mlsanityData to 7 Oct 2026

The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware

arXiv 2106.07030 ↗First posted 13 Jun 2021cs.NE cs.AI cs.LGcited by 19

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Abstract (arXiv)

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. However, it has been argued that most modern machine learning algorithms are not neurophysiologically plausible. In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware. In this study, we present a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing, implemented on Intel's Loihi neuromorphic research processor. We demonstrate a proof-of-principle three-layer circuit that learns to classify digits from the MNIST dataset. To our knowledge, this is the first work to show a Spiking Neural Network (SNN) implementation of the backpropagation algorithm that is fully on-chip, without a computer in the loop. It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge computing. This implementation shows a path for using in-memory, massively parallel neuromorphic processors for low-power, low-latency implementation of modern deep learning applications.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 41 of its 137 references that are in the map. First posted on arXiv 13 Jun 2021.

Builds on

41 of 137 references.

References · 41

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  3. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  4. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  5. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  6. 2018Deep Learning in Spiking Neural Networkscited by 258
  7. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  8. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  9. 2016Training Deep Spiking Neural Networks using Backpropagationcited by 203
  10. 2017SuperSpike: Supervised learning in multi-layer spiking neural networkscited by 178
  11. 2014Difference Target Propagationcited by 144
  12. 2020Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networkscited by 98
29 more
  1. 2018Dendritic cortical microcircuits approximate the backpropagation algorithmcited by 96
  2. 2019Deep Learning without Weight Transportcited by 87
  3. 2016Supervised learning based on temporal coding in spiking neural networkscited by 75
  4. 2016Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computingcited by 68
  5. 2015How Important is Weight Symmetry in Backpropagation?cited by 57
  6. 2019Fast and energy-efficient neuromorphic deep learning with first-spike timescited by 52
  7. 2017Gradient Descent for Spiking Neural Networkscited by 47
  8. 2019Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagationcited by 43
  9. 2016Neuromorphic Deep Learning Machinescited by 38
  10. 2018Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networkscited by 30
  11. 2017BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticitycited by 23
  12. 2016Recurrent Network Models Of Sequence Generation And Memorycited by 21
  13. 2019Rapid online learning and robust recall in a neuromorphic olfactory circuitcited by 17
  14. 2018Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communicationcited by 14
  15. 2020Nengo and low-power AI hardware for robust, embedded neuroroboticscited by 14
  16. 2018NengoDL: Combining deep learning and neuromorphic modelling methodscited by 11
  17. 2021In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processorcited by 11
  18. 2020A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinascited by 9
  19. 2019On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processorcited by 8
  20. 2016Deep Spiking Networkscited by 7
  21. 2016Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware Systemcited by 7
  22. 2020On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networkscited by 4
  23. 2015Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chainscited by 2
  24. 2019Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapsescited by 2
  25. 2020Visual Pattern Recognition with on On-chip Learning: towards a Fully Neuromorphic Approachcited by 2
  26. 2014Exact, Dynamically Routable Current Propagation in Pulse-Gated Synfire Chainscited by 1
  27. 2015A Fokker-Planck approach to graded information propagation in pulse-gated feedforward neuronal networkscited by 1
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  29. 2019Mapping high-performance RNNs to in-memory neuromorphic chipscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 3; 2025: 5; 2026: 4.

19 papers.

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  1. 2022Spiking Neural Networks for Frame-based and Event-based Single Object Localizationcited by 18
  2. 2021Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignmentscited by 14
  3. 2023Applications of Spiking Neural Networks in Visual Place Recognitioncited by 9
  4. 2024DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardwarecited by 9
  5. 2022Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognitioncited by 7
  6. 2022Visual Odometry with Neuromorphic Resonator Networkscited by 5
  7. 2022Intelligence Processing Units Accelerate Neuromorphic Learningcited by 3
  8. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  9. 2024Neuromorphic on-chip reservoir computing with spiking neural network architecturescited by 1
  10. 2024Training Multi-Layer Binary Neural Networks With Local Binary Error Signalscited by 1
  11. 2025Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguationcited by 1
  12. 2025SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networkscited by 1
7 more
  1. 2026Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representationscited by 1
  2. 2023A method for the ethical analysis of brain-inspired AIcited by 0
  3. 2025Frozen Backpropagation: Relaxing Weight Symmetry in Deep Spiking Neural Networkscited by 0
  4. 2025Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networkscited by 0
  5. 2026Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systemscited by 0
  6. 2026Adaptive Speech-to-Spike Encoding for Spiking Neural Networkscited by 0
  7. 2026All On-Board: Fully On-Chip Neuromorphic Q-Learning with Embedded CartPole Simulationcited by 0