reservoir_states — CONNGRAPH reservoir op

• Data kinds: conn_graph × matrix → matrix

• Call: import fullseye as fs; fs.ledger.reservoir_states(W: 'Any', U: 'Any', in_scale: 'float' = 1.0, leak: 'float' = 1.0, nonlinearity: 'str' = 'tanh', seed: 'int' = 0, washout: 'int' = 0, W_in: 'Any' = None) -> 'np.ndarray' (to call the implementation directly, import conngraph; conngraph.reservoir_states(W: 'Any', U: 'Any', in_scale: 'float' = 1.0, leak: 'float' = 1.0, nonlinearity: 'str' = 'tanh', seed: 'int' = 0, washout: 'int' = 0, W_in: 'Any' = None) -> 'np.ndarray'; from the registry, opsconngraph.get("reservoir_states"))

Usage

Reservoir state sequence: x_{t+1} = (1−leak) x_t + leak · f(Wᵀ x_t + W_in u_t). Returns (T − washout, n).

U is the (T, d) input sequence (1-D becomes (T, 1)). W_in is an (n, d) matrix, uniform on (−in_scale, in_scale), determined by seed. nonlinearity is tanh / linear. Starting from x_0 = 0, the state after each step's update is stacked. washout rows are discarded from the start (T or more is rejected).

If W_in is passed, that (n, d) matrix is used instead of random numbers (in_scale / seed are ignored) -- use this to stimulate a fixed group of nodes (columns = indicators of the stimulated nodes).

Detailed usage guide

• conngraph family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_malecns_activity_wave — py -3.11 examples/poc_malecns_activity_wave.py

• poc_microns_brain_wave — py -3.11 examples/poc_microns_brain_wave.py

Ops the type connects to (they accept matrix as input)

reservoir_encode · ridge_readout · ridge_predict · graph_activation_latency · graph_activity_spread · points_activity_video · graph_layer_propagate · states_participation_ratio

Same category (reservoir)

reservoir_from_graph · reservoir_encode · ridge_readout · ridge_predict

*Provenance: conngraph.py — CONNGRAPH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.