{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:K4FVBM43SG5M75LHCHMKVUUYEM","short_pith_number":"pith:K4FVBM43","schema_version":"1.0","canonical_sha256":"570b50b39b91bacff56711d8aad2982307baadf26ca092a56012ab86553c2b08","source":{"kind":"arxiv","id":"2204.04431","version":2},"attestation_state":"computed","paper":{"title":"A Spiking Neural Network Structure Implementing Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Mikhail Kiselev","submitted_at":"2022-04-09T09:08:10Z","abstract_excerpt":"At present, implementation of learning mechanisms in spiking neural networks (SNN) cannot be considered as a solved scientific problem despite plenty of SNN learning algorithms proposed. It is also true for SNN implementation of reinforcement learning (RL), while RL is especially important for SNNs because of its close relationship to the domains most promising from the viewpoint of SNN application such as robotics. In the present paper, I describe an SNN structure which, seemingly, can be used in wide range of RL tasks. The distinctive feature of my approach is usage of only the spike forms o"},"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":"2204.04431","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NE","submitted_at":"2022-04-09T09:08:10Z","cross_cats_sorted":[],"title_canon_sha256":"77a60e961f6e8fbffb58f77863c82538a7094e254097f93f503bf1ecde05e13b","abstract_canon_sha256":"ae778e3a2fab29457aab784e6f9ec2b7f602c87b4845dfae5a9f4e630fc01a31"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:53:29.956608Z","signature_b64":"F9N/k4Lg7LRbZjJQcn0UyalPn+UAE2Ah4Zc6GqwPCbE7kp/rdqnr3LeuOH137RQFcoGQmPRZuFkdcqqD+j95Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"570b50b39b91bacff56711d8aad2982307baadf26ca092a56012ab86553c2b08","last_reissued_at":"2026-07-05T06:53:29.956164Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:53:29.956164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Spiking Neural Network Structure Implementing Reinforcement Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Mikhail Kiselev","submitted_at":"2022-04-09T09:08:10Z","abstract_excerpt":"At present, implementation of learning mechanisms in spiking neural networks (SNN) cannot be considered as a solved scientific problem despite plenty of SNN learning algorithms proposed. It is also true for SNN implementation of reinforcement learning (RL), while RL is especially important for SNNs because of its close relationship to the domains most promising from the viewpoint of SNN application such as robotics. In the present paper, I describe an SNN structure which, seemingly, can be used in wide range of RL tasks. The distinctive feature of my approach is usage of only the spike forms o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.04431","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2204.04431/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":"2204.04431","created_at":"2026-07-05T06:53:29.956221+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.04431v2","created_at":"2026-07-05T06:53:29.956221+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.04431","created_at":"2026-07-05T06:53:29.956221+00:00"},{"alias_kind":"pith_short_12","alias_value":"K4FVBM43SG5M","created_at":"2026-07-05T06:53:29.956221+00:00"},{"alias_kind":"pith_short_16","alias_value":"K4FVBM43SG5M75LH","created_at":"2026-07-05T06:53:29.956221+00:00"},{"alias_kind":"pith_short_8","alias_value":"K4FVBM43","created_at":"2026-07-05T06:53:29.956221+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/K4FVBM43SG5M75LHCHMKVUUYEM","json":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM.json","graph_json":"https://pith.science/api/pith-number/K4FVBM43SG5M75LHCHMKVUUYEM/graph.json","events_json":"https://pith.science/api/pith-number/K4FVBM43SG5M75LHCHMKVUUYEM/events.json","paper":"https://pith.science/paper/K4FVBM43"},"agent_actions":{"view_html":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM","download_json":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM.json","view_paper":"https://pith.science/paper/K4FVBM43","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.04431&json=true","fetch_graph":"https://pith.science/api/pith-number/K4FVBM43SG5M75LHCHMKVUUYEM/graph.json","fetch_events":"https://pith.science/api/pith-number/K4FVBM43SG5M75LHCHMKVUUYEM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM/action/storage_attestation","attest_author":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM/action/author_attestation","sign_citation":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM/action/citation_signature","submit_replication":"https://pith.science/pith/K4FVBM43SG5M75LHCHMKVUUYEM/action/replication_record"}},"created_at":"2026-07-05T06:53:29.956221+00:00","updated_at":"2026-07-05T06:53:29.956221+00:00"}