Direct Training for Spiking Neural Networks: Faster, Larger, Better

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive performance compared with artificial neural networks (ANNs), due to the lack of effective learning algorithms and efficient programming frameworks. We address this issue from two aspects: (1) We propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for deep SNNs. (2) Via narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version, we present a Pytorch-based implementation method towards the training of…

• NS
  National Science Foundation

  Awards: 1730309, 61332007, 1725447
• NN
  National Natural Science Foundation of China

  Awards: 1725447, 61621136008, 61620106010, 61876215, 61836004, 61332007, 61327902
• NK
  National Key Research and Development Program of China

  Awards: 2017YFA0700900, 61332007, 61876215