{"as_of":"2026-08-16T10:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2bb3eca6fca04664158a2fbdc807a95a0ca8d2355a5150d421b1dcf852b715bd","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:03:26.184050Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.07428/citation-record","integrity":"/paper/1908.07428/integrity","json":"/paper/1908.07428/citation-record.json","paper":"/paper/1908.07428"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:03:27.021568Z","title":"Dynamics of brain activation during picture naming","venue":null,"work_id":"e9afd989-910a-40f5-9cf7-2c02c3253660","year":1994},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.940503Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:122c8b6625a41797bbde5b405d01258ee6a1efd892718f34e62c011387ad4113","observation_id":"a5f852a0-d123-4040-863c-9bcddfd8c04c","resolution":{"observed_at":"2026-08-14T14:03:27.027821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:27.002748Z","title":"The human brain is intrinsically organized into dynamic, anticorrelated functional networks","venue":null,"work_id":"ab2445c7-9038-4125-b598-3fd1ace36cf3","year":2005},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.945615Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:5e6e612445304de9bd79e4b26aa264662c1b7aa1f019769d60607eda1ad0f036","observation_id":"b43e17ef-bd6e-4564-93bd-fbb00e78ce91","resolution":{"observed_at":"2026-08-14T14:03:27.008980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:03:25.950612Z","title":"A hierarchy of time-scales and the brain","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.950612Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:2489d8a9fdd1a5aca04fd151bec44c9078a88770d30357df2736a68b44a14f8c","observation_id":"10d64e0e-5457-4a5a-92ae-a85416a79518","resolution":{"observed_at":"2026-08-14T14:03:25.950612Z","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":"2026-08-14T14:03:25.958028Z","title":"Cambridge University Press, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.958028Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:a630e433a5cb138a272299d5bbb05494b2a70d253516ff7ffe3d6166c7c19131","observation_id":"acc02470-c9cc-4da0-9afb-da07a18c7972","resolution":{"observed_at":"2026-08-14T14:03:25.958028Z","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":"2026-08-14T14:03:26.953217Z","title":"Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools","venue":null,"work_id":"b440e1af-fef9-44d6-a6b8-5a30b781bb9c","year":2016},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.963231Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:c358a38999b0b00dd40e0ae01a73eddf972dff9fa5b63e4fc39e9bef6a301805","observation_id":"8cbc1fd8-9f17-463d-a70c-8c04a9f1b8f9","resolution":{"observed_at":"2026-08-14T14:03:26.960282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.917717Z","title":"Dynamic models of large-scale brain activity","venue":null,"work_id":"65b83910-0470-42ee-ba04-da734eb35e16","year":2017},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.968617Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:4df051accc6761fc788a4e7b5a0aa4b1b97272032bb12b19bd87af2f857507ba","observation_id":"2bed98b0-d6f0-48aa-8a71-6972d654f9a7","resolution":{"observed_at":"2026-08-14T14:03:26.931771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.888646Z","title":"Modeling single-neuron dynamics and computations: a balance of detail and abstraction","venue":null,"work_id":"56407ab6-4a91-49b2-aa01-8dcf29c9780a","year":2006},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.976910Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:9b17b41a8c7953ef4eb2cdb88c6643965b7c1b7dcbff0d5e617a235dad8a83c4","observation_id":"ea9338c0-0c2e-4e68-9122-70ccfc710c2c","resolution":{"observed_at":"2026-08-14T14:03:26.895311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.865441Z","title":"How good are neuron models? Science, 326(5951):379–380, 2009","venue":null,"work_id":"eb35298f-0b0b-436a-ad1c-3ce49580ec4c","year":2009},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.983163Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:0a0a96556b40f79dcd84220c0ab61df6696ae40119e493f76c305f4e8671b84e","observation_id":"d915a0ee-8977-408d-b0ae-862fd7c6de07","resolution":{"observed_at":"2026-08-14T14:03:26.874124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.847199Z","title":"Neural networks for nonlinear dynamic system modelling and