1 · Reservoir activations how it reacts to input
Each dot is a reservoir neuron and the lines are its connections; colour is the activation as the input drives the network, and node size grows with degree (so scale-free hubs stand out). Switch the structure (ring, chain, lattice, small-world, scale-free, modular, star or random) and the input to see how connectivity and drive shape the dynamics. The right panel shows the activation of each neuron over time — one coloured trace per neuron, scrolling as the reservoir responds to the input. A higher spectral radius lengthens the memory. Pick a structure and input, then press play.
2 · Central vs Federated vs Local vs Ensemble the strategies
A forecasting task — pick synthetic, the real TED spread (a credit-risk series, 1986–2022) or chaotic Mackey-Glass — split across clients. Federated ridge pools the clients' summary statistics and lands exactly on centralized; local-only models degrade as each client is starved of data; the ensemble of heterogeneous reservoirs sits in between. Left: the animated test-set forecast; right: test NRMSE (lower is better).
3 · Short-term memory how far back it remembers
Can the reservoir reconstruct the input it saw k steps ago? Each bar is the recovery (R², 0–1) at delay k. Memory fades with delay, and reaches further as the spectral radius approaches 1.
4 · Speaker ID on Japanese Vowels classification · real data
A real UCI benchmark: classify which of 9 speakers uttered a short Japanese vowel (12-dimensional cepstral frames). Each speaker is one federated client — an extreme label skew. Centralized and federated agree exactly and get it right; a local-only client (which has only ever heard one speaker) is stuck at chance. Press play to cycle through held-out test utterances, or scrub to pick one.