In this paper our aim is to identify layered hierarchical generic network topologies which could closely mimic brain's connectivity. Recent analyses have compared the brain's connectivity (based both on a cortical-equivalent Rent's rule and on neurological data) with well-known network topologies used in supercomputers and massively parallel computers (using two different interpretations of Rent's rule). These have revealed that all the well-known computer network topologies fall short of being strong contenders for mimicking the brain's connectivity. That is why in this paper we perform a high-level analysis of two-layer hierarchical generic networks. The range of granularities (i.e., number of gates/cores/neurons) as well as the fan-ins and the particular combinations of the two generic networks which would make such a mimicking achievable are identified and discussed.