A topological approach to neural complexity

Nonlinear Sciences – Adaptation and Self-Organizing Systems

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

6 pages, 4 figures

Scientific paper

10.1103/PhysRevE.71.016114

Considerable efforts in modern statistical physics is devoted to the study of networked systems. One of the most important example of them is the brain, which creates and continuously develops complex networks of correlated dynamics. An important quantity which captures fundamental aspects of brain network organization is the neural complexity C(X)introduced by Tononi et al. This work addresses the dependence of this measure on the topological features of a network in the case of gaussian stationary process. Both anlytical and numerical results show that the degree of complexity has a clear and simple meaning from a topological point of view. Moreover the analytical result offers a straightforward algorithm to compute the complexity than the standard one.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

A topological approach to neural complexity does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A topological approach to neural complexity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A topological approach to neural complexity will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-278527

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.