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

2026-02-11

AI Summary

This study addresses the fundamental challenge of deciphering the underlying structural and functional mechanisms—such as connectivity matrices, neuronal cell types, signaling dynamics, and latent external stimuli—from the complex spatiotemporal activity of heterogeneous neural systems. To this end, the authors propose an interpretable modeling framework based on graph neural networks that integrates neural dynamics simulation with graph structure learning. By doing so, the method overcomes the limitations of conventional black-box models while maintaining high predictive accuracy. Notably, it achieves the first joint inference of connectivity architecture, cell-type identity, signaling mechanisms, and hidden stimuli in large-scale simulated neural ensembles. The approach successfully reconstructs ground-truth connectivity matrices, neuronal types, and signal transmission functions at the scale of thousands of neurons and, in certain scenarios, accurately identifies unknown external inputs.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• graph neural networks
• neural assemblies
• interpretable representations
• connectivity matrix
• neural dynamics

Innovation

• graph neural networks
• interpretable representation
• neural assemblies
• connectivity inference
• dynamical systems