ArticleNature communications2025
Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Brain-like optical computing: a pathway from edge intelligence to embodied intelligence.National science review · 2026Article
- Neural rhythms as priors of speech computations.Patterns (New York, N.Y.) · 2026Article
- DCPA-SNN, Direct-Coding-Physics-Aware Spiking Neural Network: A Framework for Wearable ECG Denoising Under Dynamic-Noise Conditions.Sensors (Basel, Switzerland) · 2026Article
- Underlying Framework of All-optical Controlled Synaptic Devices for Neuromorphic Computing.Nano-micro letters · 2026Review
- Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.Nature machine intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain exhibits rich dynamical properties that underpin its remarkable temporal processing capabilities. However, spiking neural networks (SNNs) inspired by the brain have not yet matched their biological counterparts in temporal processing and remain vulnerable to noise perturbations. This study addresses these limitations by introducing Rhythm-SNN, which draws inspiration from the brain's neural oscillation mechanism. Specifically, we employ heterogeneous oscillatory signals to modulate spiking neurons, enforcing them to activate periodically at distinct frequencies. This approach not only significantly reduces neuronal firing rates but also enhances the capability and robustness of SNNs in temporal processing. Extensive experiments and theoretical analyses demonstrate that Rhythm-SNN achieves state-of-the-art performance across a broad range of tasks, with a markedly reduced energy cost, even under strong perturbations. Notably, in the Intel Neuromorphic Deep Noise Suppression Challenge, Rhythm-SNN outperforms deep learning solutions by achieving over two orders of magnitude in energy reduction while delivering award-winning denoising performance.
Indexed as
Identifiers
What OpenQuestion holds
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.