The combination of neuroscience-oriented and computer-science-oriented approaches is the most promising method to develop artificial general intelligence (AGI) that can learn general tasks similar to humans. Currently, two main routes of learning exist, including neuroscience-inspired methods , represented by local synaptic plasticity, and machine-learning methods, represented by backpropagation . Both have advantages and complement each other , but neither  can solve all learning problems well. Integrating these two methods into one network may provide better learning abilities for general tasks.  Here, we report a hybrid spiking neural network model that integrates the two approaches by introducing a  meta-local module and a two-phase causality modellingmethod. The model can not only optimize local plasticityrules, but also receive top-down supervision information . In addition to flexibly supporting multiple spike-based coding schemes, we demonstrate that this  model facilitates learning of many general tasks, including fault-tolerance learning, few-shot learning and multiple-task learning , and show its efficiency on the Tianjic neuromorphic platform . This work provides a new route for brain-inspired computing and facilitates AGI development .
Two main routes of learning methods exist at present including neuroscience-inspired methods and machine learning methods . 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 learning and 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 .
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The combination of neuroscience-oriented and computer-science-oriented approaches is the most promising method to develop artificial general intelligence (AGI) that can learn general tasks similar to humans. Currently, two
Two
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exist,
methods exist at present
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, represented by local synaptic plasticity, and machine-learning methods, represented by backpropagation
and machine learning methods
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advantages and complement each other
own advantages
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currently
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these two methods
them
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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.
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hybrid spiking neural network
spike-based general learning
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the two approaches
two learning routes
A
brain-inspired
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causality modellingmethod. The model can not only optimize local plasticityrules, but also
parametric modelling. The hybrid model can meta-learn general local plasticity, and
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. In addition to flexibly supporting multiple spike-based coding schemes, we
for multi-scale learning. We
A
hybrid
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, and show its efficiency
. Furthermore, the implementation of the hybrid model
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. This work provides a new route for brain-inspired computing and facilitates AGI development
proves that it can fully utilize the advantages of neuromorphic hardware architecture and promote energy-efficient on-chip applications