identification","venue":null,"work_id":"8163bb9d-51a0-4ce2-84a8-02110c3bdc39","year":1992},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.989068Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:f993e9f3e073e80f2813c6c5a2db087b301ed9bf56f93e25b734ca7498937e73","observation_id":"06dfb72d-8d0d-46c6-8452-26f40928d5ea","resolution":{"observed_at":"2026-08-14T14:03:26.853123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.830141Z","title":"Nonlinear system identification using neural networks","venue":null,"work_id":"f756c93a-7340-4d63-a470-6bcd8e0d7b85","year":2007},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:25.996119Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:62c4229bf06169c809c40225e2b7f84e1172a6f5518b1b80be9dbc19d354ee9a","observation_id":"2646553e-db4d-40b2-b7a0-019f21ca2732","resolution":{"observed_at":"2026-08-14T14:03:26.835953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.812238Z","title":"Application of feedforward neural networks to dynamical system identification and control","venue":null,"work_id":"a7d18763-3908-47e9-b56c-7f9efd9c1739","year":1993},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.001565Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:ea3987dc7c6447bb31296400a7a2bc75c303ca35f7cd46329a04aaf7bde43f24","observation_id":"0c187e80-0e69-4236-bdfc-c036d4909ffd","resolution":{"observed_at":"2026-08-14T14:03:26.818117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:03:26.008114Z","title":"Long-time predictive modeling of nonlinear dynamical systems using neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.008114Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:b0c9953fe9f00bd03a013059fb89314033f23b2e49b7dd1a5c71739f33883cbe","observation_id":"d16a089e-1a93-488c-b0ed-03f8cda25f3d","resolution":{"observed_at":"2026-08-14T14:03:26.008114Z","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":"2026-08-14T14:03:26.764486Z","title":"Modeling robot dynamics using dynamic neural networks","venue":null,"work_id":"5cf38bfd-4519-4a55-a999-2f8fbafcdf4c","year":1997},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.017783Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:03f51a563ba31ccf568dfc500851e49c483b7ef78a97e7532291ac0a14612743","observation_id":"f3186e23-33a0-4052-8957-a33a0fde5b67","resolution":{"observed_at":"2026-08-14T14:03:26.770881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.745302Z","title":"Identification of nonlinear dynamic systems using functional link artificial neural networks","venue":null,"work_id":"dfb680b4-246d-4647-9bcb-ea5d086b943d","year":1999},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.023574Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:22bc4512fce4d29228b2412aefe8375e4779fffd0d3736590d5ae8a1ce1c64fc","observation_id":"faa574a4-4d6e-4d97-8b06-16f8af619f41","resolution":{"observed_at":"2026-08-14T14:03:26.750914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.726465Z","title":"Neural network dynamics for model- based deep reinforcement learning with model-free fine-tuning","venue":null,"work_id":"b7ff4100-2f4a-4c62-914f-1f002a609ac5","year":2018},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.029587Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:dffc89b632cdf3a494b0dce4b6d7e064d072948a0c6187af27856c3a065d8ff6","observation_id":"dc5271d3-24aa-40b2-b1ed-ff42e075add6","resolution":{"observed_at":"2026-08-14T14:03:26.732082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:03:26.034731Z","title":"Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.034731Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:90c192a80d8647862b25e8cdc2012109e38aa4abb8cbf9436a351d2aa4f9dd1d","observation_id":"d5e876f4-ee3c-49f7-90cc-bcb9e456d5f0","resolution":{"observed_at":"2026-08-14T14:03:26.034731Z","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":"2026-08-14T14:03:26.691030Z","title":"A recurrent neural network for modelling dynamical systems","venue":null,"work_id":"84727826-2195-4553-a72a-1d212dc5413a","year":1998},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.040038Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:b0a1055f27d411fa83d66bee5c9f415fdd41fb097349c264e02ba7da002561b5","observation_id":"a03c83d1-2941-4a05-ac5d-4d6e359f1853","resolution":{"observed_at":"2026-08-14T14:03:26.698008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.670294Z","title":"Deepmpc: Learning deep latent