{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:57MYS2BFWUJOAENJDWEH4QMLP7","short_pith_number":"pith:57MYS2BF","canonical_record":{"source":{"id":"1711.01861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-06T12:36:07Z","cross_cats_sorted":[],"title_canon_sha256":"cbe5e513548321c500307da78a945d3bd0dc3345120525d1b89dbb0046b3a27a","abstract_canon_sha256":"3c1b9180ead2cfef8ed006606f1fb95d74684730adb61234fc51b04da63da9fd"},"schema_version":"1.0"},"canonical_sha256":"efd9896825b512e011a91d887e418b7ff5cb15c26aff47a44f89129699664cc0","source":{"kind":"arxiv","id":"1711.01861","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.01861","created_at":"2026-05-18T00:31:16Z"},{"alias_kind":"arxiv_version","alias_value":"1711.01861v1","created_at":"2026-05-18T00:31:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01861","created_at":"2026-05-18T00:31:16Z"},{"alias_kind":"pith_short_12","alias_value":"57MYS2BFWUJO","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"57MYS2BFWUJOAENJ","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"57MYS2BF","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:57MYS2BFWUJOAENJDWEH4QMLP7","target":"record","payload":{"canonical_record":{"source":{"id":"1711.01861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-06T12:36:07Z","cross_cats_sorted":[],"title_canon_sha256":"cbe5e513548321c500307da78a945d3bd0dc3345120525d1b89dbb0046b3a27a","abstract_canon_sha256":"3c1b9180ead2cfef8ed006606f1fb95d74684730adb61234fc51b04da63da9fd"},"schema_version":"1.0"},"canonical_sha256":"efd9896825b512e011a91d887e418b7ff5cb15c26aff47a44f89129699664cc0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:16.694547Z","signature_b64":"yehLX4jkZQifl/RBAbGugfae6i0PVdqsF3v4+nvdsKsJAme6yEt5rJvGmN0QyUwuWmMQToP3/XnN5EbCqJRYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efd9896825b512e011a91d887e418b7ff5cb15c26aff47a44f89129699664cc0","last_reissued_at":"2026-05-18T00:31:16.693927Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:16.693927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.01861","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G1si8H9tr+3N+QjPecZ7xSgsXwx10EK5/5eMy8xU/EYnrUsRxXa2vvhfaWnm+DjvsHt2jOgDUITRTZo24gK9DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:03:19.241084Z"},"content_sha256":"643bc17ce75e84c205ed739aa0c73ca912a1b14ea321b988b865902ebdfce57d","schema_version":"1.0","event_id":"sha256:643bc17ce75e84c205ed739aa0c73ca912a1b14ea321b988b865902ebdfce57d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:57MYS2BFWUJOAENJDWEH4QMLP7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Flexible statistical inference for mechanistic models of neural dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Giacomo Bassetto, Jakob H. Macke, Jan-Matthis Lueckmann, Kaan \\\"Ocal, Marcel Nonnenmacher, Pedro J. Goncalves","submitted_at":"2017-11-06T12:36:07Z","abstract_excerpt":"Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically measured data has been challenging. We propose to overcome this limitation by using likelihood-free inference approaches (also known as Approximate Bayesian Computation, ABC) to perform full Bayesian inference on single-neuron models. Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters. We learn a Bayesian m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01861","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+DupFVlf+4bMuvvhUUzwq7W3c+LDJjU8kyWYkQfP/IQpveETQRKYPER5SdjtG9rg5RpowcTqVus+welzpijKCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:03:19.241741Z"},"content_sha256":"c99a9ca68ea9e8e433fae8fcd77fa3caefb86f27aa1051304851da746e6d567b","schema_version":"1.0","event_id":"sha256:c99a9ca68ea9e8e433fae8fcd77fa3caefb86f27aa1051304851da746e6d567b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/57MYS2BFWUJOAENJDWEH4QMLP7/bundle.json","state_url":"https://pith.science/pith/57MYS2BFWUJOAENJDWEH4QMLP7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/57MYS2BFWUJOAENJDWEH4QMLP7/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":"2026-05-25T21:03:19Z","links":{"resolver":"https://pith.science/pith/57MYS2BFWUJOAENJDWEH4QMLP7","bundle":"https://pith.science/pith/57MYS2BFWUJOAENJDWEH4QMLP7/bundle.json","state":"https://pith.science/pith/57MYS2BFWUJOAENJDWEH4QMLP7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/57MYS2BFWUJOAENJDWEH4QMLP7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:57MYS2BFWUJOAENJDWEH4QMLP7","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":"3c1b9180ead2cfef8ed006606f1fb95d74684730adb61234fc51b04da63da9fd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-06T12:36:07Z","title_canon_sha256":"cbe5e513548321c500307da78a945d3bd0dc3345120525d1b89dbb0046b3a27a"},"schema_version":"1.0","source":{"id":"1711.01861","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.01861","created_at":"2026-05-18T00:31:16Z"},{"alias_kind":"arxiv_version","alias_value":"1711.01861v1","created_at":"2026-05-18T00:31:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01861","created_at":"2026-05-18T00:31:16Z"},{"alias_kind":"pith_short_12","alias_value":"57MYS2BFWUJO","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"57MYS2BFWUJOAENJ","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"57MYS2BF","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:c99a9ca68ea9e8e433fae8fcd77fa3caefb86f27aa1051304851da746e6d567b","target":"graph","created_at":"2026-05-18T00:31:16Z","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":"Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically measured data has been challenging. We propose to overcome this limitation by using likelihood-free inference approaches (also known as Approximate Bayesian Computation, ABC) to perform full Bayesian inference on single-neuron models. Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters. We learn a Bayesian m","authors_text":"Giacomo Bassetto, Jakob H. Macke, Jan-Matthis Lueckmann, Kaan \\\"Ocal, Marcel Nonnenmacher, Pedro J. Goncalves","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-06T12:36:07Z","title":"Flexible statistical inference for mechanistic models of neural dynamics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01861","kind":"arxiv","version":1},"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":"sha256:643bc17ce75e84c205ed739aa0c73ca912a1b14ea321b988b865902ebdfce57d","target":"record","created_at":"2026-05-18T00:31:16Z","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":"3c1b9180ead2cfef8ed006606f1fb95d74684730adb61234fc51b04da63da9fd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-06T12:36:07Z","title_canon_sha256":"cbe5e513548321c500307da78a945d3bd0dc3345120525d1b89dbb0046b3a27a"},"schema_version":"1.0","source":{"id":"1711.01861","kind":"arxiv","version":1}},"canonical_sha256":"efd9896825b512e011a91d887e418b7ff5cb15c26aff47a44f89129699664cc0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efd9896825b512e011a91d887e418b7ff5cb15c26aff47a44f89129699664cc0","first_computed_at":"2026-05-18T00:31:16.693927Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:16.693927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yehLX4jkZQifl/RBAbGugfae6i0PVdqsF3v4+nvdsKsJAme6yEt5rJvGmN0QyUwuWmMQToP3/XnN5EbCqJRYAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:16.694547Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.01861","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:643bc17ce75e84c205ed739aa0c73ca912a1b14ea321b988b865902ebdfce57d","sha256:c99a9ca68ea9e8e433fae8fcd77fa3caefb86f27aa1051304851da746e6d567b"],"state_sha256":"b2afb09cab084a4f83ced00f8f0e0b7748216f83eba2ef536db3334c91d35c80"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b26IdflwHu/j1FgRILjA432NCdC79l78dgEsBRJ5rnjUrUHoVdcSnTYQVa3nQFjyG1DJNYjbzuJLL/ptpUrqAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:03:19.245195Z","bundle_sha256":"8094b310599b937a41c83488394daea8c382d156db89f0e550e229626ab62e24"}}