Machine Learning Methods for Studying Latent Neural Activity Dynamics | Paper Citation Record

Observation 4e30a17f-8e41-4864-923c-8bcdcc94056a · outbound

This paper cites Multiscale low-dimensional mo- tor cortical state dynamics predict naturalistic reach-and-grasp behavior.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Multiscale low-dimensional mo- tor cortical state dynamics predict naturalistic reach-and-grasp behavior.Nat

Reference 1

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This paper cites A brain-wide map of neural ac- tivity during complex behaviour.Nature, 645(8079):177–191,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics A brain-wide map of neural ac- tivity during complex behaviour.Nature, 645(8079):177–191,

Reference 2

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This paper cites A unified, scalable framework for neural population decoding.NeurIPS, 36:44937–44956,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics A unified, scalable framework for neural population decoding.NeurIPS, 36:44937–44956,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-22T01:22:11.906751Z

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Observation 534a310a-dbe3-417d-b0bb-a6b34511b4f8 · outbound

This paper cites Neural latent aligner: cross-trial alignment for learning representations of complex, naturalistic neural data.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Neural latent aligner: cross-trial alignment for learning representations of complex, naturalistic neural data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Geometry linked to untangling efficiency reveals structure and computation in neural populations.bioRxiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Geometry linked to untangling efficiency reveals structure and computation in neural populations.bioRxiv,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex.Na- ture neuroscience, 23(1):138–151,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex.Na- ture neuroscience, 23(1):138–151,

Reference 7

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This paper cites full-force: A target- based method for training recurrent networks.PloS one, 13(2):e0191527,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics full-force: A target- based method for training recurrent networks.PloS one, 13(2):e0191527,

Reference 8

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This paper cites Nonlinear multiregion neural dynamics with parametric impulse response communication channels.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Nonlinear multiregion neural dynamics with parametric impulse response communication channels

Reference 9

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This paper cites A theory of multineuronal dimensionality, dynamics and measure- ment.BioRxiv, page 214262,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics A theory of multineuronal dimensionality, dynamics and measure- ment.BioRxiv, page 214262,

Reference 10

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This paper cites Energy-based autoregressive generation for neural population dynamics.Proceedings of the AAAI Conference on Artificial In- telligence, 40(1):309–317, Mar.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Energy-based autoregressive generation for neural population dynamics.Proceedings of the AAAI Conference on Artificial In- telligence, 40(1):309–317, Mar

Reference 11

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Observation 8a9dd4d3-b207-4341-81a0-d2f720f15b5f · outbound

This paper cites Recurrent switching dynam- ical systems models for multiple interacting neural populations.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Recurrent switching dynam- ical systems models for multiple interacting neural populations

Reference 12

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Observation 9e10d947-fe5d-4b4a-a111-a645d772e82c · outbound

This paper cites Disentangling the flow of signals between populations of neurons.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Disentangling the flow of signals between populations of neurons.Nat

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Uncovering motifs of concurrent signal- ing across multiple neuronal populations.NeurIPS, 36:34711– 34722,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Uncovering motifs of concurrent signal- ing across multiple neuronal populations.NeurIPS, 36:34711– 34722,

Reference 14

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This paper cites Self-supervised contrastive learning performs non-linear system identification.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Self-supervised contrastive learning performs non-linear system identification

Reference 15

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This paper cites Marble: interpretable rep- resentations of neural population dynamics using geometric deep learning.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Marble: interpretable rep- resentations of neural population dynamics using geometric deep learning.Nat

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Between-area communication through the lens of within-area neuronal dynamics.Sci.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Between-area communication through the lens of within-area neuronal dynamics.Sci

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Splice: fully tractable hierarchical extension of ica with pooling.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Splice: fully tractable hierarchical extension of ica with pooling

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Modeling latent neural dynamics with gaus- sian process switching linear dynamical systems.NeurIPS, 37:33805–33835,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Modeling latent neural dynamics with gaus- sian process switching linear dynamical systems.NeurIPS, 37:33805–33835,

Reference 19

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This paper cites Disentangling the roles of distinct cell classes with cell-type dynamical systems.NeurIPS, 37:33668–33690,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Disentangling the roles of distinct cell classes with cell-type dynamical systems.NeurIPS, 37:33668–33690,

Reference 20

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This paper cites Robust alignment of cross-session record- ings of neural population activity by behaviour via unsupervised domain adaptation.arXiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Robust alignment of cross-session record- ings of neural population activity by behaviour via unsupervised domain adaptation.arXiv,

Reference 21

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This paper cites Latent diffusion for neural spiking data.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Latent diffusion for neural spiking data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Modeling state- dependent communication between brain regions with switching nonlinear dynamical systems.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Modeling state- dependent communication between brain regions with switching nonlinear dynamical systems