features for model predictive control","venue":null,"work_id":"5db50723-a29a-4d8f-8874-8df6e029c30f","year":2015},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.044882Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:53e8590a48daca33530eed97099a31d78cdbc690a45acd26fc8433b6ad3a73fa","observation_id":"25e741a8-c8ae-4cc5-aa44-7ce99cbf61cd","resolution":{"observed_at":"2026-08-14T14:03:26.677142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.648604Z","title":"On the difficulty of training recurrent neural networks","venue":null,"work_id":"676cb440-b5c2-48c2-bea0-584c03fddc7c","year":2013},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.049147Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:27ec9c50c29521099c1503be6fd0459a25d55f8b49dafc3e71ded930b865c012","observation_id":"6b3a0cf2-5f58-473d-9e76-bea29542d652","resolution":{"observed_at":"2026-08-14T14:03:26.656615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.627206Z","title":"Multistep prediction of dynamic systems with recurrent neural networks","venue":null,"work_id":"7bd230bc-26bf-4684-882e-3871bacbc5ce","year":2019},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.054103Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:1330244bc713fd75235af412ef525654b272f5ccd86100251ebdf0647430b774","observation_id":"9628240d-3f91-43de-b5f7-4d3fcdc01b6d","resolution":{"observed_at":"2026-08-14T14:03:26.632965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.608510Z","title":"Deep learning-based human-driven vehicle trajectory prediction and its application for platoon control of connected and autonomous vehicles","venue":null,"work_id":"19118aac-35b9-400a-aa33-58f93190c218","year":2018},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.060107Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:8e61e99dc2ef6fda83a12312d4f1076fe11e6c9215f5b31ad4b4cf3aec768ff9","observation_id":"17fc5ddc-16d6-4788-96b5-699cec304af2","resolution":{"observed_at":"2026-08-14T14:03:26.614246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.591381Z","title":"Non-linear system modeling using lstm neural networks","venue":null,"work_id":"b0d0ff8f-0eff-4790-b849-d26ea2710e0a","year":2018},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.066518Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:3e520d39265d6a6da0536cfa2040f841e3aa89dfab83084ed70bc154419b3166","observation_id":"9db9aa6d-b103-46cc-bf56-8afae8f40d0c","resolution":{"observed_at":"2026-08-14T14:03:26.597352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.573900Z","title":"A new concept using LSTM neural networks for dynamic system identification","venue":null,"work_id":"b9688233-9595-4204-87fe-53b7c75e32e1","year":2017},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.074467Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:e686900c606898cc73436ec68af27859261c9ee679a5ec2f26a4c1d49fdce257","observation_id":"c1c17e98-239a-491d-9307-1aadc8343df4","resolution":{"observed_at":"2026-08-14T14:03:26.579424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.558213Z","title":"Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks","venue":null,"work_id":"1694f829-8cbc-445e-bc21-a6b901911898","year":2018},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.081034Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:3a4b15d2979d29f83b5317bf46b4b87d3baece72a82b5ae11e193c74b722aaf0","observation_id":"07f34e25-c16a-4ebc-b74b-879de4f855ee","resolution":{"observed_at":"2026-08-14T14:03:26.563338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:03:26.086320Z","title":"Long short-term memory","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.086320Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:a4ad5ff65ff0a20e4d97d4010953044ccbe9a17cfc5b7f05835a590431d5c3e7","observation_id":"03d09a21-1876-407c-985c-66c2461e7a15","resolution":{"observed_at":"2026-08-14T14:03:26.086320Z","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":"2026-08-14T14:03:26.091257Z","title":"Understanding the difficulty of training deep feedforward neural networks","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.091257Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:598644641425ddc80db6c5fcb7460b89f95221c12b41c5e3648359183678f663","observation_id":"c816e86f-779d-49ec-995b-2f503243cb62","resolution":{"observed_at":"2026-08-14T14:03:26.091257Z","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":"2026-08-14T14:03:26.096439Z","title":"Sequence