{"as_of":"2026-08-14T20:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b6c96ab0c17ddb5ba73babc1f5a11eb50cc3dae59760d345562ed102958f035a","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":"2026-08-14T05:16:30.890782Z","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":"2026-08-14T06:32:32.682623+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/1909.01771/citation-record","integrity":"/paper/1909.01771/integrity","json":"/paper/1909.01771/citation-record.json","paper":"/paper/1909.01771"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:16:30.439210Z","title":"Abbott and W.G","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.439210Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:21799af425718e313b2407e5f86348757504d512696d1b97e9476640c8cbfe9a","observation_id":"302a1aae-685c-459f-a21b-44b43d82e789","resolution":{"observed_at":"2026-08-14T05:16:30.439210Z","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":"2026-08-14T05:16:30.444942Z","title":"Naous, E","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.444942Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:a8b6503b985665f221a630c30e9dd6b5c5685154d79df5246767fe5a80e3888b","observation_id":"a57ab367-a4e4-49dd-b9b3-9200701b9bc7","resolution":{"observed_at":"2026-08-14T05:16:30.444942Z","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":"2026-08-14T05:16:30.450169Z","title":"Yodann: An ultra-low power convolutional neural network accelerator based on binary weights","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.450169Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:474352f6ce61a5c6cc3373f7726048287854ef1f13f395fd9b1138ec44957825","observation_id":"6888f3b2-12df-4d6e-adf0-1e8f15702902","resolution":{"observed_at":"2026-08-14T05:16:30.450169Z","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":"2026-08-14T05:16:30.454854Z","title":"Normad-normalized approximate descent based supervised learning rule for spiking neurons","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.454854Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:2d05e25d44133e379702278dd3a260baa62fc9f88f32c9c09177f8d9b7cae0e8","observation_id":"13ca1b06-9430-4b78-8fee-c7c066843eb6","resolution":{"observed_at":"2026-08-14T05:16:30.454854Z","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":"2026-08-14T05:16:30.459754Z","title":"Endurance/retention trade off in hfox and taox based rram","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.459754Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:ec9304f84420fd4d9feb89df08ff33afa0656794410b07b0aa6ebecc3fcccd6c","observation_id":"77d0be93-2b9b-403e-b53b-4f22b3212186","resolution":{"observed_at":"2026-08-14T05:16:30.459754Z","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":"2026-08-14T05:16:30.464884Z","title":"Bartolozzi and G","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.464884Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3b19d5cdf83b1b697e52b5acc07378e303f406c1a2043ec1f594e76746c51c6b","observation_id":"0dc105d9-388c-481a-b31b-3cf4f0867b57","resolution":{"observed_at":"2026-08-14T05:16:30.464884Z","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":"2026-08-14T05:16:30.470027Z","title":"Bartolozzi and G","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.470027Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:bd7fd2bf8fd0f0d0ceb93d34da356121290f74c3748d1f42d4fa98cf82dd63c0","observation_id":"a7837c3c-2584-488e-9dff-69a67cd42946","resolution":{"observed_at":"2026-08-14T05:16:30.470027Z","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":"2026-08-14T05:16:30.474667Z","title":"Bartolozzi and G","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.474667Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:8e81c1ed774942bc19dc383ee57804f7c8ae3bb7fe3fcb92cafe4cce27f7d32f","observation_id":"65fd060c-5fc9-4622-9794-a0f4415ba6e9","resolution":{"observed_at":"2026-08-14T05:16:30.474667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.09049","last_updated":"2019-02-21T17:15:18Z","snapshot_observed_at":"2026-08-14T17:25:45.886549Z","submitted_at":"2019-01-25T19:07:36Z","title":"Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.09049","snapshot_observed_at":"2026-08-14T05:16:30.479109Z","title":"Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.479109Z"},"links":{"cited_paper":"/paper/1901.09049","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:ab15f715a1783dff1bb7f297810dda8fdb9dd2726da5b641978ed6d3048bee4e","observation_id":"424f2646-3f12-4f5d-a88a-76affc08effd","resolution":{"observed_at":"2026-08-14T05:16:30.479109Z","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":"2026-08-14T05:16:30.484014Z","title":"Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.484014Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:9108905f979954787c8c94517cf7a2f92af1b3f8851e16d783420a3ec510c345","observation_id":"f056caf7-287a-40c8-8548-7f2c8f1fde58","resolution":{"observed_at":"2026-08-14T05:16:30.484014Z","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":"2026-08-14T05:16:30.488832Z","title":"Bi and M-M","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.488832Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:0ef9d0b3c2bb1e290f19fec8d0ed7cac333aedfcd9e28eaa80369c9a955dc5fc","observation_id":"4a73fe4f-8d7f-4b99-abe8-f2680af2e269","resolution":{"observed_at":"2026-08-14T05:16:30.488832Z","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":"2026-08-14T05:16:30.493866Z","title":"Spikeprop: backpropagation for networks of spiking neurons","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.493866Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:9b3057d7e8baece7070f28fc119fdcedad2afc47c74863a30422c8c703c737e3","observation_id":"193e916c-f11e-476a-b59e-82fc6ba88c7a","resolution":{"observed_at":"2026-08-14T05:16:30.493866Z","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":"2026-08-14T05:16:30.499012Z","title":"The probability of neurotransmitter release: variability and feedback control at single synapses","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.499012Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:a706ba40fd4083ccba0b0db4b8cf7998f843884465d28d4e84d5020323ee99de","observation_id":"780a8026-a304-4236-9574-c812482bc1ff","resolution":{"observed_at":"2026-08-14T05:16:30.499012Z","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":"2026-08-14T05:16:30.504473Z","title":"Mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.504473Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:da24d6b8ee43db0449b5cc5852d83d5d0951c6f72b81b7cdd1740cbbb0c975e0","observation_id":"d80a18f5-c16a-4854-8787-356d7b9ef739","resolution":{"observed_at":"2026-08-14T05:16:30.504473Z","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":"2026-08-14T05:16:30.509388Z","title":"Physical