Neocortex's architecture optimizes computation, information transfer and synchronizability at given total connection length

Abstract

It is experimental evidence that biological neocortical neurons are arranged in a columnar clustered architecture and coupled according to a bi-power law connection probability function. Using a bi-power connection probability function paradigm, we scan a wide range of network types, for which we compare speed of information propagation. Whereas the information propagation increases linearly in the neighbor order $n$ for $n$-nearest neighbor coupled networks, in our elaborate model of the neocortex, the information propagation speed saturates at a high level even more quickly than in single-power law models, expressing the superiority of the modified network type. We study similarly the network synchronizability as a function of the architecture. The investigations reveal that bi-power connection distributions, which on this level of description are the most refined architectures of the mammalian cortex, optimize information propagation and synchronizability under the constraint of constant total connection length.