotonics. 4: 031222. PMID 28680907 DOI: 10.1117/1.Nph.4.3.031222 |
0.33 |
|
| 2017 |
Rubchinsky LL, Ahn S, Klijn W, Cumming B, Yates S, Karakasis V, Peyser A, Woodman M, Diaz-Pier S, Deraeve J, Vassena E, Alexander W, Beeman D, Kudela P, Boatman-Reich D, ... ... Masquelier T, et al. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2 Bmc Neuroscience. 18. DOI: 10.1186/S12868-017-0371-2 |
0.71 |
|
| 2017 |
Thorpe S, Yousefzadeh A, Martin J, Masquelier T. Unsupervised learning of repeating patterns using a novel STDP based algorithm Journal of Vision. 17: 1079. DOI: 10.1167/17.10.1079 |
0.733 |
|
| 2016 |
Kheradpisheh SR, Ghodrati M, Ganjtabesh M, Masquelier T. Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder. Frontiers in Computational Neuroscience. 10: 92. PMID 27642281 DOI: 10.3389/Fncom.2016.00092 |
0.757 |
|
| 2016 |
Kheradpisheh SR, Ghodrati M, Ganjtabesh M, Masquelier T. Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition. Scientific Reports. 6: 32672. PMID 27601096 DOI: 10.1038/Srep32672 |
0.76 |
|
| 2016 |
Kheradpisheh SR, Ganjtabesh M, Masquelier T. Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition Neurocomputing. 205: 382-392. DOI: 10.1016/J.Neucom.2016.04.029 |
0.796 |
|
| 2013 |
Serrano-Gotarredona T, Masquelier T, Prodromakis T, Indiveri G, Linares-Barranco B. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Frontiers in Neuroscience. 7: 2. PMID 23423540 DOI: 10.3389/Fnins.2013.00002 |
0.778 |
|
| 2011 |
Zamarreño-Ramos C, Camuñas-Mesa LA, Pérez-Carrasco JA, Masquelier T, Serrano-Gotarredona T, Linares-Barranco B. On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Frontiers in Neuroscience. 5: 26. PMID 21442012 DOI: 10.3389/Fnins.2011.00026 |
0.782 |
|
| 2010 |
Masquelier T, Serre T, Thorpe S, Poggio T. Learning simple and complex cells-like receptive fields from natural images: a plausibility proof Journal of Vision. 7: 81-81. DOI: 10.1167/7.9.81 |
0.48 |
|
| 2010 |
Masquelier T, Thorpe SJ. Learning to recognize objects using waves of spikes and spike timing-dependent plasticity Proceedings of the International Joint Conference On Neural Networks. DOI: 10.1109/IJCNN.2010.5596934 |
0.541 |
|
| 2009 |
Masquelier T, Hugues E, Deco G, Thorpe SJ. Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 29: 13484-93. PMID 19864561 DOI: 10.1523/Jneurosci.2207-09.2009 |
0.639 |
|
| 2009 |
Masquelier T, Guyonneau R, Thorpe SJ. Competitive STDP-based spike pattern learning. Neural Computation. 21: 1259-76. PMID 19718815 DOI: 10.1162/neco.2008.06-08-804 |
0.798 |
|
| 2008 |
Masquelier T, Guyonneau R, Thorpe SJ. Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. Plos One. 3: e1377. PMID 18167538 DOI: 10.1371/journal.pone.0001377 |
0.787 |
|
| 2007 |
Masquelier T, Thorpe SJ. Unsupervised learning of visual features through spike timing dependent plasticity. Plos Computational Biology. 3: e31. PMID 17305422 DOI: 10.1371/journal.pcbi.0030031 |
0.672 |
|
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