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Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) because of their sparse, asynchronous, and binary event-driven processing.
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Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type
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Spiking neuron models: Single neurons, populations, plasticity
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The organization of behavior: A neuropsychological theory
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Activity-driven, event-based vision sensors
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Implementing spike-timing-dependent plasticity on spinnaker neuromorphic hardware
Jin, X., Rast, A., Galluppi, F., Davies, S., Furber, S.: · 2010
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Segmentation and edge detection based on spiking neural network model
Meftah, B., Lezoray, O., Benyettou, A.: · 2010
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2010
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Semantic contours from inverse detectors
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The spinnaker project
Furber, S.B., Galluppi, F., Temple, S., Plana, L.A.: · 2014
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Retinomorphic event-based vision sensors: bioinspired cameras with spiking output
Posch, C., Serrano-Gotarredona, T., Linares-Barranco, B., Delbruck, T.: · 2014
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1.1 computing’s energy problem (and what we can do about it)
Horowitz, M.: · 2014
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Simonyan, K., Zisserman, A.: · 2015
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Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip
Akopyan, F., Sawada, J., Cassidy, A., Alvarez-Icaza, R., Arthur, J., Merolla, P., Imam, N., Nakamura, Y., Datta, P., Nam, G.J., et al.: · 2015
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P.U., Neil, D., Binas, J., Cook, M., Liu, S.C., Pfeiffer, M.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Spiking deep convolutional neural networks for energy-efficient object recognition
Cao, Y., Chen, Y., Khosla, D.: · 2015
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Diehl, P.U., Cook, M.: · 2015
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U-net: Convolutional networks for biomedical image segmentation
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TensorFlow: Large-scale machine learning on heterogeneous systems (2015) Software available from tensorflow.org
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Speeding up semantic segmentation for autonomous driving
Treml, M., Arjona-Medina, J., Unterthiner, T., Durgesh, R., Friedmann, F., Schuberth, P., Mayr, A., Heusel, M., Hofmarcher, M., Widrich, M., et al.: · 2016
Surrogate gradient learning in spiking neural networks
Neftci, E.O., Mostafa, H., Zenke, F.: · 2019
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A swarm optimization solver based on ferroelectric spiking neural networks
Fang, Y., Wang, Z., Gomez, J., Datta, S., Khan, A.I., Raychowdhury, A.: · 2019
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Image segmentation method based on spiking neural network with adaptive synaptic weights
Zheng, D., Lin, X., Wang, X.: · 2019
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Ev-segnet: Semantic segmentation for event-based cameras
Alonso, I., Murillo, A.C.: · 2019
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Rethinking the performance comparison between snns and anns
Deng, L., Wu, Y., Hu, X., Liang, L., Ding, Y., Li, G., Zhao, G., Li, P., Xie, Y.: · 2020
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Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
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Training deep spiking neural networks using backpropagation
Lee, J.H., Delbruck, T., Pfeiffer, M.: · 2016
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3d u-net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: · 2016
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Efficient processing of deep neural networks: A tutorial and survey
Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.: · 2017
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Rueckauer, B., Lungu, I.A., Hu, Y., Pfeiffer, M., Liu, S.C.: · 2017
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Supervised learning based on temporal coding in spiking neural networks
Mostafa, H.: · 2017
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Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2017
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A survey of autonomous driving: Common practices and emerging technologies
Yurtsever, E., Lambert, J., Carballo, A., Takeda, K.: · 2020
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Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Kim, Y., Panda, P.: · 2020
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Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization
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Temporal coding in spiking neural networks with alpha synaptic function
Comsa, I.M., Fischbacher, T., Potempa, K., Gesmundo, A., Versari, L., Alakuijala, J.: · 2020
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Park, S., Kim, S., Na, B., Yoon, S.: · 2020
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Neuromorphic nearest neighbor search using intel’s pohoiki springs
Frady, E.P., Orchard, G., Florey, D., Imam, N., Liu, R., Mishra, J., Tse, J., Wild, A., Sommer, F.T., Davies, M.: · 2020
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Spiking-yolo: spiking neural network for energy-efficient object detection
Kim, S., Park, S., Na, B., Yoon, S.: · 2020
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Computer vision for autonomous vehicles: Problems, datasets and state of the art
Janai, J., Güney, F., Behl, A., Geiger, A., et al.: · 2020
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Image segmentation using deep learning: A survey
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: · 2020
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Rathi, N., Roy, K.: · 2020
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Federated learning with spiking neural networks
Venkatesha, Y., Kim, Y., Tassiulas, L., Panda, P.: · 2021
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Optimizing deeper spiking neural networks for dynamic vision sensing
Kim, Y., Panda, P.: · 2021
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Privatesnn: Fully privacy-preserving spiking neural networks
Kim, Y., Venkatesha, Y., Panda, P.: · 2021
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Visual explanations from spiking neural networks using interspike intervals
Kim, Y., Panda, P.: · 2021
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A 128 x 128 120db 30mw asynchronous vision sensor that responds to relative intensity change
Lichtsteiner, P., Posch, C., Delbruck, T.: · 2069
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