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With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption.
Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Reinforcement learning using a continuous time actor-critic framework with spiking neurons
Nicolas Frémaux, Henning Sprekeler, and Wulfram Gerstner · 2013
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Synaptic electronics: materials, devices and applications
Duygu Kuzum, Shimeng Yu, and HS Philip Wong · 2013
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The spinnaker project
Steve B Furber, Francesco Galluppi, Steve Temple, and Luis A Plana · 2014
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Neuronal dynamics: From single neurons to networks and models of cognition
Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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1.1 computing’s energy problem (and what we can do about it)
Mark Horowitz · 2014
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, Andrew S Cassidy, Jun Sawada, Filipp Akopyan, Bryan L Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, et al · 2014
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Memory and information processing in neuromorphic systems
Giacomo Indiveri and Shih-Chii Liu · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
Nicolas Frémaux and Wulfram Gerstner · 2016
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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Navigating mobile robots to target in near shortest time using reinforcement learning with spiking neural networks
Amarnath Mahadevuni and Peng Li · 2017
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu · 2017
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Six-legged walking in insects: how cpgs, peripheral feedback, and descending signals generate coordinated and adaptive motor rhythms
Salil S Bidaye, Till Bockemühl, and Ansgar Büschges · 2018
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End to end learning of spiking neural network based on r-stdp for a lane keeping vehicle
Zhenshan Bing, Claus Meschede, Kai Huang, Guang Chen, Florian Rohrbein, Mahmoud Akl, and Alois Knoll · 2018
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A survey of robotics control based on learning-inspired spiking neural networks
Zhenshan Bing, Claus Meschede, Florian Röhrbein, Kai Huang, and Alois C Knoll · 2018
The heidelberg spiking data sets for the systematic evaluation of spiking neural networks
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, and Friedemann Zenke · 2020
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Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations
Saima Sharmin, Nitin Rathi, Priyadarshini Panda, and Kaushik Roy · 2020
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Strategy and benchmark for converting deep q-networks to event-driven spiking neural networks
Weihao Tan, Devdhar Patel, and Robert Kozma · 2020
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Reinforcement co-learning of deep and spiking neural networks for energy-efficient mapless navigation with neuromorphic hardware
Guangzhi Tang, Neelesh Kumar, and Konstantinos P Michmizos · 2020
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Deep reinforcement learning with population-coded spiking neural network for continuous control
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Cited alongside, same era.
Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, et al · 2018
Cited alongside, same era.
Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules
Wulfram Gerstner, Marco Lehmann, Vasiliki Liakoni, Dane Corneil, and Johanni Brea · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari breakout game
Devdhar Patel, Hananel Hazan, Daniel J Saunders, Hava T Siegelmann, and Robert Kozma · 2019
Cited alongside, same era.
Towards spike-based machine intelligence with neuromorphic computing
Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda · 2019
Cited alongside, same era.
A comprehensive analysis on adversarial robustness of spiking neural networks
Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar, Chankyu Lee, Wachirawit Ponghiran, and Kaushik Roy · 2019
Cited alongside, same era.
Guangzhi Tang, Neelesh Kumar, Raymond Yoo, and Konstantinos P Michmizos · 2020
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Spikingjelly
Wei Fang, Yanqi Chen, Jianhao Ding, Ding Chen, Zhaofei Yu, Huihui Zhou, Yonghong Tian, and other contributors · 2021
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Deep residual learning in spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian · 2021
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Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Timothee Masquelier, Tiejun Huang, and Yonghong Tian · 2021
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On-chip trainable hardware-based deep q-networks approximating a backpropagation algorithm
Jangsaeng Kim, Dongseok Kwon, Sung Yun Woo, Won-Mook Kang, Soochang Lee, Seongbin Oh, Chul-Heung Kim, Jong-Ho Bae, Byung-Gook Park, and Jong-Ho Lee · 2021
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Visual explanations from spiking neural networks using inter-spike intervals
Youngeun Kim and Priyadarshini Panda · 2021
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Human-level control through directly-trained deep spiking q-networks
Guisong Liu, Wenjie Deng, Xiurui Xie, Li Huang, and Huajin Tang · 2021
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Norse - a deep learning library for spiking neural networks, January 2021
Christian Pehle and Jens Egholm Pedersen · 2021
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Population-coding and dynamic-neurons improved spiking actor network for reinforcement learning
Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Xiang Cheng, and Bo Xu · 2021
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