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The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn.
Discharge patterns and functional organization of mammalian retina
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Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings. pp. 249–256
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Short-term plasticity optimizes synaptic information transmission
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Quantifying impacts of short-term plasticity on neuronal information transfer
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Immunity to device variations in a spiking neural network with memristive nanodevices
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing, in: 2015 International joint conference on neural networks (IJCNN), ieee. pp. 1–8
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Direct training for spiking neural networks: Faster, larger, better, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1311–1318
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Lisnn: Improving spiking neural networks with lateral interactions for robust object recognition., in: IJCAI, pp. 1519–1525
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Deep spiking neural network: Energy efficiency through time based coding, in: European Conference on Computer Vision, Springer. pp. 388–404
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A biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule
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Fspinn: An optimization framework for memory-efficient and energy-efficient spiking neural networks
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Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks
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Cited alongside, same era.
Adaptive spike threshold enables robust and temporally precise neuronal encoding
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Training deep spiking neural networks using backpropagation
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Random synaptic feedback weights support error backpropagation for deep learning
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., Vollgraf, R., 2017 · 2017
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Unsupervised feature learning with winner-takes-all based stdp
Ferré, P., Mamalet, F., Thorpe, S.J., 2018 · 2018
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Stdp-based spiking deep convolutional neural networks for object recognition
Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T., 2018 · 2018
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A curiosity-based learning method for spiking neural networks
Shi, M., Zhang, T., Zeng, Y., 2020 · 2020
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Glsnn: A multi-layer spiking neural network based on global feedback alignment and local stdp plasticity
Zhao, D., Zeng, Y., Zhang, T., Shi, M., Zhao, F., 2020 · 2020
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Brain inspired sequences production by spiking neural networks with reward-modulated stdp
Fang, H., Zeng, Y., Zhao, F., 2021 · 2021
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Li, Y., Zeng, Y., Zhao, D., 2021 · 2021
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Sstdp: Supervised spike timing dependent plasticity for efficient spiking neural network training
Liu, F., Zhao, W., Chen, Y., Wang, Z., Yang, T., Jiang, L., 2021 · 2021
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On the self-repair role of astrocytes in stdp enabled unsupervised snns
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Shen, G., Zhao, D., Zeng, Y., 2021 · 2021
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Short-term synaptic plasticity makes neurons sensitive to the distribution of presynaptic population firing rates
Tauffer, L., Kumar, A., 2021 · 2021
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Zhao, D., Li, Y., Zeng, Y., Wang, J., Zhang, Q., 2021 · 2021
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A brain-inspired theory of mind spiking neural network for reducing safety risks of other agents
Zhao, Z., Lu, E., Zhao, F., Zeng, Y., Zhao, Y., 2022 · 2022
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Multi-layer unsupervised learning in a spiking convolutional neural network, in: 2017 international joint conference on neural networks (IJCNN), IEEE. pp. 2023–2030
Tavanaei, A., Maida, A.S., 2017 · 2030
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