The human and mammalian cerebrum scale by computational power and information resistance

TL;DR

This paper derives two laws explaining how mammalian cerebrum size and structure scale with computational power and information resistance, revealing fundamental principles of brain organization.

Contribution

It introduces two novel laws that quantitatively explain cerebral scaling relations based on information flow and resistance, surpassing previous models.

Findings

Long-range information flow depends on cortical surface and connection resistance.

White matter volume scales with cortical surface area with minor corrections.

Large cerebrums favor local processing over global information flow.

Abstract

The cerebrum of mammals spans a vast range of sizes and yet has a very regular structure. The amount of folding of the cortical surface and the proportion of white matter gradually increase with size, but the underlying mechanisms remain elusive. Here, two laws are derived to fully explain these cerebral scaling relations. The first law holds that the long-range information flow in the cerebrum is determined by the total cortical surface (i.e., the number of neurons) and the increasing information resistance of long-range connections. Despite having just one free parameter, the first law fits the mammalian cerebrum better than any existing function, both across species and within humans. According to the second law, the white matter volume scales, with a few minor corrections, to the cortical surface area. It follows from the first law that large cerebrums have much local processing and little global information flow. Moreover, paradoxically, a further increase in long-range connections would decrease the efficiency of information flow.