graph_layer_propagate — CONNGRAPH dimension op

• Data kinds: conn_graph × labels × matrix → matrix

• Call: import fullseye as fs; fs.ledger.graph_layer_propagate(W: 'Any', labels: 'Any', U: 'Any', activation: 'str' = 'kwta', active_frac: 'float' = 0.1, gain: 'float' = 1.0) -> 'np.ndarray' (to call the implementation directly, import conngraph; conngraph.graph_layer_propagate(W: 'Any', labels: 'Any', U: 'Any', activation: 'str' = 'kwta', active_frac: 'float' = 0.1, gain: 'float' = 1.0) -> 'np.ndarray'; from the registry, opsconngraph.get("graph_layer_propagate"))

Usage

Passes the layer-0 state U (N, n_0) forward, one stage at a time, through the blocks W[layer a → layer a+1], giving the states of all layers (N, n) (matrix; columns in node order, and the layer-0 columns are U itself).

For each receiver the input weights are normalised to sum to 1 before taking the weighted sum (independent of layer size), then activated with activation:

"linear" passes it through (mean absolute value set to gain), "tanh" saturates, "kwta" lets only the receivers whose input is in the top active_frac for each stimulus fire (by their surplus over the threshold), normalised so that the mean is 1. Connections that skip layers (layer a → a+2) and recurrence within a layer are not used (a tool that looks only at the forward, one-stage-at-a-time mapping).

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_connectome_motor_bottleneck — py -3.11 examples/poc_connectome_motor_bottleneck.py

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

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

Same category (dimension)

graph_block_shuffle · states_participation_ratio · states_layer_dimension

*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.