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THE CONSCIOUSNESS ATLAS
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ConsciousnessLearn · Explore · Joseph Anady

Hebbian weight increment

Changes a connection in proportion to a learning-rate factor and the product of post- and presynaptic values. It is an outer-product update; without additional normalization it does not impose a fixed weight bound.

MEGAMIND specificationdocumented formula
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megamind-hebbian-learningMEGAMIND specification
ΔWij=ηyixj\Delta W_{ij}=\eta y_ix_j

Read the equation

Changes a connection in proportion to a learning-rate factor and the product of post- and presynaptic values. It is an outer-product update; without additional normalization it does not impose a fixed weight bound.

Its role in the build

Changes a connection in proportion to a learning-rate factor and the product of post- and presynaptic values.

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 ΔW[i][j] 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

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

Exact original notation
ΔW[i][j] = η × y[i] × x[j]

Related builds

Sources & provenance

01
MEGAMIND_Complete_Technical_Specification.docx
§5.3 · Jan 24, 2026
ΔW[i][j] = η × y[i] × x[j]

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