• Home
  • Conferences
  • Publications
  • Association
GUIDED SELF-ORGANISATION
  • Home
  • Conferences
  • Publications
  • Association
GSO-2010:  Xin Shuai and Larry Yaeger
Motif Analysis in Evolving Neural Networks

Networks motifs are called 'building block' in complex network. Motif analysis can give us richer and multi-dimensional information about the relations among complexity, structure and function. In this presentation, both the temporal dynamic and frequency distribution of 3-node motifs in Polyworld is analyzed for both driven runs and passive runs. Two interesting observations can be made based on the results: First, all 13 types of 3-node motifs can be classified into 6 categories according to their temporal dynamic trajectories. We hypothesize that some types of motifs are favored, while others are suppressed, by natural selection. Second, the top 3 frequent motif types are symmetric while the top 3 infrequency motif types are asymmetric. We hypothesize that symmetry in motif structure possibly has some evolutionary advantage in function. Further study is needed to validate our hypothesis.

Powered by Create your own unique website with customizable templates.
  • Home
  • Conferences
  • Publications
  • Association