{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MZJTHABVD75X7MBKWGIKBP7PUS","short_pith_number":"pith:MZJTHABV","schema_version":"1.0","canonical_sha256":"66533380351ffb7fb02ab190a0bfefa485fba1a8e77aa3585179e570bc80d65f","source":{"kind":"arxiv","id":"1801.05151","version":1},"attestation_state":"computed","paper":{"title":"Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"cs.CV","authors_text":"Bin Yan, Chi Zhang, Kai Qiao, Linyuan Wang, Li Tong, Ying Zeng","submitted_at":"2018-01-16T08:34:18Z","abstract_excerpt":"In recent years, research on decoding brain activity based on functional magnetic resonance imaging (fMRI) has made remarkable achievements. However, constraint-free natural image reconstruction from brain activity is still a challenge. The existing methods simplified the problem by using semantic prior information or just reconstructing simple images such as letters and digitals. Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN). Firstly, we ex"},"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":"1801.05151","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-16T08:34:18Z","cross_cats_sorted":["cs.AI","q-bio.NC"],"title_canon_sha256":"2a47276353738a961b4f9301b56a46c7fc81142282e0918c31ea362c4084dc82","abstract_canon_sha256":"d7eded94cec6590a584f22c5237a7613128f80cc1c3ef63f9d4cb74406b94784"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:47.744145Z","signature_b64":"Ad4vLopQpbjzR/I5ZMzCSifctTKDRtQ4JqPJ6rBV0T5k2TXOVYJ4v3PtQFvtVthi/WpI1slXBwHPkl43TtBcDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66533380351ffb7fb02ab190a0bfefa485fba1a8e77aa3585179e570bc80d65f","last_reissued_at":"2026-05-18T00:25:47.743565Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:47.743565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"cs.CV","authors_text":"Bin Yan, Chi Zhang, Kai Qiao, Linyuan Wang, Li Tong, Ying Zeng","submitted_at":"2018-01-16T08:34:18Z","abstract_excerpt":"In recent years, research on decoding brain activity based on functional magnetic resonance imaging (fMRI) has made remarkable achievements. However, constraint-free natural image reconstruction from brain activity is still a challenge. The existing methods simplified the problem by using semantic prior information or just reconstructing simple images such as letters and digitals. Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN). Firstly, we ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.05151","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":"1801.05151","created_at":"2026-05-18T00:25:47.743663+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.05151v1","created_at":"2026-05-18T00:25:47.743663+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.05151","created_at":"2026-05-18T00:25:47.743663+00:00"},{"alias_kind":"pith_short_12","alias_value":"MZJTHABVD75X","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"MZJTHABVD75X7MBK","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"MZJTHABV","created_at":"2026-05-18T12:32:40.477152+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/MZJTHABVD75X7MBKWGIKBP7PUS","json":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS.json","graph_json":"https://pith.science/api/pith-number/MZJTHABVD75X7MBKWGIKBP7PUS/graph.json","events_json":"https://pith.science/api/pith-number/MZJTHABVD75X7MBKWGIKBP7PUS/events.json","paper":"https://pith.science/paper/MZJTHABV"},"agent_actions":{"view_html":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS","download_json":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS.json","view_paper":"https://pith.science/paper/MZJTHABV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.05151&json=true","fetch_graph":"https://pith.science/api/pith-number/MZJTHABVD75X7MBKWGIKBP7PUS/graph.json","fetch_events":"https://pith.science/api/pith-number/MZJTHABVD75X7MBKWGIKBP7PUS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS/action/storage_attestation","attest_author":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS/action/author_attestation","sign_citation":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS/action/citation_signature","submit_replication":"https://pith.science/pith/MZJTHABVD75X7MBKWGIKBP7PUS/action/replication_record"}},"created_at":"2026-05-18T00:25:47.743663+00:00","updated_at":"2026-05-18T00:25:47.743663+00:00"}