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Spiking is not differentiable

Limited gradients

Surrogate gradients

Approximate gradients

References
  1. Nicola, W., & Clopath, C. (2017). Supervised learning in spiking neural networks with FORCE training. Nature Communications, 8(1). 10.1038/s41467-017-01827-3
  2. Schuman, C. D., Mitchell, J. P., Patton, R. M., Potok, T. E., & Plank, J. S. (2020). Evolutionary Optimization for Neuromorphic Systems. Proceedings of the Neuro-Inspired Computational Elements Workshop, 1–9. 10.1145/3381755.3381758
  3. Mitchell, J. P., Bruer, G., Dean, M. E., Plank, J. S., Rose, G. S., & Schuman, C. D. (2017). NeoN: Neuromorphic control for autonomous robotic navigation. 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), 136–142. 10.1109/iris.2017.8250111
  4. 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, 36(6), 51–63. 10.1109/msp.2019.2931595
  5. Zenke, F., & Neftci, E. O. (2021). Brain-Inspired Learning on Neuromorphic Substrates. Proceedings of the IEEE, 109(5), 935–950. 10.1109/jproc.2020.3045625