mechanisms of endurance degradation in tmo-rram","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.509388Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:a6118f7009a96067aa5d5580d5613caeefcb26e6e013819cca4b90346782e7ca","observation_id":"27875bd7-18e4-4917-8221-b17096aa68e1","resolution":{"observed_at":"2026-08-14T05:16:30.509388Z","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":"2026-08-14T05:16:30.514923Z","title":"Chicca, F","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.514923Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:1ff18cb7decd8e6ba23218912e739c4bfea53abd9547961d6fddd40abc18e7ec","observation_id":"45df419e-18fe-4763-89f0-452b122f6636","resolution":{"observed_at":"2026-08-14T05:16:30.514923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.7024","last_updated":"2015-09-23T01:00:44Z","snapshot_observed_at":"2026-08-10T16:13:51.240000Z","submitted_at":"2014-12-22T15:22:45Z","title":"Training deep neural networks with low precision multiplications","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.7024","snapshot_observed_at":"2026-08-14T05:16:30.519499Z","title":"Low precision arithmetic for deep learning","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.519499Z"},"links":{"cited_paper":"/paper/1412.7024","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:e8af0223b9d86c006956e067d6fa82d6a546371e49a35fa0fadb8f865465dcd7","observation_id":"f014d0bf-69cd-4a45-9da8-ea4715b38cad","resolution":{"observed_at":"2026-08-14T05:16:30.519499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.02830","last_updated":"2016-03-17T14:54:25Z","snapshot_observed_at":"2026-08-10T10:55:02.784121Z","submitted_at":"2016-02-09T01:01:59Z","title":"Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.02830","snapshot_observed_at":"2026-08-14T05:16:30.524120Z","title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.524120Z"},"links":{"cited_paper":"/paper/1602.02830","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:0bc2ffad63789da4dd31e731961491a96330ff4545660f13a7636eb8f294809d","observation_id":"0893c74d-738a-4519-a7b6-d1ee40eecbaa","resolution":{"observed_at":"2026-08-14T05:16:30.524120Z","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":"2026-08-14T05:16:30.529269Z","title":"Davies, N","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.529269Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:6fd79c0f85c06f7cb3adcefa74d4ff9e7ac4338f58bed94362c3f27e216278ad","observation_id":"07d0d66e-73cd-4e77-9eb0-0bd043bf2fea","resolution":{"observed_at":"2026-08-14T05:16:30.529269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07406","last_updated":"2018-06-19T18:02:34Z","snapshot_observed_at":"2026-08-14T19:01:47.915076Z","submitted_at":"2018-06-19T18:02:34Z","title":"Contrastive Hebbian Learning with Random Feedback Weights","version":1},"cited_work":{"arxiv_id":"1806.07406","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.07406","snapshot_observed_at":"2026-08-14T05:16:31.675672Z","title":"Contrastive Hebbian Learning with Random Feedback Weights","venue":"cs.LG","work_id":"e2df2835-6f23-4875-bb7e-13a0b831eeb3","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.534561Z"},"links":{"cited_paper":"/paper/1806.07406","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:258beddfb2a40d45b6371fd8b8f26956d264a81de418270a6eb5bca5ff46e598","observation_id":"0bd49016-cc58-4936-bac5-e43b24220ae4","resolution":{"observed_at":"2026-08-14T05:16:31.680897Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.00583","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:16:31.650335Z","title":"Pedroni, Nikil Dutt, Jeffrey Krichmar, Gert Cauwenberghs, and Emre Neftci","venue":null,"work_id":"041b1e54-7253-42d3-a875-283eac00459a","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.539169Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:4390edcda1ce7c8848dd5dfeacc803bec5520a31e583ce33073f8d152453e4db","observation_id":"aab3ac3d-c400-453c-bee7-7b8e90b4c2f3","resolution":{"observed_at":"2026-08-14T05:16:31.659436Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:30.543560Z","title":"Noise in the nervous system","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.543560Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:8c36eb5b5cf50282f3231ca914e66abebfc665d33c02cfe4af0f19a523c051ba","observation_id":"1dbbd56f-a33c-49d6-b04b-67b106fb7bbd","resolution":{"observed_at":"2026-08-14T05:16:30.543560Z","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":"2026-08-14T05:16:30.547927Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.547927Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:62d00ce10144b72480d8ad52eb11e24a8186b208cba59adb069b29ba5efd1811","observation_id":"9277082a-8038-4f26-aa45-2f0cf79cfded","resolution":{"observed_at":"2026-08-14T05:16:30.547927Z","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":"2026-08-14T05:16:30.552355Z","title":"Modeling and analysis of passive switching crossbar arrays","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.552355Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:761ac2110795d9b9dfbcfad2e83fc5f70f2336c1c5a73e761252e8c38ce1a94d","observation_id":"f35f8c7f-bb94-49e2-9a33-dd17706366d4","resolution":{"observed_at":"2026-08-14T05:16:30.552355Z","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":"2026-08-14T05:16:32.589098Z","title":"Overcoming crossbar nonidealities in binary neural networks through learning","venue":null,"work_id":"dde9caf5-aa1a-4cbf-9b4e-814e051750fb","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.556906Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:2725305b143d49543994cb08eb6e6683e8849c46b13a43822f20677903209405","observation_id":"89fa7574-b99a-40e7-9893-6f07e6b7eca2","resolution":{"observed_at":"2026-08-14T05:16:32.594484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01512","last_updated":"2019-03-04T19:55:01Z","snapshot_observed_at":"2026-08-14T17:07:33.864850Z","submitted_at":"2019-03-04T19:55:01Z","title":"On Resistive Memories: One Step Row Readout Technique and Sensing Circuitry","version":1},"cited_work":{"arxiv_id":"1903.01512","doi":null,"metadata_source":"pith","pith_arxiv_id":"1903.01512","snapshot_observed_at":"2026-08-14T05:16:31.522151Z","title":"On Resistive Memories: One Step Row Readout Technique and Sensing