mlsanityData to 7 Oct 2026

Building population models for large-scale neural recordings: opportunities and pitfalls

arXiv 2102.01807 ↗First posted 3 Feb 2021q-bio.NC cs.LGcited by 9

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Abstract (arXiv)

Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 121 references that are in the map. First posted on arXiv 3 Feb 2021.

Builds on

17 of 121 references.

References · 17

  1. 2020Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAEcited by 48
  2. 2016Linear dynamical neural population models through nonlinear embeddingscited by 36
  3. 2016A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurementscited by 24
  4. 2016Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trainscited by 22
  5. 2014Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameterscited by 15
  6. 2019Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networkscited by 13
  7. 2016Interpretable Nonlinear Dynamic Modeling of Neural Trajectoriescited by 6
  8. 2019LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximationscited by 3
  9. 2020A new inference approach for training shallow and deep generalized linear models of noisy interacting neuronscited by 3
  10. 2014Correlations and functional connections in a population of grid cellscited by 2
  11. 2019A study of dependency features of spike trains through copulascited by 2
  12. 2019Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inferencecited by 2
5 more
  1. 2019Inference of a mesoscopic population model from population spike trainscited by 2
  2. 2020Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationshipscited by 2
  3. 2016A tractable method for describing complex couplings between neurons and population ratecited by 1
  4. 2017Predicting how and when hidden neurons skew measured synaptic interactionscited by 1
  5. 2018Scaling Spike Detection and Sorting for Next Generation Electrophysiologycited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 3; 2025: 1.

9 papers.

  1. 2023A Unified, Scalable Framework for Neural Population Decodingcited by 59
  2. 2021Neural Latents Benchmark '21: Evaluating latent variable models of neural population activitycited by 57
  3. 2025Neural Encoding and Decoding at Scalecited by 17
  4. 2022Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptationcited by 13
  5. 2024Self-supervised contrastive learning performs non-linear system identificationcited by 9
  6. 2022Expressive architectures enhance interpretability of dynamics-based neural population modelscited by 6
  7. 2024Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systemscited by 5
  8. 2024Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equationscited by 4
  9. 2022Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensemblescited by 1