{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:GTUBHC2FZVBWZXB2DS6XRM45UV","short_pith_number":"pith:GTUBHC2F","schema_version":"1.0","canonical_sha256":"34e8138b45cd436cdc3a1cbd78b39da55fd2e3fc5f98644d4ac73a16f385f348","source":{"kind":"arxiv","id":"2008.03198","version":1},"attestation_state":"computed","paper":{"title":"Learning as filtering: implications for spike-based plasticity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Jannes Jegminat, Jean-Pascal Pfister","submitted_at":"2020-08-07T14:26:29Z","abstract_excerpt":"Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time varying environment during the learning process; and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network - the Synaptic Filter - and show its computational and biolo"},"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":"2008.03198","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2020-08-07T14:26:29Z","cross_cats_sorted":[],"title_canon_sha256":"07d2471094559f83c4e13e3c19d938ca27741b0119b6a3943e9cf377e1272e69","abstract_canon_sha256":"8a2afcca20e4246e87293183db9bdd132c2a7df0339902a21837347fc2300c02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:25:31.795069Z","signature_b64":"6cjPgODhmEEHIkSA4+ug1HVxGFj4OSZHid4u8kmxVIhjF8ora1artRltm3SFY7TGbHdk/LcSnXnB8sclG7CJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34e8138b45cd436cdc3a1cbd78b39da55fd2e3fc5f98644d4ac73a16f385f348","last_reissued_at":"2026-07-05T01:25:31.794562Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:25:31.794562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning as filtering: implications for spike-based plasticity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Jannes Jegminat, Jean-Pascal Pfister","submitted_at":"2020-08-07T14:26:29Z","abstract_excerpt":"Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time varying environment during the learning process; and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network - the Synaptic Filter - and show its computational and biolo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.03198","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/2008.03198/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":"2008.03198","created_at":"2026-07-05T01:25:31.794629+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.03198v1","created_at":"2026-07-05T01:25:31.794629+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.03198","created_at":"2026-07-05T01:25:31.794629+00:00"},{"alias_kind":"pith_short_12","alias_value":"GTUBHC2FZVBW","created_at":"2026-07-05T01:25:31.794629+00:00"},{"alias_kind":"pith_short_16","alias_value":"GTUBHC2FZVBWZXB2","created_at":"2026-07-05T01:25:31.794629+00:00"},{"alias_kind":"pith_short_8","alias_value":"GTUBHC2F","created_at":"2026-07-05T01:25:31.794629+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/GTUBHC2FZVBWZXB2DS6XRM45UV","json":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV.json","graph_json":"https://pith.science/api/pith-number/GTUBHC2FZVBWZXB2DS6XRM45UV/graph.json","events_json":"https://pith.science/api/pith-number/GTUBHC2FZVBWZXB2DS6XRM45UV/events.json","paper":"https://pith.science/paper/GTUBHC2F"},"agent_actions":{"view_html":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV","download_json":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV.json","view_paper":"https://pith.science/paper/GTUBHC2F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.03198&json=true","fetch_graph":"https://pith.science/api/pith-number/GTUBHC2FZVBWZXB2DS6XRM45UV/graph.json","fetch_events":"https://pith.science/api/pith-number/GTUBHC2FZVBWZXB2DS6XRM45UV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV/action/storage_attestation","attest_author":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV/action/author_attestation","sign_citation":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV/action/citation_signature","submit_replication":"https://pith.science/pith/GTUBHC2FZVBWZXB2DS6XRM45UV/action/replication_record"}},"created_at":"2026-07-05T01:25:31.794629+00:00","updated_at":"2026-07-05T01:25:31.794629+00:00"}