Circuitry","venue":"cs.ET","work_id":"03bfab0f-8e42-4b3b-bde7-f1351c8e5a52","year":2019},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.561499Z"},"links":{"cited_paper":"/paper/1903.01512","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:5b6b35bb1c7c23b65c1e6b25c0f202cdbc2da94ea97f36dac96869acc44c4177","observation_id":"4795c204-bc19-4162-95ee-7d881e376ba3","resolution":{"observed_at":"2026-08-14T05:16:31.526698Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.574592Z","title":"The spinnaker project","venue":null,"work_id":"3a28ce0e-7287-4a4d-bc02-1282678f4757","year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.566508Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:f3a788c9750fa29353dc2d80ccbb7d8ed6b1273a364722f427d5222e6618582e","observation_id":"5ab3e511-4dd7-41f2-bfdb-91f53e931dde","resolution":{"observed_at":"2026-08-14T05:16:32.579561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.559325Z","title":"Gerstner and W","venue":null,"work_id":"a1169c52-dba9-421b-a292-ccf4e7f08bc9","year":2002},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.571172Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:65567546dca0ec8f9cd549a597cba90acc0cf03ee5c024d8daf6eb7c2052b6d6","observation_id":"37998c44-b786-4d01-add7-eafe6c485e0d","resolution":{"observed_at":"2026-08-14T05:16:32.564009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:30.575632Z","title":"Neuronal dynamics: From single neurons to networks and models of cognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.575632Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:b29934546edc9e23914c2042f05084e092a2c58eac85b2b25a66fa6dfdb8a2c9","observation_id":"3e0fcfe8-12c2-41cc-ac62-eeb55d9839e4","resolution":{"observed_at":"2026-08-14T05:16:30.575632Z","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":"2026-08-14T05:16:32.533634Z","title":"Goldberg, G","venue":null,"work_id":"9ae230e0-ca5e-4528-a643-2d019dec1d7f","year":2001},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.580739Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:696faef9353c83f20d867970c9fdf6d3f3801422b492caa9e71e4f0fdcc512b4","observation_id":"a439deb9-d789-41f0-9e82-70dcc2e6bb00","resolution":{"observed_at":"2026-08-14T05:16:32.538634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:30.585189Z","title":"G \\\"u tig and H","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.585189Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:9e975d5d0bc59439f15160d2bf5b705ee587f42bece29ebf003b0b7d6e34c763","observation_id":"cacb2e20-757c-4cc2-9e2c-70e9b62b5fb6","resolution":{"observed_at":"2026-08-14T05:16:30.585189Z","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":"2026-08-14T05:16:32.518356Z","title":"Training products of experts by minimizing contrastive divergence","venue":null,"work_id":"39258409-2680-413a-84d7-204f2ae264e6","year":2002},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.590775Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:6188e84716546591426fe0c32b89beb19972826072037906bccc0e1e811cc0da","observation_id":"e1722d71-3f0e-4fa1-8bd9-63681473190b","resolution":{"observed_at":"2026-08-14T05:16:32.523603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.503339Z","title":"Hopfield","venue":null,"work_id":"daac94a6-c6c4-4973-93a2-5030b93b5302","year":1982},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.595803Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3f2673a0f28201450f172b646f4a8f408d4ca2675a4a780c2c71ac63d79e1942","observation_id":"55021bd6-abeb-45a2-a94b-78b797ede286","resolution":{"observed_at":"2026-08-14T05:16:32.508253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2016.26161","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:16:31.497856Z","title":"Maldonado Huayaney, Stephen Nease, and Elisabetta Chicca","venue":null,"work_id":"c60bbb35-763f-4612-bbb7-b0fe5d1a0e5c","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.600327Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:f62717e0feb77e16e5f73858300cae1276017cdc7fda22262bbff690521473f6","observation_id":"6636a1bc-9dbd-4b58-9b55-57b1fbdb2ae7","resolution":{"observed_at":"2026-08-14T05:16:31.504801Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.04698","last_updated":"2017-06-19T22:11:20Z","snapshot_observed_at":"2026-08-01T00:59:51.790251Z","submitted_at":"2017-06-14T23:56:57Z","title":"Gradient Descent for Spiking Neural Networks","version":2},"cited_work":{"arxiv_id":"1706.04698","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.04698","snapshot_observed_at":"2026-08-14T05:16:31.429787Z","title":"Gradient Descent for Spiking Neural Networks","venue":"q-bio.NC","work_id":"53d170fb-b910-4c3c-9e55-4dd4daeb5059","year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.606204Z"},"links":{"cited_paper":"/paper/1706.04698","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:f2c35bcfd60238944dd744ff094e38219702b753b24b73b1ba401fe23278567d","observation_id":"55400f3b-42a2-48f1-864a-cf38f9e80e70","resolution":{"observed_at":"2026-08-14T05:16:31.435049Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.487831Z","title":"Hyv \\\"a rinen","venue":null,"work_id":"bf456ab8-5a05-498a-afed-fbc29757861c","year":2004},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.611089Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3f08e24071b57e7cf09da677fdebb7e1f779312ac0161ee1b88472955e9ffaa1","observation_id":"7c5175ae-2df6-4e33-8cec-7d63efabe211","resolution":{"observed_at":"2026-08-14T05:16:32.492695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.472519Z","title":"Brain-inspired computing with resistive switching memory (rram): Devices, synapses and neural networks","venue":null,"work_id":"c5529413-adf6-4f9f-817c-7d0c6f2634ce","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.615674Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:6372a8697f92c7759171784fe9f8eeb413e6e16e8bb44d9d5580533786b7fbb5","observation_id":"3bc7837d-1bf4-4895-a80e-b40e62883275","resolution":{"observed_at":"2026-08-14T05:16:32.477395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.00073","last_updated":"2020-10-30T19:37:48Z","snapshot_observed_at":"2026-08-12T15:42:28.002579Z","submitted_at":"2020-10-30T19:37:48Z","title":"Resource-Aware Pareto-Optimal Automated Machine Learning Platform","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.00073","snapshot_observed_at":"2026-08-14T05:16:30.620888Z","title":"Indiveri, B","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.620888Z"},"links":{"cited_paper":"/paper/2011.00073","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:25633fcbde5d71f3f85f7e131adeffd25ae809a35b5939895bbce69c4b1fffd6","observation_id":"4f1e01c8-be58-4204-bf51-4ed430797f2f","resolution":{"observed_at":"2026-08-14T05:16:30.620888Z","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":"2026-08-14T05:16:32.456869Z","title":"A