Paper Citation Record · LEDGER

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2511.02584.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2511.02584 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:14:44.007356Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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verified fuzzy0
unresolved36
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External citation measurements

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Outbound references

Observation 16c2f96e-44ec-4b70-ad70-4c2ad4298c12 · outbound

This paper cites Neural networks and physical systems with emergent collective computational abilities.Proceed- ings of the national academy of sciences, 79(8):2554–2558, 1982.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Neural networks and physical systems with emergent collective computational abilities.Proceed- ings of the national academy of sciences, 79(8):2554–2558, 1982

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.388060Z

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Observation b736b24f-38cd-4916-90fe-87fccafeeabd · outbound

This paper cites The nobel prize in physics 2024 – press release.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks The nobel prize in physics 2024 – press release

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.444557Z

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Observation d5bf3769-5c97-486b-8bc3-f2ab0afb48b0 · outbound

This paper cites Hopfield networks is all you need.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Hopfield networks is all you need

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.520866Z

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Observation 0c69128e-93af-4c9a-94a3-367f57ce0fbd · outbound

This paper cites Efficient and optimal binary Hopfield associative memory storage using minimum probability flow.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Efficient and optimal binary Hopfield associative memory storage using minimum probability flow

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.591186Z

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Observation 78748593-18e4-495e-86ad-f9070c9e327f · outbound

This paper cites an unresolved cited work.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.692258Z

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Observation 17a02d0d-cd45-4d95-b56a-ac1b9239ee82 · outbound

This paper cites an unresolved cited work.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.742783Z

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Observation 18a4a34c-2628-484e-99c9-079b8067820f · outbound

This paper cites Cover and Joy A.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Cover and Joy A

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.889254Z

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Observation 99feeeb9-9a77-4371-9c58-930862d35579 · outbound

This paper cites Nonnegative Decomposition of Multivariate Information.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Nonnegative Decomposition of Multivariate Information

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.952331Z

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Observation fab62316-b0cd-417b-a4fe-89bae035c700 · outbound

This paper cites Opening the Black Box of Deep Neural Networks via Information.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Opening the Black Box of Deep Neural Networks via Information

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.026155Z

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Observation 508a1728-632c-4d12-b806-9ebc057ccd90 · outbound

This paper cites On information plane analyses of neural network classifiers—a review.IEEE Transactions on Neural Networks and Learning Systems, 33(12):7039–7051, 2021.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks On information plane analyses of neural network classifiers—a review.IEEE Transactions on Neural Networks and Learning Systems, 33(12):7039–7051, 2021

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.089956Z

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Observation 186603f3-21f9-45b7-900b-96e4fa708c67 · outbound

This paper cites A measure of the complexity of neural representations based on partial information decomposition.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks A measure of the complexity of neural representations based on partial information decomposition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.184462Z

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Observation 67650ec6-fef0-4473-866d-2f2bd7e37788 · outbound

This paper cites Shannon invariants: A scalable approach to information decomposition.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Shannon invariants: A scalable approach to information decomposition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.246691Z

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Observation 366c8c68-da1f-452d-a3d7-688445d0990e · outbound

This paper cites Schneider, David A.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Schneider, David A

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.313049Z

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Observation 3a691441-79d8-4179-8c88-a659acaaae93 · outbound

This paper cites Schneider, Valentin Neuhaus, David A.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Schneider, Valentin Neuhaus, David A

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.368117Z

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Observation 0e2f9ee9-4c24-43e6-8fab-31b122afc241 · outbound

This paper cites Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the national academy of sciences, 81(10):3088–3092, 1984.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the national academy of sciences, 81(10):3088–3092, 1984

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.432071Z

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Observation e4bac725-6c00-4b4f-ad69-caa9b5729dc5 · outbound

This paper cites Storing infinite numbers of patterns in a spin-glass model of neural networks.Physical Review Letters, 55(14):1530, 1985.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Storing infinite numbers of patterns in a spin-glass model of neural networks.Physical Review Letters, 55(14):1530, 1985

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.505365Z

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Observation ce134858-78a4-4865-a608-3a2f5a1aa047 · outbound

This paper cites an unresolved cited work.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.568344Z

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Observation 30ef3b35-bb8b-4b88-a033-26dd62811478 · outbound

This paper cites The space of interactions in neural network models.Journal of physics A: Mathematical and general, 21(1):257, 1988.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks The space of interactions in neural network models.Journal of physics A: Mathematical and general, 21(1):257, 1988

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.635011Z

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Observation 6dfa50c9-5b2f-4306-9256-74910be5acfd · outbound

This paper cites Bits and pieces: Understanding information decomposition from part-whole relationships and formal logic.Proceedings of the Royal Society A, 477(2251): 20210110, 2021.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Bits and pieces: Understanding information decomposition from part-whole relationships and formal logic.Proceedings of the Royal Society A, 477(2251): 20210110, 2021

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.672837Z

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Observation 82991655-c223-4cd9-9598-028bed14be21 · outbound

This paper cites Information decomposition of target effects from multi-source interactions: Perspectives on previous, current and future work.Entropy, 20(4):307, 2018.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Information decomposition of target effects from multi-source interactions: Perspectives on previous, current and future work.Entropy, 20(4):307, 2018

