{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BKMUM6VNDHX6T4RKDJFMTGEX2Z","short_pith_number":"pith:BKMUM6VN","schema_version":"1.0","canonical_sha256":"0a99467aad19efe9f22a1a4ac99897d64032ba4ccdf19c558655efffe356d6d6","source":{"kind":"arxiv","id":"2605.25607","version":1},"attestation_state":"computed","paper":{"title":"Balancing structure and randomness: maximum entropy networks for context-dependent computations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cond-mat.stat-mech"],"primary_cat":"q-bio.NC","authors_text":"Ludwig Hruza, Srdjan Ostojic","submitted_at":"2026-05-25T09:00:05Z","abstract_excerpt":"Understanding how network function constrains neural connectivity is a central challenge in neuroscience. An influential approach is to train neural networks with gradient descent on cognitive tasks and characterize the resulting connectivity. A key limitation is that the resulting structure depends on the details of the training procedure. Here we propose a complementary normative approach based on the maximum entropy principle for network connectivity, independent of any particular learning algorithm. We describe connectivity as a probability distribution over single-neuron weights, express "},"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":"2605.25607","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-bio.NC","submitted_at":"2026-05-25T09:00:05Z","cross_cats_sorted":["cond-mat.stat-mech"],"title_canon_sha256":"20c2ec2e014a40cceaa08e7ec3cbbfee2bcd8880ec8fcfe8c07384fead05fb20","abstract_canon_sha256":"8103465cf314fd3b4bbba5304feea0fa1237daf472385df94f15f698c2bc62f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:04:46.233789Z","signature_b64":"WCNZVuSYZM+SIcTt5l03X27hzMdVCb+ZGR5TFrTQC11oy5UQxrpkr4VF0bs6iWeFNzIPfch/b5huYcY/DIKyDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a99467aad19efe9f22a1a4ac99897d64032ba4ccdf19c558655efffe356d6d6","last_reissued_at":"2026-05-26T02:04:46.232968Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:04:46.232968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Balancing structure and randomness: maximum entropy networks for context-dependent computations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cond-mat.stat-mech"],"primary_cat":"q-bio.NC","authors_text":"Ludwig Hruza, Srdjan Ostojic","submitted_at":"2026-05-25T09:00:05Z","abstract_excerpt":"Understanding how network function constrains neural connectivity is a central challenge in neuroscience. An influential approach is to train neural networks with gradient descent on cognitive tasks and characterize the resulting connectivity. A key limitation is that the resulting structure depends on the details of the training procedure. Here we propose a complementary normative approach based on the maximum entropy principle for network connectivity, independent of any particular learning algorithm. We describe connectivity as a probability distribution over single-neuron weights, express "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.25607","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/2605.25607/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":"2605.25607","created_at":"2026-05-26T02:04:46.233110+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.25607v1","created_at":"2026-05-26T02:04:46.233110+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.25607","created_at":"2026-05-26T02:04:46.233110+00:00"},{"alias_kind":"pith_short_12","alias_value":"BKMUM6VNDHX6","created_at":"2026-05-26T02:04:46.233110+00:00"},{"alias_kind":"pith_short_16","alias_value":"BKMUM6VNDHX6T4RK","created_at":"2026-05-26T02:04:46.233110+00:00"},{"alias_kind":"pith_short_8","alias_value":"BKMUM6VN","created_at":"2026-05-26T02:04:46.233110+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/BKMUM6VNDHX6T4RKDJFMTGEX2Z","json":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z.json","graph_json":"https://pith.science/api/pith-number/BKMUM6VNDHX6T4RKDJFMTGEX2Z/graph.json","events_json":"https://pith.science/api/pith-number/BKMUM6VNDHX6T4RKDJFMTGEX2Z/events.json","paper":"https://pith.science/paper/BKMUM6VN"},"agent_actions":{"view_html":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z","download_json":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z.json","view_paper":"https://pith.science/paper/BKMUM6VN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.25607&json=true","fetch_graph":"https://pith.science/api/pith-number/BKMUM6VNDHX6T4RKDJFMTGEX2Z/graph.json","fetch_events":"https://pith.science/api/pith-number/BKMUM6VNDHX6T4RKDJFMTGEX2Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z/action/storage_attestation","attest_author":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z/action/author_attestation","sign_citation":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z/action/citation_signature","submit_replication":"https://pith.science/pith/BKMUM6VNDHX6T4RKDJFMTGEX2Z/action/replication_record"}},"created_at":"2026-05-26T02:04:46.233110+00:00","updated_at":"2026-05-26T02:04:46.233110+00:00"}