{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SNHF4LGBQVEQBWA2VY3VCXDIMB","short_pith_number":"pith:SNHF4LGB","schema_version":"1.0","canonical_sha256":"934e5e2cc1854900d81aae37515c686055446634bd9f553dabda36a606ce40f5","source":{"kind":"arxiv","id":"2510.02545","version":1},"attestation_state":"computed","paper":{"title":"Mean-field analysis of a neural network with stochastic STDP","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.dis-nn","math.PR","q-bio.NC"],"primary_cat":"physics.bio-ph","authors_text":"Etienne Tanr\\'e, Pascal Helson, Romain Veltz","submitted_at":"2025-10-02T20:23:35Z","abstract_excerpt":"Analysing biological spiking neural network models with synaptic plasticity has proven to be challenging both theoretically and numerically. In a network with N all-to-all connected neurons, the number of synaptic connections is on the order of $N^2$, making these models computationally demanding. Furthermore, the intricate coupling between neuron and synapse dynamics, along with the heterogeneity generated by plasticity, hinder the use of classic theoretical tools such as mean-field or slow-fast analyses. To address these challenges, we introduce a new variable which we term a typical neuron "},"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":"2510.02545","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.bio-ph","submitted_at":"2025-10-02T20:23:35Z","cross_cats_sorted":["cond-mat.dis-nn","math.PR","q-bio.NC"],"title_canon_sha256":"fe9f360e46f986e90693516dfd8de5c44682e7551785d9f2766eb2a38fd9b481","abstract_canon_sha256":"d13c2dd218c8750eecabe42c5ad79ba33c5fc0e27893a2a9e3be84ee15101490"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T01:51:49.802081Z","signature_b64":"r38UopIsRK7ffoZVCSlzc+7wNOzPujYSD5GemWi+E3YWgyGcz2TbMPlkW4DN+IOal2y2VhMnjKToryGdpGKSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"934e5e2cc1854900d81aae37515c686055446634bd9f553dabda36a606ce40f5","last_reissued_at":"2026-08-04T01:51:49.800469Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T01:51:49.800469Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mean-field analysis of a neural network with stochastic STDP","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.dis-nn","math.PR","q-bio.NC"],"primary_cat":"physics.bio-ph","authors_text":"Etienne Tanr\\'e, Pascal Helson, Romain Veltz","submitted_at":"2025-10-02T20:23:35Z","abstract_excerpt":"Analysing biological spiking neural network models with synaptic plasticity has proven to be challenging both theoretically and numerically. In a network with N all-to-all connected neurons, the number of synaptic connections is on the order of $N^2$, making these models computationally demanding. Furthermore, the intricate coupling between neuron and synapse dynamics, along with the heterogeneity generated by plasticity, hinder the use of classic theoretical tools such as mean-field or slow-fast analyses. To address these challenges, we introduce a new variable which we term a typical neuron "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.02545","kind":"arxiv","version":1},"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/2510.02545/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":"2510.02545","created_at":"2026-08-04T01:51:49.801717+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.02545v1","created_at":"2026-08-04T01:51:49.801717+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.02545","created_at":"2026-08-04T01:51:49.801717+00:00"},{"alias_kind":"pith_short_12","alias_value":"SNHF4LGBQVEQ","created_at":"2026-08-04T01:51:49.801717+00:00"},{"alias_kind":"pith_short_16","alias_value":"SNHF4LGBQVEQBWA2","created_at":"2026-08-04T01:51:49.801717+00:00"},{"alias_kind":"pith_short_8","alias_value":"SNHF4LGB","created_at":"2026-08-04T01:51:49.801717+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.00306","citing_title":"Mechanistic bridges from receptors to whole-brain dynamics: mean-field reductions, validity domains, and computational trade-offs","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB","json":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB.json","graph_json":"https://pith.science/api/pith-number/SNHF4LGBQVEQBWA2VY3VCXDIMB/graph.json","events_json":"https://pith.science/api/pith-number/SNHF4LGBQVEQBWA2VY3VCXDIMB/events.json","paper":"https://pith.science/paper/SNHF4LGB"},"agent_actions":{"view_html":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB","download_json":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB.json","view_paper":"https://pith.science/paper/SNHF4LGB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.02545&json=true","fetch_graph":"https://pith.science/api/pith-number/SNHF4LGBQVEQBWA2VY3VCXDIMB/graph.json","fetch_events":"https://pith.science/api/pith-number/SNHF4LGBQVEQBWA2VY3VCXDIMB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB/action/storage_attestation","attest_author":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB/action/author_attestation","sign_citation":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB/action/citation_signature","submit_replication":"https://pith.science/pith/SNHF4LGBQVEQBWA2VY3VCXDIMB/action/replication_record"}},"created_at":"2026-08-04T01:51:49.801717+00:00","updated_at":"2026-08-04T01:51:49.801717+00:00"}