# Latent Graph Learning in Generative Models of Neural Signals

arXiv: https://arxiv.org/abs/2508.16776 · HTML: https://mlsanity.com/paper/2508.16776/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [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)
- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [Transformers are Graph Neural Networks](https://mlsanity.com/paper/2506.22084/) (2025)
- [Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution](https://mlsanity.com/paper/2407.14668/) (2024)
- [Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data](https://mlsanity.com/paper/2311.00136/) (2023)
- [Inferring network connectivity from event timing patterns](https://mlsanity.com/paper/1803.09974/) (2018)

## Abstract (arXiv)

Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience. Foundation models for large-scale neural data represent shared latent structures of neural signals. However, extracting interpretable latent graph representations in foundation models remains challenging and unsolved. Here we explore latent graph learning in generative models of neural signals. By testing against numerical simulations of neural circuits with known ground-truth connectivity, we evaluate several hypotheses for explaining learned model weights. We discover modest alignment between extracted network representations and the underlying directed graphs and strong alignment in the co-input graph representations. These findings motivate paths towards incorporating graph-based geometric constraints in the construction of large-scale foundation models for neural data.