  Humans learn multiple tasks in succession with minimal mutual interference,
through the context gating mechanism in the prefrontal cortex (PFC). The
brain-inspired models of spiking neural networks (SNN) have drawn massive
attention for their energy efficiency and biological plausibility. To overcome
catastrophic forgetting when learning multiple tasks in sequence, current SNN
models for lifelong learning focus on memory reserving or regularization-based
modification, while lacking SNN to replicate human experimental behavior.
Inspired by biological context-dependent gating mechanisms found in PFC, we
propose SNN with context gating trained by the local plasticity rule (CG-SNN)
for lifelong learning. The iterative training between global and local
plasticity for task units is designed to strengthen the connections between
task neurons and hidden neurons and preserve the multi-task relevant
information. The experiments show that the proposed model is effective in
maintaining the past learning experience and has better task-selectivity than
other methods during lifelong learning. Our results provide new insights that
the CG-SNN model can extend context gating with good scalability on different
SNN architectures with different spike-firing mechanisms. Thus, our models have
good potential for parallel implementation on neuromorphic hardware and model
human's behavior.
