Fetching the paper…
Reading the bibliography…
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation.
D. E. Rumelhart, G. E. Hinton, R. J. Williams, et al., Learning representations by back-propagating errors, Cognitive modeling 5 (3) (1988) 1
1988
Earlier work this paper cites.
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation 9 (8) (1997) 1735–1780
1997
Earlier work this paper cites.
G.-q. Bi, M.-m. Poo, Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type, J. Neurosci. 18 (24) (1998) 10464–10472
1998
Earlier work this paper cites.
G. G. Turrigiano, K. R. Leslie, N. S. Desai, L. C. Rutherford, S. B. Nelson, Activity-dependent scaling of quantal amplitude in neocortical neurons, Nature 391 (6670) (1998) 892
1998
Earlier work this paper cites.
S. Song, K. D. Miller, L. F. Abbott, Competitive hebbian learning through spike-timing-dependent synaptic plasticity, Nature neuroscience 3 (9) (2000) 919
2000
Earlier work this paper cites.
H. Jaeger, The “echo state” approach to analysing and training recurrent neural networks-with an erratum note, Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148 (34) (2001) 13
2001
Earlier work this paper cites.
C. B. Holroyd, M. G. Coles, The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity., Psychological review 109 (4) (2002) 679
2002
Earlier work this paper cites.
P. Dayan, B. W. Balleine, Reward, motivation, and reinforcement learning, Neuron 36 (2) (2002) 285–298
2002
Earlier work this paper cites.
W. Maass, T. Natschläger, H. Markram, Real-time computing without stable states: A new framework for neural computation based on perturbations, Neural computation 14 (11) (2002) 2531–2560
2002
Earlier work this paper cites.
W. Zhang, D. J. Linden, The other side of the engram: experience-driven changes in neuronal intrinsic excitability, Nature Reviews Neuroscience 4 (11) (2003) 885
2003
Earlier work this paper cites.
W. Maass, H. Markram, On the computational power of circuits of spiking neurons, J. Comput. Syst. Sci. 69 (4) (2004) 593–616
2004
Earlier work this paper cites.
L. C. Yeung, H. Z. Shouval, B. S. Blais, L. N. Cooper, Synaptic homeostasis and input selectivity follow from a calcium-dependent plasticity model, Proceedings of the National Academy of Sciences 101 (41) (2004) 14943–14948
2004
Earlier work this paper cites.
R. A. Wise, Dopamine, learning and motivation, Nature reviews neuroscience 5 (6) (2004) 483
2004
Earlier work this paper cites.
H. Jaeger, H. Haas, Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication, science 304 (5667) (2004) 78–80
2004
Earlier work this paper cites.
A. Shrestha, K. Ahmed, Y. Wang, Q. Qiu, Stable spike-timing dependent plasticity rule for multilayer unsupervised and supervised learning, in: Neural Networks (IJCNN), 2017 International Joint Conference on, IEEE, 2017, pp. 1999–2006
2006
Earlier work this paper cites.
G. W. Davis, Homeostatic control of neural activity: from phenomenology to molecular design, Annu. Rev. Neurosci. 29 (2006) 307–323
2006
Earlier work this paper cites.
N. Masuda, H. Kori, Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity, Journal of computational neuroscience 22 (3) (2007) 327–345
2007
Earlier work this paper cites.
E. M. Izhikevich, Solving the distal reward problem through linkage of stdp and dopamine signaling, Cereb. Cortex 17 (10) (2007) 2443–2452
2007
Earlier work this paper cites.
N. Caporale, Y. Dan, Spike timing-dependent plasticity : A hebbian learning rule, Annu . Rev . Neurosci 31 (2008) 25–46
2008
Earlier work this paper cites.
G. G. Turrigiano, The self-tuning neuron: Synaptic scaling of excitatory synapses, Cell 135 (3) (2008) 422–435
2008
Earlier work this paper cites.
A. Maffei, G. G. Turrigiano, Multiple modes of network homeostasis in visual cortical layer 2/3, Journal of Neuroscience 28 (17) (2008) 4377–4384
2008
Cited alongside, same era.
L. v. d. Maaten, G. Hinton, Visualizing data using t-sne, J. Mach. Learn. Res. 9 (Nov) (2008) 2579–2605
2008
Cited alongside, same era.
J. C. Zhang, P. M. Lau, G. Q. Bi, C. F. Stevens, Gain in sensitivity and loss in temporal contrast of stdp by dopaminergic modulation at hippocampal synapses, Proc. Natl. Acad. Sci. U. S. A. 106 (31) (2009) 13028–13033
2009
Cited alongside, same era.
H. Tanaka, T. Morie, K. Aihara, A cmos spiking neural network circuit with symmetric/asymmetric stdp function, IEICE transactions on fundamentals of electronics, communications and computer sciences 92 (7) (2009) 1690–1698
2009
Cited alongside, same era.
Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015) 436–444
2015
Later among the works it cites.
J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks 61 (2015) 85–117
2015
Later among the works it cites.
