{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:IQWN3YX7WKWN6V2J67SWF5QC3B","short_pith_number":"pith:IQWN3YX7","canonical_record":{"source":{"id":"1606.00825","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-06-02T19:48:22Z","cross_cats_sorted":[],"title_canon_sha256":"0eb645816fd523298b68d77493f72093aabffbd2d6edb6b0f8cad675b449b911","abstract_canon_sha256":"6dd43153f4f3a8dba78b42aca0c5113db8dbb97606bd574b9b53f495c4f4cc6c"},"schema_version":"1.0"},"canonical_sha256":"442cdde2ffb2acdf5749f7e562f602d85fac7a1e5749117eea1fc74fa3e4698f","source":{"kind":"arxiv","id":"1606.00825","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.00825","created_at":"2026-05-18T01:10:42Z"},{"alias_kind":"arxiv_version","alias_value":"1606.00825v2","created_at":"2026-05-18T01:10:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.00825","created_at":"2026-05-18T01:10:42Z"},{"alias_kind":"pith_short_12","alias_value":"IQWN3YX7WKWN","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IQWN3YX7WKWN6V2J","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IQWN3YX7","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:IQWN3YX7WKWN6V2J67SWF5QC3B","target":"record","payload":{"canonical_record":{"source":{"id":"1606.00825","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-06-02T19:48:22Z","cross_cats_sorted":[],"title_canon_sha256":"0eb645816fd523298b68d77493f72093aabffbd2d6edb6b0f8cad675b449b911","abstract_canon_sha256":"6dd43153f4f3a8dba78b42aca0c5113db8dbb97606bd574b9b53f495c4f4cc6c"},"schema_version":"1.0"},"canonical_sha256":"442cdde2ffb2acdf5749f7e562f602d85fac7a1e5749117eea1fc74fa3e4698f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:42.572223Z","signature_b64":"3pw/co2fiUiVtj0RG8yi+1xiGaAKaYkrrMQ9hHroM7EWwO9ywx6CeQgSQgcBAu/wmMyjW8vo2w+f3wmXALBFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"442cdde2ffb2acdf5749f7e562f602d85fac7a1e5749117eea1fc74fa3e4698f","last_reissued_at":"2026-05-18T01:10:42.571805Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:42.571805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.00825","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:10:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dnMOK3/Rwg4V7KFxI6h7JjetmHePyMbClmV6JSjcv+58ueyPDFfjdxaHQJ0yVu2tzIkWlpJi3Dcoa4z4P9a3AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T19:56:43.192195Z"},"content_sha256":"dd2b43103be863b3ea04aaf12724301a4ca85ed0d42d841ca89cf7bd1a48bb3b","schema_version":"1.0","event_id":"sha256:dd2b43103be863b3ea04aaf12724301a4ca85ed0d42d841ca89cf7bd1a48bb3b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:IQWN3YX7WKWN6V2J67SWF5QC3B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Training a Hidden Markov Model with a Bayesian Spiking Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Amirhossein Tavanaei, Anthony S Maida","submitted_at":"2016-06-02T19:48:22Z","abstract_excerpt":"It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential data, such as individual spoken works. The approach combines a hidden Markov model (HMM) with a spiking neural network (SNN). The HMM, consisting of states and transitions, forms a fixed backbone with nonadaptive transition probabilities. The SNN, however, implements a biologically based Bayesian computation that derives from the spike t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.00825","kind":"arxiv","version":2},"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":"2026-05-18T01:10:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rkGpu0dKnGh3OePsrLn7Oo5Ty+0V5Xfw1J4C/uWbe2l03+DtefqfFm96yE8K7ZYMsyawEst+oCLxrhrraYbMAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T19:56:43.192557Z"},"content_sha256":"b821eb474a22d8347fe5ec9cb7ba742c7db4dcdfb3f68985e59c63ac3e502879","schema_version":"1.0","event_id":"sha256:b821eb474a22d8347fe5ec9cb7ba742c7db4dcdfb3f68985e59c63ac3e502879"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IQWN3YX7WKWN6V2J67SWF5QC3B/bundle.json","state_url":"https://pith.science/pith/IQWN3YX7WKWN6V2J67SWF5QC3B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IQWN3YX7WKWN6V2J67SWF5QC3B/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":"2026-06-23T19:56:43Z","links":{"resolver":"https://pith.science/pith/IQWN3YX7WKWN6V2J67SWF5QC3B","bundle":"https://pith.science/pith/IQWN3YX7WKWN6V2J67SWF5QC3B/bundle.json","state":"https://pith.science/pith/IQWN3YX7WKWN6V2J67SWF5QC3B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IQWN3YX7WKWN6V2J67SWF5QC3B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:IQWN3YX7WKWN6V2J67SWF5QC3B","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":"6dd43153f4f3a8dba78b42aca0c5113db8dbb97606bd574b9b53f495c4f4cc6c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-06-02T19:48:22Z","title_canon_sha256":"0eb645816fd523298b68d77493f72093aabffbd2d6edb6b0f8cad675b449b911"},"schema_version":"1.0","source":{"id":"1606.00825","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.00825","created_at":"2026-05-18T01:10:42Z"},{"alias_kind":"arxiv_version","alias_value":"1606.00825v2","created_at":"2026-05-18T01:10:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.00825","created_at":"2026-05-18T01:10:42Z"},{"alias_kind":"pith_short_12","alias_value":"IQWN3YX7WKWN","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"IQWN3YX7WKWN6V2J","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"IQWN3YX7","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:b821eb474a22d8347fe5ec9cb7ba742c7db4dcdfb3f68985e59c63ac3e502879","target":"graph","created_at":"2026-05-18T01:10:42Z","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":"It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential data, such as individual spoken works. The approach combines a hidden Markov model (HMM) with a spiking neural network (SNN). The HMM, consisting of states and transitions, forms a fixed backbone with nonadaptive transition probabilities. The SNN, however, implements a biologically based Bayesian computation that derives from the spike t","authors_text":"Amirhossein Tavanaei, Anthony S Maida","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-06-02T19:48:22Z","title":"Training a Hidden Markov Model with a Bayesian Spiking Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.00825","kind":"arxiv","version":2},"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:dd2b43103be863b3ea04aaf12724301a4ca85ed0d42d841ca89cf7bd1a48bb3b","target":"record","created_at":"2026-05-18T01:10:42Z","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":"6dd43153f4f3a8dba78b42aca0c5113db8dbb97606bd574b9b53f495c4f4cc6c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-06-02T19:48:22Z","title_canon_sha256":"0eb645816fd523298b68d77493f72093aabffbd2d6edb6b0f8cad675b449b911"},"schema_version":"1.0","source":{"id":"1606.00825","kind":"arxiv","version":2}},"canonical_sha256":"442cdde2ffb2acdf5749f7e562f602d85fac7a1e5749117eea1fc74fa3e4698f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"442cdde2ffb2acdf5749f7e562f602d85fac7a1e5749117eea1fc74fa3e4698f","first_computed_at":"2026-05-18T01:10:42.571805Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:10:42.571805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3pw/co2fiUiVtj0RG8yi+1xiGaAKaYkrrMQ9hHroM7EWwO9ywx6CeQgSQgcBAu/wmMyjW8vo2w+f3wmXALBFDw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:10:42.572223Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.00825","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd2b43103be863b3ea04aaf12724301a4ca85ed0d42d841ca89cf7bd1a48bb3b","sha256:b821eb474a22d8347fe5ec9cb7ba742c7db4dcdfb3f68985e59c63ac3e502879"],"state_sha256":"1ee2d2d9d512e10faf9c4e9f9e68b0b2edeb4b0f296c8fbc7092cf202a10bb96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/r271ZSdkD5kh4lE915XHasSMve1kqCZOK2SwCt4123rKrbb8JYb1IjqDePuiTiazyr9xxlt9gdM93pDcDy+Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T19:56:43.194518Z","bundle_sha256":"56e1f91c79fd21e08d683ebdf1ff0e5b372b4cf2b431b8e4cb08ee159d9670f0"}}