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 8c657082-5252-4e09-ad1d-6008ad9db30b · outbound

This paper cites Identifying signal and noise structure in neural pop- ulation activity with gaussian process factor models.NeurIPS, 33:13795–13805,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Identifying signal and noise structure in neural pop- ulation activity with gaussian process factor models.NeurIPS, 33:13795–13805,

Reference 24

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This paper cites A large-scale neural network training framework for generalized estimation of single-trial population dynamics.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics A large-scale neural network training framework for generalized estimation of single-trial population dynamics.Nat

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Variational autoencoders and nonlinear ica: A unifying framework.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Variational autoencoders and nonlinear ica: A unifying framework

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Inferring latent dynamics underlying neural population activity via neural differential equations.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Inferring latent dynamics underlying neural population activity via neural differential equations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Flow-field inference from neural data using deep recurrent networks.bioRxiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Flow-field inference from neural data using deep recurrent networks.bioRxiv,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Multi-region markovian gaussian process: An efficient method to discover directional communications across multiple brain re- gions.PMLR, 235:28112,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Multi-region markovian gaussian process: An efficient method to discover directional communications across multiple brain re- gions.PMLR, 235:28112,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 4c6bf106-a938-4c74-9e84-57f28a5061b2 · outbound

This paper cites Learning time-varying multi-region brain communications via scalable markovian gaussian processes.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Learning time-varying multi-region brain communications via scalable markovian gaussian processes

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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

Machine Learning Methods for Studying Latent Neural Activity Dynamics Bayesian learning and inference in recurrent switching linear dynamical systems

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites an unresolved cited work.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Unresolved cited work

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 1120747a-3cac-4a24-aa62-8b89cfb049ac · outbound

This paper cites Multiplexed subspaces route neural activity across brain-wide networks.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Multiplexed subspaces route neural activity across brain-wide networks.Nat

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 9212a717-36e0-47aa-9798-bad7eba12055 · outbound

This paper cites Empirical models of spiking in neural populations.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Empirical models of spiking in neural populations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 3ed955f6-3296-4fe0-9c89-cb4bba09f95b · outbound

This paper cites Diffusion-based genera- tion of neural activity from disentangled latent codes.ArXiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Diffusion-based genera- tion of neural activity from disentangled latent codes.ArXiv,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites CREIMBO: Cross-regional ensemble in- teractions in multi-view brain observations.

Machine Learning Methods for Studying Latent Neural Activity Dynamics CREIMBO: Cross-regional ensemble in- teractions in multi-view brain observations

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Inferring stochastic low-rank recurrent neural networks from neural data.NeurIPS, 37:18225–18264,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Inferring stochastic low-rank recurrent neural networks from neural data.NeurIPS, 37:18225–18264,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Generative models of brain dynamics.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Generative models of brain dynamics

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 2d289a1e-908e-4de7-9ec8-39afbd394baa · outbound

This paper cites Inferring single-trial neural popula- tion dynamics using sequential auto-encoders.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Inferring single-trial neural popula- tion dynamics using sequential auto-encoders.Nat

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 3b31c434-65af-47a7-8424-586e41aafad5 · outbound

This paper cites Neural latents benchmark’21: evaluating latent variable models of neural population activity.arXiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Neural latents benchmark’21: evaluating latent variable models of neural population activity.arXiv,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Re- thinking brain-wide interactions through multi-region ‘network of networks’ models.Curr.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Re- thinking brain-wide interactions through multi-region ‘network of networks’ models.Curr

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation feb335d9-c45c-4668-bd82-f754469f909a · outbound

This paper cites A neural manifold view of the brain.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics A neural manifold view of the brain.Nat

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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

Machine Learning Methods for Studying Latent Neural Activity Dynamics Generalizable, real-time neural decoding with hybrid state-space models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 47afe3a9-e967-4f15-8c35-f5a7f8a59fc1 · outbound

This paper cites Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.Nat

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation b51d1f17-4085-4e79-a0b3-fbedc0de9784 · outbound

This paper cites Learnable latent embeddings for joint behavioural and neural analysis.Nature, 617(7960):360– 368,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Learnable latent embeddings for joint behavioural and neural analysis.Nature, 617(7960):360– 368,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation b6ad09ff-9e44-41a6-9d40-55569a4a6a96 · outbound

This paper cites Modeling conditional distributions of neu- ral and behavioral data with masked variational autoencoders.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Modeling conditional distributions of neu- ral and behavioral data with masked variational autoencoders

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation 9b149ffd-1079-464e-a0b0-8c64c4137bf1 · outbound