local learning rule for independent component analysis","venue":null,"work_id":"f599c009-cf82-4b69-8ba6-5fcbfd7fcf68","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.625631Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:b4fac0626f4f7e130595802a3ef7e2c3f6adfdb67ca405d092e39da776331e25","observation_id":"328a15aa-7747-45e3-8f3c-86058f866c77","resolution":{"observed_at":"2026-08-14T05:16:32.461798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1608.05343","last_updated":"2017-07-03T10:52:04Z","snapshot_observed_at":"2026-07-06T05:07:27.342709Z","submitted_at":"2016-08-18T17:29:09Z","title":"Decoupled Neural Interfaces using Synthetic Gradients","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.05343","snapshot_observed_at":"2026-08-14T05:16:30.629971Z","title":"Decoupled neural interfaces using synthetic gradients","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.629971Z"},"links":{"cited_paper":"/paper/1608.05343","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:4f72f4105a4b1445780105ff16ac113c55882b616c34e4b4d679d7ef9b7de486","observation_id":"5b49ad85-c0a4-47d7-b0df-4d870263f0c3","resolution":{"observed_at":"2026-08-14T05:16:30.629971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.00072","last_updated":"2020-06-02T03:33:11Z","snapshot_observed_at":"2026-08-14T18:34:20.926878Z","submitted_at":"2018-08-31T22:22:53Z","title":"RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.00072","snapshot_observed_at":"2026-08-14T05:16:30.635065Z","title":"Rx-caffe: Framework for evaluating and training deep neural networks on resistive crossbars","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.635065Z"},"links":{"cited_paper":"/paper/1809.00072","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:f60d95d9639d0c5403e6fb569d0447d94a40001693c16a44ca0951ed74097497","observation_id":"09a4f3e0-c7a3-4fd7-b7e3-1daf12e6e4dc","resolution":{"observed_at":"2026-08-14T05:16:30.635065Z","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":"2026-08-14T05:16:32.443114Z","title":"Predicting spike timing of neocortical pyramidal neurons by simple threshold models","venue":null,"work_id":"2cd90b99-494d-489e-bb97-c243a2306c63","year":2006},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.640052Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:bce33bcff021c7dc88ddcbe00ad1987652be043c3f657c472e0acaf9f3a0ee3e","observation_id":"e153f677-0522-4955-8353-58445b627cc9","resolution":{"observed_at":"2026-08-14T05:16:32.447375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.10766","last_updated":"2019-08-08T10:56:36Z","snapshot_observed_at":"2026-08-14T17:37:52.613150Z","submitted_at":"2018-12-27T16:47:11Z","title":"SMPLR: Deep SMPL reverse for 3D human pose and shape recovery","version":2},"cited_work":{"arxiv_id":"1812.10766","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.10766","snapshot_observed_at":"2026-08-14T05:16:31.363035Z","title":"SMPLR: Deep SMPL reverse for 3D human pose and shape recovery","venue":"cs.CV","work_id":"25843534-ac3e-4cd9-882d-1ab746bbb985","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.644928Z"},"links":{"cited_paper":"/paper/1812.10766","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:bd087523c39821f939f7be439c501b1e3cb479ced590383bf37529e338c3f0c2","observation_id":"35f47da5-a8fa-4802-bf6e-31d0177cce17","resolution":{"observed_at":"2026-08-14T05:16:31.368772Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1504.05143","last_updated":"2015-04-20T18:18:18Z","snapshot_observed_at":"2026-07-06T04:15:30.694583Z","submitted_at":"2015-04-20T18:18:18Z","title":"Network Plasticity as Bayesian Inference","version":1},"cited_work":{"arxiv_id":"1504.05143","doi":null,"metadata_source":"pith","pith_arxiv_id":"1504.05143","snapshot_observed_at":"2026-08-14T05:16:31.341458Z","title":"Network Plasticity as Bayesian Inference","venue":"cs.NE","work_id":"06493646-d311-444e-bcd3-614727d328d3","year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.650158Z"},"links":{"cited_paper":"/paper/1504.05143","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:103c43c6887f9ba95cb7013d21a4e6de8d2497928cf8468cffd53e055e8f376c","observation_id":"ba7ddeed-b64c-4736-963d-c9f39ba51aff","resolution":{"observed_at":"2026-08-14T05:16:31.346701Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.429023Z","title":null,"venue":null,"work_id":"8b393e39-fda6-4bf3-ac29-7133c48c244c","year":1966},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.654846Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3c751998479b121a5f511a34bea8d767c713c7e6d3d57723d10e41b95fc21edc","observation_id":"68fdb3c5-6a58-493f-be6e-64626cbbe460","resolution":{"observed_at":"2026-08-14T05:16:32.433599Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.413928Z","title":"Deep neural network optimized to resistive memory with nonlinear current-voltage characteristics","venue":null,"work_id":"28ca58df-f116-4433-8f58-30fdca0a299f","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.659196Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:e084397cd908e9be5a45204570dc29cec498b41eac708616194e00c6418ecd32","observation_id":"42130a82-0315-406d-9910-cb085d300ca8","resolution":{"observed_at":"2026-08-14T05:16:32.419012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.397681Z","title":"A memory frontier for complex synapses","venue":null,"work_id":"0d6984f8-565f-44e1-bdd4-cb1667ae19a2","year":2013},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.663715Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:39291e6b3472181493ab3915406ff8a194f1544185927907b89471d000b6592c","observation_id":"8b224899-e795-466e-8022-460d01fde0c9","resolution":{"observed_at":"2026-08-14T05:16:32.403015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.380401Z","title":"Energy-efficient neuronal computation via quantal synaptic failures","venue":null,"work_id":"9bc9917f-aa01-49ac-b1dd-4a566388888b","year":2002},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.668643Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:7b876cc6bc6ba323ad9552d7c94e238828949764c28abb3fcf5bcbd866adfb17","observation_id":"b13684ac-07c9-4531-a25c-18be4254f0e8","resolution":{"observed_at":"2026-08-14T05:16:32.385532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.365402Z","title":"Efficient and self-adaptive in-situ learning in multilayer memristor neural networks","venue":null,"work_id":"d7226e8e-2ccd-4ffa-866f-c757ca7fc635","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.673694Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:8e444d184f9d3cb125d890c9de03f03d1de63e253414e9a4fcfbe9a7b3c2c940","observation_id":"a34d5e5d-a08c-44fb-9b8a-6a449bc337fe","resolution":{"observed_at":"2026-08-14T05:16:32.370434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.350448Z","title":"Random