{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:2NAOFDDXB54HGYWEKNQ7APNVUP","short_pith_number":"pith:2NAOFDDX","schema_version":"1.0","canonical_sha256":"d340e28c770f787362c45361f03db5a3e9c829f4d27bac8070f9d63bb656a6be","source":{"kind":"arxiv","id":"1302.3590","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Learning of Loglinear Models for Neural Connectivity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kathryn Blackmond Laskey, Laura Martignon","submitted_at":"2013-02-13T14:15:20Z","abstract_excerpt":"This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies.  A major objective of the research is to provide statistical tools for detecting changes in firing patterns with changing stimuli. Our framework is not restricted to the well-understood case of pair interactions, but generalizes the Boltzmann machine model to allow for higher order interactions.  The paper applies a Markov Chain Monte Carlo Model Composition (MC3) algorithm to search over connectivity structures and uses Laplace's method to approximate po"},"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":"1302.3590","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-02-13T14:15:20Z","cross_cats_sorted":["q-bio.NC","stat.AP","stat.ML"],"title_canon_sha256":"8654aff7ccb20d5f399f5b2d394b4d93c4f9882a44bca4a94b22e966c42c9187","abstract_canon_sha256":"f61e55cd391aa86101c8f56084f8f1ddbb1b2f28e8eb04d9031a5766576d4483"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:33:36.608118Z","signature_b64":"y66V0zMGoLTAZAPeuv9CMazG2C7MtWsE6q5KwzgSZxEWajlWB/TA1xtSom7XmJjyw1lPYjoGWqjl3W89QpMjBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d340e28c770f787362c45361f03db5a3e9c829f4d27bac8070f9d63bb656a6be","last_reissued_at":"2026-05-18T03:33:36.607536Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:33:36.607536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Learning of Loglinear Models for Neural Connectivity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kathryn Blackmond Laskey, Laura Martignon","submitted_at":"2013-02-13T14:15:20Z","abstract_excerpt":"This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies.  A major objective of the research is to provide statistical tools for detecting changes in firing patterns with changing stimuli. Our framework is not restricted to the well-understood case of pair interactions, but generalizes the Boltzmann machine model to allow for higher order interactions.  The paper applies a Markov Chain Monte Carlo Model Composition (MC3) algorithm to search over connectivity structures and uses Laplace's method to approximate po"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.3590","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":""},"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":"1302.3590","created_at":"2026-05-18T03:33:36.607628+00:00"},{"alias_kind":"arxiv_version","alias_value":"1302.3590v1","created_at":"2026-05-18T03:33:36.607628+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.3590","created_at":"2026-05-18T03:33:36.607628+00:00"},{"alias_kind":"pith_short_12","alias_value":"2NAOFDDXB54H","created_at":"2026-05-18T12:27:32.513160+00:00"},{"alias_kind":"pith_short_16","alias_value":"2NAOFDDXB54HGYWE","created_at":"2026-05-18T12:27:32.513160+00:00"},{"alias_kind":"pith_short_8","alias_value":"2NAOFDDX","created_at":"2026-05-18T12:27:32.513160+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP","json":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP.json","graph_json":"https://pith.science/api/pith-number/2NAOFDDXB54HGYWEKNQ7APNVUP/graph.json","events_json":"https://pith.science/api/pith-number/2NAOFDDXB54HGYWEKNQ7APNVUP/events.json","paper":"https://pith.science/paper/2NAOFDDX"},"agent_actions":{"view_html":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP","download_json":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP.json","view_paper":"https://pith.science/paper/2NAOFDDX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1302.3590&json=true","fetch_graph":"https://pith.science/api/pith-number/2NAOFDDXB54HGYWEKNQ7APNVUP/graph.json","fetch_events":"https://pith.science/api/pith-number/2NAOFDDXB54HGYWEKNQ7APNVUP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP/action/storage_attestation","attest_author":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP/action/author_attestation","sign_citation":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP/action/citation_signature","submit_replication":"https://pith.science/pith/2NAOFDDXB54HGYWEKNQ7APNVUP/action/replication_record"}},"created_at":"2026-05-18T03:33:36.607628+00:00","updated_at":"2026-05-18T03:33:36.607628+00:00"}