Paper Citation Record · LEDGER

Leveraging unlabelled data for generalizable neural population decoding

As of 21 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2607.14086.

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

pith.paper-citation-record.v1
2607.14086 v1

Coverage vector

measured 59 of 59 reference resolution

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measured 60 of 60 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: pith, observed 2026-08-08T18:29:22.294622Z

Reference resolution

59 of 59 outbound references displayed

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External citation measurements

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Outbound references

Observation 2db4ef57-f15a-4dc4-826e-c793c8ba9c6d · outbound

This paper cites Machine Learning for Neural Decoding.

Leveraging unlabelled data for generalizable neural population decoding Machine Learning for Neural Decoding

Reference 1

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Observation 4c2ea886-ddfe-47d8-a939-037ce43d51d5 · outbound

This paper cites Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.

Leveraging unlabelled data for generalizable neural population decoding Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Reference 2

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Observation 88ff2858-f4e3-4a62-b284-57c850bdc55a · outbound

This paper cites BRAND: a platform for closed-loop experiments with deep network models.

Leveraging unlabelled data for generalizable neural population decoding BRAND: a platform for closed-loop experiments with deep network models

Reference 3

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Observation 94f7a408-1910-44fe-9596-4b12c2a287c1 · outbound

This paper cites Making brain–machine interfaces robust to future neural variability.

Leveraging unlabelled data for generalizable neural population decoding Making brain–machine interfaces robust to future neural variability

Reference 4

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Observation f5e5c95f-eb47-4f13-ac79-2af7aac01359 · outbound

This paper cites Attention is All you Need.

Leveraging unlabelled data for generalizable neural population decoding Attention is All you Need

Reference 5

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Observation 5516fa7d-e756-4f75-966d-3f268c382d34 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Leveraging unlabelled data for generalizable neural population decoding Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 6

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Observation a816e2c0-10cd-410c-90d8-b9135ecb8714 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Leveraging unlabelled data for generalizable neural population decoding Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 7

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Observation 3fa55f06-81ad-43b8-a4f5-9ad217c1607c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Leveraging unlabelled data for generalizable neural population decoding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 795705e7-e16f-42d0-ab2f-2676a329c9c5 · outbound

This paper cites Large Brain Model for Learning Generic Representations with Tremen- dous EEG Data in BCI.

Leveraging unlabelled data for generalizable neural population decoding Large Brain Model for Learning Generic Representations with Tremen- dous EEG Data in BCI

Reference 9

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This paper cites Jamba: Hybrid Transformer-Mamba Language Models.

Leveraging unlabelled data for generalizable neural population decoding Jamba: Hybrid Transformer-Mamba Language Models

Reference 10

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Observation 6062c590-d3d8-449e-a13e-71f0482ef18c · outbound

This paper cites Brant: Foundation Model for Intracranial Neural Signal.

Leveraging unlabelled data for generalizable neural population decoding Brant: Foundation Model for Intracranial Neural Signal

Reference 11

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Observation e1a511c3-3a1e-4197-81d1-4890a2b2c0aa · outbound

This paper cites BrainBERT: Self- supervised representation learning for intracranial recordings.

Leveraging unlabelled data for generalizable neural population decoding BrainBERT: Self- supervised representation learning for intracranial recordings

Reference 12

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Observation 21975ea5-0ba5-4bdf-93a0-69e20d789793 · outbound

This paper cites A Unified, Scalable Framework for Neural Population Decoding.

Leveraging unlabelled data for generalizable neural population decoding A Unified, Scalable Framework for Neural Population Decoding

Reference 13

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Observation 28abbdda-fcac-48ec-8458-d198a1d123a7 · outbound

This paper cites Gener- alizable, real-time neural decoding with hybrid state-space models.

Leveraging unlabelled data for generalizable neural population decoding Gener- alizable, real-time neural decoding with hybrid state-space models

Reference 14

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This paper cites Representation learning for neural population activity with Neural Data Transformers.

Leveraging unlabelled data for generalizable neural population decoding Representation learning for neural population activity with Neural Data Transformers

Reference 15

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Observation 05f4d2bf-dc3b-41aa-8dd3-25ebb2b86c1c · outbound

This paper cites Neural Encoding and Decoding at Scale.

Leveraging unlabelled data for generalizable neural population decoding Neural Encoding and Decoding at Scale

Reference 16

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Observation ebe192c8-4c45-4989-aeb3-9dbc0d23ec5b · outbound

This paper cites Deep Learning.

Leveraging unlabelled data for generalizable neural population decoding Deep Learning

Reference 17

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This paper cites Language Models are Few-Shot Learners.

Leveraging unlabelled data for generalizable neural population decoding Language Models are Few-Shot Learners

Reference 18

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This paper cites BERT: Pre-training of Deep Bidirectional Transform- ers for Language Understanding.

Leveraging unlabelled data for generalizable neural population decoding BERT: Pre-training of Deep Bidirectional Transform- ers for Language Understanding

Reference 19

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Observation db9b8c57-245c-4ddc-ba25-445f4d2577b1 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Leveraging unlabelled data for generalizable neural population decoding A simple framework for contrastive learning of visual representations

Reference 20

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This paper cites Self- Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Leveraging unlabelled data for generalizable neural population decoding Self- Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Reference 21

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Observation 77662b9c-f8ab-4e50-a76a-a234c106e6cc · outbound

This paper cites POCO: Scalable Neural Forecasting through Population Conditioning.

