# Meta-Dynamical State Space Models for Integrative Neural Data Analysis

arXiv: https://arxiv.org/abs/2410.05454 · HTML: https://mlsanity.com/paper/2410.05454/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 21 of 21)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Deep Kalman Filters](https://mlsanity.com/paper/1511.05121/) (2015)
- [Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data](https://mlsanity.com/paper/1605.06432/) (2016)
- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [Variational Sequential Monte Carlo](https://mlsanity.com/paper/1705.11140/) (2017)
- [Universality and individuality in neural dynamics across large populations of recurrent networks](https://mlsanity.com/paper/1907.08549/) (2019)
- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Generalizing to New Physical Systems via Context-Informed Dynamics Model](https://mlsanity.com/paper/2202.01889/) (2022)
- [LEADS: Learning Dynamical Systems that Generalize Across Environments](https://mlsanity.com/paper/2106.04546/) (2021)
- [Meta-Learning Dynamics Forecasting Using Task Inference](https://mlsanity.com/paper/2102.10271/) (2021)
- [Low Tensor Rank Learning of Neural Dynamics](https://mlsanity.com/paper/2308.11567/) (2023)
- [lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems](https://mlsanity.com/paper/2309.01230/) (2023)
- [Analyzing Populations of Neural Networks via Dynamical Model Embedding](https://mlsanity.com/paper/2302.14078/) (2023)
- [eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling](https://mlsanity.com/paper/2403.01371/) (2024)
- [Efficient and robust multi-task learning in the brain with modular latent primitives](https://mlsanity.com/paper/2105.14108/) (2021)
- [Gradient-based inference of abstract task representations for generalization in neural networks](https://mlsanity.com/paper/2407.17356/) (2024)
- [Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments](https://mlsanity.com/paper/2409.01280/) (2024)

## Built on by (most cited first, 8 of 8)

- [Generalizable, real-time neural decoding with hybrid state-space models](https://mlsanity.com/paper/2506.05320/) (2025)
- [InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics](https://mlsanity.com/paper/2510.25943/) (2025)
- [JEDI: Jointly Embedded Inference of Neural Dynamics](https://mlsanity.com/paper/2603.10489/) (2026)
- [Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects](https://mlsanity.com/paper/2603.14161/) (2026)
- [Integrative neurocybernetic modeling in the era of large-scale neuroscience](https://mlsanity.com/paper/2604.23903/) (2026)
- [Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction](https://mlsanity.com/paper/2606.22969/) (2026)
- [A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems](https://mlsanity.com/paper/2607.14937/) (2026)
- [Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings](https://mlsanity.com/paper/2610.03065/) (2026)

## Abstract (arXiv)

Learning shared structure across environments facilitates rapid learning and adaptive behavior in neural systems. This has been widely demonstrated and applied in machine learning to train models that are capable of generalizing to novel settings. However, there has been limited work exploiting the shared structure in neural activity during similar tasks for learning latent dynamics from neural recordings. Existing approaches are designed to infer dynamics from a single dataset and cannot be readily adapted to account for statistical heterogeneities across recordings. In this work, we hypothesize that similar tasks admit a corresponding family of related solutions and propose a novel approach for meta-learning this solution space from task-related neural activity of trained animals. Specifically, we capture the variabilities across recordings on a low-dimensional manifold which concisely parametrizes this family of dynamics, thereby facilitating rapid learning of latent dynamics given new recordings. We demonstrate the efficacy of our approach on few-shot reconstruction and forecasting of synthetic dynamical systems, and neural recordings from the motor cortex during different arm reaching tasks.