Frequency-Based Reservoir computing

• URL: http://arxiv.org/abs/2607.24420v1
• Date: Mon, 27 Jul 2026 13:33:13 GMT
• Title: Frequency-Based Reservoir computing
• Abstract summary: Reservoir computing is an efficient machine learning framework for predicting time generated by dynamical systems.<n>Here, we propose a frequency-based reservoir inspired by the brain's oscillatory dynamics and its hierarchy of timescales.<n>We find that units of the frequency-based reservoir amplify and store specific input frequencies, which are then used for prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reservoir computing has emerged as an efficient machine learning framework for predicting time series generated by dynamical systems. In contrast to other machine and deep learning approaches, a reservoir computing trains only the output layer via linear regression, leaving the reservoir (recurrent layer) untrained. This simplification makes reservoir computers easier to train and more amenable to experimentation. However, because current reservoirs consist of networks of randomly connected nodes and require the optimization of numerous hyperparameters, a framework that precisely explains how reservoir computing operates and how it can be optimized remains missing. Here, we propose a frequency-based reservoir inspired by the brain's oscillatory dynamics and its hierarchy of timescales. The frequency-based reservoir can be interpreted as an ensemble of independent oscillatory units, each processing a portion of the input's frequency content. This allows us to understand the reservoir's internal behavior by modeling it as a single unit driven by an external input. Borrowing from the theory of a nonlinear oscillator forced by complex periodic inputs, we found that units of the frequency-based reservoir selectively amplify and store specific input frequencies, which are then used for prediction. The frequency-based reservoir performs as well as or better than equivalent random reservoirs. Furthermore, the frequency-based approach can be optimized to improve short-term prediction, a property that random reservoirs lack. Finally, we show that the frequency-based reservoir can also predict complex spatiotemporal dynamics. Our results show that reservoir computing can be designed using brain properties and theoretical insights borrowed from the physics of forced nonlinear oscillators.

Related papers

• Training-Free Universal Approximation by Prompting Random Transformers [48.556633593447465]
  We show that a softmax attention network with random, untrained weights can approximate any Hlder function on a compact manifold when steered by an appropriate soft prompt.<n>We further quantify the cost of prompting, exposing a tradeoff between the norm of the constructed soft prompt tokens, prompt length, and hidden dimension.
  arXiv  Detail & Related papers  (2026-08-10T12:57:22Z)
• Quantum Reservoir Computing: Recent Advances and Future Directions [34.77938621771246]
  Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features.<n>Its computational power is often attributed to the exponentially large Hilbert space of the quantum system.<n>On hardware, these capabilities are further constrained by finite sampling, hardware noise, measurement backaction, and the cost of estimating observables.
  arXiv  Detail & Related papers  (2026-07-20T22:38:49Z)
• Diagnosing quantum reservoirs at scale based on expressivity and coverage [42.13843953705695]
  Quantum reservoirs offer a hardware-friendly route to quantum machine learning.<n>Because the reservoir is not optimized, performance depends entirely on the choice of reservoir family.<n>We introduce a scalable, hardware-agnostic framework built on two complementary quantities.
  arXiv  Detail & Related papers  (2026-07-10T14:16:13Z)
• Recurrent Quantum Feature Maps for Reservoir Computing [0.6299766708197881]
  We introduce a reservoir based on recurrent quantum feature maps where a fixed quantum circuit is reused to encode both current inputs and a classical feedback signal derived from previous outputs.<n>We evaluate the model on the Mackey-Glass time-series prediction task using our recently introduced CP feature map, and find that it achieves lower mean squared error than standard classical baselines.
  arXiv  Detail & Related papers  (2026-04-03T21:33:10Z)
• Smooth embeddings in contracting recurrent networks driven by regular dynamics: A synthesis for neural representation [45.88028371034407]
  Recent empirical work has documented topology-preserving latent organization in trained recurrent models.<n>Recent theoretical results in reservoir computing establish conditions under which the synchronization map is an embedding.<n>Our contribution is an integrated framework that assembles generalized synchronization and embedding guarantees for contracting reservoirs.
  arXiv  Detail & Related papers  (2026-01-26T23:10:39Z)
• Oscillations enhance time-series prediction in reservoir computing with feedback [3.3686252536891454]
  Reservoir computing is a machine learning framework used for modeling the brain. It is difficult to accurately reproduce the long-term target time series because the reservoir system becomes unstable. This study proposes oscillation-driven reservoir computing (ODRC) with feedback.
  arXiv  Detail & Related papers  (2024-06-05T02:30:29Z)
• Reservoir Computing with Generalized Readout based on Generalized Synchronization [0.0]
  Reservoir computing is a machine learning framework that exploits nonlinear dynamics. We propose a novel reservoir computing framework with generalized readout, including a nonlinear combination of reservoir variables. In a numerical study, we find that introducing the generalized readout leads to a significant improvement in accuracy and an unexpected enhancement in robustness.
  arXiv  Detail & Related papers  (2024-05-03T10:03:59Z)
• QuantumReservoirPy: A Software Package for Time Series Prediction [44.99833362998488]
  We have developed a software package to allow for quantum reservoirs to fit a common structure. Our package results in simplified development and logical methods of comparison between quantum reservoir architectures.
  arXiv  Detail & Related papers  (2024-01-19T13:31:29Z)
• Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing [68.8204255655161]
  Controlling nonlinear dynamical systems using machine learning allows to drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. We show first that classical reservoir computing excels at this task. In a next step, we compare those results based on different amounts of training data to an alternative setup, where next-generation reservoir computing is used instead. It turns out that while delivering comparable performance for usual amounts of training data, next-generation RC significantly outperforms in situations where only very limited data is available.
  arXiv  Detail & Related papers  (2023-07-14T07:05:17Z)
• Optimization of a Hydrodynamic Computational Reservoir through Evolution [58.720142291102135]
  We interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters.
  arXiv  Detail & Related papers  (2023-04-20T19:15:02Z)
• Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing [0.0]
  Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. This study analyzes the effect of sampling on the ability of reservoir computing to autonomously regenerate chaotic time series.
  arXiv  Detail & Related papers  (2023-01-27T13:31:15Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Natural quantum reservoir computing for temporal information processing [4.785845498722406]
  Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits.
  arXiv  Detail & Related papers  (2021-07-13T01:58:57Z)
• Fast and differentiable simulation of driven quantum systems [58.720142291102135]
  We introduce a semi-analytic method based on the Dyson expansion that allows us to time-evolve driven quantum systems much faster than standard numerical methods. We show results of the optimization of a two-qubit gate using transmon qubits in the circuit QED architecture.
  arXiv  Detail & Related papers  (2020-12-16T21:43:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.