synaptic feedback weights support error backpropagation for deep learning","venue":null,"work_id":"97e7f5cb-ac50-48ab-86fc-d7e9ba69fb8b","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.678111Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:d5f5b53f3a36764fd9dc87f27aa2388661d88c00fe51fd0e8cf8e7c8939d6d52","observation_id":"d899c3c4-9ea2-4b76-8431-92f97d3c889e","resolution":{"observed_at":"2026-08-14T05:16:32.355534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.335360Z","title":"Maass, T","venue":null,"work_id":"d541ad15-95cf-4ffa-9297-77e184dc4ae2","year":2002},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.682689Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:11f03c795cc9808118bbedf87fe5080431f7adf732282ed7775fd85ce42583a8","observation_id":"b6248c1a-a4fb-406f-82b8-ced2e6b3542b","resolution":{"observed_at":"2026-08-14T05:16:32.340027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.320854Z","title":null,"venue":null,"work_id":"c242f8f4-155d-47a7-bfe1-e2efbfed16b7","year":1990},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.687026Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3e8ee7b102b38e32bd81246b6d47d6e16e94fd873cec96c10e50d44e163ab613","observation_id":"3c574af5-3da5-4a6d-9bad-f02bce250fed","resolution":{"observed_at":"2026-08-14T05:16:32.325456Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:30.692352Z","title":"A million spiking-neuron integrated circuit with a scalable communication network and interface","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.692352Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:f27ca21f775901ac9353c0fb0755079a958335e47b4da791524c7022f973756a","observation_id":"d9d5d53a-e8f9-46e1-a1ec-661e77c644e9","resolution":{"observed_at":"2026-08-14T05:16:30.692352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16932","last_updated":"2020-06-30T16:02:11Z","snapshot_observed_at":"2026-07-06T09:34:09.418067Z","submitted_at":"2020-06-30T16:02:11Z","title":"Interval fragmentations with choice: equidistribution and the evolution of tagged fragments","version":1},"cited_work":{"arxiv_id":"2006.16932","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.16932","snapshot_observed_at":"2026-08-14T05:16:31.320744Z","title":"Interval fragmentations with choice: equidistribution and the evolution of tagged fragments","venue":"math.PR","work_id":"081143ca-01d6-4a25-948c-439c1d7b3ad9","year":2020},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.696843Z"},"links":{"cited_paper":"/paper/2006.16932","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:0a077cfa6aa9f4b8c3ca082c4d6b8ff2cdde7dfe43a10ecee1f682acf35f0b90","observation_id":"6fdfa4cd-68a2-414e-b01e-b8b4c2833694","resolution":{"observed_at":"2026-08-14T05:16:31.326075Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.296524Z","title":"Moreno-Bote","venue":null,"work_id":"b4acafd0-ea1b-49bf-a635-0bc327fdf090","year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.701452Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3ce529e4a8462d9318a021be81ce28c0ce9a13c7a4c4b37110b4952622d04264","observation_id":"0a433c03-42ac-4ff2-9259-6777ed060e1c","resolution":{"observed_at":"2026-08-14T05:16:32.301272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.06756","last_updated":"2017-11-17T22:48:02Z","snapshot_observed_at":"2026-08-14T20:12:25.629473Z","submitted_at":"2017-11-17T22:48:02Z","title":"Deep supervised learning using local errors","version":1},"cited_work":{"arxiv_id":"1711.06756","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.06756","snapshot_observed_at":"2026-08-14T05:16:31.299041Z","title":"Deep supervised learning using local errors","venue":"cs.NE","work_id":"ec91b970-902a-4a27-a993-3fb986a9a6f6","year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.705817Z"},"links":{"cited_paper":"/paper/1711.06756","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:1c14396b4fc401b97026934c1a857a58feb1830f9c3a98f03803bc52d1befe9f","observation_id":"a2e0599f-ad96-4838-aa01-cd40fc948f51","resolution":{"observed_at":"2026-08-14T05:16:31.304373Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.278916Z","title":"Understanding rram endurance, retention and window margin trade-off using experimental results and simulations","venue":null,"work_id":"ee8ac083-f84e-47d1-955f-bdbd8fa00bb0","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.711220Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:99fe443e90c1333e8a28244ae19504e81a81a2e4d1676b8f0e6c53d61744d5fb","observation_id":"60abbcd5-1391-4de0-9d99-7ca413f73e2c","resolution":{"observed_at":"2026-08-14T05:16:32.283800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.262057Z","title":"Memristor-based neural networks: Synaptic versus neuronal stochasticity","venue":null,"work_id":"d91bafbd-5e9c-4726-b0b1-5f3748763f5e","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.715815Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:e2fa87e31576345603f0bc4ab6393847545ae3eebe43537713c584966b2c8ba3","observation_id":"b0c1193b-9f89-4a18-9081-cb9f21fd145c","resolution":{"observed_at":"2026-08-14T05:16:32.267497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.247507Z","title":"Stochastic synapses as resource for efficient deep learning machines","venue":null,"work_id":"05abae57-f480-4db1-ad79-8585ea6da1b5","year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.720644Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:6f5c17dc347e8d3cf0853f2abae199cdac283f24800c6f08767b0884fcee71b3","observation_id":"9d481d09-00cd-40b3-bcf8-0b7c21a57177","resolution":{"observed_at":"2026-08-14T05:16:32.252236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.233759Z","title":"Event-driven random back-propagation: Enabling neuromorphic deep learning machines","venue":null,"work_id":"acd8fcd1-b02f-4627-acaf-1fb546e49c42","year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.725059Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:5eb7c3e6a6ff48ad43003852b83523b7b83934044845c022d97d886728de306f","observation_id":"65c68b4b-1ffa-476d-b98c-4466e067ba03","resolution":{"observed_at":"2026-08-14T05:16:32.238260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.isci.2018.06.010","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:16:30.943169Z","title":null,"venue":null,"work_id":"4e222724-7b01-41f9-87a9-7d8a69eb7b14","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.729551Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:8c2b6b8a9dda482869e5c7b016e22c0defe910dc3f7e0221a806382d233d9863","observation_id":"df20c34e-7ebb-463e-bd76-e9b984e3487e","resolution":{"observed_at":"2026-08-14T05:16:30.948031Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2016.00241","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:16:31.275970Z","title":"Stochastic