Brain-inspired global-local learning incorporated with neuromorphic computing

Understand

Two main routes of learning methods exist at present including error-driven global learning and neuroscience-oriented local learning.

• Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios.
• At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs for exploiting the advantages.
• Here, we report a neuromorphic hybrid learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity.