{"as_of":"2026-08-13T11:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d802b08329391e59236a7e68eee643c03cea8073b9d671358545dd58e26cda67","coverage":[{"denominator":96,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":96,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T14:56:51.558194Z","state":"measured"},{"denominator":96,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":96,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.14801/citation-record","integrity":"/paper/2411.14801/integrity","json":"/paper/2411.14801/citation-record.json","paper":"/paper/2411.14801"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.125308Z","title":"Probabilistic decision making by slow reverberation in cortical circuits","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.125308Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:22e451541d2525133c44f5a89638041bb7d12147e2108b5add3d820e0d1775c6","observation_id":"a8b5336a-4f26-4be1-8127-2806c4a3d703","resolution":{"observed_at":"2026-08-12T14:56:51.125308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.130459Z","title":"Large-scale model of mammalian thalamocortical systems","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.130459Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:e5d5d66e12eeec1041c604e8ec7bd1b3d24c53c2184ad8c8ebfbe8c1582acd28","observation_id":"ae1951b8-957c-48e0-80f0-181a9a909070","resolution":{"observed_at":"2026-08-12T14:56:51.130459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.136021Z","title":"A large-scale model of the functioning brain","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.136021Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:1b45ce0e1aa1884e279e648e31f26f8a8d13a629a8c5781e2d6de9efe137dc13","observation_id":"2db4fb44-27be-4d4c-873e-f82de2fc2c4a","resolution":{"observed_at":"2026-08-12T14:56:51.136021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.141334Z","title":"The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.141334Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b882cd7f8a70e0040075f3098d35470ead5cb2f78075056b0db49c44e1bb9b22","observation_id":"70f7a7a9-0b0f-4b26-ac10-3ea29cbf8aa0","resolution":{"observed_at":"2026-08-12T14:56:51.141334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.146021Z","title":"Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.146021Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:fd81c463ae2605695b4d3567bd1712559f64a5598777a029331ecd8bd443fbfc","observation_id":"8e734785-49a3-4520-98b3-6a7dd7595e22","resolution":{"observed_at":"2026-08-12T14:56:51.146021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.150704Z","title":"Reconstruction and simulation of neocortical microcircuitry","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.150704Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:6df78844caf8796b8328b38fbbc3b51fa186af1a72693ff408869bb6d4e9ff35","observation_id":"5e121327-ff36-4c83-9761-3c91a41afa79","resolution":{"observed_at":"2026-08-12T14:56:51.150704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.155996Z","title":"Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.155996Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:41497bc834c8d934883b7ea4285385e74a4ecd6b039e3c26f9f79bcc3ff525f6","observation_id":"4b26885c-75aa-4580-8ae2-3a60e947b7a4","resolution":{"observed_at":"2026-08-12T14:56:51.155996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.160288Z","title":"Orientation selectivity from very sparse LGN inputs in a comprehensive model of macaque V1 cortex","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.160288Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:25bfe803471917cbf2d9e30b0981abaecc98387f503f07b78002bd94add9a698","observation_id":"1c8c35f9-9bd4-492e-9214-abd99f9d3d49","resolution":{"observed_at":"2026-08-12T14:56:51.160288Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.164727Z","title":"Dissecting the synapse-and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.164727Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:6248b679acceaf1e3f78c1bdc5e7f7ad9573c3ff874a54b5c31ea02f66b6108a","observation_id":"0b12dea9-45a4-4085-8210-e61889a63e5d","resolution":{"observed_at":"2026-08-12T14:56:51.164727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.169253Z","title":"A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.169253Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:88c0d5ab4c15edf87e129c06419ca42ad54e59046d1c177516d422d418d69ebd","observation_id":"6dc736c7-5d4e-4105-b03f-5f03e0742efd","resolution":{"observed_at":"2026-08-12T14:56:51.169253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.173988Z","title":"Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.173988Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:7c6e0d9eee4accd700672c3a552bbfd5779bb31aa9fe7f2dcb3f1dfb1adea47e","observation_id":"cb525f3d-f604-48b8-92a8-7389effd5012","resolution":{"observed_at":"2026-08-12T14:56:51.173988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.179095Z","title":"Survey of spiking in the mouse visual system reveals functional hierarchy","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.179095Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:8da3d4958bc41c2194d792453f2b4ed5fb9856f2d5014f5c1510541c817c56d6","observation_id":"5cce1233-91eb-4d30-9364-298e83be6002","resolution":{"observed_at":"2026-08-12T14:56:51.179095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.183811Z","title":"The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.183811Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:088abfae93eae22d4c0357a3bd18c104d0ce541c16bb555cbffd157829f7fc44","observation_id":"45453738-a0da-4a38-bf20-073c9bf70189","resolution":{"observed_at":"2026-08-12T14:56:51.183811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.188361Z","title":"A computational model of direction selectivity in Macaque V1 cortex based on dynamic differences between ON and OFF pathways","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.188361Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:85d86855561027d531af1385d10dd09de69ad5928a941128a32553be572368b6","observation_id":"20bd81a5-d784-440e-b4ff-ca588b298be7","resolution":{"observed_at":"2026-08-12T14:56:51.188361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.193906Z","title":"Reading a neural code","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.193906Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:37ab5cb46a6b30ba22f0a5440c6130f5aaa36e097a64aa2ad39f22ee5b672d20","observation_id":"e85338c8-d8c2-4075-b88e-09504360dec6","resolution":{"observed_at":"2026-08-12T14:56:51.193906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.198430Z","title":"Reliability of spike timing in neocortical neurons","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.198430Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:7a4f0e7b0d6eb2f432f129b3e711ca7a6c78cca1cf23e438d6f4386cba3f3937","observation_id":"14208409-78c1-4891-a622-b19d7ca2412c","resolution":{"observed_at":"2026-08-12T14:56:51.198430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.721477Z","title":"Primary cortical representation of sounds by the coordination of action-potential timing","venue":null,"work_id":"2f879b91-3817-4936-8820-20f07b35526b","year":1996},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.203344Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d30a3699f267987299a210e9c4b82da4ae9ee32a75eb649d4b0ecf977515e552","observation_id":"1db171f5-f096-4ce0-a774-6126b731167c","resolution":{"observed_at":"2026-08-12T14:56:52.726033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.707765Z","title":"Rieke et al","venue":null,"work_id":"20fb680b-afcd-42f2-bfa9-362c8624cc45","year":1997},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.207861Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:bff7b3aa4f7634332b6b20f5e40539af2b9fa317f77a48ff8da39019dd3c5676","observation_id":"2b277189-540f-473a-b492-a046131e41d9","resolution":{"observed_at":"2026-08-12T14:56:52.712272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.694069Z","title":"Neuronal synchrony: a versatile code for the definition of relations?","venue":null,"work_id":"8f3a8a84-4f27-4609-aef6-ad9f3b231da6","year":1999},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.212131Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:cd85c76baa9e66078801fd679c4c1c12f3e27eaedec636f82706a5035896e7b5","observation_id":"8b45dab2-56db-4845-a97c-0d312267bfaf","resolution":{"observed_at":"2026-08-12T14:56:52.698587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.678252Z","title":"Regulation of spike timing in visual cortical circuits","venue":null,"work_id":"38559d68-7c29-4a39-af5d-10fe3d038368","year":2008},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.217554Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:98614d6fcd46712dc8a7105b62c9f76960f5fec9536de47c358ffc3665330045","observation_id":"ec9a9ac7-7674-4b07-8772-bc5b41e74e60","resolution":{"observed_at":"2026-08-12T14:56:52.683601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.663959Z","title":"Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type","venue":null,"work_id":"fb9fb417-435a-4264-9927-0a8652c98d67","year":1998},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.221892Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:6905a01d8f33d92a26ace19727c68e99bd39a7310423f9fad38af60015bccc91","observation_id":"529dc6bc-2aff-48a7-9315-844ed855729b","resolution":{"observed_at":"2026-08-12T14:56:52.668688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.649938Z","title":"Competitive Hebbian learning through spike-timing-dependent synaptic plasticity","venue":null,"work_id":"a9a4cfe0-41bc-48b7-aaed-d68540b99395","year":2000},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.226380Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:f8420dac6180b9a6129891245a4cd87ae8a72aa49e87b7427ce45346b7346011","observation_id":"0809e451-c2a8-4b24-8dbf-50d7b7e63250","resolution":{"observed_at":"2026-08-12T14:56:52.654670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.635613Z","title":"Spike timing-dependent plasticity of neural circuits","venue":null,"work_id":"d961b4db-1be9-4aed-b199-2eb643c66935","year":2004},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.231395Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:ae2f3e565c36237ba8b673fe55402f1da648e21f5b018686468502e2634c30b4","observation_id":"192b3f59-8be5-49c8-b8be-a784950ffdb6","resolution":{"observed_at":"2026-08-12T14:56:52.640482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.621504Z","title":"Neuromodulation of spike-timing-dependent plasticity: past, present, and future","venue":null,"work_id":"c66efade-9451-4a46-b88a-e8723d455ba1","year":2019},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.235858Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:ffa9fa48e5d555362d91f118c514e7bb331009db53cf4700f1ee3fdbc4911840","observation_id":"fae5bc84-6a42-435d-805a-215aedbfa262","resolution":{"observed_at":"2026-08-12T14:56:52.626104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.607818Z","title":"Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits","venue":null,"work_id":"6db4bc8d-5eeb-425a-9ee4-27a73481d4dd","year":2021},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.240202Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d374993cb0e5c24a0f19b942533119058c1158199c4d27b6e615ecab3a913ee3","observation_id":"5d9000e1-f874-47b1-a130-4cf51c03f490","resolution":{"observed_at":"2026-08-12T14:56:52.612432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.593451Z","title":"Integrator or coincidence detector? The role of the cortical neuron revisited","venue":null,"work_id":"7dcaeefc-19dc-4a8b-8ac9-a2b13605c4f1","year":1996},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.244849Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:6c778d6e7e10fcc99353603bda5b50a11cc26bfbe7c342986e0e27125291f8a8","observation_id":"3d8ab0d1-676e-42b6-b4f8-39f7934fc3be","resolution":{"observed_at":"2026-08-12T14:56:52.597948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.579720Z","title":"Computing with neural synchrony","venue":null,"work_id":"605edd3b-6842-40d1-b27f-9f95fe154171","year":2012},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.249931Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:cce5a45a26948347b432dbf901aa591d5f5a5165935fbe5df4667511dc3e516c","observation_id":"1fa37406-ce5b-484b-aa4a-8563b1b30b98","resolution":{"observed_at":"2026-08-12T14:56:52.584193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.564989Z","title":"Neurophysiological and computational principles of cortical rhythms in cognition","venue":null,"work_id":"ef2555f1-7534-4404-8dcf-549ae087df57","year":2010},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.255044Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:21a0158fd0cf714e78cd4da9b25e28ac0f4b6eb3ad514669d3caa62afedb7eaf","observation_id":"20e30187-8763-4d1e-a9b5-f21c539f3f13","resolution":{"observed_at":"2026-08-12T14:56:52.569766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.550665Z","title":"First spikes in ensembles of human tactile afferents code complex spatial fingertip events","venue":null,"work_id":"7e084334-4d8c-4d37-9a9d-ffef85211267","year":2004},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.259614Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b76438cefef97fc3e33b076727e2dc36006207c96c2c9207983bc4c181041b41","observation_id":"f5f0cf08-3698-4706-a6ac-dd8900c16785","resolution":{"observed_at":"2026-08-12T14:56:52.555313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.536758Z","title":"Rhythms for cognition: communication through coherence","venue":null,"work_id":"ac47386f-7fba-4354-9469-6a068c25c3e9","year":2015},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.264397Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:50b4d77b73e9c147e8b37a3a041565b7bc815ba86febcbc0527b83af8480b7e0","observation_id":"758ee832-0a9c-4b1f-a082-f199e5232713","resolution":{"observed_at":"2026-08-12T14:56:52.541339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.522556Z","title":"Cracking the neural code for sensory perception by combining statistics, intervention, and behavior","venue":null,"work_id":"43f9e211-f6b2-4649-850e-5ec8088bbf4d","year":2017},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.269749Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:8cee42c79f02b4cae2681eb0d86e6927d180ff66c45e73dbafa389f182a3155f","observation_id":"67c01e02-ab57-43cd-90dc-806392e9bafb","resolution":{"observed_at":"2026-08-12T14:56:52.527278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.508878Z","title":"Gamma and beta bursts underlie working memory","venue":null,"work_id":"86ae87e2-d664-4b45-a298-6d35329139a7","year":2016},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.274328Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:c28c40ebbc3b657e8626ac56f3aac032964ac38d74b490e2260c82eb30b05ce0","observation_id":"23cef9af-0c0d-499e-9e55-682c8097315e","resolution":{"observed_at":"2026-08-12T14:56:52.513157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.495403Z","title":"Neuronal oscillations in cortical networks","venue":null,"work_id":"3d77b398-f76e-484d-8b73-0fea6f9c3773","year":2004},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.278659Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:7eb25e2ecd8ae73ad72b1f2a57f3772b0cf27fe97cb760dbd6c70b9e5c1db174","observation_id":"24d0ae07-3eb6-444f-8091-886a68744145","resolution":{"observed_at":"2026-08-12T14:56:52.499817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.481294Z","title":"Which model to use for cortical spiking neurons?","venue":null,"work_id":"d9c1efc5-598e-4c06-87ba-75ea11d3bb94","year":2004},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.282898Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:bfbbec4e223580159101289413fc22ee92e472b124b829321bcdbd8a967f5fd3","observation_id":"39a75b98-21e1-4a68-8909-f40f3fea22c1","resolution":{"observed_at":"2026-08-12T14:56:52.485798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.467801Z","title":"Chaos in neuronal networks with balanced excitatory and inhibitory activity","venue":null,"work_id":"e075ebfb-4844-4014-9469-f8c9118e7d7e","year":1996},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.287585Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:491350ca3b03022f1e226d9b230a2bab00ecfa36030140865d97a91d50708476","observation_id":"6055d261-8671-4ac8-914c-9a875e5ca8e5","resolution":{"observed_at":"2026-08-12T14:56:52.472211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.454157Z","title":"Neuronal dynamics: From single neurons to networks and models of cognition","venue":null,"work_id":"0e1325b3-4835-4ed5-8e26-c30e7103dc86","year":2014},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.292522Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d0ff77c5be7737da3e503aa41a5bc6a8d251dc4238b9223b6ccfbf334472d4e5","observation_id":"37b71b1a-b81c-478c-b482-a2faf5d33d27","resolution":{"observed_at":"2026-08-12T14:56:52.458788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.439147Z","title":"Chaotic resonance in typical routes to chaos in the Izhikevich neuron model","venue":null,"work_id":"4a541f6e-db08-42e4-86a0-475ff7bf3c1c","year":2017},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.297041Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d6e516bb95c211e2deaeea6348b10e485f50be3089382e4adbb5027a9da7a1b4","observation_id":"c164853f-8d90-4347-8746-41bc3a89b241","resolution":{"observed_at":"2026-08-12T14:56:52.444009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.424515Z","title":"Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons","venue":null,"work_id":"7ccfbf78-6727-44af-9bf8-9258827b789d","year":2000},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.301482Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b794b67cbc440e413bad6da12e1218751c9c8cdfe417d4774ff30190db6f17a0","observation_id":"16655bd0-e2fb-4f9b-85b3-4df44a8c2edb","resolution":{"observed_at":"2026-08-12T14:56:52.429216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.410397Z","title":"Irregular dynamics in up and down cortical states","venue":null,"work_id":"942e1b89-b576-46a5-87cd-9011bf228761","year":2010},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.305892Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:db0011b65e2ebfb87f05352ac7081862d8d9409124c150eb5772ee30a565f614","observation_id":"b37ff064-f749-48a0-9a3e-fcf6914ba2f3","resolution":{"observed_at":"2026-08-12T14:56:52.415181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.396026Z","title":"The impact of structural heterogeneity on excitation-inhibition balance in cortical networks","venue":null,"work_id":"9bc46f06-fff2-4aa3-908a-a0f52fb1b339","year":2016},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.310275Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d014f23be0d08d59cbba0da41ccf5c845355d1a7f495013aef4f4478835e86ab","observation_id":"36f9f405-738e-4f47-931a-0b30da0d773f","resolution":{"observed_at":"2026-08-12T14:56:52.400846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.382206Z","title":"A data-informed mean-field approach to mapping of cortical parameter landscapes","venue":null,"work_id":"1ff8261a-a4be-4986-ad1b-8af373c1934a","year":2021},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.316637Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:135a8b80e67d942f7c3205dc866dc1c6dc4b87ed3c3d0cc5f7124a814ee1daf4","observation_id":"81d010d6-ede3-4c26-b99d-e8a8aa8afaa0","resolution":{"observed_at":"2026-08-12T14:56:52.386741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.367480Z","title":"From spiking neuron models to linear-nonlinear models","venue":null,"work_id":"8a488ee3-b1b6-450e-80c5-1b30e6c18b56","year":2011},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.321277Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:e65dc606aba56f92c1aabeb673bd930ceb9fb6f0f6eea68a92e5f5417dd48acd","observation_id":"a405b577-3841-40c1-86d2-039505753c59","resolution":{"observed_at":"2026-08-12T14:56:52.372392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.353472Z","title":"Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size","venue":null,"work_id":"e44d2c4b-1a2f-4dc7-b4ef-91a2335f4c78","year":2017},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.325662Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:54348bb954e93fbb1cf7cb0de7d31575227dbd11f4b7058de0073a2ea9688118","observation_id":"27b91452-d51b-4c99-8354-7fe2f50bdd0c","resolution":{"observed_at":"2026-08-12T14:56:52.358039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.339409Z","title":"Macroscopic description for networks of spiking neurons","venue":null,"work_id":"78174cea-b893-47c9-a726-70910dc8f619","year":2015},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.330959Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b02780adad05a077a12f0cada3c7dd91e15165c2a370178128ce490ee9d48d09","observation_id":"e88ba786-926e-458c-ae29-9ef22304db8d","resolution":{"observed_at":"2026-08-12T14:56:52.344015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.324909Z","title":"Dynamic finite size effects in spiking neural networks","venue":null,"work_id":"72f1b6f5-532b-462d-87d9-fac84ceccbf5","year":2013},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.335438Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b0fd3f3bbccdc663ae7cdc20fa50cace551e6623dbf0a9a88c4ad723b92afa65","observation_id":"fee11c71-ba32-4135-8d28-4eb7953ac1fc","resolution":{"observed_at":"2026-08-12T14:56:52.329957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.311917Z","title":"A quantitative description of membrane current and its application to conduction and excitation in nerve","venue":null,"work_id":"085b600c-643f-4cda-8e26-527e32164c06","year":1952},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.339722Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:7c4a8a39c493ddcee647ddaf1c4904d5ec19ec02e40a28a71588024b323fef58","observation_id":"56e65f6e-f065-41b9-bbc0-c230cfae1ba7","resolution":{"observed_at":"2026-08-12T14:56:52.316209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.298210Z","title":"Stable propagation of synchronous spiking in cortical neural net- works","venue":null,"work_id":"2e5f5bd5-f006-4973-8e8e-8edb999753eb","year":1999},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.344050Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:a9829e0692b1e991a4b6d13e297dafbe94b8dbeb600aee34e49d3c557fcee62a","observation_id":"7bab1650-2d76-41ef-9f20-19d9ffe0215b","resolution":{"observed_at":"2026-08-12T14:56:52.302927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.284238Z","title":"Desynchronization in diluted neural networks","venue":null,"work_id":"6e48f751-27d2-4612-bb7e-5108d65dfac6","year":2006},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.348309Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:859c2fff986dab15cc1311991e1d1ea2913617e7b1a7640a306b13b018234f86","observation_id":"57d4faa5-90cc-4acb-8881-cc3d7b36b4e6","resolution":{"observed_at":"2026-08-12T14:56:52.288731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.269870Z","title":"Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex","venue":null,"work_id":"3aeccccb-1321-464f-99cf-b3f61fecc2bb","year":2010},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.352569Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:adbd97237646c3cd6b432641990a3f2b86399175f6ed4d40dd260eb7d4eb3ce3","observation_id":"e5503748-57ba-4fcf-819f-134a149928c1","resolution":{"observed_at":"2026-08-12T14:56:52.274360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.255880Z","title":"The columnar organization of the neocortex","venue":null,"work_id":"4cd83f94-4ebd-40dc-a22d-c1373c5569d6","year":1997},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.356885Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:5c8c5e1f2f7989182b53485a9eee6338270cd398ec977004418d2bb71dba88f3","observation_id":"ac8b01f2-f85a-4e84-afb7-f9990ae20435","resolution":{"observed_at":"2026-08-12T14:56:52.260319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.242860Z","title":"The basic uniformity in structure of the