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THE CONSCIOUSNESS ATLAS
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ConsciousnessLearn · Explore · Joseph Anady

G5: Analog activation update (unchanged, already efficient) · aᵢ(t+1)

This relation defines aᵢ(t+1) in the source section “G5: Analog activation update (unchanged, already efficient) · aᵢ(t+1)”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on the source’s stated variables…

Implementation mathematicsSource recovered; runtime not executed
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gh-eq-e4a887ba5990ff55source-comment-equation
ai(t+1)=tanh(α⋅Σuji⋅aj+β)aᵢ(t+1) = tanh(α·Σu_{ji}·aⱼ + β)

Read the equation

This relation defines aᵢ(t+1) in the source section “G5: Analog activation update (unchanged, already efficient) · aᵢ(t+1)”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on the source’s stated variables and conditions; this record does not assign an unprovided physical or cognitive meaning to them.

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 aᵢ(t+1) as the quantity being defined or updated by this relation.

  2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

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

Assumptions & limits

  • This source expression was recovered from the pinned code revision. Its presence does not establish that the associated program was built, run, or scientifically validated.

Original context

Read the surrounding source passage
            state[i] = (mix & threshold_mask) | (old_state[i] & ~threshold_mask);
        }
    }

    /**
     * @brief G5: Analog activation update (unchanged, already efficient)
     *
     * aᵢ(t+1) = tanh(α·Σu_{ji}·aⱼ + β)
     */
    void update_analog() {
        prev_activation = activation;

        // Compute new activation from density
        const double d = density();
        const double input = ALPHA * d * dna_modulation + BETA;

        // Apply tanh with golden scaling (branchless)
        const double x = input * PHI;
Exact original notation
aᵢ(t+1) = tanh(α·Σu_{ji}·aⱼ + β)

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