{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QZ6K2U4YUWS5RN66YXXF5XTKWU","short_pith_number":"pith:QZ6K2U4Y","canonical_record":{"source":{"id":"1707.04582","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-07-14T17:56:50Z","cross_cats_sorted":["cs.LG","q-bio.NC"],"title_canon_sha256":"97aaf76d38669e4b5fc2f3023ad1022217d60950c449b746e6acca458d935268","abstract_canon_sha256":"b3fa73887c34b415a3c0b2d0974b44f2e0ed4f4bd26f9d8a1d3fa145dae70a9e"},"schema_version":"1.0"},"canonical_sha256":"867cad5398a5a5d8b7dec5ee5ede6ab513cb496793d7ca33f8c747f254e9baa3","source":{"kind":"arxiv","id":"1707.04582","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.04582","created_at":"2026-05-18T00:39:57Z"},{"alias_kind":"arxiv_version","alias_value":"1707.04582v3","created_at":"2026-05-18T00:39:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.04582","created_at":"2026-05-18T00:39:57Z"},{"alias_kind":"pith_short_12","alias_value":"QZ6K2U4YUWS5","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QZ6K2U4YUWS5RN66","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QZ6K2U4Y","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QZ6K2U4YUWS5RN66YXXF5XTKWU","target":"record","payload":{"canonical_record":{"source":{"id":"1707.04582","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-07-14T17:56:50Z","cross_cats_sorted":["cs.LG","q-bio.NC"],"title_canon_sha256":"97aaf76d38669e4b5fc2f3023ad1022217d60950c449b746e6acca458d935268","abstract_canon_sha256":"b3fa73887c34b415a3c0b2d0974b44f2e0ed4f4bd26f9d8a1d3fa145dae70a9e"},"schema_version":"1.0"},"canonical_sha256":"867cad5398a5a5d8b7dec5ee5ede6ab513cb496793d7ca33f8c747f254e9baa3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:57.983794Z","signature_b64":"JjoTrH2yr4DBuVC+dJ8DWL650wfAX7m7DgcKf8OwtLh5q3MCH6RWtu//fhVtwIhny9GOnYEGLp5Y/1ytxJ/BDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"867cad5398a5a5d8b7dec5ee5ede6ab513cb496793d7ca33f8c747f254e9baa3","last_reissued_at":"2026-05-18T00:39:57.983042Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:57.983042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.04582","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kzp89DQRY1AWIaz5ToBLOTYd89RDjXSv6nxW2th0V8ZxXtAwzdCBExC58MxDoxsz9ot7vQLRVAQoHwELIQP1BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T12:18:33.128281Z"},"content_sha256":"c7a302238d4a3f7a5657ab9093bd33f6bd7f1f21ac38d137354ff71d7d84c485","schema_version":"1.0","event_id":"sha256:c7a302238d4a3f7a5657ab9093bd33f6bd7f1f21ac38d137354ff71d7d84c485"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QZ6K2U4YUWS5RN66YXXF5XTKWU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Capturing the diversity of biological tuning curves using generative adversarial networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.NC"],"primary_cat":"q-bio.QM","authors_text":"G. Barello, Takafumi Arakaki, Yashar Ahmadian","submitted_at":"2017-07-14T17:56:50Z","abstract_excerpt":"Tuning curves characterizing the response selectivities of biological neurons often exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves. However, a general framework for fitting such models to experimentally measured tuning curves is lacking. We address this problem by proposing to view mechanistic network models as generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.04582","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/QcuITBWbHOjqdn495Pt0iZpvdscD4rxAsRfnBcFGArYdxPR9ZRYVu8zX7DCvGLbncpy0QmQCsRex5KJbf6mBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T12:18:33.128638Z"},"content_sha256":"c3f8f0cc84798078ee1ce9d1d7c1166ef4c504f69c97e76767d71ff1fc658823","schema_version":"1.0","event_id":"sha256:c3f8f0cc84798078ee1ce9d1d7c1166ef4c504f69c97e76767d71ff1fc658823"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QZ6K2U4YUWS5RN66YXXF5XTKWU/bundle.json","state_url":"https://pith.science/pith/QZ6K2U4YUWS5RN66YXXF5XTKWU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