neocortex","venue":null,"work_id":"bd64467d-3163-40d9-99e1-077ccfe3cd1a","year":1980},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.361286Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:5e6a12e6cbd3bc66439bdf7fab08fb176bae2f00a2504796f0522cc8b5e4ff76","observation_id":"7fc2e462-d70b-4bd4-a222-580e2d80603c","resolution":{"observed_at":"2026-08-12T14:56:52.247115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.229497Z","title":"The minicolumn hypothesis in neuroscience","venue":null,"work_id":"f7187c92-57bc-47f9-a272-62625dafb742","year":2002},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.365716Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b31d04b35e8a2c1fad0d12ddc66d225d898e4a4dc251f085c48fab11903a0e0d","observation_id":"6104d81c-1774-46b8-b848-2d3b821d4f0c","resolution":{"observed_at":"2026-08-12T14:56:52.233784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.215446Z","title":"Barrel cortex function","venue":null,"work_id":"c6ff723f-1f10-4981-b7cb-ce6fa698af25","year":2013},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.370060Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d3d50b1c77b44bc6d743e2beba2e9c0b9815df82be1d56ab0610fe099446f555","observation_id":"32baa8ea-3588-4f56-bd0a-007614b1eb69","resolution":{"observed_at":"2026-08-12T14:56:52.219975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.202149Z","title":"Excitatory and inhibitory interactions in localized populations of model neurons","venue":null,"work_id":"1c504744-f22d-4811-96af-2ee1a70b5188","year":1972},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.374429Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:20322cc93aa4827d3e7a6b50ec03c75ec8ae09fd103a55ca15976e2095c56b4a","observation_id":"2236e094-b092-4d18-a290-1163ebece551","resolution":{"observed_at":"2026-08-12T14:56:52.206545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.188141Z","title":"A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue","venue":null,"work_id":"dce9039c-7b65-4c49-9800-b2264fa2b6e6","year":1973},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.378787Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:aba6faed3cdddcf64a32f7e18e30db0552a9de68bf4210aae561fd844ee5e3c2","observation_id":"80e902e0-b213-4471-8500-32d30e61d3dc","resolution":{"observed_at":"2026-08-12T14:56:52.192678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.174533Z","title":"Kinetic theory for neuronal network dynamics","venue":null,"work_id":"15a5f94c-ef34-46fa-bfb1-56deb4365699","year":2006},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.383242Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:209d547bf5a3957c5bdfb5b5257b6432363f751baf7c06b1b8d2ec308a1b167c","observation_id":"a997a145-8fa7-4d94-b0d5-ad84296295de","resolution":{"observed_at":"2026-08-12T14:56:52.178976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.160673Z","title":"Self-consistent stochastic dynamics for finite-size networks of spiking neurons","venue":null,"work_id":"a6f9c545-a882-4190-b219-4d4599deefab","year":2023},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.387646Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:ef2c3377326fa294c7c0e0c89f5769098125b05c8010d321d5a58f73165ed5b5","observation_id":"2d64ef88-9cf5-4457-a7a1-415233cb4d0f","resolution":{"observed_at":"2026-08-12T14:56:52.165067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.146799Z","title":"Beyond mean field theory: statistical field theory for neural networks","venue":null,"work_id":"e10d9f99-e6d4-4bef-bc49-48a0a86e8f7f","year":2013},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.392278Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:817f94795e5780ebd83c2e7c20603ce86ee7ee273caeb91710ab6da544e7768d","observation_id":"8bb3c33b-81d4-4bac-9c53-8964f1f1e19f","resolution":{"observed_at":"2026-08-12T14:56:52.151226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.132862Z","title":"Biophysics of computations","venue":null,"work_id":"a42518ac-c6b5-415c-a79b-473f22297703","year":1999},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.396760Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:8ee5b42bfd95c04e1cf34cf891c0c046865fcf30fa92d3d2331b44c9b21d7120","observation_id":"27eee162-4e57-469f-b868-1bb37345657a","resolution":{"observed_at":"2026-08-12T14:56:52.137232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.118896Z","title":"A neuronal network model of macaque primary visual cortex (V1): Orientation selectivity and dynamics in the input layer 4Ca","venue":null,"work_id":"67cbef4f-8379-47b9-a0e8-0e3b27e02fe5","year":2000},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.401491Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b66957258bde727fb4883969fbfc6ef40f8193515bd24101e444af92e1bfd90c","observation_id":"29764757-40a7-49a3-87ee-69ea633d092f","resolution":{"observed_at":"2026-08-12T14:56:52.123606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.104820Z","title":"Pharmacology and nerve-endings","venue":null,"work_id":"03c27ed8-cdcb-42df-9570-716609ff501d","year":1935},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.405951Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:854a530a39e6f7cec77d93110aadb09794fad88d79b4c60979f7dd286dce4e59","observation_id":"b48628db-7b2d-47a6-8075-ed230d7e6f3c","resolution":{"observed_at":"2026-08-12T14:56:52.109257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.091356Z","title":"Principles of neural science","venue":null,"work_id":"83bda9f3-58c6-46ef-a7fe-cfb1a9f5d170","year":2000},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.410371Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d69697184fc6296aedb9b5d0c41040d0dae2c79e659a8c1ebca619a9c1a59b68","observation_id":"7e007b60-f111-4fca-9299-93637e1958b2","resolution":{"observed_at":"2026-08-12T14:56:52.095717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.076571Z","title":"Fast global oscillations in networks of integrate-and-fire neurons with low firing