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This paper will start by comparing brain's connectivity (based on different analyses of neurological data) versus well-known network topologies (originally used in massively parallel super-computers), in view of the latest interpretation of Rent's rule. These will reveal that the brain is in very good agreement with Rent's rule average growth rate. With respect to classical network topologies, the crossbar (only for quite small sizes) and the cube connected cycles (for a wider range) look like promising contenders (for the brain), while in fact any network topology falls short of properly mimicking brain's connectivity. That is why, we will go on exploring hybrid (hierarchical) combination of two network topologies, allowing us to identify those (hybrid network topologies) which could closely emulate brain's connectivity (as well as the particular ranges where this is happening).

This paper starts from very fresh analyses comparing brain's connectivity with those of well-known network topologies, based on the latest interpretation of Rent's rule. Those analyses have revealed how close the brain comes to the latest Rent's rule averages. On the other hand, all the known network topologies seems to fall short of being strong contenders for mimicking the brain. That is why this paper performs a detailed Rent-based (top-down) connectivity analysis of many two-level hybrid network topologies. This analysis aims to identify those two-level hybrid network topologies which are able to closely mimic brain's connectivity. The ranges of granularity (as given by the total number of gates and the number of processors) where this mimicking is happening are identified. These results should have implications for the design of networks(-on-chip) and for the burgeoning field of multi/manycore processors (in the short to medium term), as well as for investigations on future nano-architectures (in the long run). Complementary results using a bottom-up approach have also been obtained, and will be mentioned.

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