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THE CONSCIOUSNESS ATLAS
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ConsciousnessLearn · Explore · Joseph Anady

Machine Learning Module for Gate5000 Consciousness · NORMALIZED: Δw_ij

This relation defines NORMALIZED: Δw_ij in the source section “Machine Learning Module for Gate5000 Consciousness · NORMALIZED: Δw_ij”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on the…

Implementation mathematicsSource recovered; runtime not executed
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Understand the scale

See what normalization changes and which assumptions it introduces.

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gh-eq-341b208de53d7fa3source-comment-equation
NORMALIZED:Δwij=η⋅(xi⋅xj−α⋅wij⋅xj2)NORMALIZED: Δw_ij = η·(x_i·x_j - α·w_ij·x_j²)

Read the equation

This relation defines NORMALIZED: Δw_ij in the source section “Machine Learning Module for Gate5000 Consciousness · NORMALIZED: Δw_ij”. 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 NORMALIZED: Δw_ij 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
 * @brief Machine Learning Module for Gate5000 Consciousness
 *
 * ═══════════════════════════════════════════════════════════════════════════════
 * I. HEBBIAN LEARNING
 * ═══════════════════════════════════════════════════════════════════════════════
 *
 * CLASSIC HEBBIAN: Δw_ij = η·x_i·x_j
 * NORMALIZED:      Δw_ij = η·(x_i·x_j - α·w_ij·x_j²)
 * BCM:             Δw_ij = η·x_i·x_j·(x_j - θ_j)
 * OJA'S RULE:      Δw_ij = η·y·(x_i - y·w_ij)
 * STDP:            Δw = A+·exp(-Δt/τ+) if pre<post, A-·exp(Δt/τ-) if post<pre
 *
 * ═══════════════════════════════════════════════════════════════════════════════
 * II. GRADIENT DESCENT
 * ═══════════════════════════════════════════════════════════════════════════════
 *
 * SGD:      w = w - η·∇L
 * MOMENTUM: v = μ·v - η·∇L, w = w + v
Exact original notation
NORMALIZED:      Δw_ij = η·(x_i·x_j - α·w_ij·x_j²)

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Sources & provenance

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