synapses enable efficient brain-inspired learning machines","venue":null,"work_id":"6c589efc-cd96-43c4-99a9-e613c17f1647","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.735735Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:2ce71b4e1034cbd04ecfe0db04b0b2874d00295b9f7e2c87718329f7e634ab57","observation_id":"35ac945b-bfbe-4d0e-9c8f-20f3ee362393","resolution":{"observed_at":"2026-08-14T05:16:31.283242Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2017.00324","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:16:31.199945Z","title":"Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis","venue":null,"work_id":"5021cfcf-f041-4fc1-b88d-1b6cc5008616","year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.740369Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:af2bf4e6c1bb65b9420dbb2527ecda47b641e11bdd7bb4399be4e5b7338d80ab","observation_id":"04115488-815c-4465-88ab-3838e4ca1eff","resolution":{"observed_at":"2026-08-14T05:16:31.207935Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.09948","last_updated":"2019-05-03T16:24:45Z","snapshot_observed_at":"2026-08-14T20:47:24.442275Z","submitted_at":"2019-01-28T19:13:55Z","title":"Surrogate Gradient Learning in Spiking Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.09948","snapshot_observed_at":"2026-08-14T05:16:30.744750Z","title":"Surrogate gradient learning in spiking neural networks","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.744750Z"},"links":{"cited_paper":"/paper/1901.09948","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:ff48391437f9382ce49ce5e7a9d0f3b4d975e85452c1f1ea3b3e5a9400a9ad5e","observation_id":"b3c6f49d-482f-4db8-af6f-991bffe9d77b","resolution":{"observed_at":"2026-08-14T05:16:30.744750Z","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":"10.1523/jneurosci.5796-12.2013","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:16:30.925121Z","title":"Gabaergic circuits control spike-timing-dependent plasticity","venue":null,"work_id":"e04b1590-5477-400f-b50a-89cdb4283a3b","year":2013},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.749446Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:b1eeeee43223057906ad22aae1ed34fef309c2379a1ff69f71cfd80edc46212a","observation_id":"c4cd5b3f-f1fc-400c-b442-aa5ef08226ef","resolution":{"observed_at":"2026-08-14T05:16:30.931847Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.219561Z","title":null,"venue":null,"work_id":"c37af8a9-cd4d-4ddc-8fa8-32486ee7939d","year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.754017Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:41032da0aa7aa9420e72aab38bcd0b8a1c892b01dd684fc3266564773ad0538b","observation_id":"c06fe0a2-99ca-48c2-b70b-41effa65ab0e","resolution":{"observed_at":"2026-08-14T05:16:32.224237Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.204981Z","title":"Tio x-based rram synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing","venue":null,"work_id":"72e751df-8bd2-4b6a-9bc3-2391083107f9","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.758862Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:c28ffe7393684dc3dc06e30f002d5affc0a26c26d50b16454737f7e27b4d4371","observation_id":"11a1fd4d-22d6-470f-961e-420fec31818d","resolution":{"observed_at":"2026-08-14T05:16:32.209886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.188519Z","title":"Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning","venue":null,"work_id":"314d3472-9b0a-41c9-92af-9363adc0560d","year":2006},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.763399Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:6c206f3bb127a8cac387a2777eb6b832dd7390b42f0696ae714b23a31a52d7a4","observation_id":"a23e5d5e-7327-4145-a255-fb3869efc571","resolution":{"observed_at":"2026-08-14T05:16:32.194244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.172437Z","title":"Training and operation of an integrated neuromorphic network based on metal-oxide memristors","venue":null,"work_id":"d84677ca-926a-469f-a5ab-6ea6ff765d9a","year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.767825Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:827decfe43f61f6edeb5e632ca0d8ba580ba262f28309987042cc247321e7ecd","observation_id":"28fbd135-b06a-4874-a7f1-4ee309e800a2","resolution":{"observed_at":"2026-08-14T05:16:32.178475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.157952Z","title":"Training and operation of an integrated neuromorphic network based on metal-oxide memristors","venue":null,"work_id":"728fea96-c849-48ee-952f-74f69c90973d","year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.772770Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:1cf05ab89e4ce8d4807f75b79552612e74490ce53caad697369957bf58dfb91a","observation_id":"44df6d07-aaea-4434-aa7e-65d4077e6d93","resolution":{"observed_at":"2026-08-14T05:16:32.162786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.142839Z","title":"A novel program-verify algorithm for multi-bit operation in hfo 2 rram","venue":null,"work_id":"0dd313fc-e2ae-40da-be58-32066b8743fd","year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.777092Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:391942f4314022d4ae678559c8e900e23eb8c9fb13567581ee29848bb0e25765","observation_id":"e5036c57-3a61-4e6f-a564-085868e03a8b","resolution":{"observed_at":"2026-08-14T05:16:32.147781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.127479Z","title":"A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses","venue":null,"work_id":"8c3dc872-6e95-40b3-885e-76ad9b448de7","year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.781344Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3dd350989812e674a15cef23c3fbb2aa76013e79922b35ca921d58d67eff2dac","observation_id":"e589bc68-ed55-4b04-b01a-389eb74b677e","resolution":{"observed_at":"2026-08-14T05:16:32.132590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.111628Z","title":"Bioinspired programming of memory devices for implementing an inference engine","venue":null,"work_id":"d910df06-5ca8-41fe-a0ea-01ccf700d705","year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.785608Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:5203b3bf93b1fea5fae61a1c6b37301431c81e32b47d6f13e25cbcbd99ac0032","observation_id":"5a372425-d208-4648-8899-0cb6e8665123","resolution":{"observed_at":"2026-08-14T05:16:32.117023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.096444Z","title":"Xnor-net: Imagenet classification using binary convolutional neural networks","venue":null,"work_id":"cd9d1d01-08b3-4dae-a81e-56f2b050cba6","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.790057Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:eac0550f7094da1a493aa4ec10c8e6e6d2ccf4edadf2b5acbc4c13203a3419ee","observation_id":"94ea0576-2895-48c0-bb5e-3d59b780acfc","resolution":{"observed_at":"2026-08-14T05:16:32.101074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.082567Z","title":"ghi, Christian G Mayr, Teresa Serrano-Gotarredona, Heidemarie Schmidt, Gwendal Lecerf, Jean Tomas, Julie Grollier, S \\","venue":null,"work_id":"9483b9e2-a5ef-4549-9a5e-fef98a013815","year":2015},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.794517Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:f895a1d34381dae38fabac365096ab6fd89b0ec702e4d9bc28f065aa39afbbf0","observation_id":"a2961c3c-b4ab-40ec-a320-7e3a9d04afe7","resolution":{"observed_at":"2026-08-14T05:16:32.086921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.067628Z","title":"Independent component analysis in spiking neurons","venue":null,"work_id":"cb53690d-a725-4411-aa92-e0c0e7103329","year":2010},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.798759Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:41931a95341d36a0febc5f9a88693a88fe856a1714af7f74308c42cde45b6bf1","observation_id":"0904a34a-0e8e-472b-a91b-6435ef315897","resolution":{"observed_at":"2026-08-14T05:16:32.072473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.051344Z","title":"Schemmel, J","venue":null,"work_id":"10ed775b-a036-44df-a533-2884d9da84b9","year":2008},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.803297Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:462a87ee8792cd8285fd758a5dd79326934c3cacd6bcc0bc9251b4eb90538307","observation_id":"98eb8fcd-8b1a-4239-8453-f09f9960f674","resolution":{"observed_at":"2026-08-14T05:16:32.056356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.035552Z","title":"u derle, A. Gr\\","venue":null,"work_id":"d4ac0122-24fa-4133-b512-72d5454525b1","year":2010},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.808026Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:a9609b62d3bb082dfb431935597b9cc5345db231476eb81ace5ae73b3555451c","observation_id":"dbf8a722-b367-46c8-80dd-ab43771754f1","resolution":{"observed_at":"2026-08-14T05:16:32.040299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.019184Z","title":"Getting formal with dopamine and reward","venue":null,"work_id":"51ac85cd-48e9-4f1e-927b-1f17d08b5f98","year":2002},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.812479Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:4474e12beb99bc3d7438087fcb22f499e335cc66bf62e3a8c7eaac07777c9710","observation_id":"f385df36-3aa4-48af-ae2e-84074391bca0","resolution":{"observed_at":"2026-08-14T05:16:32.024414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:32.002554Z","title":"Spike timing dependent plasticity: a consequence of more fundamental learning rules","venue":null,"work_id":"8a6090ba-88ff-4523-98a6-4c727607a9db","year":2010},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.816676Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:7302fa72746ab7f45c4e7c331da04b956436b1f7992ad4c4015900694adca59f","observation_id":"4cc72dff-6fd4-4753-9ccc-a753434c019c","resolution":{"observed_at":"2026-08-14T05:16:32.008054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08646","last_updated":"2018-09-05T10:10:03Z","snapshot_observed_at":"2026-08-14T18:32:47.858084Z","submitted_at":"2018-09-05T10:10:03Z","title":"SLAYER: Spike Layer Error Reassignment in Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08646","snapshot_observed_at":"2026-08-14T05:16:30.821284Z","title":"Slayer: Spike layer error reassignment in time","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.821284Z"},"links":{"cited_paper":"/paper/1810.08646","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:325f5fbdd7e20146e50d35e70a36701f32566b8f7eccb4b8283b902e6c4cac08","observation_id":"406b9b0e-42be-4ecb-abc2-afcc56ea2d66","resolution":{"observed_at":"2026-08-14T05:16:30.821284Z","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":"2026-08-14T05:16:31.984982Z","title":"Fully parallel rram synaptic array for implementing binary neural network with (+ 1,- 1) weights and (+ 1, 0) neurons","venue":null,"work_id":"1d3bb046-9a15-488d-b58c-6a8befadbd07","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.825865Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:f4985a2666d48120794e3433ed42952febc46879e595f46132d410fc67bf42c5","observation_id":"f9dfec58-f2f4-4dee-be52-9405e2c0c5f1","resolution":{"observed_at":"2026-08-14T05:16:31.989797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.969716Z","title":"Finn: A framework for fast, scalable binarized neural network inference","venue":null,"work_id":"3769a209-c132-4e23-b49e-bec2359ba07e","year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.830444Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:5720d340e77e9fbfd105e79434ae6ae92d95352563d6345c68627706412387ee","observation_id":"53bd889b-4890-42c7-a643-9f9928d53477","resolution":{"observed_at":"2026-08-14T05:16:31.974737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.954488Z","title":"Learning by the dendritic prediction of somatic spiking","venue":null,"work_id":"b619738d-63dc-428a-aaad-bb73524f88af","year":2014},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.834852Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:1227b23e2d003b3200025682242b193ef732945901fc69c9e0ee5fe5dff9af0c","observation_id":"14a090e0-6e66-4693-ac29-4902a46da079","resolution":{"observed_at":"2026-08-14T05:16:31.959348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.940014Z","title":"Regularization of neural networks using dropconnect","venue":null,"work_id":"40bb085f-8d4b-4c5b-a338-49995b2f3e18","year":2013},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.839679Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:eb51964284df52276228aadd68f5d259fd6d5078587bfcce3cdcd8dccad7fdaf","observation_id":"6dde0f13-06fe-4da4-ad7b-d6811fc1d526","resolution":{"observed_at":"2026-08-14T05:16:31.944972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.925931Z","title":"Fully memristive neural networks for pattern classification with unsupervised learning","venue":null,"work_id":"0aa0854b-5370-4852-8a7f-7e27c1762240","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.844190Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:1f19c074d866b849a3ed1d3413272f0c9e10f4bff3ed9b2b9bfac59f623fb119","observation_id":"78f0389e-3a26-4043-a6fd-b371cbe09bf7","resolution":{"observed_at":"2026-08-14T05:16:31.930425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:30.848508Z","title":"A