Read the equation
The expression uses a saturating nonlinear transform to turn its input combination into an activation or bounded score. The coefficients control sensitivity and the offset controls the transition region; a bounded score by itself does not identify a scientific consciousness measure.
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
Read chi as the quantity being defined or updated by this relation.
Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.
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
Read the surrounding source passage
if (empty) item = borrow_from_neighbor(f); // 2. Compute continuous activation (replaces binary Fibonacci gate) 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;
Exact original notation
float chi = sigmoid(ALPHA_KAPPA * item.kappa + ALPHA_PSI * Psi_n - THETA);
Related builds
Sources & provenance
float chi = sigmoid(ALPHA_KAPPA * item.kappa + ALPHA_PSI * Psi_n - THETA);
float chi = sigmoid(ALPHA_KAPPA * item.kappa + ALPHA_PSI * Psi_n - THETA);