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Spiking neural networks combine analog computation with event-based communication using discrete spikes.
Fast and deep: energy-efficient neuromorphic learning with first-spike times
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Spikeprop: backpropagation for networks of spiking neurons
Bohte, S. M., Kok, J. N. & La Poutré, J. A · 2000
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Learning methods of recurrent spiking neural networks
Selvaratnam, K., Kuroe, Y. & Mori, T · 2000
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Modeling, simulation, sensitivity analysis, and optimization of hybrid systems
Barton, P. I. & Lee, C. K · 2002
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Spiking Neuron Models: Single Neurons, Populations, Plasticity
Gerstner, W. & Kistler, W · 2002
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Addressing the memory bottleneck in AI model training
Ojika, D. et al · 2003
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A gradient descent rule for spiking neurons emitting multiple spikes
Booij, O. & tat Nguyen, H · 2005
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Maximising sensitivity in a spiking network
Bell, A. J. & Parra, L. C · 2005
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A learning method for synthesizing spiking neural oscillators
Kuroe, Y. & Iima, H · 2006
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The tempotron: a neuron that learns spike timing–based decisions
Gütig, R. & Sompolinsky, H · 2006
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Surrogate gradients for analog neuromorphic computing
Cramer, B. et al · 2006
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Aer ear: A matched silicon cochlea pair with address event representation interface
Chan, V., Liu, S. & van Schaik, A · 2007
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Learning methods of recurrent spiking neural networks based on adjoint equations approach
Kuroe, Y. & Ueyama, T · 2010
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Scikit-learn: Machine learning in Python
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The chronotron: A neuron that learns to fire temporally precise spike patterns
Florian, R. V · 2012
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The Implicit Function Theorem: History, Theory, and Applications
Krantz, S. & Parks, H · 2012
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A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
Xu, Y., Zeng, X., Han, L. & Yang, J · 2013
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Adam: A method for stochastic optimization
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The spinnaker project
Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A · 2014
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An accelerated lif neuronal network array for a large-scale mixed-signal neuromorphic architecture
Aamir, S. A. et al · 2018
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Loihi: A neuromorphic manycore processor with on-chip learning
Davies, M. et al · 2018
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A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (dynaps)
Moradi, S., Qiao, N., Stefanini, F. & Indiveri, G · 2018
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Towards spike-based machine intelligence with neuromorphic computing
Roy, K., Jaiswal, A. & Panda, P · 2019
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Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Neftci, E. O., Mostafa, H. & Zenke, F · 2019
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A. et al · 2014
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A proof of a key formula in the error-backpropagation learning algorithm for multiple spiking neural networks
Yang, W., Yang, D. & Fan, Y · 2014
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Convolutional networks for fast, energy-efficient neuromorphic computing
Esser, S. K. et al · 2016
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Spiking neurons can discover predictive features by aggregate-label learning
Gütig, R · 2016
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Training deep spiking neural networks using backpropagation
Lee, J. H., Delbruck, T. & Pfeiffer, M · 2016
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Memory-efficient implementation of densenets
Pleiss, G. et al · 2017
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Rozenwasser, E. & Yusupov, R · 2019
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Sensitivity analysis for hybrid systems and systems with memory
Serban, R. & Recuero, A · 2019
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Efficient rematerialization for deep networks
Kumar, R., Purohit, M., Svitkina, Z., Vee, E. & Wang, J · 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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Neural jump stochastic differential equations
Jia, J. & Benson, A. R · 2019
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Braindrop: A mixed-signal neuromorphic architecture with a dynamical systems-based programming model
Neckar, A. et al · 2019
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Towards artificial general intelligence with hybrid tianjic chip architecture
Pei, J. et al · 2019
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All-optical spiking neurosynaptic networks with self-learning capabilities
Feldmann, J., Youngblood, N., Wright, C., Bhaskaran, H. & Pernice, W · 2019
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Demonstrating advantages of neuromorphic computation: A pilot study
Wunderlich, T. et al · 2019
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Pde-constrained optimization and the adjoint method (2019)
Bradley, A. M · 2019
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Temporal coding in spiking neural networks with alpha synaptic function
Comsa, I. M. et al · 2020
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Temporal backpropagation for spiking neural networks with one spike per neuron
Kheradpisheh, S. R. & Masquelier, T · 2020
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Yin-yang dataset
Kriener, L · 2020
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Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
Billaudelle, S. et al · 2020
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Sparse spiking gradient descent
Perez-Nieves, N. & Goodman, D. F. M · 2021
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The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks
Zenke, F. & Vogels, T. P · 2021
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Adjoint equations of spiking neural networks
Pehle, C.-G · 2021
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