Clipped plasticity parameter

Read the equation

Forms a confidence-dependent learning rate and clips it between the configured minimum and maximum. The bounds control allowed update intensity.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read eta as the quantity being defined or updated by this relation.

2. Applies the stated minimum, maximum, or clamp. These bounds are part of the actual operation.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

float Psi_n = state.families[f].Psi / (state.families[f].Psi + PSI_0);
float chi = sigmoid(ALPHA_KAPPA * item.kappa + ALPHA_PSI * Psi_n - THETA);

// 3. Compute bounded plasticity
float eta = clamp(ETA_BASE + ETA_GAIN * item.kappa, ETA_MIN, ETA_MAX);

// 4. Learning drive
float Gamma = chi * eta * item.kappa;

// 5. Allocator computes π^eff across substrates
auto pi_eff = allocator.compute(item, state.families[f]);

float eta = clamp(ETA_BASE + ETA_GAIN * item.kappa, ETA_MIN, ETA_MAX);

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