ArticleNature communications2024
Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks.
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 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Few-shot neuromorphic vision in a nonlinear photonic network laser.Science advances · 2026Article
- Physics-Guided Generative Inverse Design of Low-Carbon Concrete via Multi-Objective Latent Space Optimization.ACS omega · 2026Article
- Metrics for spin-based computing.Nature reviews. Physics · 2026Article
- Coplanar Floating-Gate Antiferroelectric Transistor with Multifunctionality for All-in-One Analog Reservoir Computing.Nano-micro letters · 2026Article
- Noise-aware training of neuromorphic dynamic device networks.Nature communications · 2025Article
- Controllable gliders in a nanomagnetic metamaterial.Nature communications · 2025Article
- Exploring subthreshold processing for next-generation TinyAI.Frontiers in computational neuroscience · 2025Article
- Ultrastrong magnon-magnon coupling and chiral spin-texture control in a dipolar 3D multilayered artificial spin-vortex ice.Nature communications · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach's efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.
Identifiers
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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.