rates","venue":null,"work_id":"5e75d26e-8789-4af0-a8b5-57b67eda46f1","year":1999},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.414780Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:179f8ed5d17c8b7c77d5fe413e06588db59548793703fb157b666ddce40d4501","observation_id":"1f6184bc-c4ae-4a25-8568-a75e479cda60","resolution":{"observed_at":"2026-08-12T14:56:52.081594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.062434Z","title":"Field-theoretic approach to fluctuation effects in neural networks","venue":null,"work_id":"9342f724-b0a6-400b-8e54-f3d51da3393d","year":2007},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.418960Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:f9d200d21227babd3d2aa9f96b8d48881c8c9228542908bb791b1083a52c76f5","observation_id":"1cd89f23-5c0b-4700-8d19-8ec8faafd935","resolution":{"observed_at":"2026-08-12T14:56:52.066976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.048511Z","title":"Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds","venue":null,"work_id":"2883ef2a-2272-4187-bf61-31d537b5cfa2","year":2021},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.423102Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:6b1ef9a4e7d8be15d80b6d423c91e7da1c89dec59c948979ace12052e9344f6c","observation_id":"5817307e-ad75-4932-9530-486a72c10598","resolution":{"observed_at":"2026-08-12T14:56:52.053099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.14942","last_updated":"2022-06-29T23:03:32Z","snapshot_observed_at":"2026-07-06T13:26:10.177972Z","submitted_at":"2022-06-29T23:03:32Z","title":"Multi-band oscillations emerge from a simple spiking network","version":1},"cited_work":{"arxiv_id":"2206.14942","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.14942","snapshot_observed_at":"2026-08-12T14:56:51.611390Z","title":"Multi-band oscillations emerge from a simple spiking network","venue":"q-bio.NC","work_id":"8b29a648-f466-44d2-9339-330f0117081e","year":2022},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.427536Z"},"links":{"cited_paper":"/paper/2206.14942","citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:262c77712d97b74966b43c87ccc04e78a36fdbda5e3c1a109c24ccac08c53e32","observation_id":"2cedddaf-5160-4539-aa00-6781da0bcb45","resolution":{"observed_at":"2026-08-12T14:56:51.617927Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.034347Z","title":"Kinetic models of synaptic transmission","venue":null,"work_id":"07679ad8-a85a-4e74-9447-d14edac4ec18","year":1998},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.432299Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:6f1da0e16cfa9d288c36991c8d31b9638e0b5ece1de14eb040035fdb4d07c970","observation_id":"e8b521d6-a492-42bb-be2d-91c523f754a2","resolution":{"observed_at":"2026-08-12T14:56:52.038758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.020076Z","title":"Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons in vivo","venue":null,"work_id":"6a25c952-4786-432d-9ed3-d94b47f2dc5c","year":1998},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.436843Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:eccf4d43468ce2418854bf086d2b96ed965e99b4964daaeb5f247fe679076719","observation_id":"616c9e99-f741-4880-86d3-978dfc5bd11c","resolution":{"observed_at":"2026-08-12T14:56:52.024778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:52.006085Z","title":"Averaging for Markov Chains","venue":null,"work_id":"21991098-92b4-4414-afd0-78c21204b1c0","year":2008},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.440830Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:e8eed53d66c6166fe750f6228c58d1124c5dd19b12665a2aef302b1106cb34a7","observation_id":"649f5719-30be-4965-b2ab-c7a3a6ffc6ba","resolution":{"observed_at":"2026-08-12T14:56:52.010733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.991452Z","title":"Stochastic neural field model: multiple firing events and correlations","venue":null,"work_id":"8db2f65a-f8d8-49f9-8ea2-f30c53ff49f3","year":2019},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.445056Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:00ea045d8b20418dc76c52c6dd240dca9d2ec0ad7a7957d88206e86c3a666b47","observation_id":"9c4352d2-d56a-40ab-98ab-64d90d60c35e","resolution":{"observed_at":"2026-08-12T14:56:51.995866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.978172Z","title":"Emergent dynamics in a model of visual cortex","venue":null,"work_id":"512aef25-8caf-4881-a02e-db82ea2336d0","year":2013},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.449380Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:7a8b5d044797204a7b28f1e9239d663a4542a22d008eb5b13f17757110d876e1","observation_id":"9c67a77e-db80-49be-a74d-60639cd160e7","resolution":{"observed_at":"2026-08-12T14:56:51.982526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.963993Z","title":"Dynamics of multistable states during ongoing and evoked cortical activity","venue":null,"work_id":"7851fb8c-993c-48f7-ba13-7ae10b722ea4","year":2015},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.453687Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:328b7d7d2aede61c6cd228724314311bf9ba9b4a04d5057a91df2566c2fdc064","observation_id":"8479d130-c105-4b47-b8a0-61a78eb4cf0b","resolution":{"observed_at":"2026-08-12T14:56:51.968843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.949466Z","title":"Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity","venue":null,"work_id":"771d911b-507f-47ae-a804-714219b0256e","year":2003},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.457875Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:3580e03df00ed28bdd8a01d554d70ea903de2df34f5892309ef62f53623726d6","observation_id":"eff26b6a-ed5b-47ff-9af1-d93c6b1e69f1","resolution":{"observed_at":"2026-08-12T14:56:51.954289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.934357Z","title":"Firing rate models for gamma oscillations","venue":null,"work_id":"8409dfd8-b194-4a46-8519-6dc66911d642","year":2019},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.462292Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:4aaad5c4149f3c0a06650b9c325441092a597746e94d1646881e089f18232410","observation_id":"48b32607-d540-4bac-9e44-0ad755c08759","resolution":{"observed_at":"2026-08-12T14:56:51.938968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.919585Z","title":"A