Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks

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…

• NS
  National Science Foundation

  Award: 1640081
• IC
  Intel Corporation

  Award: P2ZHP2_164960
• W
  Wellcome
• WT
  Wellcome Trust

  Award: 110124/Z/15/Z]