On brain-inspired connectivity and hybrid network topologies

Abstract

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.

Robustness of logic gates and reconfigurability of neuromorphic switching networks

“…There are several current lines of development of nanoelectronics trying to cope with the increasing density and smallness of components, and the needs for new architectures, self-assembly and fault tolerance [1], [2], [3], [4], [5]. In neuromorphic computation, usually the functionality of simple neural networks is mimicked by forming a network of tunable resistive links (weight functions) connecting nonlinear summation nodes, forming e.g.…”