NLP4Neuro: Sequence-to-sequence learning for neural population decoding | Paper Citation Record

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This paper cites https://www.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding https://www

Reference 1

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This paper cites Neuronal dynamics regulating brain and behavioral state transitions.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Neuronal dynamics regulating brain and behavioral state transitions

Reference 2

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Observation 4b40fcfc-28f3-44a1-8a6d-198b1f7a8667 · outbound

This paper cites Pretectal neurons control hunting behaviour.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Pretectal neurons control hunting behaviour

Reference 3

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

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Multi-session, multi-task neural decoding from distinct cell-types and brain regions

Reference 4

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This paper cites Population transformer.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Population transformer

Reference 5

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This paper cites Elegans-AI: How the connectome of a living organism could model artificial neural networks.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Elegans-AI: How the connectome of a living organism could model artificial neural networks

Reference 6

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This paper cites Laplacian eigenmaps for dimensionality reduction and data representation.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Laplacian eigenmaps for dimensionality reduction and data representation

Reference 7

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This paper cites MeLM, a generative pretrained language modeling framework that solves for- ward and inverse mechanics problems.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding MeLM, a generative pretrained language modeling framework that solves for- ward and inverse mechanics problems

Reference 8

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Observation 101dc1d0-bbd7-4788-bee6-43d28b8f6df8 · outbound

This paper cites Population transformer: Learning population- level representations of neural activity.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Population transformer: Learning population- level representations of neural activity

Reference 9

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This paper cites PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling

Reference 10

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This paper cites Visual control of walking speed in drosophila.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Visual control of walking speed in drosophila

Reference 11

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This paper cites Bridging the gap between the connectome and whole-brain activity in C.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Bridging the gap between the connectome and whole-brain activity in C

Reference 12

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This paper cites Sparse autoencoders find highly interpretable features in language models.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Sparse autoencoders find highly interpretable features in language models

Reference 13

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This paper cites Extraction of salient sentences from labelled documents.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Extraction of salient sentences from labelled documents

Reference 14

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This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 15

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This paper cites Exploring deep learning models for EEG neural decoding.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Exploring deep learning models for EEG neural decoding

Reference 16

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This paper cites Effects of connectivity on narrative temporal processing in structured reservoir computing.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Effects of connectivity on narrative temporal processing in structured reservoir computing

Reference 17

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This paper cites Neural circuits underlying divergent visuomotor strategies of zebrafish and danionella cerebrum.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Neural circuits underlying divergent visuomotor strategies of zebrafish and danionella cerebrum

Reference 18

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This paper cites Next generation reservoir computing.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Next generation reservoir computing

Reference 19

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This paper cites CaImAn an open source tool for scalable calcium imaging data analysis.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding CaImAn an open source tool for scalable calcium imaging data analysis

Reference 20

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This paper cites Behavioral assessment of visual function via optomotor response and cognitive function via Y-maze in diabetic rats.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Behavioral assessment of visual function via optomotor response and cognitive function via Y-maze in diabetic rats

Reference 21

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This paper cites DeepSeek-coder: When the large language model meets programming – the rise of code intelligence.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding DeepSeek-coder: When the large language model meets programming – the rise of code intelligence

Reference 22

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This paper cites A brain-wide circuit model of heat-evoked swimming behavior in larval zebrafish.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding A brain-wide circuit model of heat-evoked swimming behavior in larval zebrafish

Reference 23

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This paper cites Decoding layer saliency in language transformers.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Decoding layer saliency in language transformers

Reference 24

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This paper cites Long short-term memory-based neural decoding of object categories evoked by natural images.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Long short-term memory-based neural decoding of object categories evoked by natural images

Reference 25

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This paper cites Adam: A method for stochastic optimization.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Adam: A method for stochastic optimization

Reference 26

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This paper cites Connectome-constrained networks predict neural activity across the fly visual system.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Connectome-constrained networks predict neural activity across the fly visual system

Reference 27

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This paper cites Do emergent abilities exist in quantized large language models: An empirical study.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Do emergent abilities exist in quantized large language models: An empirical study

Reference 28

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NLP4Neuro: Sequence-to-sequence learning for neural population decoding Decoupled weight decay regularization

Reference 29

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This paper cites ZAPBench: A benchmark for whole-brain activity prediction in zebrafish.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding ZAPBench: A benchmark for whole-brain activity prediction in zebrafish

Reference 30

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This paper cites Decoding the brain: From neural representations to mechanistic models.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Decoding the brain: From neural representations to mechanistic models

Reference 31

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This paper cites Connectomes inform function: from time-varying dynamics to animal behaviour.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Connectomes inform function: from time-varying dynamics to animal behaviour

Reference 32

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This paper cites Harnessing behavioral diversity to understand neural computations for cognition.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Harnessing behavioral diversity to understand neural computations for cognition

Reference 33

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This paper cites From whole-brain data to functional circuit models: The zebrafish optomotor response.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding From whole-brain data to functional circuit models: The zebrafish optomotor response

Reference 34

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NLP4Neuro: Sequence-to-sequence learning for neural population decoding Unresolved cited work

Reference 35

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This paper cites Suite2p: beyond 10,000 neurons with standard two-photon microscopy.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Suite2p: beyond 10,000 neurons with standard two-photon microscopy

Reference 36

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This paper cites PyTorch: An imperative style, high-performance deep learning library.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding PyTorch: An imperative style, high-performance deep learning library

