ReviewNature communications2024
Emerging opportunities and challenges for the future of reservoir computing.
Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 48 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
48 citing papers in PubMed.
- Research Progress on Acidizing Techniques for Complex-Type Reservoirs.Molecules (Basel, Switzerland) · 2026Review
- Time series learning in a many-body Rydberg system with emergent collective amplification.Nature communications · 2026Article
- Multiphoton Quantum Reservoirs For Robust Multidimensional Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Tailored forecasting from short time series via meta-learning.Science advances · 2026Article
- Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing.Nature communications · 2026Article
- Triple-Mode Ferroelectric Thin-Film Transistor for Hybrid Electrical-Optical Reservoir Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Memristive Physical Reservoir Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Theory of temporal pattern learning in echo state networks.PNAS nexus · 2026Article
- Trainable neuromorphic spintronic hardware Via analog finite-difference gradient methods.Nature communications · 2026Article
- A Wilson-Cowan reservoir computer for interpretable spatiotemporal vision.Scientific reports · 2026Article
- HERA: a Hybrid Euler Reservoir Architecture for multivariate time series classification.Scientific reports · 2026Article
- Black hole information turbulence and the Hubble tension.Scientific reports · 2026Article
- Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks.Nature communications · 2026Article
- Evolving reservoir computers reveal bidirectional coupling between predictive power and emergent dynamics.Patterns (New York, N.Y.) · 2026Article
- Physical echo state network based on the nonlinearity and dynamic response of ambipolar heterostructure transistors.Nature communications · 2026Article
- Metrics for spin-based computing.Nature reviews. Physics · 2026Article
- Efficient next-generation reservoir computing: An analog in-memory implementation using memristor crossbar arrays.iScience · 2026Article
- Topological Entropy Correlates with the Predictive Power of Multiplexed Ensemble Reservoir Computing.bioRxiv : the preprint server for biology · 2026Article
- Compact Optical Reservoir Computing via Luminescence Dynamics in Rare-Earth Ions-Doped Nanocrystals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Organic Electrochemical Transistors for Neuromorphic Devices and Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines.
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.