title: Neural complexity and structural connectivity.
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.
authors.affiliation: Department 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
authors.forename: L
authors.initials: L
authors.lastname: Barnett
authors.forename: C L
authors.initials: CL
authors.lastname: Buckley
authors.forename: S
authors.initials: S
authors.lastname: Bullock
mesh_terms: D003198:Computer Simulation; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*