learning algorithm for continually running fully recurrent neural networks","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.848508Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:a94c81119d26fe01d37d5bf97610040168da3cd42bece883badfbadc212067ab","observation_id":"a6a989ca-11a1-450b-9928-b5d97fef2556","resolution":{"observed_at":"2026-08-14T05:16:30.848508Z","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":"2026-08-14T05:16:31.901526Z","title":"International technology roadmap for semiconductors","venue":null,"work_id":"cc51ac25-fc82-473b-bb2f-89d794e83bc6","year":2013},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.852883Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:0329a72077c88fd02140cce42ce16d04fefcd7edbd760af0f58b09fdd0fb840e","observation_id":"7fb1ee6c-e3a5-46c5-a6fd-a89a5d413ef3","resolution":{"observed_at":"2026-08-14T05:16:31.906056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.885389Z","title":"Resistive memory-based analog synapse: The pursuit for linear and symmetric weight update","venue":null,"work_id":"2c1e5026-925b-4fa0-98ff-e21776897a95","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.857351Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:ec4a1b48f73f6e8a9511098f6a325c786f16f982d24ec2e9be024929dd16867d","observation_id":"d6e978b9-d2be-4025-9fd5-fee9ad4cc8c6","resolution":{"observed_at":"2026-08-14T05:16:31.890887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.868751Z","title":"Equivalence of backpropagation and contrastive hebbian learning in a layered network","venue":null,"work_id":"bf109fcb-d9f8-4061-95cb-370e99078d83","year":2003},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.862519Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3357574f785d13d6d343cc2619ef4a2be16890dcc02ba34f86341adb4cda0a42","observation_id":"17060ee1-1aab-4e60-bfd8-861337dd7b1a","resolution":{"observed_at":"2026-08-14T05:16:31.874394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.852285Z","title":"Voltage fluctuations in neurons: signal or noise? Physiological Reviews, 91 0 (3): 0 917--929, 2011","venue":null,"work_id":"b1d1a939-c5ea-43e9-bb14-0aff838de94f","year":2011},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.867415Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:395e1f42a7d54c3ec0dcba479367271934e8812e1d8ad87f0bcf294b3a42f327","observation_id":"e2867a27-a9e8-4c4d-afe9-eee8c30f9375","resolution":{"observed_at":"2026-08-14T05:16:31.857017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.836997Z","title":"Neuro-inspired computing with emerging nonvolatile memorys","venue":null,"work_id":"f701ec07-ab18-4f23-a822-31284e48bcb3","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.872148Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:8ccf9393e43d92344feb3084cb65bf30d1b7524478b8ad22ffa18186fa3f92b6","observation_id":"4c418d2d-e9b7-4810-b342-06198ccb93a2","resolution":{"observed_at":"2026-08-14T05:16:31.842025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.821430Z","title":"Stochastic learning in oxide binary synaptic device for neuromorphic computing","venue":null,"work_id":"251bd953-ed94-40cf-b120-594b33018d30","year":2013},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.876607Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:3c478c73063e8b0a9a564fe8f6a9eff93e8c9d6ba0a512e6ebd3cac3a8abb63a","observation_id":"7c70a4d7-df5b-438c-9102-42f9770950d1","resolution":{"observed_at":"2026-08-14T05:16:31.826726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02053","last_updated":"2016-09-07T16:30:01Z","snapshot_observed_at":"2026-07-06T05:09:58.250430Z","submitted_at":"2016-09-07T16:30:01Z","title":"Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks","version":1},"cited_work":{"arxiv_id":"1609.02053","doi":null,"metadata_source":"pith","pith_arxiv_id":"1609.02053","snapshot_observed_at":"2026-08-14T05:16:31.008469Z","title":"Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks","venue":"cs.NE","work_id":"7c285be7-2c51-4f69-be33-0ced6be522b5","year":2016},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.881065Z"},"links":{"cited_paper":"/paper/1609.02053","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:b137e5ee8606390ce30feb4293c060f3beb4a9ae6578497b01e65ff5da55875b","observation_id":"24b81df1-530e-4709-a210-4281bcd08e91","resolution":{"observed_at":"2026-08-14T05:16:31.013498Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.11146","last_updated":"2017-10-14T15:08:04Z","snapshot_observed_at":"2026-07-06T05:45:01.045689Z","submitted_at":"2017-05-31T15:31:26Z","title":"SuperSpike: Supervised learning in multi-layer spiking neural networks","version":2},"cited_work":{"arxiv_id":"1705.11146","doi":null,"metadata_source":"pith","pith_arxiv_id":"1705.11146","snapshot_observed_at":"2026-08-14T05:16:30.984926Z","title":"SuperSpike: Supervised learning in multi-layer spiking neural networks","venue":"q-bio.NC","work_id":"adf7ab31-40a3-49d7-9bb1-d2a966cd1d6b","year":2017},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.885840Z"},"links":{"cited_paper":"/paper/1705.11146","citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:1504f1a4b44f45032cba039e66971326bda16e8d60e0d746c073ed161d557cf0","observation_id":"2b8769c2-f61f-4d8c-b484-a96c56bca314","resolution":{"observed_at":"2026-08-14T05:16:30.990921Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-14T05:16:31.804893Z","title":"Characterizing endurance degradation of incremental switching in analog rram for neuromorphic systems","venue":null,"work_id":"41efa8a9-50a7-429d-abc3-10e8f3533dab","year":2018},"citing_paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective","version":2},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-14T05:16:30.890782Z"},"links":{"citing_paper":"/paper/1909.01771"},"observation_digest":"sha256:8c9e2e1a2b533db5048d12a87579e35bf0e927833d92576b58878ed219fa273b","observation_id":"15257247-260e-41bf-9da1-ed91ba89c31d","resolution":{"observed_at":"2026-08-14T05:16:31.810202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1909.01771","last_updated":"2019-10-08T09:45:24Z","latest_version":2,"primary_category":"cs.ET","snapshot_observed_at":"2026-08-14T05:05:56.384380Z","submitted_at":"2019-09-04T13:09:27Z","title":"Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective"},"reference_resolution":{"displayed":96,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":34,"verified_exact":14,"verified_fuzzy":47},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 0 inbound Pith citation observations for arXiv:1909.01771."}