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The capabilities of natural neural systems have inspired new generations of machine learning algorithms as well as neuromorphic very large-scale integrated (VLSI) circuits capable of fast, low-power information processing.
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A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses
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A 640m pixel/s 3.65 mw sparse event-driven neuromorphic object recognition processor with on-chip learning
Kim, J. K., Knag, P., Chen, T. & Zhang, Z · 2015
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
Frémaux, N. & Gerstner, W · 2015
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A mechanism for graded, dynamically routable current propagation in pulse-gated synfire chains and implications for information coding
Sornborger, A., Wang, Z. & Tao, L · 2015
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Wang, C., Xiao, Z., Wang, Z., Sornborger, A. T. & Tao, L · 2015
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Backpropagation for energy-efficient neuromorphic computing
Esser, S. K., Appuswamy, R., Merolla, P., Arthur, J. V. & Modha, D. S · 2015
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Scalable energy-efficient, low-latency implementations of trained spiking deep belief networks on spinnaker
Stromatias, E. et al · 2015
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A 4096-neuron 1m-synapse 3.8-pj/sop spiking neural network with on-chip stdp learning and sparse weights in 10-nm finfet cmos
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Programming spiking neural networks on intel’s loihi
Lin, C.-K. et al · 2018
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Jin, Y., Zhang, W. & Li, P · 2018
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Rueckauer, B. & Liu, S.-C · 2018
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Training deep neural networks for binary communication with the whetstone method
Severa, W., Vineyard, C. M., Dellana, R., Verzi, S. J. & Aimone, J. B · 2019
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Diehl, P. U. & Cook, M · 2015
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Packet-based communication in the cortex
Luczak, A., McNaughton, B. L. & Harris, K. D · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S. & Sun, J · 2015
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Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. & DiCarlo, J. J · 2016
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Convolutional networks for fast, energy-efficient neuromorphic computing
Esser, S. et al · 2016
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How important is weight symmetry in backpropagation?
Liao, Q., Leibo, J. & Poggio, T · 2016
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Demonstrating hybrid learning in a flexible neuromorphic hardware system
Friedmann, S. et al · 2016
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Nengodl: Combining deep learning and neuromorphic modelling methods
Rasmussen, D · 2019
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Going deeper in spiking neural networks: VGG and residual architectures
Sengupta, A., Ye, Y., Wang, R., Liu, C. & Roy, K · 2019
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Mapping high-performance rnns to in-memory neuromorphic chips
Nair, M. V. & Indiveri, G · 2019
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7.6 a 65nm 236.5 nj/classification neuromorphic processor with 7.5% energy overhead on-chip learning using direct spike-only feedback
Park, J., Lee, J. & Jeon, D · 2019
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Simple framework for constructing functional spiking recurrent neural networks
Kim, R., Li, Y. & Sejnowski, T. J · 2019
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Surrogate gradient learning in spiking neural networks
Neftci, E. O., Mostafa, H. & Zenke, F · 2019
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Deep learning in spiking neural networks
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T. & Maida, A · 2019
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A pulse-gated, neural implementation of the backpropagation algorithm
Sornborger, A., Tao, L., Snyder, J. & Zlotnik, A · 2019
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Fast and deep: energy-efficient neuromorphic learning with first-spike times
Göltz, J. et al · 2019
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Approximating back-propagation for a biologically plausible local learning rule in spiking neural networks
Shrestha, A., Fang, H., Wu, Q. & Qiu, Q · 2019
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Bp-stdp: Approximating backpropagation using spike timing dependent plasticity
Tavanaei, A. & Maida, A · 2019
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Neural state machines for robust learning and control of neuromorphic agents
Liang, D. et al · 2019
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Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
Pang, R. & Fairhall, A. L · 2019
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A review of binarized neural networks
Simons, T. & Lee, D.-J · 2019
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Backpropagation and the brain
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J. & Hinton, G · 2020
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On-chip few-shot learning with surrogate gradient descent on a neuromorphic processor
Stewart, K., Orchard, G., Shrestha, S. B. & Neftci, E · 2020
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Nengo and low-power ai hardware for robust, embedded neurorobotics
DeWolf, T., Jaworski, P. & Eliasmith, C · 2020
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experimental demonstration of supervised learning in spiking neural networks with phase-change memory synapses
Nandakumar, S. et al · 2020
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A 28-nm convolutional neuromorphic processor enabling online learning with spike-based retinas
Frenkel, C., Legat, J.-D. & Bol, D · 2020
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Rapid online learning and robust recall in a neuromorphic olfactory circuit
Imam, N. & Cleland, T. A · 2020
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Mixed-precision deep learning based on computational memory
Nandakumar, S. et al · 2020
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On-chip error-triggered learning of multi-layer memristive spiking neural networks
Payvand, M., Fouda, M. E., Kurdahi, F., Eltawil, A. M. & Neftci, E. O · 2020
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Payeur, A., Guerguiev, J., Zenke, F., Richards, B. & Naud, R · 2020
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A solution to the learning dilemma for recurrent networks of spiking neurons
Bellec, G. et al · 2020
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Temporal coding in spiking neural networks with alpha synaptic function
Comsa, I. M. et al · 2020
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Visual pattern recognition with on on-chip learning: towards a fully neuromorphic approach
Baumgartner, S. et al · 2020
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Nxtf: An api and compiler for deep spiking neural networks on intel loihi
Rueckauer, B. et al · 2021
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In-hardware learning of multilayer spiking neural networks on a neuromorphic processor
Shrestha, A., Fang, H., Rider, D., Mei, Z. & Qui, Q · 2021
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Advancing neuromorphic computing with loihi: A survey of results and outlook
Davies, M. et al · 2021
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Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes
Stöckl, C. & Maass, W · 2021
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