Computational Analysis of Connectivity in the Mammalian Cerebral Cortex

Neuroanatomical connectivity and functional connectivity are to be studied for understand the mechanism of neural systems accepting the sensory inputs and combining different information. We examined the structural features of three mammalian cerebral cortex networks and a number of randomized control networks expressed as graphs and patterns of functional connectivity to which they give rise when implemented as dynamic systems. We found that the cerebral cortex of macaque, cat and rat have smaller characteristic path length and diameter of graph but higher reciprocal fraction and cluster index in structure connectivity, which shows features characteristic of small-world networks. At the same time, the cerebral cortex networks have higher entropy and complexity in function connectivity compared with the same size and density random networks. Multidimensional Scaling analysis showed that the cerebral cortex areas with similar functions connect with each other more closely.