This paper cites Feedforward and feedback in- teractions between visual cortical areas use different population activity patterns.Nat.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Feedforward and feedback in- teractions between visual cortical areas use different population activity patterns.Nat

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Survey of spiking in the mouse visual system reveals functional hierarchy.Nature, 592(7852):86–92,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Survey of spiking in the mouse visual system reveals functional hierarchy.Nature, 592(7852):86–92,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Anderson Keller, Yisong Yue, Pietro Perona, and Max Welling.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Anderson Keller, Yisong Yue, Pietro Perona, and Max Welling

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Training biologically plausible recurrent neural net- works on cognitive tasks with long-term dependencies.NeurIPS, 36:32061–32074,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Training biologically plausible recurrent neural net- works on cognitive tasks with long-term dependencies.NeurIPS, 36:32061–32074,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation c859f1de-4fa9-47e7-b65c-ca0ece34bb5c · outbound

This paper cites Disentangling shared and private neural dynamics with spire.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Disentangling shared and private neural dynamics with spire

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-07-03T03:57:38.288166Z

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Observation ad36ea20-c7c5-4fc5-a953-7d5ff3a1f552 · outbound

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

Machine Learning Methods for Studying Latent Neural Activity Dynamics Large-scale neural recordings call for new insights to link brain and behavior.Nat

Reference 53

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Modeling and dissociation of intrinsic and input- driven neural population dynamics underlying behavior.PNAS, 121(7):e2212887121,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Modeling and dissociation of intrinsic and input- driven neural population dynamics underlying behavior.PNAS, 121(7):e2212887121,

Reference 54

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Observation 145a3e63-a303-47a6-97fc-173b0135f48f · outbound

This paper cites Braid: Input-driven nonlinear dynamical modeling of neural-behavioral data.arXiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Braid: Input-driven nonlinear dynamical modeling of neural-behavioral data.arXiv,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation bd092306-40ac-43b0-b3d6-0be882f2cc34 · outbound

This paper cites Extracting computational mechanisms from neural data using low-rank rnns.NeurIPS, 35:24072–24086,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Extracting computational mechanisms from neural data using low-rank rnns.NeurIPS, 35:24072–24086,

Reference 56

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Observation 13bbb0a8-4e89-4b15-a6d0-896fd7e4928c · outbound

This paper cites Expressive dy- namics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity.arXiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Expressive dy- namics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity.arXiv,

Reference 57

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This paper cites Exploring behavior-relevant and disentangled neural dy- namics with generative diffusion models.NeurIPS, 37:34712– 34736,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Exploring behavior-relevant and disentangled neural dy- namics with generative diffusion models.NeurIPS, 37:34712– 34736,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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Observation f9538a05-579b-45d4-8f57-6e7e1d1c5b06 · outbound

This paper cites Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Gaussian process based nonlinear latent structure discovery in multivariate spike train data.Advances in neural information processing systems, 30,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Gaussian process based nonlinear latent structure discovery in multivariate spike train data.Advances in neural information processing systems, 30,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Identifying in- teractions across brain areas while accounting for individual- neuron dynamics with a transformer-based variational autoen- coder.arXiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Identifying in- teractions across brain areas while accounting for individual- neuron dynamics with a transformer-based variational autoen- coder.arXiv,

Reference 61

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Observation f199533b-396d-4f9b-a4ff-2c5541f98f21 · outbound

This paper cites Rep- resentation learning for neural population activity with neural data transformers.arXiv,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Rep- resentation learning for neural population activity with neural data transformers.arXiv,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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

Machine Learning Methods for Studying Latent Neural Activity Dynamics Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population ac- tivity

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites In- ference of neural dynamics using switching recurrent neural net- works.NeurIPS, 37:131456–131481,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics In- ference of neural dynamics using switching recurrent neural net- works.NeurIPS, 37:131456–131481,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites universal translator.

Machine Learning Methods for Studying Latent Neural Activity Dynamics universal translator

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Neural encoding and decoding at scale.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Neural encoding and decoding at scale

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites Varia- tional latent gaussian process for recovering single-trial dynam- ics from population spike trains.Neural Comput., 29(5):1293– 1316,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Varia- tional latent gaussian process for recovering single-trial dynam- ics from population spike trains.Neural Comput., 29(5):1293– 1316,

Reference 67

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This paper cites Brain foundation models: A survey on advancements in neural signal processing and brain discovery.IEEE Signal Processing Magazine,.

Machine Learning Methods for Studying Latent Neural Activity Dynamics Brain foundation models: A survey on advancements in neural signal processing and brain discovery.IEEE Signal Processing Magazine,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-27T14:23:43.529547Z

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This paper cites planning.

Machine Learning Methods for Studying Latent Neural Activity Dynamics planning

Reference 69

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