Neural complexity and structural connectivity
Barnett, Lionel, Buckley, Christopher L. and Bullock, Seth (2009) Neural complexity and structural connectivity. Physical Review E, 79, (5), 051914-[12pp]. (doi:10.1103/PhysRevE.79.051914).
- Published Version
Tononi et al. 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.
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
|Date Deposited:||20 May 2009 15:30|
|Last Modified:||27 Mar 2014 20:13|
|Further Information:||Google Scholar|
|ISI Citation Count:||15|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)