In this paper our aim is to identify layered hierarchical generic net- work topologies which could closely mimic brain's connectivity. Recent analy- ses 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 inter- pretations of Rent's rule). These have revealed that none of the well-known computer network topologies by themselves are strong contenders for mimick- ing 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.
['Valeriu Beiu', 'Basheer A. M. Madappuram', 'Peter M. Kelly', 'Liam McDaid']
On Two-Layer Hierarchical Networks How Does the Brain Do This?