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To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time.
A silicon neuron
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A solution to the learning dilemma for recurrent networks of spiking neurons
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Mastering the game of go without human knowledge
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Equivalent-accuracy accelerated neural-network training using analogue memory
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Loihi: A neuromorphic manycore processor with on-chip learning
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Gradient descent for spiking neural networks
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Large-scale neuromorphic spiking array processors: A quest to mimic the brain
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Language models are few-shot learners
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The heidelberg spiking data sets for the systematic evaluation of spiking neural networks
B. Cramer, Y. Stradmann, J. Schemmel, and F. Zenke · 2020
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A 28-nm convolutional neuromorphic processor enabling online learning with spike-based retinas
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Accurate deep neural network inference using computational phase-change memory
V. Joshi, M. Le Gallo, S. Haefeli, I. Boybat, S. R. Nandakumar, C. Piveteau, M. Dazzi, B. Rajendran, A. Sebastian, and E. Eleftheriou · 2020
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Physics for neuromorphic computing
D. Marković, A. Mizrahi, D. Querlioz, and J. Grollier · 2020
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Extending brainscales os for brainscales-2
E. Müller, C. Mauch, P. Spilger, O. J. Breitwieser, J. Klähn, D. Stöckel, T. Wunderlich, and J. Schemmel · 2020
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Neural heterogeneity promotes robust learning
N. Perez-Nieves, V. C. H. Leung, P. L. Dragotti, and D. F. M. Goodman · 2020
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J. Büchel, D. Zendrikov, S. Solinas, G. Indiveri, and D. R. Muir · 2021
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In situ learning using intrinsic memristor variability via markov chain monte carlo sampling
T. Dalgaty, N. Castellani, C. Turck, K.-E. Harabi, D. Querlioz, and E. Vianello · 2021
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Comparison of Artificial and Spiking Neural Networks on Digital Hardware
S. Davidson and S. B. Furber · 2021
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Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
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