ArticleNature communications2024
Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 89 citations in OpenAlex.
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- Enhancing time scalability of spiking neural networks with dynamic time constants.Scientific reports · 2026Article
- Pulvinar pathways as skip connections in deep neural networks for vision.Frontiers in neuroimaging · 2026Article
- Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.Nature machine intelligence · 2026Article
- Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks.Frontiers in neuroscience · 2026Article
- Neuromorphic computing paradigms enhance robustness through spiking neural networks.Nature communications · 2025Article
- Modeling macroscopic brain dynamics with brain-inspired computing architecture.Nature communications · 2025Article
- Dynamic Vision Sensor-Driven Spiking Neural Networks for Low-Power Event-Based Tracking and Recognition.Sensors (Basel, Switzerland) · 2025Article
- Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.Nature communications · 2025Article
- A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics.Nature communications · 2025Article
- Advancing spatio-temporal processing through adaptation in spiking neural networks.Nature communications · 2025Article
- The effectiveness evaluation of industry education integration model for applied universities under back propagation neural network.Scientific reports · 2025Article
- Complex Spiking Neural Network Evaluated by Injury Resistance Under Stochastic Attacks.Brain sciences · 2025Article
- Bio-plausible reconfigurable spiking neuron for neuromorphic computing.Science advances · 2025Article
- Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.Neuroinformatics · 2025Article
- Memristive Ion Dynamics to Enable Biorealistic Computing.Chemical reviews · 2025Review
- Real-time human-robot interaction and service provision using hybrid intelligent computing framework.PloS one · 2025Article
- Innovating beyond electrophysiology through multimodal neural interfaces.Nature reviews electrical engineering · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.
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