Leveraging unlabelled data for generalizable neural population decoding POCO: Scalable Neural Forecasting through Population Conditioning

Reference 22

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This paper cites OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens.

Leveraging unlabelled data for generalizable neural population decoding OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Reference 23

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Leveraging unlabelled data for generalizable neural population decoding Masked Autoencoders Are Scalable Vision Learners

Reference 24

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This paper cites wav2vec 2.0: a framework for self-supervised learning of speech representations.

Leveraging unlabelled data for generalizable neural population decoding wav2vec 2.0: a framework for self-supervised learning of speech representations

Reference 25

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This paper cites Multi-session, multi-task neural decoding from distinct cell-types and brain regions.

Leveraging unlabelled data for generalizable neural population decoding Multi-session, multi-task neural decoding from distinct cell-types and brain regions

Reference 26

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Leveraging unlabelled data for generalizable neural population decoding Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity

Reference 27

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Leveraging unlabelled data for generalizable neural population decoding A Generalist Intracortical Motor Decoder

Reference 28

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Leveraging unlabelled data for generalizable neural population decoding Sharing neurophysiology data from the Allen Brain Observa- tory

Reference 29

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Leveraging unlabelled data for generalizable neural population decoding Survey of spiking in the mouse visual system reveals functional hierarchy

Reference 30

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Leveraging unlabelled data for generalizable neural population decoding Transcriptomic cell type structures in vivo neuronal activity across multiple timescales

Reference 31

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Leveraging unlabelled data for generalizable neural population decoding Reproducibility of in vivo electrophysiological measurements in mice

Reference 32

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Leveraging unlabelled data for generalizable neural population decoding Functional organization of human sensori- motor cortex for speech articulation

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Leveraging unlabelled data for generalizable neural population decoding Human ECoG speaking consonant-vowel syllables

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Leveraging unlabelled data for generalizable neural population decoding Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals

Reference 35

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Leveraging unlabelled data for generalizable neural population decoding A New Approach to Linear Filtering and Prediction Problems

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Leveraging unlabelled data for generalizable neural population decoding Neural Decoding of Cursor Motion Using a Kalman Filter

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Leveraging unlabelled data for generalizable neural population decoding Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control

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Leveraging unlabelled data for generalizable neural population decoding Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model

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Leveraging unlabelled data for generalizable neural population decoding A recurrent neural network for closed-loop intracortical brain–machine interface decoders

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Leveraging unlabelled data for generalizable neural population decoding A Recurrent Latent Variable Model for Sequential Data

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Leveraging unlabelled data for generalizable neural population decoding Inferring single-trial neural population dynamics using sequential auto-encoders

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Leveraging unlabelled data for generalizable neural population decoding Efficiently Modeling Long Sequences with Structured State Spaces

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Leveraging unlabelled data for generalizable neural population decoding Towards a

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Leveraging unlabelled data for generalizable neural population decoding Know Thyself by Knowing Others: Learning Neuron Identity from Population Context

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Leveraging unlabelled data for generalizable neural population decoding Long-term recordings of motor and premotor cortical spiking activity during reaching in monkeys

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Leveraging unlabelled data for generalizable neural population decoding Accurate decoding of reaching movements from field potentials in the absence of spikes

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Leveraging unlabelled data for generalizable neural population decoding Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology

Reference 48

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Leveraging unlabelled data for generalizable neural population decoding Neural population dynamics during reaching

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Leveraging unlabelled data for generalizable neural population decoding Neural Latents Benchmark ‘21: Evaluating latent variable models of neural population activity

Reference 50

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Leveraging unlabelled data for generalizable neural population decoding RoFormer: Enhanced transformer with Rotary Position Embedding

Reference 51

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Leveraging unlabelled data for generalizable neural population decoding Perceiver IO: A General Architecture for Structured Inputs & Outputs

Reference 52

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Leveraging unlabelled data for generalizable neural population decoding TorchEEGEMO: A deep learning toolbox towards EEG-based emotion recognition

Reference 53

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Leveraging unlabelled data for generalizable neural population decoding Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Reference 54

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Leveraging unlabelled data for generalizable neural population decoding Decoupled Weight Decay Regularization

Reference 55

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Leveraging unlabelled data for generalizable neural population decoding xLSTM: Extended Long Short-Term Memory

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Leveraging unlabelled data for generalizable neural population decoding Representation Learning with Contrastive Predictive Coding

Reference 57

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Leveraging unlabelled data for generalizable neural population decoding High-performance brain-to-text communication via handwriting

Reference 58

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Leveraging unlabelled data for generalizable neural population decoding neural unit

Reference 59

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Pith citing papers

Observation 76e028d8-1b5c-4cbe-8dfa-48f14be1e455 · inbound

NeuroPB: Scaling Neural Decoding with Pretrained Behavioral Representations cites this paper.

NeuroPB: Scaling Neural Decoding with Pretrained Behavioral Representations Leveraging unlabelled data for generalizable neural population decoding

Reference 33

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T18:29:22.301224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.