master equation formalism for macroscopic modeling of asynchronous irregular activity states","venue":null,"work_id":"befc162f-32e0-405f-a022-e935d393c667","year":2009},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.466510Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:7f1bb316cc7decfa7f11f30b353b8358a780e73bcc4dd7c28c481f14945c9e63","observation_id":"dcc245d3-dec3-431d-85c2-162c9348a39f","resolution":{"observed_at":"2026-08-12T14:56:51.924342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.905292Z","title":"Stochastic neural field theory and the system-size expansion","venue":null,"work_id":"0225a7a1-a8d8-42a1-9230-1d399e4a3d7a","year":2010},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.471021Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:c1fce07f5e701974c64f4cc3f4035292615a64a16db49cc94ec443f8d2cd411c","observation_id":"e22b867e-3022-4e29-8683-a9fcf50c9dea","resolution":{"observed_at":"2026-08-12T14:56:51.909964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.891268Z","title":"Impact of network structure and cellular response on spike time correlations","venue":null,"work_id":"dcd7876d-a01f-4fef-9eae-1f4ea8a52b3a","year":2012},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.475105Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:73034f4a17712d94cb5cd764fcb0e29c95fce8cfb5c9285e47f3265b5f2cabf3","observation_id":"68855802-1a10-4e6c-acbe-a040e5c97b4f","resolution":{"observed_at":"2026-08-12T14:56:51.895722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.876714Z","title":"Systematic fluctuation expansion for neural network activity equations","venue":null,"work_id":"00dc635d-20b1-4f72-8571-1a7e3a9d84ce","year":2010},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.479451Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:8f83213a6f77e4e560b21bce8e1e122ec760314842d52aa0f2a2bbb9283762be","observation_id":"74d13b5b-a4c8-4800-8f12-c6785a46cabb","resolution":{"observed_at":"2026-08-12T14:56:51.881398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.862264Z","title":"A case study in the functional consequences of scaling the sizes of realistic cortical models","venue":null,"work_id":"2e09f2b8-34e8-4c58-a5d1-2c7d1c0eb9fb","year":2019},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.483822Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b2776d47432c09414b2916eb91968fc48c74a719b988c2befae3fcff435450cd","observation_id":"7ce4cf7a-ad11-4a39-b01f-119bf4aaf207","resolution":{"observed_at":"2026-08-12T14:56:51.867257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.848001Z","title":"Beyond blow-up in excitatory integrate and fire neuronal networks: refractory period and spontaneous activity","venue":null,"work_id":"77204261-44c6-4c32-8b45-9464abf4022c","year":2014},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.488105Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:9eb84fa67ec59d10c097f2d72d56b571d4404635f19616b1555872e28af1152e","observation_id":"ad59d6c1-0c7d-41d5-95d2-1998c16cfbe8","resolution":{"observed_at":"2026-08-12T14:56:51.852718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.834510Z","title":"How well do reduced models capture the dynamics in models of interacting neurons?","venue":null,"work_id":"c2fa324c-da89-4a3a-bd0c-eada7a2cddba","year":2019},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.492326Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:eb18d1dcb1be79906f7c5677cef5318629892acab9e9797c704f99164214a257","observation_id":"47feef4b-0067-4a66-8627-647ecf1f7b74","resolution":{"observed_at":"2026-08-12T14:56:51.838859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.820409Z","title":"Learning spiking neuronal networks with artificial neural networks: neural oscillations","venue":null,"work_id":"b72ca33a-9308-4673-b64e-70e3238e5f5e","year":2024},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.496561Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:2460a7ffe6bd094f8f43ed5c0c991fd06c85aeb4dd0cc94739eb9134445cecba","observation_id":"3bfef939-cfa6-4538-bdeb-16b6e265591f","resolution":{"observed_at":"2026-08-12T14:56:51.825159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.806303Z","title":"Data-driven discovery of partial differential equations","venue":null,"work_id":"67692a30-6737-4097-a516-eaa0691a0d6e","year":2017},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.500823Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:b394c69e81523dbc88a06c972ae04c94fe2a450a2721d399d2263654c8a3b42b","observation_id":"090e49d1-21d1-4996-bbe5-29b6a63eb7f9","resolution":{"observed_at":"2026-08-12T14:56:51.810889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.792043Z","title":"Deep learning for universal linear embeddings of nonlinear dynamics","venue":null,"work_id":"137eec96-d6ba-4222-93c0-6e4df8c3ece8","year":2018},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.505160Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:8edc854445de078454543c586cea119e022ae5fe8acbfaeebd30770b5bc7f525","observation_id":"5191f5fc-ce4d-421a-92b4-ae6aec6f633f","resolution":{"observed_at":"2026-08-12T14:56:51.796661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.777468Z","title":"A tour of reinforcement learning: The view from continuous control","venue":null,"work_id":"1318ad87-f770-4fc8-8d04-51db8cb5a48a","year":2019},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.509481Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:f2e7d51a6f0b3614a35d346f624d1da1209cf1607de25e1525e52911f72e9210","observation_id":"9a474228-749b-4b2d-b0a6-77bf89b04384","resolution":{"observed_at":"2026-08-12T14:56:51.782345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.762036Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","venue":null,"work_id":"003b3674-69d6-4308-9a42-a42f3120fb02","year":2019},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.513855Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:4563781b9653ac358bdef96cbe0c934591178193ca68a81ddf3ed34e2a30025c","observation_id":"61621bbc-6a37-42f9-b1a5-00cea610f0e0","resolution":{"observed_at":"2026-08-12T14:56:51.767268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.747861Z","title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems","venue":null,"work_id":"521c03df-0ad3-464b-a668-5fcea28b74f9","year":2016},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.518206Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:618c4347753e01c79a800ebbac9b482781cc0e59211f79d5d7ce07d067d3730a","observation_id":"5d0bc7d8-1f03-4708-a90b-110ca790598c","resolution":{"observed_at":"2026-08-12T14:56:51.752337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.01558","last_updated":"2017-09-05T19:20:44Z","snapshot_observed_at":"2026-08-11T03:10:07.160328Z","submitted_at":"2017-09-05T19:20:44Z","title":"Learning