The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks

Key points

• However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neurons.1
• Quantitative analysis reveals that up to 44% of neurons in an ImageNet-trained ResNet-18 model reside in a deeply inhibited state, imposing a persistent computational burden despite having a negligible probability of firing.1
• To address this inefficiency, the Dormant Spiking Neuron (DSN) model is introduced.1
• By incorporating a state-driven dynamic gate, the DSN allows a neuron to enter a dormant state when its membrane potential falls below a predefined negative threshold, dynamically pruning redundant computations at the instance level.1

Sources (1)

• [1]The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 12:05 PM

  Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity.

  However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neurons.

Extractive summary: sentences quoted from the sources.