{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ET74KPWNQJFWQXCNH3CM4VQPXC","short_pith_number":"pith:ET74KPWN","schema_version":"1.0","canonical_sha256":"24ffc53ecd824b685c4d3ec4ce560fb8905837d69f34a710d95b529511884606","source":{"kind":"arxiv","id":"2209.11741","version":2},"attestation_state":"computed","paper":{"title":"Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adarsh Kumar Kosta, Kaushik Roy","submitted_at":"2022-09-21T21:17:56Z","abstract_excerpt":"Event-based cameras have recently shown great potential for high-speed motion estimation owing to their ability to capture temporally rich information asynchronously. Spiking Neural Networks (SNNs), with their neuro-inspired event-driven processing can efficiently handle such asynchronous data, while neuron models such as the leaky-integrate and fire (LIF) can keep track of the quintessential timing information contained in the inputs. SNNs achieve this by maintaining a dynamic state in the neuron memory, retaining important information while forgetting redundant data over time. Thus, we posit"},"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":"2209.11741","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-09-21T21:17:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fbc810e8c6fd1473f1d9699e393bf9c6ea3dbc1c44ec14199cf17a840abc61cc","abstract_canon_sha256":"5cd22696df25c849edeee5c66253185d75d33132ead035c74271d0596f63bd2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:50:46.739889Z","signature_b64":"k8nImkZErUewIy9UtAW0vNv9/fgOJ6unpNIXT5oxJpgxmXp1yxX9CVMYA421W/hFlNn0KOmhxfkZ/+cnmS39DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24ffc53ecd824b685c4d3ec4ce560fb8905837d69f34a710d95b529511884606","last_reissued_at":"2026-07-05T05:50:46.739428Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:50:46.739428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adarsh Kumar Kosta, Kaushik Roy","submitted_at":"2022-09-21T21:17:56Z","abstract_excerpt":"Event-based cameras have recently shown great potential for high-speed motion estimation owing to their ability to capture temporally rich information asynchronously. Spiking Neural Networks (SNNs), with their neuro-inspired event-driven processing can efficiently handle such asynchronous data, while neuron models such as the leaky-integrate and fire (LIF) can keep track of the quintessential timing information contained in the inputs. SNNs achieve this by maintaining a dynamic state in the neuron memory, retaining important information while forgetting redundant data over time. Thus, we posit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.11741","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/2209.11741/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":"2209.11741","created_at":"2026-07-05T05:50:46.739488+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.11741v2","created_at":"2026-07-05T05:50:46.739488+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.11741","created_at":"2026-07-05T05:50:46.739488+00:00"},{"alias_kind":"pith_short_12","alias_value":"ET74KPWNQJFW","created_at":"2026-07-05T05:50:46.739488+00:00"},{"alias_kind":"pith_short_16","alias_value":"ET74KPWNQJFWQXCN","created_at":"2026-07-05T05:50:46.739488+00:00"},{"alias_kind":"pith_short_8","alias_value":"ET74KPWN","created_at":"2026-07-05T05:50:46.739488+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/ET74KPWNQJFWQXCNH3CM4VQPXC","json":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC.json","graph_json":"https://pith.science/api/pith-number/ET74KPWNQJFWQXCNH3CM4VQPXC/graph.json","events_json":"https://pith.science/api/pith-number/ET74KPWNQJFWQXCNH3CM4VQPXC/events.json","paper":"https://pith.science/paper/ET74KPWN"},"agent_actions":{"view_html":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC","download_json":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC.json","view_paper":"https://pith.science/paper/ET74KPWN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.11741&json=true","fetch_graph":"https://pith.science/api/pith-number/ET74KPWNQJFWQXCNH3CM4VQPXC/graph.json","fetch_events":"https://pith.science/api/pith-number/ET74KPWNQJFWQXCNH3CM4VQPXC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC/action/storage_attestation","attest_author":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC/action/author_attestation","sign_citation":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC/action/citation_signature","submit_replication":"https://pith.science/pith/ET74KPWNQJFWQXCNH3CM4VQPXC/action/replication_record"}},"created_at":"2026-07-05T05:50:46.739488+00:00","updated_at":"2026-07-05T05:50:46.739488+00:00"}