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- spikingjelly.activation_based.surrogate package
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- Module contents
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- .. automodule:: spikingjelly.activation_based.surrogate
- :members:
- :undoc-members:
- :show-inheritance:
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- References
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- .. [#esser2015backpropagation] Esser S K, Appuswamy R, Merolla P, et al. Backpropagation for energy-efficient neuromorphic computing[J]. Advances in neural information processing systems, 2015, 28: 1117-1125.
- .. [#esser2016convolutional] Esser S K, Merolla P A, Arthur J V, et al. Convolutional networks for fast, energy-efficient neuromorphic computing[J]. Proceedings of the national academy of sciences, 2016, 113(41): 11441-11446.
- .. [#yin2017algorithm] Yin S, Venkataramanaiah S K, Chen G K, et al. Algorithm and hardware design of discrete-time spiking neural networks based on back propagation with binary activations[C]//2017 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2017: 1-5.
- .. [#STBP] Wu Y, Deng L, Li G, et al. Spatio-temporal backpropagation for training high-performance spiking neural networks[J]. Frontiers in neuroscience, 2018, 12: 331.
- .. [#huh2018gradient] Huh D, Sejnowski T J. Gradient descent for spiking neural networks[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018: 1440-1450.
- .. [#SLAYER] Shrestha S B, Orchard G. SLAYER: spike layer error reassignment in time[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018: 1419-1428.
- .. [#LSNN] Bellec G, Salaj D, Subramoney A, et al. Long short-term memory and learning-to-learn in networks of spiking neurons[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018: 795-805.
- .. [#SuperSpike] Zenke F, Ganguli S. Superspike: Supervised learning in multilayer spiking neural networks[J]. Neural computation, 2018, 30(6): 1514-1541.
- .. [#wu2019direct] Wu Y, Deng L, Li G, et al. Direct training for spiking neural networks: Faster, larger, better[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(01): 1311-1318.
- .. [#STCA] Gu P, Xiao R, Pan G, et al. STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks[C]//IJCAI. 2019: 1366-1372.
- .. [#neftci2019surrogate] Neftci E O, Mostafa H, Zenke F. Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks[J]. IEEE Signal Processing Magazine, 2019, 36(6): 51-63.
- .. [#roy2019scaling] Roy D, Chakraborty I, Roy K. Scaling deep spiking neural networks with binary stochastic activations[C]//2019 IEEE International Conference on Cognitive Computing (ICCC). IEEE, 2019: 50-58.
- .. [#panda2020toward] Panda P, Aketi S A, Roy K. Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization[J]. Frontiers in Neuroscience, 2020, 14.
- .. [#SNNLSTM] Lotfi Rezaabad A, Vishwanath S. Long Short-Term Memory Spiking Networks and Their Applications[C]//International Conference on Neuromorphic Systems 2020. 2020: 1-9.
- .. [#SNU] Woźniak S, Pantazi A, Bohnstingl T, et al. Deep learning incorporating biologically inspired neural dynamics and in-memory computing[J]. Nature Machine Intelligence, 2020, 2(6): 325-336.
- .. [#LISNN] Cheng X, Hao Y, Xu J, et al. LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition[C]//IJCAI. 1519-1525.
- .. [#DECOLLE] Kaiser J, Mostafa H, Neftci E. Synaptic plasticity dynamics for deep continuous local learning (DECOLLE)[J]. Frontiers in Neuroscience, 2020, 14: 424.
- .. [#SRNN] Yin B, Corradi F, Bohté S M. Effective and efficient computation with multiple-timescale spiking recurrent neural networks[C]//International Conference on Neuromorphic Systems 2020. 2020: 1-8.
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