Y. Cao, Y. Chen, D. Khosla, Spiking deep convolutional neural networks for energy-efficient object recognition, Int. J. Comput. Vision 113 (1) (2015) 54–66
2015
Later among the works it cites.
P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, M. Pfeiffer, Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing, in: IJCNN, IEEE, 2015, pp. 1–8
2015
Later among the works it cites.
S. K. Esser, R. Appuswamy, P. A. Merolla, J. V. Arthur, D. S. Modha, Backpropagation for energy-efficient neuromorphic computing, NIPS (2015) 1117–1125
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2009
Cited alongside, same era.
M. Lukoševičius, H. Jaeger, Reservoir computing approaches to recurrent neural network training, Comput. Sci. Rev. 3 (3) (2009) 127–149
2009
Cited alongside, same era.
K. Pozo, Y. Goda, Unraveling mechanisms of homeostatic synaptic plasticity, Neuron 66 (3) (2010) 337–351
2010
Cited alongside, same era.
T. P. Vogels, H. Sprekeler, F. Zenke, C. Clopath, W. Gerstner, Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks, Science 334 (6062) (2011) 1569–1573
2011
Cited alongside, same era.
P. W. Glimcher, Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis, Proceedings of the National Academy of Sciences 108 (Supplement 3) (2011) 15647–15654
2011
Cited alongside, same era.
L. N. Cooper, M. F. Bear, The bcm theory of synapse modification at 30: interaction of theory with experiment, Nature Reviews Neuroscience 13 (11) (2012) 798
2012
Cited alongside, same era.
N. X. Tritsch, B. L. Sabatini, Dopaminergic modulation of synaptic transmission in cortex and striatum, Neuron 76 (1) (2012) 33–50
2012
Cited alongside, same era.
M. Lukoševičius, H. Jaeger, B. Schrauwen, Reservoir computing trends, KI-Künstliche Intelligenz 26 (4) (2012) 365–371
2012
Cited alongside, same era.
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, J. Schmidhuber, Lstm: A search space odyssey, IEEE transactions on neural networks and learning systems 28 (10) (2016) 2222–2232
2016
Later among the works it cites.
P. U. Diehl, G. Zarrella, A. Cassidy, B. U. Pedroni, E. Neftci, Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware, in: ICRC, IEEE, 2016, pp. 1–8
2016
Later among the works it cites.
D. Neil, S.-C. Liu, Effective sensor fusion with event-based sensors and deep network architectures, in: ISCAS, IEEE, 2016, pp. 2282–2285
2016
Later among the works it cites.
S. K. Esser, P. A. Merolla, J. V. Arthur, A. S. Cassidy, R. Appuswamy, A. Andreopoulos, D. J. Berg, J. L. Mckinstry, T. Melano, D. R, et al., Convolutional networks for fast, energy-efficient neuromorphic computing, Proc. Natl. Acad. Sci. U. S. A. 113 (41) (2016) 11441–11446
2016
Later among the works it cites.
R. K. Mishra, S. Kim, S. J. Guzman, P. Jonas, Symmetric spike timing-dependent plasticity at ca3–ca3 synapses optimizes storage and recall in autoassociative networks, Nature communications 7 (2016) 11552
2016
Later among the works it cites.
E. Yavuz, J. Turner, T. Nowotny, Genn: a code generation framework for accelerated brain simulations, Scientific reports 6 (2016) 18854
2016
Later among the works it cites.
A. Samadi, T. P. Lillicrap, D. B. Tweed, Deep learning with dynamic spiking neurons and fixed feedback weights, Neural Comput. 29 (3) (2017) 578–602
2017
Later among the works it cites.
Z. Hu, T. Wang, X. Hu, An stdp-based supervised learning algorithm for spiking neural networks, in: ICONIP, Springer, 2017, pp. 92–100
2017
Later among the works it cites.
T. Zhang, Y. Zeng, D. Zhao, M. Shi, A plasticity-centric approach to train the non-differential spiking neural networks, in: AAAI, 2018
2018
Closest in time.
S. R. Kheradpisheh, M. Ganjtabesh, S. J. Thorpe, T. Masquelier, Stdp-based spiking deep convolutional neural networks for object recognition, Neural Networks 99 (2018) 56–67
2018
Closest in time.
C. Lee, P. Panda, G. Srinivasan, K. Roy, Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised fine-tuning, Frontiers in Neuroscience 12 (2018) 435
2018
Closest in time.
Z. Lin, D. Ma, J. Meng, L. Chen, Relative ordering learning in spiking neural network for pattern recognition, Neurocomputing 275 (2018) 94–106
2018
Closest in time.
Q. Xu, Y. Qi, H. Yu, J. Shen, H. Tang, G. Pan, Csnn: An augmented spiking based framework with perceptron-inception., in: IJCAI, 2018, pp. 1646–1652
2018
Closest in time.
N. K. Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Vol. 7, Springer, 2018
2018
Closest in time.