to sequence learning with neural networks","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.096439Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:83f7d1c73f954613a4e856c1ece49c8d96de0e0e23aec9e1b2f4ff31ce6b4277","observation_id":"0a12edbe-6352-47da-9b96-766a1f7067d0","resolution":{"observed_at":"2026-08-14T14:03:26.096439Z","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":"2026-08-14T14:03:26.499409Z","title":"CA1 pyramidal cells have diverse biophysical properties, affected by development, experience, and aging","venue":null,"work_id":"dd2ba2fd-4fe6-4b2e-9caf-699c790fd1fd","year":2017},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.101883Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:20910801eff3a38c7e54ca81c40ee2bd098bbe0561a8b5a3e192bc74470e6c99","observation_id":"b5da8ab6-c78b-480a-adc1-1cfa26735c77","resolution":{"observed_at":"2026-08-14T14:03:26.505629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.480662Z","title":"Contribution of persistent na+ current and m-type k+ current to somatic bursting in ca1 pyramidal cells: combined experimental and modeling study","venue":null,"work_id":"eba3b3ed-6086-475d-9a6e-ccb42900de8e","year":1912},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.107286Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:022dec618984d9ff5f69d6ab75d6234cd217c3fcf7eb91aea256a729bcaacb61","observation_id":"2f7b7bc9-d937-4346-b38b-127e7b876694","resolution":{"observed_at":"2026-08-14T14:03:26.487560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.461833Z","title":"A unified model of CA1/3 pyramidal cells: An investigation into excitability","venue":null,"work_id":"ed0fd62a-3ed1-421c-8eb7-379e1c774e32","year":2011},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.112200Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:8c6f05de20a2892a9fa1469d8f755b9b04c10386ddac65cf0c302adeaea22d21","observation_id":"2366064f-afc1-4f60-866c-33f994bfe86a","resolution":{"observed_at":"2026-08-14T14:03:26.467858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.444145Z","title":"Simple, biologically-constrained CA1 pyramidal cell models using an intact, whole hippocampus context","venue":null,"work_id":"d61a918c-469a-428f-8d45-0bcef64d2675","year":2014},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.117040Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:1a5f74054d8524f9dfbbe85203bd3c75481df2dcfc335548363571c7899020c6","observation_id":"22923d4f-63ce-4401-b6a3-035c35193a94","resolution":{"observed_at":"2026-08-14T14:03:26.449867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.427066Z","title":"Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell","venue":null,"work_id":"6abd930c-5c23-46a3-8e37-251c6b87478a","year":2003},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.121674Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:e2726aa92ae3b219031e00fd6c0f71e341b8197252a99f13b6bc0aa913cd98ae","observation_id":"9055e361-d79b-4fe6-857c-9706b3316c87","resolution":{"observed_at":"2026-08-14T14:03:26.432766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.406883Z","title":"Role of ax- onal NaV1","venue":null,"work_id":"69008ac9-d398-483f-a60d-d09e36c008a8","year":2008},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.126338Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:d21712992a222da20e8516dd32335cd62ad89d3fab36fb7ef9929e62dddd819c","observation_id":"3511dcad-d367-45e7-b3ac-c259d349852c","resolution":{"observed_at":"2026-08-14T14:03:26.413069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.383224Z","title":"Synapse distribution suggests a two-stage model of dendritic integration in CA1 pyramidal neurons.Neuron, 63(2):171–177, 2009","venue":null,"work_id":"4e6ba197-357e-47a6-830a-64e68154635e","year":2009},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.131965Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:b41d169a38907feb04be2c3bc6ea84fd46c9c23327b6aa18ba614fe01b1a440f","observation_id":"f731c890-d23c-4e28-a8e9-41027874ae5e","resolution":{"observed_at":"2026-08-14T14:03:26.389515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.365249Z","title":"On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons","venue":null,"work_id":"b62ba9b3-fba4-494a-a4e1-530b8bdf12f2","year":2012},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.137002Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:9782484b1b875ebc60a91a3b8ebadabf46c38242d6c905ef2d25ef0bd9d6adf8","observation_id":"0f7794df-764f-4216-b4e7-2feb1d85bd33","resolution":{"observed_at":"2026-08-14T14:03:26.370614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.346446Z","title":"Fast