Paper Citation Record · LEDGER

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data

As of 23 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:1908.07896.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.07896 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:00:07.167306Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy16
unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eceb62a6-0fa2-418a-a7d8-fea766436fd9 · outbound

This paper cites Empirical models of spiking in neural populations.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Empirical models of spiking in neural populations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.514815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c1272d4-4100-466a-b55c-55cee7316191 · outbound

This paper cites Spectral learning of linear dynamics from generalised-linear observations with application to neural population data.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Spectral learning of linear dynamics from generalised-linear observations with application to neural population data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.502076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cd0b0640-403e-4438-83cf-b5cfe30bd965 · outbound

This paper cites Linear dynamical neural population models through nonlinear embeddings.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Linear dynamical neural population models through nonlinear embeddings

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.489742Z

Source-reported events for the cited work

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Observation b6d909e7-e464-4632-85e6-54c186164782 · outbound

This paper cites Bayesian learning and inference in recurrent switching linear dynamical systems.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Bayesian learning and inference in recurrent switching linear dynamical systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.475843Z

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Observation aec383ee-a617-4ae1-91a2-bfcdbfabdc74 · outbound

This paper cites Learning structured neural dynamics from single trial population recording.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Learning structured neural dynamics from single trial population recording

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.462310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ba9e0322-58af-4ba3-a6b7-6357f9f6c129 · outbound

This paper cites Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.449114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6f0cf5c8-622f-4482-80e2-b5f5cf58f0f3 · outbound

This paper cites Variational latent gaussian process for recovering single-trial dynamics from population spike trains.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Variational latent gaussian process for recovering single-trial dynamics from population spike trains

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.436283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 95e6e335-f520-492b-ab5f-b6e1b7723e5b · outbound

This paper cites Gaussian process based nonlinear latent structure discovery in multivariate spike train data.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Gaussian process based nonlinear latent structure discovery in multivariate spike train data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.423855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c2ff1623-cd53-48f9-b035-26db58bc8470 · outbound

This paper cites Temporal alignment and latent gaussian process factor inference in population spike trains.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Temporal alignment and latent gaussian process factor inference in population spike trains

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.411420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f3c60525-6917-4951-9488-5f94e11aa67b · outbound

This paper cites LFADS - Latent Factor Analysis via Dynamical Systems.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data LFADS - Latent Factor Analysis via Dynamical Systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T12:00:07.101767Z

Source-reported events for the cited work

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Observation d0ac55e6-621a-4ee0-94d8-1f6104b4ab11 · outbound

This paper cites Interpretable nonlinear dynamic modeling of neural trajecto- ries.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Interpretable nonlinear dynamic modeling of neural trajecto- ries

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.399270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation de4c60cb-f333-4b40-972b-b7889bc804b0 · outbound

This paper cites Inferring single-trial neural population dynamics using sequential auto-encoders.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Inferring single-trial neural population dynamics using sequential auto-encoders

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T12:00:07.111380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe0b5d43-6ebc-4c3f-a068-9156396f9024 · outbound

This paper cites Learning interpretable continuous-time models of latent stochastic dynamical systems.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Learning interpretable continuous-time models of latent stochastic dynamical systems

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:00:07.275206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 79c9dbcb-c806-415d-8192-6bd14599b87f · outbound

This paper cites Auto-Encoding Variational Bayes.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Auto-Encoding Variational Bayes

Reference 14

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Source-reported events for the cited work

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Observation 764e01cc-e515-4151-aa2b-b6f8ee164a3a · outbound

This paper cites Stochastic Backpropagation and Approximate Inference in Deep Generative Models.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T12:00:07.125004Z

Source-reported events for the cited work

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Observation e3cf8138-ff4e-46df-a03e-fe14f339d146 · outbound

This paper cites DRAW: A Recurrent Neural Network For Image Generation.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data DRAW: A Recurrent Neural Network For Image Generation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T12:00:07.129647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ebe5f4f-d832-4702-8b67-dd275ac426bc · outbound

This paper cites Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T12:00:07.134262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ecf163a-9a9f-476e-8c85-5e671ea5ce21 · outbound

This paper cites Structured inference networks for nonlinear state space models.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Structured inference networks for nonlinear state space models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.378858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 41209ed9-3133-429e-b3e7-e2204ef14dbe · outbound

This paper cites Neural population dynamics during reaching.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Neural population dynamics during reaching

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.365755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e0d1b291-5bb3-45f9-b92d-3792f291e812 · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.353073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4415befe-0162-4858-820b-c080d0266c5a · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Dropout: a simple way to prevent neural networks from overﬁtting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T12:00:07.151315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f14b7aa6-2b5f-416e-8404-c6660894d124 · outbound

This paper cites Cross-validatory estimation of the number of components in factor and principal components models.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Cross-validatory estimation of the number of components in factor and principal components models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.332236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3a32d296-f73c-4360-be9b-6099c08a7914 · outbound

This paper cites Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor compo- nent analysis.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor compo- nent analysis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.318482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c36408c2-3121-4351-a553-2bf3f006ada3 · outbound

This paper cites Decoding vectorial information from ﬁring rates.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Decoding vectorial information from ﬁring rates

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:00:07.304428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4c6c0b75-d3b2-4a2d-b0ac-f0eb1ed12f0d · outbound

This paper cites Population Based Training of Neural Networks.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Population Based Training of Neural Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T12:00:07.167306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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