Two main routes of learning methods exist at present including neuroscience-inspired methods and machine learningmethods. Both have own advantages, but neither currently can solve all learning problems well. Integrating them into one network may provide better learning abilities for general tasks. On the other hand, spiking neural network embodies "computation" in spatiotemporal domain with unique features of rich coding scheme and threshold switching, which is very suitable for low power and high parallel neuromorphic computing . Here, we report a spike-based general learning model that integrates two learning routes by introducing a brain-inspired meta-local module and a two-phase parametric modelling. The hybrid model can meta-learn general local plasticity , and receive top-down supervision information for multi-scale learning. We demonstrate that this hybrid model facilitates learning of many general tasks, including fault-tolerance learning, few-shot learningand multiple-task learning . Furthermore, the implementation of the hybrid model on the Tianjic neuromorphic platform proves that it can fully utilize the advantages of neuromorphic hardware architecture and promote energy-efficient on-chip applications .
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 .
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neuroscience-inspired methods and machine learningmethods. Both have own advantages, but neither currently can solve all learning problems well.
error-driven global learning and neuroscience-oriented local learning.
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better learning abilities for general tasks. On the other hand, spiking neural network embodies "computation" in spatiotemporal domain with unique features of rich coding scheme and threshold switching, which is very suitable for low power and high parallel neuromorphic computing
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
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spike-based general learning model that integrates two learning routes
neuromorphic hybrid learning model
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meta-local module and a two-phase parametric modelling. The hybrid model
meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It
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general local plasticity ,
local plasticity
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multi-scale
multiscale synergic
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that this hybrid model facilitates learning of many general
the advantages of this model in multiple different
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fault-tolerance learning, few-shot learningand multiple-task learning . Furthermore,
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
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implementation of the hybrid model on the
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proves that it
by exploiting algorithm-hardware co-designs and proved that the model
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the advantages of neuromorphic hardware architecture and promote energy-efficient on-chip applications
neuromorphic many-core architecture to develop hybrid computation paradigm