parametrically-driven-oscillator-neuromorphic - Reservoir Computing with Parametric Oscillators
Models reservoir computing with a two-mode parametrically driven oscillator across sub-threshold, resonance, and frequency-comb regimes.
Tags
Updated: 2026-10-01Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Model two-mode oscillators
- Encode inputs via drive amplitude
- Simulate oscillator responses
- Sample temporal and spectral states
- Train ridge-regression readouts
- Predict chaotic time series
- Map bifurcation and error landscapes
- Compare dynamical regimes
Inputs
- Input time series
- Oscillator parameters
- Drive amplitude settings
- Sampling windows and frequency bins
- Training and testing splits
- Prediction targets
Outputs
- Reservoir state vectors
- Temporal and spectral responses
- One-step-ahead predictions
- Prediction error metrics
- Parameter-space error maps
- Bifurcation diagrams
Requirements
- Numerical ODE solver
- Fourier transform support
- Ridge regression support