and accurate low-dimensional reduction of biophysically detailed neuron models","venue":null,"work_id":"29383e1a-ddb9-4fa5-9231-2bd09c8d163b","year":2012},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.143622Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:161ae65cb7296e699cac59f055703b9d24c2efef1150b9d180570cbb4c9c443b","observation_id":"afbfb7c3-3149-4c58-bc77-4aa810da6e74","resolution":{"observed_at":"2026-08-14T14:03:26.352344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.328704Z","title":"Dendritic sodium spikes are required for long-term potentiation at distal synapses on hippocampal pyramidal neurons","venue":null,"work_id":"368c54ee-6c56-4bb2-967c-cbc32aad27aa","year":2015},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.148909Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:96d92557944e9f17440d735c0a02b5b2f067cdaf456df16f5ed6e0ee6215448a","observation_id":"71075af5-47ee-44b9-8e23-48fd46a29490","resolution":{"observed_at":"2026-08-14T14:03:26.334196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T14:03:26.311772Z","title":"Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit","venue":null,"work_id":"8263acdc-dfe2-49cb-92c1-93ffe28ce966","year":2016},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.155268Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:c7975259d03f9c7bb11fa68f94f219e4b0a1395ec73c98a47c462bba0beee970","observation_id":"45d9339d-05d5-4fbc-8f4a-0b178814eb67","resolution":{"observed_at":"2026-08-14T14:03:26.317398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:03:26.160655Z","title":"Generalization of backpropagation with application to a recurrent gas market model","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.160655Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:e162b0a51325a2b4fa00e5cf228a68ddf17987c293d98eaa67d9a31ebcd1cf5d","observation_id":"7f0efe8b-1d67-43e7-9189-c5011ab66763","resolution":{"observed_at":"2026-08-14T14:03:26.160655Z","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":"2026-08-14T14:03:26.278970Z","title":"Backpropagation through time: what it does and how to do it","venue":null,"work_id":"b86230ca-47c3-44b5-93be-5d2881df2b49","year":1990},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.166164Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:4cd0a3088f68de2f0fe414e9f1fd815c8c1b3f216e5aa5baa435070b4ca73fcf","observation_id":"90c01be8-5219-42b2-9516-e8946ffacf40","resolution":{"observed_at":"2026-08-14T14:03:26.287181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:03:26.171574Z","title":"A focused backpropagation algorithm for temporal","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.171574Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:3974d00b780dcabb0f3170a47608ee06ad744c1c15e179cb7b81fead008c0aea","observation_id":"71e623c5-beba-4c7d-9cb4-f076af7e6bbe","resolution":{"observed_at":"2026-08-14T14:03:26.171574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T14:03:26.177324Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.177324Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:bfae065248bcb6566960a3735ef408644c6e5f6e7466cfb0d6d93207b0f322a4","observation_id":"5515073e-1923-49c0-88ba-fd1f407a8c66","resolution":{"observed_at":"2026-08-14T14:03:26.177324Z","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":"2026-08-14T14:03:26.246193Z","title":"HH Model","venue":null,"work_id":"8c318e5a-9809-4186-9c9b-06e06f3d12b1","year":null},"citing_paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T14:03:26.184050Z"},"links":{"citing_paper":"/paper/1908.07428"},"observation_digest":"sha256:26c7c0efce9c3495ce48a13362e4bf70cd7ac6bea22d7bb3fe5a6157d2ddbfc8","observation_id":"f222f695-5e76-4860-b231-d7164462c8f9","resolution":{"observed_at":"2026-08-14T14:03:26.252890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.07428","last_updated":"2019-08-11T17:36:46Z","latest_version":1,"primary_category":"q-bio.NC","snapshot_observed_at":"2026-08-16T03:22:13.009400Z","submitted_at":"2019-08-11T17:36:46Z","title":"Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":33},"total_outbound_references":43},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:1908.07428."}