Neuromorphic Computing Based on Parametrically-Driven Oscillators and Frequency Combs

• URL: http://arxiv.org/abs/2604.21861v1
• Date: Thu, 23 Apr 2026 16:57:58 GMT
• Title: Neuromorphic Computing Based on Parametrically-Driven Oscillators and Frequency Combs
• Abstract summary: We investigate a two-mode system exhibiting 2:1 parametric resonance and demonstrate its operation as a reservoir computer.<n>We find that optimal computational performance is achieved within the parametric resonance regime.<n>We also show that the input modulation, the detuning from the frequency matching condition, damping ratio, and input data rate systematically control the accessible dynamical regimes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parametrically driven oscillators provide a natural platform for neuromorphic computation, where nonlinear mode coupling and intrinsic dynamics enable both memory and high-dimensional transformation. Here, we investigate a two-mode system exhibiting 2:1 parametric resonance and demonstrate its operation as a reservoir computer across distinct dynamical regimes, including sub-threshold, parametric resonance, and frequency-comb states. By encoding input signals into the drive amplitude and sampling the resulting temporal and spectral responses, we perform one step-ahead prediction of benchmark chaotic systems, including Mackey-Glass, Rossler, and Lorenz dynamics. We find that optimal computational performance is achieved within the parametric resonance regime, where nonlinear interactions are activated while temporal coherence is preserved. In contrast, although frequency-comb states introduce increased spectral dimensionality, their performance is not consistently good across their existence band and also degrades in the chaotic comb regime due to loss of phase coherence. Mapping prediction error over parameter space reveals a direct correspondence between computational capability and the underlying bifurcation structure, with low-error regions aligned with the parametric resonance boundary. We further show that the input modulation, the detuning from the frequency matching condition, damping ratio, and input data rate systematically control the accessible dynamical regimes and thereby the computational performance. These results establish parametric resonance as a robust operating regime for oscillator-based reservoir computing and provide design principles for tuning physical systems toward optimal neuromorphic functionality.

Related papers

• Parametric and feedback-controlled multiparameter quantum estimation in a double cavity optomechanics: steady and dynamical state [0.0]
  We investigate a coupled-cavity optomechanical platform in which two mechanical modes interact with driven optical cavities.<n>We find that coherent feedback acts as a tunable resource for engineering the quantum fluctuations and parameter-dependent correlations of the optomechanical state.
  arXiv  Detail & Related papers  (2026-09-04T17:48:44Z)
• KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra [65.11254608352982]
  We introduce a generator-based neural Koopman framework that models dynamics through a structured, state-dependent representation of Koopman generators.<n>By exploiting the intrinsic Cartesian decomposition into skew-adjoint and self-adjoint components, KoopGen separates conservative transport from irreversible dissipation.
  arXiv  Detail & Related papers  (2026-02-15T06:32:23Z)
• Dynamical Phase Transitions Across Slow and Fast Regimes in a Two-Tone Driven Duffing Resonator [36.94429692322632]
  We study a Duffing resonator under bichromatic excitation and identify a competition between the two drives.<n>In the slow-beating regime, where the tones are closely spaced, the secondary drive acts as a modulation that induces dynamical phase transitions.<n>Our results provide a framework for controlling driven nonlinear systems, enabling state manipulation, and sensing in nanomechanical, optical, and superconducting circuit platforms.
  arXiv  Detail & Related papers  (2025-11-03T19:00:24Z)
• Parametric resonance and nonlinear dynamics in a coupled double-pendulum system [4.364587148820591]
  We investigate a collision-coupled double pendulum system within the framework of Lagrangian mechanics.<n>Experiments demonstrate that parametric resonance consistently occurs within a characteristic frequency ratio range.<n>We also find, under periodic driving at moderate frequencies, the system requires initial perturbations to stabilize into periodic states.
  arXiv  Detail & Related papers  (2025-09-10T11:29:30Z)
• Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing [0.0]
  This paper presents a novel Reservoir Computing (RC) method for noise filtering and reconstructing unobserved nonlinear dynamics.<n>The performance of the RC in terms of noise intensity, noise frequency content, and drastic shifts in dynamical parameters is studied.<n>The framework yields competitive accuracy at low signal-to-noise ratios and high-frequency ranges.
  arXiv  Detail & Related papers  (2025-04-17T21:47:13Z)
• Generative System Dynamics in Recurrent Neural Networks [56.958984970518564]
  We investigate the continuous time dynamics of Recurrent Neural Networks (RNNs)<n>We show that skew-symmetric weight matrices are fundamental to enable stable limit cycles in both linear and nonlinear configurations.<n> Numerical simulations showcase how nonlinear activation functions not only maintain limit cycles, but also enhance the numerical stability of the system integration process.
  arXiv  Detail & Related papers  (2025-04-16T10:39:43Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• Efficiency of Dynamical Decoupling for (Almost) Any Spin-Boson Model [44.99833362998488]
  We analytically study the dynamical decoupling of a two-level system coupled with a structured bosonic environment.<n>We find sufficient conditions under which dynamical decoupling works for such systems.<n>Our bounds reproduce the correct scaling in various relevant system parameters.
  arXiv  Detail & Related papers  (2024-09-24T04:58:28Z)
• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• Convergence of Iterative Quadratic Programming for Robust Fixed-Endpoint Transfer of Bilinear Systems [0.0]
  We present a computational method for open-loop minimum-norm control synthesis for fixed-endpoint transfer of bilinear ensemble systems. We use a two-stage computation to first ensure transfer to the desired terminal state, and then minimize the norm of the control function.
  arXiv  Detail & Related papers  (2024-03-26T22:24:11Z)
• Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling [47.081318079190595]
  We investigate the existence of underlying structures in the empirical coefficients using autoencoders. We design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models.
  arXiv  Detail & Related papers  (2021-07-09T09:03:08Z)

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.