# Machine Learning Methods for Studying Latent Neural Activity Dynamics

arXiv: https://arxiv.org/abs/2606.10530 · HTML: https://mlsanity.com/paper/2606.10530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 68 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 30 of 32)

- [Variational Autoencoders and Nonlinear ICA: A Unifying Framework](https://mlsanity.com/paper/1907.04809/) (2019)
- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [Large-scale neural recordings call for new insights to link brain and behavior](https://mlsanity.com/paper/2103.14662/) (2021)
- [Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution](https://mlsanity.com/paper/2407.14668/) (2024)
- [Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains](https://mlsanity.com/paper/1604.03053/) (2016)
- [Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery](https://mlsanity.com/paper/2503.00580/) (2025)
- [Neural Encoding and Decoding at Scale](https://mlsanity.com/paper/2504.08201/) (2025)
- [full-FORCE: A Target-Based Method for Training Recurrent Networks](https://mlsanity.com/paper/1710.03070/) (2017)
- [Inferring stochastic low-rank recurrent neural networks from neural data](https://mlsanity.com/paper/2406.16749/) (2024)
- [Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation](https://mlsanity.com/paper/2202.06159/) (2022)
- [Interpretable statistical representations of neural population dynamics and geometry](https://mlsanity.com/paper/2304.03376/) (2023)
- [Latent Diffusion for Neural Spiking Data](https://mlsanity.com/paper/2407.08751/) (2024)
- [Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems](https://mlsanity.com/paper/2408.03330/) (2024)
- [Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models](https://mlsanity.com/paper/2410.09614/) (2024)
- [Self-supervised contrastive learning performs non-linear system identification](https://mlsanity.com/paper/2410.14673/) (2024)
- [Generalizable, real-time neural decoding with hybrid state-space models](https://mlsanity.com/paper/2506.05320/) (2025)
- [BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data](https://mlsanity.com/paper/2509.18627/) (2025)
- [Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity](https://mlsanity.com/paper/2309.06402/) (2023)
- [Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data](https://mlsanity.com/paper/2308.06443/) (2023)
- [Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions](https://mlsanity.com/paper/2402.02686/) (2024)
- [Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining](https://mlsanity.com/paper/2507.09513/) (2025)
- [CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations](https://mlsanity.com/paper/2405.17395/) (2024)
- [Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes](https://mlsanity.com/paper/2407.00397/) (2024)
- [Accurate identification of communication between multiple interacting neural populations](https://mlsanity.com/paper/2506.19094/) (2025)
- [Energy-based Autoregressive Generation for Neural Population Dynamics](https://mlsanity.com/paper/2511.17606/) (2025)
- [Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes](https://mlsanity.com/paper/2407.21195/) (2024)
- [Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder](https://mlsanity.com/paper/2506.02263/) (2025)

## Abstract (arXiv)

Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons. In this paper, we provide a comprehensive survey that outlines the trajectory of Latent Variable Models (LVMs) from early state-space models to more recent deep generative models. We organize the literature into three closely related domains: (1) Single-Region Latent Dynamics, which includes models such as linear dynamical systems to more complex dynamics represented by Recurrent Neural Networks (RNNs) and Neural Ordinary Differential Equations (ODEs); (2) Multi-Region Communication, which employs probabilistic as well as subspace methods to study how information is transferred across different brain areas considering synaptic propagation delays and network connectivity; and (3) Behavior-Aligned Modeling, which seeks to disentangle neural activity related to task performance from other internal states via supervised or contrastive learning. This survey also includes large-scale neural foundation models, such as Transformers and diffusion models, that rely on large-scale pre-training for optimal performance across subjects. Finally, we conclude and discuss benchmarks, evaluation criteria, and open challenges, such as the ability to identify causal links or directionality of communication, to facilitate future research for bridging interpretable brain dynamics with reliable neural decoding.