---
slug: anti_hebbian_learning
name: Anti-Hebbian Learning
one_liner: "A connection weakens as a function of its endpoints' correlated activity through a local, correlational, unsupervised, cumulative update — the sign-flipped, decorrelating dual of Hebbian strengthening."
type: prime
schema_version: 2
status: density_v2_draft
categories: [computational, systemic, biological]
origin_domain: neuroscience
alternate_origin_domains: [machine_learning, signal_processing, statistics]
aliases: [decorrelation_learning, anti_hebbian_plasticity, hebbian_decorrelation, competitive_weakening]
related: []
solution_archetypes: []
review_flags:
- "OPERATOR-DERIVED (op-01 dualization, sub-flavor 1b) — the sign-reversal dual of `hebbian_learning` along the update-polarity (strengthen-on-correlation vs weaken-on-correlation) axis. Discovered in the op-01 full sweep 2026-07-03. Verified absent via search_prime + slug check; nearest in-corpus is the input `hebbian_learning`. STATUS: HOLD — pending owner curation."
- "Created 2026-07-03; STUB ONLY (frontmatter + Core Idea + Notes). Did NOT go through Phase-A triage. SI/SF/similarity grades are PROPOSER ESTIMATES pending triangulation. Body sections (Structural Signature onward) NOT yet written."
- "Phase-C action: re-similarity-check vs hebbian_learning / negative_feedback / normalization / decorrelation to confirm it is a distinct local-update rule and not a re-labeling of generic negative feedback; if kept, draw the dual-sibling edge to hebbian_learning and a shared-parent (correlational-local-plasticity) edge at incorporation. one_liner must be synced to a v1 draft per CLAUDE.md before merge."
substrate_independence:
  composite_substrate_independence: 3
  domain_breadth: 3
  structural_abstraction: 4
  transfer_evidence: 3
  reasoning: 'The rule "adjust a connection down in proportion to the correlated activity of its two endpoints, using only local, unsupervised, cumulative information" is a medium-neutral update relation on a weighted graph, so structural abstraction is high (4) — it does not require neurons, only two co-active units and a modifiable coupling. It recurs beyond neuroscience: anti-Hebbian rules drive independent-component analysis and decorrelating networks in machine learning, lateral-inhibition and whitening filters in signal processing, and competitive/decorrelating updates in statistics — a domain breadth of 3 spanning mind, ML, and signal processing but clustered in learning-adjacent fields. Transfer is real but the evidence is thinner and more computational than a canonical structural pattern, so transfer evidence is 3 and the composite is 3: a genuinely substrate-independent local update whose demonstrated range is narrower than feedback or random_walk.'
similarity_to_nearest_existing_prime:
  slug: hebbian_learning
  similarity: 0.72
structural_framed:
  label: structural
  aggregate: 0.12
  criteria: {vocab_travels: 0.1, evaluative_weight: 0.0, institutional_origin: 0.1, human_practice_bound: 0.0, import_vs_recognize: 0.2}
  rationale: 'Weaken-a-coupling-in-proportion-to-endpoint-correlation is a local update relation on a weighted graph, medium-neutral and carrying no normative or institutional content; evaluative weight and human-practice binding read zero, since decorrelating filters and ICA networks run the pattern with no human in the loop. The faint non-zeros come from "Hebbian"/"anti-Hebbian" being terms of art carried in from neuroscience (vocab_travels 0.1, import_vs_recognize 0.2) and the rule''s neuroscience naming origin (institutional_origin 0.1), but the underlying correlation-driven weakening is recognized wherever local decorrelation happens.'
phase_b_passes_completed: []
---
# Anti-Hebbian Learning

## Core Idea

Anti-Hebbian learning is the local update rule in which the strength of a coupling between two units *decreases* as a function of their correlated activity — the exact sign-reversal of Hebbian strengthening. It occupies one pole of a single structural axis, *the polarity of the correlational update*: Hebbian learning raises a weight when its endpoints are jointly active ("fire together, wire together"), while anti-Hebbian learning lowers it under the same joint activity ("fire together, unwire"). Crucially, only the sign of the update flips; the other three commitments of the correlational-plasticity family are inherited unchanged. The update is *local* (only the two endpoints' activities and the existing weight matter, no global view), *unsupervised* (no teacher or error signal), and *cumulative* (repeated coactivation accrues, so structure emerges from input statistics rather than from a designer's intent).

