The incessant search to understand human cognitive functions has led to the hypothesis that the brain works similar to a packet switched network such as the Internet [28]. In this thesis, I have developed a top-down simulator of brain-like networks which uses prob- ability routing to route data and a distance vector routing algorithm [21] to propagate feedback to varying depths. I investigate the impact of the feedback depth on routing table metrics. The results indicate that important performance metrics are affected by the feedback depth of the routing algorithm but also, to a large extent, by the topological features of such networks [17, 44]. The results indicate feedback depths from 25 to 30 fill the routing table most efficiently in terms of routing table fill percentage, routing table fill time and packet rejection ratio. There is also a strong correlation between the macaque monkey brain and sparse topologies.