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Thebrain 9 local server11/3/2023 It achieves significantly higher performance than single-learning methods. 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 can meta-learn local plasticity and receive top-down supervision information for multiscale learning. Here, we present a neuromorphic global-local synergic learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs to fully exploit its advantages. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. There are two principle approaches for learning in artificial intelligence: error-driven global learning and neuroscience-oriented local learning.
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