The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks
Abstract
Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-efficient signal processing. To translate these benefits into hardware, a growing number of neuromorphic spiking NN processors have attempted to emulate biological NNs. These developments have created an imminent need for methods and tools that enable such systems to solve real-world signal processing problems. Like conventional NNs, SNNs can be trained on real, domain-specific data; however, their training requires the overcoming of a number of challenges linked to their binary and dynamical nature. This article elucidates step-by-step the problems typically encountered when training SNNs and guides the reader through the key concepts of synaptic plasticity and data-driven learning in the spiking setting. Accordingly, it gives an overview of existing approaches and provides an introduction to surrogate gradient (SG) methods, specifically, as a particularly flexible and efficient method to overcome the aforementioned challenges.
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Cites
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Opportunities for neuromorphic computing algorithms and applications
Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B. (2022) · Nature Computational Science · doi:10.1038/s43588-021-00184-y · Cited by 1,117
The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity
Pehle, C., Billaudelle, S., Cramer, B., Kaiser, J., Schreiber, K., Stradmann, Y., Weis, J., Leibfried, A., Müller, E., & Schemmel, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.795876 · Cited by 233
Predictive Coding: a Theoretical and Experimental Review
Millidge, B., Seth, A., & Buckley, C. L. (2021) · arXiv · doi:10.48550/arxiv.2107.12979 · Cited by 106
New light on cortical neuropeptides and synaptic network plasticity
Smith, S. J., Hawrylycz, M., Rossier, J., & Sümbül, U. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.04.002 · Cited by 26
2022 roadmap on neuromorphic computing and engineering
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4a83
Self-citation
Metadata
- Authors
- Neftci, E. O., Mostafa, H., & Zenke, F.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2019-11
- Volume
- 36
- Issue
- 6
- Pages
- 51-63
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2984844508
- Cited by
- 1,538
- References
- 35
- FWCI
- 28.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- Neftci, E. O., Mostafa, H., & Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2019.2931595
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