QZ6K2U4YUWS5RN66YXXF5XTKWU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T12:18:33Z","links":{"resolver":"https://pith.science/pith/QZ6K2U4YUWS5RN66YXXF5XTKWU","bundle":"https://pith.science/pith/QZ6K2U4YUWS5RN66YXXF5XTKWU/bundle.json","state":"https://pith.science/pith/QZ6K2U4YUWS5RN66YXXF5XTKWU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QZ6K2U4YUWS5RN66YXXF5XTKWU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QZ6K2U4YUWS5RN66YXXF5XTKWU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b3fa73887c34b415a3c0b2d0974b44f2e0ed4f4bd26f9d8a1d3fa145dae70a9e","cross_cats_sorted":["cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-07-14T17:56:50Z","title_canon_sha256":"97aaf76d38669e4b5fc2f3023ad1022217d60950c449b746e6acca458d935268"},"schema_version":"1.0","source":{"id":"1707.04582","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.04582","created_at":"2026-05-18T00:39:57Z"},{"alias_kind":"arxiv_version","alias_value":"1707.04582v3","created_at":"2026-05-18T00:39:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.04582","created_at":"2026-05-18T00:39:57Z"},{"alias_kind":"pith_short_12","alias_value":"QZ6K2U4YUWS5","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QZ6K2U4YUWS5RN66","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QZ6K2U4Y","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:c3f8f0cc84798078ee1ce9d1d7c1166ef4c504f69c97e76767d71ff1fc658823","target":"graph","created_at":"2026-05-18T00:39:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Tuning curves characterizing the response selectivities of biological neurons often exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves. However, a general framework for fitting such models to experimentally measured tuning curves is lacking. We address this problem by proposing to view mechanistic network models as generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstac","authors_text":"G. Barello, Takafumi Arakaki, Yashar Ahmadian","cross_cats":["cs.LG","q-bio.NC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-07-14T17:56:50Z","title":"Capturing the diversity of biological tuning curves using generative adversarial networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.04582","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c7a302238d4a3f7a5657ab9093bd33f6bd7f1f21ac38d137354ff71d7d84c485","target":"record","created_at":"2026-05-18T00:39:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b3fa73887c34b415a3c0b2d0974b44f2e0ed4f4bd26f9d8a1d3fa145dae70a9e","cross_cats_sorted":["cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2017-07-14T17:56:50Z","title_canon_sha256":"97aaf76d38669e4b5fc2f3023ad1022217d60950c449b746e6acca458d935268"},"schema_version":"1.0","source":{"id":"1707.04582","kind":"arxiv","version":3}},"canonical_sha256":"867cad5398a5a5d8b7dec5ee5ede6ab513cb496793d7ca33f8c747f254e9baa3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"867cad5398a5a5d8b7dec5ee5ede6ab513cb496793d7ca33f8c747f254e9baa3","first_computed_at":"2026-05-18T00:39:57.983042Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:57.983042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JjoTrH2yr4DBuVC+dJ8DWL650wfAX7m7DgcKf8OwtLh5q3MCH6RWtu//fhVtwIhny9GOnYEGLp5Y/1ytxJ/BDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:57.983794Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.04582","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7a302238d4a3f7a5657ab9093bd33f6bd7f1f21ac38d137354ff71d7d84c485","sha256:c3f8f0cc84798078ee1ce9d1d7c1166ef4c504f69c97e76767d71ff1fc658823"],"state_sha256":"c7e1e545b91891eb1ac25157d48e1d5e2abac97a14638ec43833b894fb1b8cef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eAJTIkXpiQZz4kxaSpiCGtd7oEOlEYXNFqdNAZQGM0T8SXF3V9WHKp6bturLM9MGzF8yz90cggqLSr5PgFS1AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T12:18:33.130652Z","bundle_sha256":"ded7fcf0ea28a92b5a852ab4a00ad63fb7e1d7a9f09b614a42fb63a4a94b9db2"}}