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Updated: Jun 22, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
Neural complexity and structural connectivity
L Barnett1, C L Buckley, S Bullock
1Department of Informatics, Centre for Computational Neuroscience and Robotics, School of Science and Technology, University of Sussex, Brighton BN1 9QH, United Kingdom. l.c.barnett@sussex.ac.uk
Abstract:
Tononi [Proc. Natl. Acad. Sci. U.S.A. 91, 5033 (1994)] proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond. We develop an approximation of the measure for a popular Gaussian model which, applied to a continuous-time process, elucidates the relationship between the complexity of a neural system and its structural connectivity. Moreover, the approximation is accurate for weakly coupled systems and computationally cheap, scaling polynomially with system size in contrast to the full complexity measure, which scales exponentially. We also discuss connectivity normalization and resolve some issues stemming from an ambiguity in the original Gaussian model.
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