Reference 37

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This paper cites Language models are unsupervised multitask learners.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Language models are unsupervised multitask learners

Reference 38

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This paper cites Scaling language models: Methods, analysis & insights from training gopher.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Scaling language models: Methods, analysis & insights from training gopher

Reference 39

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This paper cites Is attention interpretable? arXiv [cs.CL], June 2019.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Is attention interpretable? arXiv [cs.CL], June 2019

Reference 40

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This paper cites Self-attention with relative position repre- sentations.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Self-attention with relative position repre- sentations

Reference 41

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This paper cites Comparative analysis of neural decoding algorithms for brain-machine interfaces.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Comparative analysis of neural decoding algorithms for brain-machine interfaces

Reference 42

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This paper cites Optimization of optomotor response-based visual function assessment in mice.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Optimization of optomotor response-based visual function assessment in mice

Reference 43

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This paper cites Deep inside convolutional networks: Visualising image classification models and saliency maps.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Deep inside convolutional networks: Visualising image classification models and saliency maps

Reference 44

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This paper cites Applications of brain-computer interfaces in neurodegenerative diseases.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Applications of brain-computer interfaces in neurodegenerative diseases

Reference 45

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This paper cites A connectome based hexagonal lattice convolutional network model of the drosophila visual system.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding A connectome based hexagonal lattice convolutional network model of the drosophila visual system

Reference 46

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Observation d5b47956-2506-46ce-a24d-e9e97bd10a38 · outbound

This paper cites Large-scale neural recordings call for new insights to link brain and behavior.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Large-scale neural recordings call for new insights to link brain and behavior

Reference 47

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Observation c5b9c87f-d7a2-4bc8-8b31-04f601fe55a7 · outbound

This paper cites Attention is all you need.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Attention is all you need

Reference 48

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Observation 9d5d30de-cf28-4fa3-9fc5-51e5fa6a838e · outbound

This paper cites Computation through neural population dynamics.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Computation through neural population dynamics

Reference 49

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Observation 53c3cb40-e02b-4f34-a985-b1514a950fd8 · outbound

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

NLP4Neuro: Sequence-to-sequence learning for neural population decoding BrainBERT: Self-supervised representation learning for intracranial recordings

Reference 50

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This paper cites Pre-trained language models and their applications.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Pre-trained language models and their applications

Reference 51

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Observation c0623d7f-99ec-4553-8106-c4d5dda62b7c · outbound

This paper cites Learning natural language inference with LSTM.arXiv [cs.CL], December 2015.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Learning natural language inference with LSTM.arXiv [cs.CL], December 2015

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:55.551521Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d8c0cf9e-ea02-447b-a26a-b7a1de9166f2 · outbound

This paper cites Emergent abilities of large language models.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Emergent abilities of large language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:55.344099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bccf392a-c652-4d27-b330-748b6ebbfcad · outbound

This paper cites Nystromformer: A nystrom-based algorithm for approximating self-attention.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Nystromformer: A nystrom-based algorithm for approximating self-attention

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:55.150073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b444855d-7a12-49ef-9201-725d8c1193eb · outbound

This paper cites Representation learning for neural population activity with neural data transformers.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Representation learning for neural population activity with neural data transformers

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:54.931657Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ef4b2d4-29ad-4af7-882f-23c29833d238 · outbound

This paper cites Neural data transformer 2: Multi-context pretraining for neural spiking activity.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Neural data transformer 2: Multi-context pretraining for neural spiking activity

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:54.693738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 515fc600-d9fa-442b-8215-19ce41e125b4 · outbound

This paper cites Generalizable LLM learning of graph synthetic data with reinforcement learning.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Generalizable LLM learning of graph synthetic data with reinforcement learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:54.505654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 295bc953-b788-45ed-81ec-113619cf177c · outbound

This paper cites universal translator.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding universal translator

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:54.266041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d25b4b62-0a68-48ce-bdfb-13b1c255e87a · outbound

This paper cites MLPST: MLP is all you need for spatio-temporal prediction.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding MLPST: MLP is all you need for spatio-temporal prediction

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:54.050419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44335948-45dc-422f-8225-98872a6cf483 · outbound

This paper cites Stytra: An open-source, integrated system for stimulation, tracking and closed-loop behavioral experiments.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Stytra: An open-source, integrated system for stimulation, tracking and closed-loop behavioral experiments

Reference 60

Resolution: verified exact; doi, observed 2026-08-06T20:38:53.836954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1925914e-0701-4e83-bde4-c3fccfef9553 · outbound

This paper cites an unresolved cited work.

NLP4Neuro: Sequence-to-sequence learning for neural population decoding Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:38:53.591302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5509b34a-dc68-4761-bd76-45fa1beb3552 · outbound

This paper cites u′(t) = LayerNorm u(t) + z(t).

NLP4Neuro: Sequence-to-sequence learning for neural population decoding u′(t) = LayerNorm u(t) + z(t)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:38:53.405847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3370c7ae-514a-4f6c-a0d1-63940f65460c · outbound

This paper cites In BERT, the gaussian error linear unit, or GELU activation function is used, as described in [15].

NLP4Neuro: Sequence-to-sequence learning for neural population decoding In BERT, the gaussian error linear unit, or GELU activation function is used, as described in [15]

Reference 63

Resolution: verified exact; doi, observed 2026-08-06T20:38:53.233795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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