On Brain-inspired Hybrid Topologies for Nano-architectures - A Rent's Rule Approach

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).