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The advantage of spiking neural networks (SNNs) over their predecessors is their ability to spike, enabling them to use spike timing for coding and efficient computing.
Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex
C. Grey and W. Singer · 1989
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A circuit for detection of interaural time differences in the brain stem of the barn owl
C. Carr and M. Konishi · 1990
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Neural Nets in Electric Fish
W. Heiligenberg · 1991
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Listening with two ears
M. Konishi · 1993
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Hebbian learning of pulse timing in the barn owl auditory system
W. Gerstner, R. Kempter, J. van Hemmen, and H. Wagner · 1999
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Hebbian learning and spiking neurons
R. Kempter, W. Gerstner, and J. L. van Hemmen · 1999
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Neuronal synchrony: a versatile code for the definition of relations?
W. Singer · 1999
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The what and why of binding: the modeler’s perspective
C. von der Malsburg · 1999
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Control of Movement for the Physically Disabled
D. Popovic and T. Sinkjaer · 2000
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Competitive hebbian learning through spike-timing dependent synaptic plasticity
S. Song, K. Miller, and L. Abbott · 2000
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Neural information processing
F. Gabbiani and J. Midtgaard · 2001
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Natural image statistics and neural representations
E. Simoncelli and B. Olshausen · 2001
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Spike-based strategies for rapid processing
S. Thorpe, A. Delorme, and R. Rullen · 2001
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Rate coding vs temporal order coding: what the retinal ganglion cells tell the visual cortex
R. VanRullen and S. Thorpe · 2001
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Statistical learning of higher-order temporal structure from visual shape sequences
J. Fiser and R. Aslin · 2002
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Vision and the statistics of the visual environment
E. Simoncelli · 2003
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First spikes in ensembles of human tactile afferents code complex spatial fingertip events
S. Johansson and I. Birznieks · 2004
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The role of temporal structure in human vision
R. Blake and S.-H. Lee · 2005
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Microsecond precision of phase delay in the auditory system of the barn owl
H. Wagner, S. Brill, R. Kempter, and C. E. Carr · 2005
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Polychronization: Computation with spikes
E. Izhikevich · 2006
Cited alongside, same era.
Ultra-rapid object detection with saccadic eye movements: Visual processing speed revisited
H. Kirchner and S. Thorpe · 2006
Cited alongside, same era.
Temporal precision in the neural code and the timescales of natural vision
D. Butts, C. Weng, J. Jin, C.-I. Yeh, N. Lesica, J.-M. Alonso, and G. Stanley · 2007
Cited alongside, same era.
Neural variability, detection thresholds, and information transmission in the vestibular system
S. Sadeghi, M. T. M.J. Chacron, and K. Cullen · 2007
Cited alongside, same era.
Bayesian spiking neurons 1: Inference
S. Deneve · 2008
Cited alongside, same era.
Visual perception and the statistical properties of natural scenes
W. Geisler · 2008
Cited alongside, same era.
Hfirst: A temporal approach to object recognition
G. Orchard, C. Meyer, R. Etienne-Cummings, C. Posch, N. Thakor, and R. Benosman · 2015
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Rate and timing of cortical responses driven by separate sensory channels
H. Saal, M. Harvey, and S. Bensmania · 2015
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Complementary contributions of spike timing and spike rate to perceptual decisions in rat s1 and s2 cortex
Y. Zuo, H. Safaai, G. Notaro, A. Mazzoni, S. Panzeri, and M. Diamond · 2015
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The role of conduction delay in creating sensitivity to interaural time differences
C. Carr, G. Ashida, H. Wagner, T. McColgan, and R. Kempter · 2016
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Skimming digits: Neuromorphic classification of spike-encoded images
G. Cohen, G. Orchard, S.-H. Leng, J. Tapson, R. Benosman, and A. van Schaik · 2016
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How the brain might work: a hierarchical and temporal model for learning and recognition
D. George · 2008
Cited alongside, same era.
Information about complex fingertip parameters in individual human tactile afferent neurons
H. Saal, S. Vijayakumar, and R. Johansson · 2009
Cited alongside, same era.
Fast saccades toward faces: Face detection in just 100 ms
S. Crouzet, H. Kirchner, and S. Thorpe · 2010
Cited alongside, same era.
Cortical dynamics during naturalistic sensory stimulations: Experiments and models
A. Mazzoni, N. Brunel, S. Cavallari, N. Logothetis, and S. Panzeri · 2011
Cited alongside, same era.
A QVGA 143 dB dynamic range frame-free PWM image sensor with lossless pixel-level video compression and time-domain CDS
C. Posch, D. Matolin, and R. Wohlgenannt · 2011
Cited alongside, same era.
Millisecond precision spike timing shapes tactile perception
E. Mackevicius, M. Best, H. Saal, and S. Bensmaia · 2012
Cited alongside, same era.
J. Elder, J. Victor, and S. W. Zucker · 2016
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Building machines that learn and think like people
B. Lake, T. Ullman, J. Tenenbaum, and S. Gershman · 2016
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Training deep spiking neural networks using backpropagation
J. Lee, T. Belbruck, and M. Pfeiffer · 2016
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Burst firing in the electrosensory system of gymnotiform weakly electric fish: Mechanisms and functional roles
M. Metzen, R. Krahe, and M. Chacron · 2016
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Effective sensor fusion with event-based sensors and deep network architectures
D. Neil and S.-C. Liu · 2016
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Phased lstm: Accelerating recurrent network training for long or event-based sequences
D. Neil, M. Pfeiffer, and S.-C. Liu · 2016
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Unsupervised learning of event-based image recordings using spike-timing-dependent plasticity
L. Iyer and A. Basu · 2017
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Visual cortex responses reflect temporal structure of continuous quasi-rhythmic sensory stimulation
C. Keitel, G. Thut, and J. Gross · 2017
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STDP-based spiking deep convolutional neural networks for object recognition
S. Kheradpisheh, M. Ganjtabesh, S. Thorpe, and T. Masquelier · 2017
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HOTS: a hierarchy of event-based time-surfaces for pattern recognition
X. Lagorce, G. Orchard, F. Gallupi, B. Shi, and R. B. Benosman · 2017
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Classifying neuromorphic data using a deep learning framework for image classification
R. Gopalakrishnan, Y. Chua, and L. Iyer · 2018
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HATS: Histograms of averaged time surfaces for robust event-based object classification
A. Sironi, Sironi, M. Brambilla, N. Bourdis, X. Lagorce, and R. Benosman · 2018
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Active perception with dynamic vision sensors. minimum saccades with optimum recognition
A. Yousefzadeh, G. Orchard, T. Serrano-Gotarredona, and B. Linares-Barranco · 2018
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