# Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching

arXiv: https://arxiv.org/abs/2604.11178 · HTML: https://mlsanity.com/paper/2604.11178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 32 of its 80 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [An Introduction to Deep Generative Modeling](https://mlsanity.com/paper/2103.05180/) (2021)
- [Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway](https://mlsanity.com/paper/1411.6422/) (2014)
- [Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2307.11494/) (2023)
- [Predictive Coding: a Theoretical and Experimental Review](https://mlsanity.com/paper/2107.12979/) (2021)
- [Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2410.03024/) (2024)
- [The Rise of Diffusion Models in Time-Series Forecasting](https://mlsanity.com/paper/2401.03006/) (2024)
- [Trajectory Flow Matching with Applications to Clinical Time Series Modeling](https://mlsanity.com/paper/2410.21154/) (2024)
- [FlowTS: Time Series Generation via Rectified Flow](https://mlsanity.com/paper/2411.07506/) (2024)
- [The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies](https://mlsanity.com/paper/2501.00504/) (2024)
- [TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction](https://mlsanity.com/paper/2507.22229/) (2025)
- [Modeling the dynamics of human brain activity with recurrent neural networks](https://mlsanity.com/paper/1606.03071/) (2016)
- [Generalization in Neural Networks: A Broad Survey](https://mlsanity.com/paper/2209.01610/) (2022)
- [Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data](https://mlsanity.com/paper/2311.00136/) (2023)
- [ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish](https://mlsanity.com/paper/2503.02618/) (2025)
- [Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI](https://mlsanity.com/paper/1910.06950/) (2019)
- [Probabilistic Forecasting via Autoregressive Flow Matching](https://mlsanity.com/paper/2503.10375/) (2025)
- [VIBE: Video-Input Brain Encoder for fMRI Response Modeling](https://mlsanity.com/paper/2507.17958/) (2025)
- [POCO: Scalable Neural Forecasting through Population Conditioning](https://mlsanity.com/paper/2506.14957/) (2025)
- [Universal Differential Equations as a Common Modeling Language for Neuroscience](https://mlsanity.com/paper/2403.14510/) (2024)
- [Predicting Human Brain States with Transformer](https://mlsanity.com/paper/2412.19814/) (2024)
- [Forecasting Whole-Brain Neuronal Activity from Volumetric Video](https://mlsanity.com/paper/2503.00073/) (2025)
- [Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)](https://mlsanity.com/paper/2507.17897/) (2025)
- [Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures](https://mlsanity.com/paper/2112.04266/) (2021)
- [Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations](https://mlsanity.com/paper/2412.12112/) (2024)
- [The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction](https://mlsanity.com/paper/2508.06499/) (2025)

## Abstract (arXiv)

Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neurotechnological applications. Here, we introduce a generative forecasting framework for modeling neural dynamics based on autoregressive flow matching (AFM). Building on recent advances in transport-based generative modeling, our approach probabilistically predicts neural responses at scale from multimodal sensory input. Specifically, we learn the conditional distribution of future neural activity given past neural dynamics and concurrent sensory input, explicitly modeling neural activity as a temporally evolving process in which future states depend on recent neural history. We evaluate our framework on the Algonauts project 2025 challenge functional magnetic resonance imaging dataset using subject-specific models. AFM significantly outperforms both a non-autoregressive flow-matching baseline and the official challenge general linear model baseline in predicting short-term parcel-wise blood oxygenation level-dependent (BOLD) activity, demonstrating improved generalization and widespread cortical prediction performance. Ablation analyses show that access to past BOLD dynamics is a dominant driver of performance, while autoregressive factorization yields consistent, modest gains under short-horizon, context-rich conditions. Together, these findings position autoregressive flow-based generative modeling as an effective approach for short-term probabilistic forecasting of neural dynamics with promising applications in closed-loop neurotechnology.