{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6FBRQLPEJVOXHYTZ2S7OEQXVIT","short_pith_number":"pith:6FBRQLPE","schema_version":"1.0","canonical_sha256":"f143182de44d5d73e279d4bee242f544d5e7b229a654e5b22a23dc433a93de5c","source":{"kind":"arxiv","id":"2508.04983","version":3},"attestation_state":"computed","paper":{"title":"Kinetic energy in random recurrent neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["nlin.CD","q-bio.NC"],"primary_cat":"cond-mat.stat-mech","authors_text":"Haiping Huang, Li-Ru Zhang","submitted_at":"2025-08-07T02:28:51Z","abstract_excerpt":"High-dimensional chaotic dynamics can emerge in a large random recurrent neural network when the synaptic gain crosses a threshold. Recent works showed that the kinetic energy of neural activity links the chaotic dynamics and the supporting unstable fixed points (equilibria) in the phase space. Here, we investigate the kinetic-energy-centric properties of random recurrent neural networks by combining dynamical mean-field theory with extensive numerical simulations. We find that the average kinetic energy shifts continuously from zero to a positive value at the known critical value of coupling "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.04983","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2025-08-07T02:28:51Z","cross_cats_sorted":["nlin.CD","q-bio.NC"],"title_canon_sha256":"8a0b4b82b465ce144a18503c3b0331cbf6a736b00d32129916b95f757152c148","abstract_canon_sha256":"b693b93c4da1d253be27f84373b162bd3efa5833a63fa49a0efef8b296e3173a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:05.360163Z","signature_b64":"dKWVFNoMMiAoY5mWa/bou3Bkz7KjPyJpUtu/c8hB6h6UUETTNR87m52wHp6CNPmDZYpHeXIakwlUReh9VT4rAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f143182de44d5d73e279d4bee242f544d5e7b229a654e5b22a23dc433a93de5c","last_reissued_at":"2026-06-03T01:05:05.359671Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:05.359671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kinetic energy in random recurrent neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["nlin.CD","q-bio.NC"],"primary_cat":"cond-mat.stat-mech","authors_text":"Haiping Huang, Li-Ru Zhang","submitted_at":"2025-08-07T02:28:51Z","abstract_excerpt":"High-dimensional chaotic dynamics can emerge in a large random recurrent neural network when the synaptic gain crosses a threshold. Recent works showed that the kinetic energy of neural activity links the chaotic dynamics and the supporting unstable fixed points (equilibria) in the phase space. Here, we investigate the kinetic-energy-centric properties of random recurrent neural networks by combining dynamical mean-field theory with extensive numerical simulations. We find that the average kinetic energy shifts continuously from zero to a positive value at the known critical value of coupling "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.04983","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.04983/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.04983","created_at":"2026-06-03T01:05:05.359731+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.04983v3","created_at":"2026-06-03T01:05:05.359731+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.04983","created_at":"2026-06-03T01:05:05.359731+00:00"},{"alias_kind":"pith_short_12","alias_value":"6FBRQLPEJVOX","created_at":"2026-06-03T01:05:05.359731+00:00"},{"alias_kind":"pith_short_16","alias_value":"6FBRQLPEJVOXHYTZ","created_at":"2026-06-03T01:05:05.359731+00:00"},{"alias_kind":"pith_short_8","alias_value":"6FBRQLPE","created_at":"2026-06-03T01:05:05.359731+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2601.04702","citing_title":"Chaos in high-dimensional dynamical systems with tunable non-reciprocity","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT","json":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT.json","graph_json":"https://pith.science/api/pith-number/6FBRQLPEJVOXHYTZ2S7OEQXVIT/graph.json","events_json":"https://pith.science/api/pith-number/6FBRQLPEJVOXHYTZ2S7OEQXVIT/events.json","paper":"https://pith.science/paper/6FBRQLPE"},"agent_actions":{"view_html":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT","download_json":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT.json","view_paper":"https://pith.science/paper/6FBRQLPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.04983&json=true","fetch_graph":"https://pith.science/api/pith-number/6FBRQLPEJVOXHYTZ2S7OEQXVIT/graph.json","fetch_events":"https://pith.science/api/pith-number/6FBRQLPEJVOXHYTZ2S7OEQXVIT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT/action/storage_attestation","attest_author":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT/action/author_attestation","sign_citation":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT/action/citation_signature","submit_replication":"https://pith.science/pith/6FBRQLPEJVOXHYTZ2S7OEQXVIT/action/replication_record"}},"created_at":"2026-06-03T01:05:05.359731+00:00","updated_at":"2026-06-03T01:05:05.359731+00:00"}