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

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

URL: http://arxiv.org/abs/2602.13325v1
Date: Wed, 11 Feb 2026 01:59:27 GMT
Title: Graph neural networks uncover structure and functions underlying the activity of simulated neural assemblies
Authors: Cédric Allier, Larissa Heinrich, Magdalena Schneider, Stephan Saalfeld,
Abstract summary: Graph neural networks trained to predict observable dynamics can be used to decompose the temporal activity of complex heterogeneous systems into simple, interpretable representations.<n>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.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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.

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