ArticleScience advances2021
Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence.Science advances · 2026Article
- Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.Nano-micro letters · 2026Review
- Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks.Frontiers in neuroscience · 2026Article
- Bidirectional cross-day alignment of neural spikes and behavior using a hybrid SNN-ANN algorithm.Frontiers in neuroscience · 2026Article
- Neuromorphic chips for biomedical engineering.Mechanobiology in medicine · 2025Review
- Competitive plasticity to reduce the energetic costs of learning.PLoS computational biology · 2024Article
- Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability.Nature communications · 2024Article
- Brain topology improved spiking neural network for efficient reinforcement learning of continuous control.Frontiers in neuroscience · 2024Article
- Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification.Frontiers in neuroscience · 2024Article
- Large language models and brain-inspired general intelligence.National science review · 2023Article
- A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost.Science advances · 2023Article
- Emotional brain network decoded by biological spiking neural network.Frontiers in neuroscience · 2023Article
- Explaining cocktail party effect and McGurk effect with a spiking neural network improved by Motif-topology.Frontiers in neuroscience · 2023Article
- N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.Scientific data · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many synaptic plasticity rules found in natural circuits have not been incorporated into artificial neural networks (ANNs). We showed that incorporating a nonlocal feature of synaptic plasticity found in natural neural networks, whereby synaptic modification at output synapses of a neuron backpropagates to its input synapses made by upstream neurons, markedly reduced the computational cost without affecting the accuracy of spiking neural networks (SNNs) and ANNs in supervised learning for three benchmark tasks. For SNNs, synaptic modification at output neurons generated by spike timing–dependent plasticity was allowed to self-propagate to limited upstream synapses. For ANNs, modified synaptic weights via conventional backpropagation algorithm at output neurons self-backpropagated to limited upstream synapses. Such self-propagating plasticity may produce coordinated synaptic modifications across neuronal layers that reduce computational cost.
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Registered trials
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