{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:PS3WYCNKBO7TUNNNRXWK6KX4IE","short_pith_number":"pith:PS3WYCNK","schema_version":"1.0","canonical_sha256":"7cb76c09aa0bbf3a35ad8decaf2afc4114a648a62466dfef0aa2fd527049aeba","source":{"kind":"arxiv","id":"1711.00193","version":1},"attestation_state":"computed","paper":{"title":"Functional Connectomics from Data: Probabilistic Graphical Models for Neuronal Network of C. elegans","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Eli Shlizerman, Hexuan Liu, Jimin Kim","submitted_at":"2017-11-01T03:43:52Z","abstract_excerpt":"We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate through the network and thus represents functional dependencies between neurons, i.e., functional connectome. The benefit of using a PGM as the functional connectome is that posterior inference can be done efficiently and circumvent the complexities in direct inference of response p"},"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":"1711.00193","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2017-11-01T03:43:52Z","cross_cats_sorted":[],"title_canon_sha256":"c9e32faafa46c2443f9399c21b9733ace886bb8774bef0c9fb3fe0694be13d14","abstract_canon_sha256":"24002238016a4276dccf39fb4bac1b0e2273498383a5d71cfba9d7d3b7dd0d1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:34.858440Z","signature_b64":"R11eEfpQvCphPFa4YqXF6Vkg6wXW+4EYq3rVYl1MndmD6DGyOOl+wCciLTfWn4breFkjLpLtxCfIYHM3YR2GCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cb76c09aa0bbf3a35ad8decaf2afc4114a648a62466dfef0aa2fd527049aeba","last_reissued_at":"2026-05-18T00:31:34.857774Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:34.857774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Functional Connectomics from Data: Probabilistic Graphical Models for Neuronal Network of C. elegans","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Eli Shlizerman, Hexuan Liu, Jimin Kim","submitted_at":"2017-11-01T03:43:52Z","abstract_excerpt":"We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate through the network and thus represents functional dependencies between neurons, i.e., functional connectome. The benefit of using a PGM as the functional connectome is that posterior inference can be done efficiently and circumvent the complexities in direct inference of response p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00193","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":"1711.00193","created_at":"2026-05-18T00:31:34.857899+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.00193v1","created_at":"2026-05-18T00:31:34.857899+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00193","created_at":"2026-05-18T00:31:34.857899+00:00"},{"alias_kind":"pith_short_12","alias_value":"PS3WYCNKBO7T","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"PS3WYCNKBO7TUNNN","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"PS3WYCNK","created_at":"2026-05-18T12:31:37.085036+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/PS3WYCNKBO7TUNNNRXWK6KX4IE","json":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE.json","graph_json":"https://pith.science/api/pith-number/PS3WYCNKBO7TUNNNRXWK6KX4IE/graph.json","events_json":"https://pith.science/api/pith-number/PS3WYCNKBO7TUNNNRXWK6KX4IE/events.json","paper":"https://pith.science/paper/PS3WYCNK"},"agent_actions":{"view_html":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE","download_json":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE.json","view_paper":"https://pith.science/paper/PS3WYCNK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.00193&json=true","fetch_graph":"https://pith.science/api/pith-number/PS3WYCNKBO7TUNNNRXWK6KX4IE/graph.json","fetch_events":"https://pith.science/api/pith-number/PS3WYCNKBO7TUNNNRXWK6KX4IE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE/action/storage_attestation","attest_author":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE/action/author_attestation","sign_citation":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE/action/citation_signature","submit_replication":"https://pith.science/pith/PS3WYCNKBO7TUNNNRXWK6KX4IE/action/replication_record"}},"created_at":"2026-05-18T00:31:34.857899+00:00","updated_at":"2026-05-18T00:31:34.857899+00:00"}