Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks

Understand

Spiking Neural Networks (SNNs) are more biologically plausible and computationally efficient.

• Therefore, SNNs have the natural advantage of drawing the sparse structural plasticity of brain development to alleviate the energy problems of deep neural networks caused by their complex and fixed structures.
• However, previous SNNs compression works are lack of in-depth inspiration from the brain development plasticity mechanism.
• This paper proposed a novel method for the adaptive structural development of SNN (SD-SNN), introducing dendritic spine plasticity-based synaptic constraint, neuronal pruning and synaptic regeneration.