The load-bearing consequence of flipping the sign is *decorrelation*. Where Hebbian learning drives units to encode and reinforce the co-occurrence statistics of their inputs — amplifying whatever is already correlated — anti-Hebbian learning drives units *apart*: connections between things that fire together are eroded, so the surviving structure represents what is *distinct* rather than what is *shared*. This is a self-limiting, stabilizing dynamic. Pure Hebbian updates are positive feedback and tend to run away (correlated weights grow without bound), so a coupled Hebbian/anti-Hebbian system is a common motif: the Hebbian term captures structure, the anti-Hebbian term subtracts out redundancy and holds the system in check. The characteristic payoff is a representation whose components are competing and decorrelated — a whitening or independence-seeking transform. The characteristic cost is that the erosion is indiscriminate with respect to usefulness: it removes *whatever* is correlated, including correlations one might have wanted to keep, so anti-Hebbian dynamics can suppress genuine shared signal along with redundant noise.

Because the rule is stated purely as "adjust a coupling down in proportion to the joint activity of its two endpoints," it is one pattern across several substrates. In **neuroscience** it appears as decorrelating synaptic plasticity and lateral inhibition; in **machine learning** as the anti-Hebbian terms that power independent-component analysis and decorrelating feature layers; in **signal processing** as adaptive whitening and interference-cancelling filters that null out correlated components; and in **statistics** as competitive, redundancy-reducing updates. In each the recognizable move is identical: use only local, unsupervised, cumulative correlation to *weaken* rather than strengthen, and thereby push a system toward independence and away from redundancy.

## Notes

**Provenance.** Produced by the op-01 dualization operator, sub-flavor 1b (sign/polarity reversal), from the input prime `hebbian_learning` along the update-polarity axis in the op-01 full sweep 2026-07-03. The dual is a well-attested, named rule in neuroscience and machine learning (anti-Hebbian / decorrelation plasticity), which is a moderate convergence signal that the pattern is real rather than a coined artifact.

**For owner evaluation (keep/cut).** The case FOR: it is a clean sign-reversal dual of an in-corpus prime, it inherits three of Hebbian learning's four structural commitments verbatim (local, unsupervised, cumulative) and flips only the fourth (update polarity), and it names a genuinely distinct *outcome* — decorrelation / redundancy reduction — that its parent does not produce. It also completes a natural pair. The specific risk flagged in the manifest is **neuroscience domain-instance risk plus a structure-collapse question**: is anti-Hebbian learning a distinct prime, or is it (a) merely "the negative of Hebbian learning" with no independent content, and/or (b) a domain-specific re-labeling of generic `negative_feedback`? The distinguishing claims to weigh: unlike bare negative feedback, anti-Hebbian learning is a *correlational, local, unsupervised, cumulative weight update* — it is specifically the *decorrelating* structure, not any stabilizing loop — and its signature product (whitening / independent components) is not something plain negative feedback yields. If Kurt judges that content to be inseparable from Hebbian learning, the better hold may be to fold both poles into a single `correlational_plasticity` (or `hebbian_learning`-as-genus) prime that carries the polarity as an internal axis, rather than list the dual separately. Decision hinges on whether the decorrelating outcome earns its own name.

**At incorporation.** Re-run similarity against `hebbian_learning`, `negative_feedback`, `normalization`, and any `decorrelation`/`whitening` entries to confirm it neither collapses into its parent nor duplicates negative feedback. If kept as a separate prime, draw the dual-sibling edge to `hebbian_learning` and a shared-parent edge to whatever correlational-local-plasticity genus is chosen; if merged, record the polarity as an internal axis of `hebbian_learning`. Sync the one_liner to a v1 draft per CLAUDE.md before merge.