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.762382Z

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Observation 9f8b82fb-9ae8-47f7-9e6d-8140d64a3ce0 · outbound

This paper cites Introducing a differentiable measure of pointwise shared information.Physical Review E, 103(3):032149, 2021.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Introducing a differentiable measure of pointwise shared information.Physical Review E, 103(3):032149, 2021

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.859454Z

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Observation 7a9778fe-76b1-46b0-8271-6cec162f3c74 · outbound

This paper cites Coherent infomax as a computational goal for neural systems.Bulletin of mathematical biology, 73(2):344–372, 2011.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Coherent infomax as a computational goal for neural systems.Bulletin of mathematical biology, 73(2):344–372, 2011

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:42.998887Z

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Observation e32c3710-231b-4384-b003-c78b388a5827 · outbound

This paper cites Completely derandomized self-adaptation in evolution strategies.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Completely derandomized self-adaptation in evolution strategies

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.073396Z

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Observation 1ec52c57-38df-414a-a6a7-324fee72bc12 · outbound

This paper cites Learning of correlated patterns in spin-glass networks by local learning rules.Phys.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Learning of correlated patterns in spin-glass networks by local learning rules.Phys

Reference 24

Resolution: verified exact; doi, observed 2026-08-04T00:18:21.594098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47d45a05-22f8-42d6-9e3e-e9cb3587111e · outbound

This paper cites Amit, Hanoch Gutfreund, and H.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Amit, Hanoch Gutfreund, and H

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.234122Z

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Observation 94061c03-81d1-47e7-948c-a3c695f453cd · outbound

This paper cites New method for parameter estimation in probabilistic models: minimum probability flow.Physical review letters, 107(22):220601, 2011.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks New method for parameter estimation in probabilistic models: minimum probability flow.Physical review letters, 107(22):220601, 2011

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.339377Z

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Observation 9af60b40-ff90-49c2-92b7-273ba3c077ab · outbound

This paper cites Self-control in sparsely coded networks.Physical review letters, 80(13):2961, 1998.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Self-control in sparsely coded networks.Physical review letters, 80(13):2961, 1998

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.428030Z

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Observation 408c0841-3334-4013-ab76-d334ac5d39ed · outbound

This paper cites Mutual information and topology 1: Asymmetric neural network.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Mutual information and topology 1: Asymmetric neural network

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.490942Z

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Observation e468ec6c-384e-4c14-a986-773a27201f9e · outbound

This paper cites Structured information in small-world neural networks.Physical Review E—Statistical, Nonlinear , and Soft Matter Physics, 79(2):021909, 2009.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Structured information in small-world neural networks.Physical Review E—Statistical, Nonlinear , and Soft Matter Physics, 79(2):021909, 2009

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.534303Z

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Observation e4c2053f-fea0-44f5-a74a-fe112ae189ce · outbound

This paper cites An application of the principle of maximum information preservation to linear systems.Advances in neural information processing systems, 1, 1988.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks An application of the principle of maximum information preservation to linear systems.Advances in neural information processing systems, 1, 1988

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.600153Z

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Observation da8c7fde-36d2-4d4d-aac5-da37166526c8 · outbound

This paper cites Hopfield.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Hopfield

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.651492Z

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Observation 8fb2c352-89e5-4c1c-974e-aaac40ac8eae · outbound

This paper cites A new frontier for Hopfield Networks.Nature Reviews Physics, pages 1–2, 2023.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks A new frontier for Hopfield Networks.Nature Reviews Physics, pages 1–2, 2023

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.759802Z

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Observation 53fa8110-c5a4-4797-bac6-d3cb7b855fcd · outbound

This paper cites On the storage capacity of hopfield models with correlated patterns.Annals of Applied Probability, 8, 11 1998.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks On the storage capacity of hopfield models with correlated patterns.Annals of Applied Probability, 8, 11 1998

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.831740Z

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Observation 6d0e43e4-cb82-4d19-885e-93824af552ac · outbound

This paper cites Formal theory of creativity, fun, and intrinsic motivation (1990–2010).IEEE Transactions on Autonomous Mental Development, 2(3):230–247, 2010.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Formal theory of creativity, fun, and intrinsic motivation (1990–2010).IEEE Transactions on Autonomous Mental Development, 2(3):230–247, 2010

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:43.884059Z

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Observation edf4cb6a-f7d4-4920-89a9-0bb30bd5b0e8 · outbound

This paper cites Partial information decomposition for continuous variables based on shared exclusions: Analytical formulation and estimation.Physical Review E, 110(1):014115, 2024.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Partial information decomposition for continuous variables based on shared exclusions: Analytical formulation and estimation.Physical Review E, 110(1):014115, 2024

Reference 35

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Observation c222b827-eb18-4e48-b124-a42801a0c834 · outbound

This paper cites Information-theoretic neural networks for unsupervised learning: mathematical and statistical considera- tions.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Information-theoretic neural networks for unsupervised learning: mathematical and statistical considera- tions

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:44.007356Z

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Observation e57c8807-bddd-45f1-a685-e445318c3c70 · outbound

This paper cites an unresolved cited work.

Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks Unresolved cited work

Reference 1948

Resolution: unresolved; no resolver link, observed 2026-08-04T00:14:41.803674Z

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Pith citing papers

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