  Networks are widely used to represent interaction pattern among the
components in complex systems. Structures of real networks from differ- ent
domains may vary quite significantly. Since there is an interplay be- tween
network architecture and dynamics, structure plays an important role in
communication and information spreading on a network. Here we investigate the
underlying undirected topology of different biological networks which support
faster spreading of information and are better in communication. We analyze the
good expansion property by using the spectral gap and communicability between
nodes. Different epidemic models are also used to study the transmission of
information in terms of disease spreading through individuals (nodes) in those
networks. More- over, we explore the structural conformation and properties
which may be responsible for better communication. Among all biological
networks studied here, the undirected structure of neuronal networks not only
pos- sesses the small-world property but the same is expressed remarkably to a
higher degree than any randomly generated network which possesses the same
degree sequence. A relatively high percentage of nodes, in neuronal networks,
form a higher core in their structure. Our study shows that the underlying
undirected topology in neuronal networks is significantly qualitatively
different than the same from other biological networks and that they may have
evolved in such a way that they inherit a (undirected) structure which is
excellent and robust in communication.