Dynamical Systems and Bifurcation via Group Sparsity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.01558","snapshot_observed_at":"2026-08-12T14:56:51.522513Z","title":"Learning dynamical systems and bifurcation via group sparsity","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.522513Z"},"links":{"cited_paper":"/paper/1709.01558","citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:4aea92b7f29b5c168ac7bf076d9931573576ac099d5af7f94bd3c02619c83d52","observation_id":"4c67503d-8e49-42d7-87a8-d01dd1df6471","resolution":{"observed_at":"2026-08-12T14:56:51.522513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.733525Z","title":"Using scientific machine learning for experimental bifurcation analysis of dynamic systems","venue":null,"work_id":"2bbbbbd3-8fa0-4eec-b649-4bf783eaa6bb","year":2023},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.527203Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:e8110d595c6c9f103ac9c150f96f26279d757c1b3577232f8d75b1a7c1693b4d","observation_id":"cdae8290-8b29-4ac2-9356-6b6ae5822abd","resolution":{"observed_at":"2026-08-12T14:56:51.738346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.719290Z","title":"Impulses and physiological states in theoretical models of nerve membrane","venue":null,"work_id":"ffbdd55b-9b6e-4160-b809-abaeeeb592a0","year":1961},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.531939Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:d955a40d590975a7cd1274e201274ec33f6d0e0d0d6063e048f74d2edba350d7","observation_id":"c45e4d19-dea8-4643-b1a6-4b94a382d09d","resolution":{"observed_at":"2026-08-12T14:56:51.723933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.703816Z","title":"The finite state projection algorithm for the solution of the chemical master equation","venue":null,"work_id":"b6816548-ffd3-4012-a8d7-6210ce399e0f","year":2006},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.536310Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:49e26b8ce19509174911b053d08016ded76d624882b2a620bce81b5d5dec59da","observation_id":"84386a48-45dd-4b11-a1a5-135ef8e576cd","resolution":{"observed_at":"2026-08-12T14:56:51.709368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.687911Z","title":"Adaptive discrete Galerkin methods applied to the chemical master equation","venue":null,"work_id":"6ac1e567-0c33-4d42-99c1-bffa88ac0ce9","year":2008},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.540757Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:4ce62346cf1b78fc699a0a7216a09168a2168c326c70e5ffb820f84eb81955e4","observation_id":"793dd382-b848-455c-a33b-ea0c46642bbb","resolution":{"observed_at":"2026-08-12T14:56:51.692862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.673618Z","title":"A review of the adjoint-state method for computing the gradient of a functional with geophysical applications","venue":null,"work_id":"94f7719c-9087-465f-a772-b50062e3732b","year":2006},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.545119Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:1ddb59c29709e6c78a50008a83bafbb68dd562ffd3c51b82c04a9862b9a1995c","observation_id":"a3e92a9c-fdf3-405f-ac6f-0d2b34ae7c1c","resolution":{"observed_at":"2026-08-12T14:56:51.678389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.658869Z","title":"Historical development of the Newton–Raphson method","venue":null,"work_id":"fb4b9602-eb59-4925-adfd-92cbc87d4389","year":1995},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.549448Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:9bd61d61fbdf6020fe88b76b9b8106bd8734c7678cbadcb7699f531d969785c1","observation_id":"b8a89643-1f9b-4faa-986e-537de1ea4fc7","resolution":{"observed_at":"2026-08-12T14:56:51.663837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.644090Z","title":"A quantitative population model of whisker barrels: re-examining the Wilson-Cowan equations","venue":null,"work_id":"6588574f-547b-4104-a5e9-8811fc9ea9f2","year":1996},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.553808Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:254a353a0db1965fd18335d684712022c56e82b2cf4678a96f67cf261258980a","observation_id":"b267b9dd-e123-4aff-a53b-da67fe249486","resolution":{"observed_at":"2026-08-12T14:56:51.648704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:56:51.629032Z","title":"Rhythm and synchrony in a cortical network model","venue":null,"work_id":"dbb044bd-1cb5-4abb-9480-32dc95cebcc6","year":2018},"citing_paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T14:56:51.558194Z"},"links":{"citing_paper":"/paper/2411.14801"},"observation_digest":"sha256:03c078ac9ddbeb27283454f975b5e2447555ff57a04f3331579bd923b3dc50c4","observation_id":"bddaa5cd-74ad-49ac-ba56-5145967a034e","resolution":{"observed_at":"2026-08-12T14:56:51.633879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.14801","last_updated":"2024-11-22T08:56:43Z","latest_version":1,"primary_category":"q-bio.NC","snapshot_observed_at":"2026-08-13T07:50:34.431413Z","submitted_at":"2024-11-22T08:56:43Z","title":"Minimizing information loss reduces spiking neuronal networks to differential equations"},"reference_resolution":{"displayed":96,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":78},"total_outbound_references":96},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 0 inbound Pith citation observations for arXiv:2411.14801."}