# Graph neural networks uncover structure and functions underlying the activity of simulated neural assemblies

arXiv: https://arxiv.org/abs/2602.13325 · HTML: https://mlsanity.com/paper/2602.13325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish](https://mlsanity.com/paper/2503.02618/) (2025)
- [Dynamics of Random Neural Networks with Bistable Units](https://mlsanity.com/paper/1409.2535/) (2014)
- [Decomposing heterogeneous dynamical systems with graph neural networks](https://mlsanity.com/paper/2407.19160/) (2024)

## Abstract (arXiv)

Graph neural networks trained to predict observable dynamics can be used to decompose the temporal activity of complex heterogeneous systems into simple, interpretable representations. Here we apply this framework to simulated neural assemblies with thousands of neurons and demonstrate that it can jointly reveal the connectivity matrix, the neuron types, the signaling functions, and in some cases hidden external stimuli. In contrast to existing machine learning approaches such as recurrent neural networks and transformers, which emphasize predictive accuracy but offer limited interpretability, our method provides both reliable forecasts of neural activity and interpretable decomposition of the mechanisms governing large neural assemblies.