Chaos · reservoir computing

Minimal Reservoir Computing

Minimal Reservoir Computing
fig. Minimal Reservoir Computing

The notebook · live in your browser, code and all

open full screen ↗

The notebook runs on its own once it loads, so give it about 10 seconds while Python starts up in your browser, and the simulations begin animating. Move the sliders and everything recomputes live. The code is shown alongside the output so you can read exactly how it works; the full editable source is linked below.

A reservoir computer forecasts chaos by pushing a signal through a big fixed random network and training only a linear readout. The question I cared about: how small can the reservoir get before it stops working? I map prediction quality across the sparsity and spectral-radius plane, scoring each setup by how many Lyapunov times its forecast stays valid, with the Lyapunov exponent computed straight from the dynamics rather than looked up. I run it on the Lorenz and Rossler flows and the Henon map to find the smallest network that still tracks each attractor.

Code & resources

← back to explorations