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Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering".
Surrogate gradient learning in spiking neural networks
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Matching properties of MOS transistors
Pelgrom, M., Duinmaijer, A. & Welbers, A · 1989
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Neuromorphic electronic systems
Mead, C · 1990
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Backpropagation through time: what it does and how to do it
Werbos, P. J · 1990
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Spike-driven synaptic plasticity: Theory, simulation, vlsi implementation
Fusi, S., Annunziato, M., Badoni, D., Salamon, A. & Amit, D. J · 2000
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Spike-based strategies for rapid processing
Thorpe, S., Delorme, A. & Van Rullen, R · 2001
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Real-time computing without stable states: A new framework for neural computation based on perturbations
Maass, W., Natschlager, T. & Markram, H · 2002
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On the computational power of circuits of spiking neurons
Maass, W. & Markram, H · 2004
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A unified approach to building and controlling spiking attractor networks
Eliasmith, C · 2005
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Learning real-world stimuli in a neural network with spike-driven synaptic dynamics
Brader, J. M., Senn, W. & Fusi, S · 2007
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A spatial contrast retina with on-chip calibration for neuromorphic spike-based aer vision systems
Costas-Santos, J., Serrano-Gotarredona, T., Serrano-Gotarredona, R. & Linares-Barranco, B · 2007
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Minimizing the effect of process mismatch in a neuromorphic system using spike-timing-dependent adaptation
Cameron, K. & Murray, A · 2008
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Parametric mismatch characterization for mixed-signal technologies
Tuinhout, H. & Wils, N · 2009
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Eventprop: Backpropagation for exact gradients in spiking neural networks (2020)
Wunderlich, T. C. & Pehle, C · 2009
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Real-time classification of complex patterns using spike-based learning in neuromorphic vlsi
Mitra, S., Fusi, S. & Indiveri, G · 2009
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A wafer-scale neuromorphic hardware system for large-scale neural modeling
Schemmel, J. et al · 2010
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A device mismatch compensation method for vlsi neural networks
Neftci, E. & Indiveri, G · 2010
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Implementing efficient balanced networks with mixed-signal spike-based learning circuits (2020)
Büchel, J., Kakon, J., Perez, M. & Indiveri, G · 2010
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Neuromorphic silicon neuron circuits
Indiveri, G. et al · 2011
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A systematic method for configuring vlsi networks of spiking neurons
Neftci, E., Chicca, E., Indiveri, G. & Douglas, R · 2011
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Exploiting device mismatch in neuromorphic vlsi systems to implement axonal delays
Sheik, S., Chicca, E. & Indiveri, G · 2012
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Spike-timing-dependent plasticity: a comprehensive overview
Markram, H., Gerstner, W. & Sjöström, P. J · 2012
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Learning optimal spike-based representations
Bourdoukan, R., Barrett, D. G. T., Machens, C. K. & Denève, S · 2012
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Real-time classification and sensor fusion with a spiking deep belief network
O’Connor, P., Neil, D., Liu, S.-C., Delbruck, T. & Pfeiffer, M · 2013
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Spinnaker: A 1-w 18-core system-on-chip for massively-parallel neural network simulation
Painkras, E. et al · 2013
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Computation using mismatch: Neuromorphic extreme learning machines
Yao, E., Hussain, S., Basu, A. & Huang, G.-B · 2013
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Predictive coding of dynamical variables in balanced spiking networks
Boerlin, M., Machens, C. K. & Denève, S · 2013
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DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments (2013)
The brain as an efficient and robust adaptive learner
Denève, S., Alemi, A. & Bourdoukan, R · 2017
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Loihi: A neuromorphic manycore processor with on-chip learning
Davies, M. et al · 2018
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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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Eligibility traces and plasticity on behavioral time scales: Experimental support of neohebbian three-factor learning rules
Gerstner, W., Lehmann, M., Liakoni, V., Corneil, D. & Brea, J · 2018
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Learning nonlinear dynamics in efficient, balanced spiking networks using local plasticity rules
Alemi, A., Machens, C. K., Denève, S. & Slotine, J.-J. E · 2018
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Efficient keyword spotting using dilated convolutions and gating
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Thiemann, J., Ito, N. & Vincent, E · 2013
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Neuromorphic learning towards nano second precision
Pfeil, T., Scherzer, A., Schemmel, J. & Meier, K · 2013
Cited alongside, same era.
A neuromorphic event-based neural recording system for smart brain-machine-interfaces
Corradi, F. & Indiveri, G · 2015
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A simple way to initialize recurrent networks of rectified linear units (2015)
Le, Q. V., Jaitly, N. & Hinton, G. E · 2015
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Truenorth: A high-performance, low-power neurosynaptic processor for multi-sensory perception, action, and cognition (2016)
Cassidy, A. S. et al · 2016
Cited alongside, same era.
Scaling mixed-signal neuromorphic processors to 28 nm fd-soi technologies
Qiao, N. & Indiveri, G · 2016
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Training deep spiking neural networks using backpropagation
Lee, J. H., Delbruck, T. & Pfeiffer, M · 2016
Cited alongside, same era.
Coucke, A. et al · 2018
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JAX: composable transformations of Python+NumPy programs (2018)
Bradbury, J. et al · 2018
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Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W. & Müller, K.-R · 2018
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The importance of space and time for signal processing in neuromorphic agents: The challenge of developing low-power, autonomous agents that interact with the environment
Indiveri, G. & Sandamirskaya, Y · 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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Weight agnostic neural networks
Gaier, A. & Ha, D · 2019
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Demonstrating advantages of neuromorphic computation: A pilot study
Wunderlich, T. et al · 2019
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Rockpool documentaton (2019)
Muir, D., Bauer, F. & Weidel, P · 2019
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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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Synaptic plasticity dynamics for deep continuous local learning (decolle)
Kaiser, J., Mostafa, H. & Neftci, E · 2020
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Learning to represent signals spike by spike
Brendel, W., Bourdoukan, R., Vertechi, P., Machens, C. K. & Denève, S · 2020
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Robust coding with spiking networks: a geometric perspective
Calaim, N., Alexander Dehmelt, F., Gonçalves, P. J. & Machens, C. K · 2020
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URL https://www.st.com/resource/en/datasheet/stm32l552cc.pdf
STM32L552xx Ultra-low-power Arm® Cortex®-M33 32-bit MCU+TrustZone®+FPU, 165 DMIPS, up to 512 KB Flash memory, 256 KB SRAM, SMPS (2020) · 2020
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A review of deep learning with special emphasis on architectures, applications and recent trends
Sengupta, S. et al · 2020
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STM32L552xx and STM32L562xx advanced Arm®-based 32-bit MCUs (2020)
ST · 2020
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In situ learning using intrinsic memristor variability via markov chain monte carlo sampling
Dalgaty, T. et al · 2021
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