ArticleNature communications2022
Brain-inspired global-local learning incorporated with neuromorphic computing.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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Who cites it
27 citing papers in PubMed.
- Trap-Programmable Ruddlesden-Popper Perovskite Nanocrystals for Persistent Optoelectronic Memory and Photonic Synapses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.Research square · 2026Article
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.bioRxiv : the preprint server for biology · 2026Article
- Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks.Frontiers in neuroscience · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.Nature machine intelligence · 2026Article
- A Reinforced, Event-Driven, and Attention-Based Convolution Spiking Neural Network for Multivariate Time Series Prediction.Biomimetics (Basel, Switzerland) · 2025Article
- Rapid learning with phase-change memory-based in-memory computing through learning-to-learn.Nature communications · 2025Article
- Multi-gate neuron-like transistors based on ensembles of aligned nanowires on flexible substrates.Nano convergence · 2025Article
- AM-MTEEG: multi-task EEG classification based on impulsive associative memory.Frontiers in neuroscience · 2025Article
- SemiSynBio: A new era for neuromorphic computing.Synthetic and systems biotechnology · 2024Review
- Adaptive spatiotemporal neural networks through complementary hybridization.Nature communications · 2024Article
- Brain-inspired chaotic spiking backpropagation.National science review · 2024Article
- Advancing brain-inspired computing with hybrid neural networks.National science review · 2024Review
- Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.Frontiers in computational neuroscience · 2024Article
- Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Exploiting noise as a resource for computation and learning in spiking neural networks.Patterns (New York, N.Y.) · 2023Article
- SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science advances · 2023Article
- BIDL: a brain-inspired deep learning framework for spatiotemporal processing.Frontiers in neuroscience · 2023Article
- A survey and perspective on neuromorphic continual learning systems.Frontiers in neuroscience · 2023Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are two principle approaches for learning in artificial intelligence: 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 to fully exploit its advantages. 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. It can meta-learn local plasticity and receive top-down supervision information for multiscale 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. We further implement the model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and prove that the model can fully utilize neuromorphic many-core architecture to develop hybrid computation paradigm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.