# LFADS - Latent Factor Analysis via Dynamical Systems

arXiv: https://arxiv.org/abs/1608.06315 · HTML: https://mlsanity.com/paper/1608.06315/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 30 of 30)

- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [Out-of-Domain Generalization in Dynamical Systems Reconstruction](https://mlsanity.com/paper/2402.18377/) (2024)
- [Diagnosing and Fixing Manifold Overfitting in Deep Generative Models](https://mlsanity.com/paper/2204.07172/) (2022)
- [Neural decoding from stereotactic EEG: accounting for electrode variability across subjects](https://mlsanity.com/paper/2411.10458/) (2024)
- [Latent Diffusion for Neural Spiking Data](https://mlsanity.com/paper/2407.08751/) (2024)
- [Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data](https://mlsanity.com/paper/2311.00136/) (2023)
- [lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems](https://mlsanity.com/paper/2309.01230/) (2023)
- [Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models](https://mlsanity.com/paper/2306.06138/) (2023)
- [POCO: Scalable Neural Forecasting through Population Conditioning](https://mlsanity.com/paper/2506.14957/) (2025)
- [Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time](https://mlsanity.com/paper/2111.00070/) (2021)
- [Expressive architectures enhance interpretability of dynamics-based neural population models](https://mlsanity.com/paper/2212.03771/) (2022)
- [Fitting summary statistics of neural data with a differentiable spiking network simulator](https://mlsanity.com/paper/2106.10064/) (2021)
- [Phase2vec: Dynamical systems embedding with a physics-informed convolutional network](https://mlsanity.com/paper/2212.03857/) (2022)
- [Recurrences reveal shared causal drivers of complex time series](https://mlsanity.com/paper/2301.13516/) (2023)
- [Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets](https://mlsanity.com/paper/2510.11924/) (2025)
- [Self-Supervised Dynamical System Representations for Physiological Time-Series](https://mlsanity.com/paper/2512.00239/) (2025)
- [TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation](https://mlsanity.com/paper/2304.13742/) (2023)
- [DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics](https://mlsanity.com/paper/2306.03088/) (2023)
- [Energy-based Autoregressive Generation for Neural Population Dynamics](https://mlsanity.com/paper/2511.17606/) (2025)
- [Learning low-dimensional dynamics from whole-brain data improves task capture](https://mlsanity.com/paper/2305.14369/) (2023)
- [Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes](https://mlsanity.com/paper/2407.21195/) (2024)
- [Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors](https://mlsanity.com/paper/2410.16136/) (2024)
- [FPGA Deployment of LFADS for Real-time Neuroscience Experiments](https://mlsanity.com/paper/2402.04274/) (2024)
- [Langevin Flows for Modeling Neural Latent Dynamics](https://mlsanity.com/paper/2507.11531/) (2025)
- [A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks](https://mlsanity.com/paper/2510.20683/) (2025)
- [Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior](https://mlsanity.com/paper/2603.05612/) (2026)
- [CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data](https://mlsanity.com/paper/2604.04958/) (2026)
- [Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics](https://mlsanity.com/paper/2606.01468/) (2026)
- [Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics](https://mlsanity.com/paper/2608.16569/) (2026)
- [TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series](https://mlsanity.com/paper/2610.19557/) (2026)

## 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.