mlsanityData to 7 Oct 2026

LFADS - Latent Factor Analysis via Dynamical Systems

arXiv 1608.06315 ↗First posted 22 Aug 2016cs.LG q-bio.NC stat.MLcited by 29

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Abstract (arXiv)

Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneously recorded, single-trial, high-dimensional neural spiking data. LFADS is a sequential model based on a variational auto-encoder. By making a dynamical systems hypothesis regarding the generation of the observed data, LFADS reduces observed spiking to a set of low-dimensional temporal factors, per-trial initial conditions, and inferred inputs. We compare LFADS to existing methods on synthetic data and show that it significantly out-performs them in inferring neural firing rates and latent dynamics.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2016.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 7; 2024: 6; 2025: 6; 2026: 4.

29 papers.

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  1. 2021Representation learning for neural population activity with Neural Data Transformerscited by 48
  2. 2024Out-of-Domain Generalization in Dynamical Systems Reconstructioncited by 33
  3. 2022Diagnosing and Fixing Manifold Overfitting in Deep Generative Modelscited by 28
  4. 2024Neural decoding from stereotactic EEG: accounting for electrode variability across subjectscited by 17
  5. 2024Latent Diffusion for Neural Spiking Datacited by 12
  6. 2023Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Datacited by 10
  7. 2023lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systemscited by 8
  8. 2023Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Modelscited by 7
  9. 2021Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timecited by 6
  10. 2022Expressive architectures enhance interpretability of dynamics-based neural population modelscited by 6
  11. 2025POCO: Scalable Neural Forecasting through Population Conditioningcited by 6
  12. 2021Fitting summary statistics of neural data with a differentiable spiking network simulatorcited by 5
17 more
  1. 2022Phase2vec: Dynamical systems embedding with a physics-informed convolutional networkcited by 5
  2. 2023Recurrences reveal shared causal drivers of complex time seriescited by 5
  3. 2025Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasetscited by 4
  4. 2023TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generationcited by 2
  5. 2023DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamicscited by 2
  6. 2025Energy-based Autoregressive Generation for Neural Population Dynamicscited by 2
  7. 2023Learning low-dimensional dynamics from whole-brain data improves task capturecited by 1
  8. 2024Diffusion-Based Generation of Neural Activity from Disentangled Latent Codescited by 1
  9. 2025Self-Supervised Dynamical System Representations for Physiological Time-Seriescited by 1
  10. 2024FPGA Deployment of LFADS for Real-time Neuroscience Experimentscited by 0
  11. 2024Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factorscited by 0
  12. 2025Langevin Flows for Modeling Neural Latent Dynamicscited by 0
  13. 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networkscited by 0
  14. 2026Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behaviorcited by 0
  15. 2026CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Datacited by 0
  16. 2026Computation-Aware Kalman Filtering with Model Selection for Neural Dynamicscited by 0
  17. 2026Learning Generalizable Reconstruction of High-Dimensional Neural Dynamicscited by 0