# Linking fast and slow: the case for generative models

arXiv: https://arxiv.org/abs/2308.10618 · HTML: https://mlsanity.com/paper/2308.10618/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2023.

## Built on by (most cited first, 3 of 3)

- [SUNTA: Hierarchical Video Prediction with Surprise-based Chunking](https://mlsanity.com/paper/2607.02087/) (2026)
- [BSD: a Bayesian framework for parametric models of neural spectra](https://mlsanity.com/paper/2410.20896/) (2024)
- [Dissociating model architectures from inference computations](https://mlsanity.com/paper/2507.15776/) (2025)

## Abstract (arXiv)

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multi-scale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.