matthew holliday / ai engineer

Projects

Mass-spring reservoir computer

An input signal shakes a grid of masses on springs, and a linear readout trained live by ridge regression reads their displacements to reproduce a target function of that input. The damping, stiffness, and drive controls show how narrow the window is where a physical reservoir works at all.

Open the live demo →

The demo running the memory task at 16 masses. A spring network is drawn above readouts showing 60% state match and NRMSE 0.400, and a trace plot below shows the readout output tracking the delayed target.

Reservoir dog

An Echo State Network watches where you drag a cursor and learns to predict where you are going next, trained online with Recursive Least Squares rather than from an offline dataset. Once it has your pattern the follower stops trailing behind you and starts cutting corners ahead of you.

Open the live demo →

The auto demo tracing a triangular path. The follower's trail sits on top of the dog's, a dashed forecast path runs ahead of both, and the side panel reports an RMSE of a few pixels alongside the reservoir and learning controls.

Echo state melody lab

An echo state network learns to play a melody back from memory, with the readout fitted by a single closed-form ridge solve instead of gradient descent. Free-run accuracy is reported next to teacher-forced accuracy, because the two diverge as soon as the output is fed back as the input.

Open the live demo →

The lab after training on Twinkle, Twinkle. Score tiles report 92.2% free-run match against 99.0% teacher-forced, and a piano roll below marks each note the free-running network got right or wrong against the target.