{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BNVHRLJ6O7JHCJPGVFNYZIAD7D","short_pith_number":"pith:BNVHRLJ6","schema_version":"1.0","canonical_sha256":"0b6a78ad3e77d27125e6a95b8ca003f8e11bba75cd14d416db9f97095dcaad9c","source":{"kind":"arxiv","id":"1806.04748","version":1},"attestation_state":"computed","paper":{"title":"Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"physics.app-ph","authors_text":"David Guerin, Dominique Vuillaume, Fabien Alibart, Jean Roncali, Maurizio Mastropasqua Talamo, Philippe Blanchard, Sebastien Pecqueur","submitted_at":"2018-06-12T20:16:01Z","abstract_excerpt":"Based on bottom-up assembly of highly variable neural cells units, the nervous system can reach unequalled level of performances with respect to standard materials and devices used in microelectronic. Reproducing these basic concepts in hardware could potentially revolutionize materials and device engineering which are used for information processing. Here, we present an innovative approach that relies on both iono-electronic materials and intrinsic device physics to show pattern classification out of a 12-unit bio-sensing array. We use the reservoir computing and learning concept to demonstra"},"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":"1806.04748","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.app-ph","submitted_at":"2018-06-12T20:16:01Z","cross_cats_sorted":["cs.ET"],"title_canon_sha256":"9344e0fe82c0a0196f0fc03a49ba3421d73865c344737e6417932208ff945ee0","abstract_canon_sha256":"40b83e467b64ba3545e37f6a51ff5ac3f912c27f21d70e8895151e2f88452642"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:00.343006Z","signature_b64":"ubGUK4LwQgVHWbTcTrrkDQRH2iZCGhIkLQpD4zR0SQBs/5gcWnYxWgIEHh4vpSKRSIbNnsr3Fe2/3PBRN2lxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b6a78ad3e77d27125e6a95b8ca003f8e11bba75cd14d416db9f97095dcaad9c","last_reissued_at":"2026-05-18T00:04:00.342271Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:00.342271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"physics.app-ph","authors_text":"David Guerin, Dominique Vuillaume, Fabien Alibart, Jean Roncali, Maurizio Mastropasqua Talamo, Philippe Blanchard, Sebastien Pecqueur","submitted_at":"2018-06-12T20:16:01Z","abstract_excerpt":"Based on bottom-up assembly of highly variable neural cells units, the nervous system can reach unequalled level of performances with respect to standard materials and devices used in microelectronic. Reproducing these basic concepts in hardware could potentially revolutionize materials and device engineering which are used for information processing. Here, we present an innovative approach that relies on both iono-electronic materials and intrinsic device physics to show pattern classification out of a 12-unit bio-sensing array. We use the reservoir computing and learning concept to demonstra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.04748","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1806.04748","created_at":"2026-05-18T00:04:00.342394+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.04748v1","created_at":"2026-05-18T00:04:00.342394+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.04748","created_at":"2026-05-18T00:04:00.342394+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNVHRLJ6O7JH","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNVHRLJ6O7JHCJPG","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNVHRLJ6","created_at":"2026-05-18T12:32:16.446611+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/BNVHRLJ6O7JHCJPGVFNYZIAD7D","json":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D.json","graph_json":"https://pith.science/api/pith-number/BNVHRLJ6O7JHCJPGVFNYZIAD7D/graph.json","events_json":"https://pith.science/api/pith-number/BNVHRLJ6O7JHCJPGVFNYZIAD7D/events.json","paper":"https://pith.science/paper/BNVHRLJ6"},"agent_actions":{"view_html":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D","download_json":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D.json","view_paper":"https://pith.science/paper/BNVHRLJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.04748&json=true","fetch_graph":"https://pith.science/api/pith-number/BNVHRLJ6O7JHCJPGVFNYZIAD7D/graph.json","fetch_events":"https://pith.science/api/pith-number/BNVHRLJ6O7JHCJPGVFNYZIAD7D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D/action/storage_attestation","attest_author":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D/action/author_attestation","sign_citation":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D/action/citation_signature","submit_replication":"https://pith.science/pith/BNVHRLJ6O7JHCJPGVFNYZIAD7D/action/replication_record"}},"created_at":"2026-05-18T00:04:00.342394+00:00","updated_at":"2026-05-18T00:04:00.342394+00:00"}