ArticleNature communications2022
A framework for the general design and computation of hybrid neural networks.
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 14 papers.
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Who cites it
14 citing papers in PubMed, 73 citations in OpenAlex.
- PS-SNN: pattern separation learning for expandable spiking neural networks in class-incremental learning.Scientific reports · 2026Article
- Research on precision prediction of heavy-duty lathes based on hybrid PINNS neural network.Scientific reports · 2026Article
- Materials and System Design for Self-Decision Bioelectronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.Nature communications · 2025Article
- Neuromorphic computing for robotic vision: algorithms to hardware advances.Communications engineering · 2025Review
- Hybrid neural networks for continual learning inspired by corticohippocampal circuits.Nature communications · 2025Article
- Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.Frontiers in computational neuroscience · 2025Article
- Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing.Nature nanotechnology · 2025Article
- Advancing brain-inspired computing with hybrid neural networks.National science review · 2024Review
- Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.Nature communications · 2024Article
- SiamEFT: adaptive-time feature extraction hybrid network for RGBE multi-domain object tracking.Frontiers in neuroscience · 2024Article
- A review of emergent intelligent systems for the detection of Parkinson's disease.Biomedical engineering letters · 2023Review
- Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions.Frontiers in neuroscience · 2023Article
- Enhancing spiking neural networks with hybrid top-down attention.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
20 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a growing trend to design hybrid neural networks (HNNs) by combining spiking neural networks and artificial neural networks to leverage the strengths of both. Here, we propose a framework for general design and computation of HNNs by introducing hybrid units (HUs) as a linkage interface. The framework not only integrates key features of these computing paradigms but also decouples them to improve flexibility and efficiency. HUs are designable and learnable to promote transmission and modulation of hybrid information flows in HNNs. Through three cases, we demonstrate that the framework can facilitate hybrid model design. The hybrid sensing network implements multi-pathway sensing, achieving high tracking accuracy and energy efficiency. The hybrid modulation network implements hierarchical information abstraction, enabling meta-continual learning of multiple tasks. The hybrid reasoning network performs multimodal reasoning in an interpretable, robust and parallel manner. This study advances cross-paradigm modeling for a broad range of intelligent tasks.
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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.