  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. It can meta-learn local plasticity and receive top-down supervision
information for multiscale synergic learning. We demonstrate the advantages of
this model in multiple different tasks, including few-shot learning, continual
learning, and fault-tolerance learning in neuromorphic vision sensors. It
achieves significantly higher performance than single-learning methods, and
shows promise in empowering neuromorphic applications revolution. We further
implemented the hybrid model in the Tianjic neuromorphic platform by exploiting
algorithm-hardware co-designs and proved that the model can fully utilize
neuromorphic many-core architecture to develop hybrid computation paradigm.
