Scholarpedia Article on Complexity


Matlab Complexity Toolbox

This toolbox contains a set of functions used to calculate complexity from the covariance (correlation) matrix of a system. Please read the comments carefully - the functions will only give valid results if certain statistical assumptions are fulfilled.  
Included is also a function for deriving covariance matrices from anatomical connection matrices, given linear dynamics and uncorrelated noise.  You can find the toolbox at this link:

Measuring Information Integration

This page contains a pdf version of the recent paper "Measuring Information Integration", by Giulio Tononi and Olaf Sporns, BMC Neuroscience vol, pg, 2003.  In addition, the page contains a Matlab toolbox with all the scripts and functions necessary to calculate the measures of information integration proposed in the paper.   Also included are the datasets (connection matrices) used to generate most of the figures in the paper.  The information integration page is at:

Movies of neural dynamics (1.4 Mb each):

These short movies show simulations described in detail in this article, to be published in "Coordination Dynamics", a book edited by Scott Kelso and Viktor Jirsa.

Here is an example of neural dynamics with high overall integration, but low complexity - it was generated using uniformly distributed connections across the map - download

In this example, connections across the map were sparse, resulting in low integration and low complexity - download

If clustered intrinsic connections are used, the resulting neural dynamics is highly complex - download - download (in 40x40 resolution)

Similarly, we find high complexity for a mixture of clustered and long-range connections - download

For relevant publications (including downloadable pdf-files) check here.